Podcasts about Apis

Llevo 15 años escribiendo notas, artículos y tutoriales. El resultado: unos 5000 archivos markdown repartidos por mi disco duro. Y, como te puedes imaginar, encontrar algo ahí dentro es como buscar una aguja en un pajar. Por eso en este episodio me he puesto manos a la obra para montar un sistema RAG (Retrieval-Augmented Generation) 100% local, sin depender de APIs externas, sin enviar tus datos a la nube, y con herramientas que ya conoces: SQLite, Ollama y Python.Este es el primero de dos episodios sobre RAG. Aquí nos centramos en construir la base de conocimiento: un pipeline que escanea tus documentos, los trocea en fragmentos manejables, extrae los metadatos del frontmatter YAML, genera embeddings con el modelo bge-m3 de Ollama, y lo guarda todo en una base de datos SQLite con búsqueda FTS5. Todo esto, además, con detección incremental de cambios: la primera ejecución tarda lo que tenga que tardar, pero las siguientes son cuestión de segundos porque solo reprocesa lo que ha cambiado.El stack es sencillo pero potente. SQLite con FTS5 para búsqueda textual, Ollama con bge-m3 para los embeddings, y seis scripts Python que suman unas 1300 líneas. Nada de LangChain, nada de frameworks pesados. Código limpio, comentado y que entiendes de un vistazo. El chunking respeta las cabeceras markdown, usa tiktoken para contar tokens con precisión, y los embeddings se almacenan como BLOBs en la propia SQLite. En el próximo episodio (el 821) usaremos esta base de conocimiento para hacer búsqueda semántica con similitud de coseno, búsqueda híbrida combinando FTS5 con embeddings, y hasta un plugin para Neovim.Puntos clave del episodio:- El problema: 15 años de notas, 5000 archivos, cero capacidad de búsqueda- La solución: RAG local con SQLite + FTS5 + Ollama, todo en tu máquina- Chunking híbrido que respeta cabeceras markdown y usa tiktoken- Pipeline incremental con detección de cambios mediante MD5- Embeddings con bge-m3 (568M parámetros, 1024 dimensiones)- Búsqueda FTS5 con snippet(), colores ANSI y sintaxis avanzada- Errores comunes y cómo solucionarlosSi te gusta el contenido, ya sabes: dale a seguir, compártelo con quien creas que le puede interesar, y déjame un comentario si tienes dudas o sugerencias. La semana que viene, en el episodio 821, montamos la búsqueda semántica y el plugin para Neovim. No te lo pierdas.Capítulos del episodio:0:00 - Introducción: RAG y base de conocimiento local2:12 - El problema: 15 años de notas sin buscar4:58 - La solución: SQLite + FTS5 + Ollama, 100% local7:00 - Escaneo de archivos y extracción de front matter10:20 - Preparación del entorno: Ollama, uv y dependencias12:00 - Chunking: cómo trocear los documentos15:45 - Estructura de la base de datos SQLite17:46 - Pipeline incremental con detección de cambios19:22 - Demo en vivo: consultas y resultados22:44 - Errores comunes y cómo solucionarlos24:10 - Resumen y adelanto del episodio 82125:15 - Despedida y cierreMás información y enlaces en las notas del episodio

We've been raising funds with blockchain and crypto for a decade. But one team believed in a vision that is only now coming to reality. And you need to hear their story.Joris believed that Blockchains will create the ability for all assets to act as 'APIs' and unlock better capital, equity financing and growth... But to do so, he needed to wait for the rest of the world to catch up to his vision.Fast forward to 2026: the entire financial services industry is sold on Blockchain technology. Tokenisation of everything is ongoing. Even the regulators are pushing things forward. So what comes next?On this show we discuss:- An intro to Joris and Fairmint- The history of on-chain equity (the last 10 years)- Getting subpoena'd by Gary Gensler- Opportunities for on-chain compliance and data oracles- What more is needed to see wider adoption of onchain equityThis is one of the best shows we've done in Season 4, so don't miss it!

Why do less than a third of independent agents actually use a CRM, and what is that gap costing them? Jason Cass sits down with Mariah Gates, Founder of Accelerated Automation, to unpack agency operations, the shifting role of AMS platforms, and what it really takes to prepare an agency for AI. Key Topics: Why less than 27% of agents actually use a CRM, per a Vertafore study Growth through acquisition creates operational struggles even for large agencies Fixed owner pay leads to healthier cash flow and tech decisions The "AMS Demotion": AMS shifting from operating system to system of record AMS platforms need open APIs and two way sync to stay relevant Point solutions expose gaps that AMS systems fail to solve Why integration philosophy separates open platforms from closed ones like EZLynx Software companies must listen closer to users to avoid disconnected decisions AI adoption requires documented processes before layering on new tools Why agentic AI replaces unlicensed work while virtual employees remain essential Reach out to: Mariah Gates Jason Cass Visit Website: Accelerated Automation Agency Intelligence Produced by PodSquad.fm

This week we are joined by Michael Collins, Principal Consulting Solutions Architect at Barracuda, to explain what a web application firewall (WAF) is and how it differs from a network firewall. Along with Nate, CIT's Director of Cybersecurity, we explore how WAFs help defend websites, web apps, and APIs against threats like injection, bot attacks, brute force attempts, and DDoS, especially as AI “vibe coding” leads to insecure public-facing applications. The episode covers choosing basic vs enterprise-grade WAF protection based on business criticality and sensitive data, tuning to reduce false positives using detect vs block modes and staged deployment, visibility into daily attacks, geofencing and IP blocking, and how WAFs support compliance efforts like PCI and HIPAA as part of a broader security strategy.00:27 What Is a WAF03:58 Defining Web Apps07:41 APIs and Vibe Coding Risks11:12 Choosing the Right WAF14:31 Tuning and False Positives20:52 Onboarding Detect to Block28:06 Compliance and Regulations31:48 Visibility and Geofencing38:13 DDoS Story and Wrap Up42:54 Testing and Deployment Options46:35 Final Thanks and CTACheck out more from Barracuda

Hoje o papo é sobre novos protocolos agênticos! Neste episódio, conversamos sobre como o Model Context Protocol (MCP) busca padronizar a integração entre agentes e ferramentas, além das diferenças e complementaridades entre MCPs, skills e APIs tradicionais. Vem ver quem participou desse papo: Paulo Silveira, o host que ainda acha automação uma bagunça Vinny Neves, cohost, dev e professor na Alura Mikaeri Ohana, Staff Developer Relations Engineer no Google Sulamita Dantas, Database Engineer e professora na FIAP Marco Antonio da Silva, Diretor de Engenharia do Conta Simples  Links:  Anthropic apresenta o MCP em 2024 MCP MCP Apps Registro oficial de servidores MCP Vinny: MCP tá onde o npm tava em 2014. e isso não é elogio Conta Simples + MCP MCP Tools Agent Gateway ADK: Agent Development Kit A2A: Agent-to-Agent UCP: Universal Commerce Protocol AP2: Agent Payments Protocol Google: Agent Identity Agent Skills Repositório oficial de Agent Skills da Anthropic  Plugins no Antigravity skills.sh, o npm de skills Criador de skills da Anthropic Roadmap do MCP Boas práticas de segurança para MCP Toda revolução tecnológica começa com quem antecipa o futuro e transforma ideias em soluções de alto impacto. Conheça os cursos da Alura + FIAP Skills & Go: Agentic Engineering, Building AI Products, e AI Data Strategy. Saiba mais sobre o Skills & Go. Vá para o Vale do Silício com Paulo Silveira, Marcell Almeida, Fabrício Carraro e Marcus Mendes na “Imersão IA Sob Controle e Alura no Vale do Silício“! Vagas limitadas, corra para reservar a sua. TechGuide.sh, um mapeamento das principais tecnologias demandadas pelo mercado para diferentes carreiras, com nossas sugestões e opiniões. #7DaysOfCode: Coloque em prática os seus conhecimentos de programação em desafios diários e gratuitos. Acesse https://7daysofcode.io/ Produção e conteúdo: Alura Cursos de Tecnologia – https://www.alura.com.br Edição e sonorização: Rede Gigahertz de Podcasts

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APIs, Multi-Channel Platforms, and Generative AI are reshaping ecommerce advertising, helping brands boost ROI by forty percent. This episode explores how integrated systems allow marketers to analyze competitors, generate winning ads in minutes, and scale creative production at speed. To learn more, visit https://www.gethookd.ai/mcp/ GetHookd LLC City: Miami Address: 40 SW 13th street Website: https://www.gethookd.ai/

Don’t miss this massive SMB partner shift! Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ In this pivotal episode, we sit down with Jose Gomez Cueto, Microsoft’s SMB leader for the Americas, to uncover the monumental shifts happening within the partner ecosystem and the $20 billion cloud opportunity currently on the table. The discussion dives deep into Microsoft’s commitment to the CSP channel, the explosion of AI agents, and why shifting from traditional headcount growth to outcome-based results is critical for survival. From navigating the complexities of the marketplace to the urgency of becoming “Customer Zero” with AI tools, this conversation provides the roadmap every MSP needs to thrive in the new era of technology. https://youtu.be/QE-1w7GeyPM Key Takeaways Microsoft operates a $20 billion cloud revenue business in the Americas alone, with 80% driven by the channel. The Cloud Solution Provider (CSP) program is now Microsoft’s primary hero motion for the fourth region. The currency of SMB growth is shifting away from headcount and moving directly toward AI-driven outcomes. MSPs must transition from traditional IT outsourcing to strategic business process consulting to survive. Failing to proactively adopt and secure AI tools creates massive liability and shadow AI risks for organizations. IT providers are urged to become “Customer Zero” by deploying and testing Copilot and autonomous agents internally before selling them. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags CSP, Agent 365, SMB cloud revenue, outcome-based selling, Copilot for business, Defender for business, shadow AI risks, AI agent deployment, Purview data security, Marketplace API integration, autonomous agents, Customer Zero, Microsoft Americas segment Transcript Jose Gomez Cueto AUDIO PODCAST [00:00:00] Jose Gomez Cueto: And, and you know, if I might say something that is confidential, avid Vince, uh, to be quite honest, please, please, uh, by definition, a marketplace is eliminating intermediaries. [00:00:11] Vince Menzione: You can feel it happening. [00:00:13] Vince Menzione: The ecosystem is shifting beneath us. The way Hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. [00:00:23] Vince Menzione: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi, own your host. And each week I sit down with leaders at the intersection of technology, partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:45] Vince Menzione: It is the strategy because being in the room changes everything. Let’s start. [00:00:55] Vince Menzione: I am absolutely thrilled for our, our next guest. Um, some of you heard me talk about this maybe earlier or in various pockets of conversation. Um, I believe both the SMB market is an, is an incredible opportunity. We’ve called it the Acre of Diamonds at Ultimate Partner at previous events. And then the MSP community, which I want to thank so many of you to for coming, coming on board now. [00:01:25] Vince Menzione: ’cause we’ve had some MSPs that have come to all our events. And doubled, tripled, quadruple the sizes of their business. From what they’ve learned in these rooms. And so we invited our next guest to come. Oh, Jose, come on up. Jose Gomez Cuerto is the leader of Microsoft’s SMB business for the Americas. Come on. [00:01:43] Vince Menzione: Come on over. Come on over. Sit down with me. And I was so thrilled to get this gentleman to come join us. His team is doing incredible work. I got to meet some of his team actually earlier this year. And we know each other for many years ago. [00:01:56] Jose Gomez Cueto: We do. [00:01:56] Vince Menzione: When I was at Microsoft, right? Yeah. So, so great to see you again. [00:01:59] Jose Gomez Cueto: It’s a pleasure to be here. Uh, thanks for the invitation. I’m thrilled to be here. And thank you all for making time, uh, or traveling here. Uh, this is the best time. To be in the industry. [00:02:10] Vince Menzione: It’s an incredible time. [00:02:11] Jose Gomez Cueto: Yeah, [00:02:11] Vince Menzione: it’s an incredible time. So sit down. Yeah, sit down. Let’s, yeah. So let’s talk about you and your organization. [00:02:17] Vince Menzione: Um, let’s talk, well, I, I, I wanna bring this up because it was like, the noise I heard in the room when I was, I went to Interven earlier this year. Yeah. Is, does Microsoft Care about this market? And, um, I was at Microsoft many years, we worked together when I was a, a gm. And, uh, it was run differently back in the day. [00:02:37] Vince Menzione: Yeah. And there’s been a lot of changes to what we call the SME and C business now. Mm-hmm. Uh, and the SMB business, which you run. So let’s talk a little bit about your organization, where you sit in the organization, and then I want to kind of dive in a little bit about what’s changed. ’cause a lot has changed for the better. [00:02:54] Jose Gomez Cueto: Yeah, it’s a great question and I think that that’s what a lot of people think about, uh, SMB and, and who’s SMB and, and who’s at Microsoft and who do I talk to. So, [00:03:02] Vince Menzione: yes. [00:03:02] Jose Gomez Cueto: Uh, even though I know a lot of, uh, friendly faces in the room, I think it’s a great, uh, starting point. Vince, so. Basically, uh, I am responsible for what we call the small and medium business, uh, segment. [00:03:15] Jose Gomez Cueto: Uh, we can also call it small and medium enterprises. Uh, I would say that it is not a monolith. Uh, we do have, uh, subsegmentation, I think that our friend Jay was talking about up to 20 subsegmentation. Uh, we think about it for simplifi simplification purposes on three. Uh, so we have, uh, the smaller organizations, the medium-sized organizations. [00:03:36] Jose Gomez Cueto: And then what we call top point manage, which is basically large enterprise that we simply don’t have an account management team, uh, assigned to. And we are the happy recipients of many of those, uh, every year. Uh, so I would say that, uh, a best definition would be also anything that is unmanaged and is primarily driven through the channel. [00:03:54] Jose Gomez Cueto: Uh, we run in the Americas approximately more than $20 billion of revenue, uh, on cloud. Uh, that’s [00:04:01] Vince Menzione: crazy. [00:04:01] Jose Gomez Cueto: So, and 80% of that is done. Through companies that are here, [00:04:05] Vince Menzione: $20 billion of business. [00:04:07] Jose Gomez Cueto: Yeah. So, um, the, the Americas region that I’m, uh, representing and under my responsibility includes basically three sales units, the United States, Canada, and Latin America. [00:04:18] Jose Gomez Cueto: Yes. Latin America is more fragmented because we have multi-country and multi, uh, subsidiary, uh, structure. Just to recap a little bit of what you asked me rewinding on what has happened in the last two or three years. Yeah. We brought basically, uh, probably something that you might remember from you were there. [00:04:36] Jose Gomez Cueto: I, yes. Uh, which is bringing the segment, uh, with the channel together. So, uh, I think that, um, uh. Earlier in the morning, uh, Steven was, uh, talking about it, what we call S-M-U-N-C, which is, uh, this segment with the channel. And the main reason is to drive, uh, that synergy, uh, and making, uh, a very bold statement that many of you might remember in the last two years, uh, Judson and Ralph, uh, heter or, or new, uh, president for this, uh, fourth region. [00:05:05] Jose Gomez Cueto: Uh, ’cause we call it fourth region. Yeah. ’cause the other one is our enterprises. [00:05:08] Vince Menzione: Yeah. So Asia, Americas Exactly. And, and EMEA. Then you’re the fourth region. [00:05:13] Jose Gomez Cueto: We’re the fourth region. So, so, um, making CSP, uh, our hero motion, and that is fantastic news. I, I started, uh, part of my journey, uh, in, in the channel, uh, way earlier in distribution in the year 2000. [00:05:30] Vince Menzione: Yep. [00:05:30] Jose Gomez Cueto: Uh, and fast forward, I would say 2011, we were launching the first commercial SaaS offering, which was Office 365. Um, I had the privilege to be, uh, leading the launch globally for that. Uh, but then we, the first thing we did was build a channel, and that was called syndication. And basically the precursor of that, uh, became CSP, basically putting, uh, the partner or customer in the middle, the partner around it for the, not only the, the opportunity, but also the responsibility to serve the customer. [00:06:04] Jose Gomez Cueto: 360 from, uh, presales all the way to, uh, uh. Upsell cross sell, and in between deployment, uh, things, uh, around, um, servicing, bundling offers, uh, troubleshooting and support, et cetera. So, uh, back to your question was, this is a very important thing because we’re basically, uh, making our channel the scale and, and the vision that we have is, is that we are gonna be continuing to scale through the channel. [00:06:33] Jose Gomez Cueto: So, um, one last thing I say, uh, in terms of the organization that I think is important for everyone to understand, and I’m gonna go a little bit into more org structure, is that we, we have these three sales units that are geographic. But, uh, what we’ve done this year is to have, uh, more depth on the solution area. [00:06:50] Jose Gomez Cueto: So you might remember that, uh, we’ve simplified them, uh, same as we used to have 8, 13, 13 areas. Now we have only three Oh yeah, same, same, uh, in the solution area. So we have, uh, the AI business solutions. Uh, cloud and AI platforms. And then, uh, security and my team basically mirrors that structure. And we have, uh, team members, uh, primarily, um, our partner, solutions specialists that are, their job is to work with companies like you. [00:07:17] Jose Gomez Cueto: Uh, some few we do, uh, direct in others. What we do is work with, uh, our top distributors, and I think we have, uh, in the room many of those. I think Google is gonna follow up, uh, for PAX eight, and that’s, uh, how we’re going to market now. [00:07:31] Vince Menzione: So just a little bit of context too for me. ’cause I, I, I had heard this at another event. [00:07:36] Vince Menzione: Yeah. And I just wanted to share this. Um, when I was at Microsoft, we, we did not put the right emphasis and energy and resources in the s and b market when I was there, or, or it was fragmented. Every group did it differently. You remember those days too, right? Well, with public sector, we didn’t necessarily have a team focused, and every business did it a little bit differently. [00:08:00] Vince Menzione: Mm-hmm. And I think one of the contexts you, you mentioned Ralph and being in Ralph’s organization. Yeah. Pulling that all together and creating the fourth region created a lot of focus that didn’t exist. And consistency in terms of execution. I think that’s what you’re talking about here. Right? And then also the fact that like, we didn’t, I don’t think we had a sep, an SMB leader back in, back in the day. [00:08:22] Vince Menzione: Like we didn’t have somebody that we can go to to think about the MSP community the way we do today. Mm-hmm. Right. We were just, they were just almost like unmanaged entities out there. Yeah. Was that, would you, would you agree with that? [00:08:32] Jose Gomez Cueto: Yeah, I, we went through several iterations that, uh, you might argue, uh, were painful or not. [00:08:38] Jose Gomez Cueto: Uh, ultimately what we’re committed is to simplify the partner experience. And make that the same for the customers. But what we have is now one center of gravity, uh, a global SMB organization. We have three area leaders. And uh, and that helps us, uh, to be quite honest and in confidence. And you and I talked about it, Jose, this is a forum for, uh. [00:08:58] Jose Gomez Cueto: Speaking the truth. Uh, we, we, we have to fight the gravitational force of the managed space. The company has a big enterprise footprint, so, uh, many of us have become, uh, the chief agitators, uh, to fight the good fight, uh, for SMB. Uh, try to under unpack, uh, in every single conversation with senior Execut. [00:09:17] Jose Gomez Cueto: What is an MSP? And no, it’s not data consulting or one of the large, uh, global design. Uh, and then we explain what they do and then what is a two tier channel, how do distributors work? And, uh, and what about this and what about that? So I think that that has been, uh, a great, uh, progress and a lot of that can be reflected, uh, into how we’re, hopefully everyone in the room is seeing it in how we’re going to market. [00:09:40] Jose Gomez Cueto: I’ll give you two examples. [00:09:41] Guest: Yes. [00:09:42] Jose Gomez Cueto: Um, for, for quite some time. We, we have very limited, uh. Product truth. That’s what the lingo that we use internally, uh, related to offer that were targeted to SMB. And I would say that, uh, business premium, uh, for M 365, uh, was the fact to offer. But now we’ve been able to in, uh, increase, uh, the not so not only commitment, uh, but also the investment that we’re doing as a company into launching offers. [00:10:08] Jose Gomez Cueto: So we have a co compiler for business that is. At a lower price point that has, uh, the same capabilities at the enterprise, uh, that we’re, uh, doing that we also have some security, uh, offers, uh, that are now unattached to business premium, which is our hero motion for sub 300 space. So you start to see, uh, an important trend and it’s great to have jobson at a CEO, uh, of the commercial business capacity because, uh, we’re making things happen. [00:10:34] Jose Gomez Cueto: So what I would say is that I love coming here to these forums. A lot of my team members are here. We’re here to learn. We’re the learner. All we, we, we don’t know much. We need to learn more. Uh, and, and just keeping us honest in terms of bringing that, uh, ethos of, of the customer that most of you are serving and, and, and things that we can improve to get better to deliver value. [00:10:58] Vince Menzione: Yeah. And the speed at which you’re moving has been pretty fast. It’s been very nimble. Like I, I, I’ve been watching this progression. It’s really like you, you’re really leaning in. I was actually hoping because I could ask you a bunch of questions. Yeah. But we have such a great audience and for the first time we really have opened it up to a lot of MSPs in the room. [00:11:18] Vince Menzione: Yeah. And I know you, you wanna get some interaction with some of these folks as well. I thought maybe we would open if you’re okay with this. Yeah, absolutely. I’d rather than I go off script a little bit. I’d rather open it up to some of the MSPs in the room. We’re sitting here eager to learn how and, and what Microsoft is going to do to help. [00:11:35] Vince Menzione: Because I think the opportunity, I personally think the opportunity is huge right [00:11:38] Jose Gomez Cueto: now. Yeah. Let’s do that and well, we get, uh, warmed up. I would say that. [00:11:43] Vince Menzione: So we need some mics. Yeah. [00:11:43] Jose Gomez Cueto: Uh, something that I’m, that I’m seeing, uh, Vince, and, and, and a question that many of you might have is why now? And, and why this an, an exciting, an exciting time. [00:11:54] Vince Menzione: Yes. [00:11:54] Jose Gomez Cueto: Um, and I would say that, uh. Right now we’re seeing, obviously Jay talked about it and, and the big transformation, but it’s a once in a generation or one in a lifetime. Yeah. Uh, shift of the entire platform. Uh, and, and a lot of the scenarios are even maybe scary, but what we see is huge opportunity. And from an SMB perspective, uh, the biggest thing that excites me is moving from, um, something that was. [00:12:23] Jose Gomez Cueto: More related to size, and now we’re moving to outcomes. So, so think about the future of SMBs, uh, with agents and things being measured on outcomes. And, and what this leads to is, uh, Jay talked about it as well, and sorry Jay, it’s such a good job that I keep quoting you. Um, we do that a lot. Uh. You got it. [00:12:49] Jose Gomez Cueto: So you talked about, uh, I noticed that Bill Gates when he said, you know, uh, uh, a pc, uh, in every desk and what we see is every human empowered with agents. Yeah. Especially in work. And what does that mean, that the currency changes being, because what you’re gonna be able to, to envision. Not in the, in the, in the near future, but now is an agentic explosion where then, uh, the currency is outcomes? [00:13:14] Jose Gomez Cueto: Yes. So if you think of an SMB growing, it’s not growing on, on, on full-time employees or headcount. It’s growing on the ability to do more through agents. So, so I think that’s an important thing and, and that’s something that we’re working very closely with our all, all our channel and the offerings that we’re launching to market as well. [00:13:32] Vince Menzione: I also think about the MSPs as being perfectly positioned because what you described, the new, the new model, the future customer and the outcomes is gonna require hands on the steering wheel at all times. [00:13:44] Jose Gomez Cueto: Yes. Yeah. So on that one, and still waiting for some, uh. Someone that is not shy to ask questions, but we’ll, we’ll keep going in the meantime. [00:13:52] Jose Gomez Cueto: Uh, I, I think that, uh, we are learning, all the [00:13:55] Vince Menzione: MSPs are lined up over here. I’m marching them all. [00:13:57] Jose Gomez Cueto: We, and, and I almost know by name all everyone in the first two rows. Yes. Uh, so, so, uh, I might pick on them. Uh, they’re too shy, but, but we’re learning together. Uh, Vince, uh, the important thing is, is the transformation, uh, and the opportunity, but also the risk of, uh, not acting. [00:14:17] Jose Gomez Cueto: Uh, what we were seeing, uh, for the first, uh, year or two was kicking tires, people testing, uh, ai. And now what we’ve seen is basically, uh, a full adoption. Uh, of the agentic technology, not even adoption of the tools, but embracing the technology. So I, I want to give you, uh, two specific, uh, examples or data points we have, uh, just in the Americas, more than almost 9 million, uh, people using copilot chat. [00:14:49] Vince Menzione: Wow, that’s amazing. [00:14:50] Jose Gomez Cueto: So imagine, uh, the, the potential that is there for people that are actively using the tool. Yeah. Uh, to en enable new scenarios of doing things. Uh, another example, and I think I have, uh, someone in my team here, is Amber in the room. Amber Kinney? No, she left. Okay. So Amber runs, uh, cloud and ai, uh, uh, or Azure platform. [00:15:12] Jose Gomez Cueto: Uh, her team has deployed, uh, more than, uh, 11 agents internally for our partner solution specialist, uh, from. Simple agents that will, uh, tell is if a specific deal is eligible for a pre-sales or post-sales program. And comparing all the complexity of our programs, oh my [00:15:29] Vince Menzione: goodness. [00:15:30] Jose Gomez Cueto: All the way to, to, to managing a pipe more effectively of opportunities. [00:15:34] Jose Gomez Cueto: So what we’re seeing is real. This is not something that people are just kicking the tires. It’s like this is the opportunity. So back to, to the point of m ms. P uh, is, is about learning together on how to transition. To, uh, a model that is gonna be based on outcomes. And, and we were discussing, uh, I was with some of our distributors, uh, many of them in the last two months in, in a specific partner advisory, uh, councils and, and some people were just sharing their experiences. [00:16:04] Jose Gomez Cueto: Oh, I decided to charge X amount for an agent. And how do you come up with that number? I don’t know. We’re just testing. Okay. And what about their current revenue? Uh, and, but what about the tokens? What if, uh, the agents start to consume and they’re gonna do the metering? So, so I think that we’re learning together in this space. [00:16:22] Jose Gomez Cueto: Um, but what it is important is just to think about the important, the, the, the critical role that the MSPs are gonna have in leading. And the biggest challenge that we’re seeing and, and we see it over and over and over is, uh, the part about scaling. [00:16:38] Vince Menzione: Yes. [00:16:39] Jose Gomez Cueto: The skilling is not, uh, about learning how to use the copilot tool or to do, uh, some, uh, you know, tuning and that, because thankfully our, at least our, our technology as a platform, uh, pretty much carries the same, uh, security, uh, and compliance configurations that you have in your Microsoft 365 tenant. [00:17:00] Jose Gomez Cueto: But it is more the, the, the skilling about understanding how to do. Customer outcome conversation. What is your AI strategy? What [00:17:08] Vince Menzione: that’s scaling? Yes. [00:17:09] Jose Gomez Cueto: What really matters? Not [00:17:10] Vince Menzione: the technical skill. It’s, it’s really the approach that they’re taking. [00:17:14] Jose Gomez Cueto: Yeah. [00:17:14] Vince Menzione: With the organization. I, it seems that MSPs for many years were down in the weeds. [00:17:20] Jose Gomez Cueto: Yeah. [00:17:20] Vince Menzione: They were turning the, the wrench, so to speak, in the organization, and yet now it seems like this. Kevin Piker, your old boss used to use this term. The, the CIO. The CEO is the new CIO. In other words, you need to be selling upstream. You need, you need to be having the conversations in the organization that are strategic [00:17:40] Jose Gomez Cueto: Yeah. [00:17:40] Vince Menzione: To that organization. [00:17:41] Jose Gomez Cueto: So, two, two twofold on, on that, uh, point, which is very important. One is, uh, not our, a lot of our MSPs are equipped right now. [00:17:49] Vince Menzione: Yeah. [00:17:49] Jose Gomez Cueto: To have a, a conversation about business strategy. Because traditionally has been more outsource it. [00:17:56] Vince Menzione: Yes. [00:17:56] Jose Gomez Cueto: Uh, we started with, you know, managing the networks, then adding services, support tickets, et cetera. [00:18:03] Jose Gomez Cueto: So being able to have that conversation is important. Uh, we, we see through a lot of our tooling that, uh, the shadow AI is everywhere. And what I always tell in any MSP conversation that I have is risk security. You’re on the hook if something happens. That’s right. So if you’re not acting. Uh, then it is a liability. [00:18:22] Vince Menzione: You’re letting things take off in your own organization. Yeah. People are using [00:18:25] Jose Gomez Cueto: philanthropic on their own. The company can go, uh, bankrupt or get sued or get, uh, if they’re in a regulated industry, they can be taken out, et cetera. So, so that’s an important point, uh, related to, to that transformation. Uh, and, and the other part of the skilling that you mentioned that is super important is being in the weeds. [00:18:45] Jose Gomez Cueto: That is where the innovation is happening. Yeah. The later research that we have is being in the front line because it’s all about, uh, reinventing those processes. So I think that it’s a, it’s a good combination that if we have the MSPs, um, and we’re working, uh, not only internally but with our distributors to develop the right skilling around those other type of, uh, consulting skills. [00:19:07] Jose Gomez Cueto: Uh, data skills, uh, business process, uh, redesign and flows. Uh, that is where, where we see the big opportunity. [00:19:14] Vince Menzione: So it’s balancing out the technical skills with the business process skills, the consulting skills. Yeah, exactly. I think we have a question over here. Yeah. [00:19:22] Guest: Good afternoon, Vince. Good. Sorry. Thanks for the great content. [00:19:26] Guest: The question is around small medium businesses and the cost around cybersecurity. So. Basically, as new tools are coming up that are AI based, such as co-pilot for security, defender for AI, are also consumption based, is there a risk that SMEs will be left out under that cybersecurity poverty line? [00:19:52] Jose Gomez Cueto: I don’t think, uh, it is, uh, a risk to being left out, uh, in the country that the, the SMBs, I would say are more help is needed. And, and the way we think about it from a perspective of, of ai and specifically I’m want to talk about agents, uh, it was mentioned by Steven in, uh, in the morning, and I’m gonna talk a little bit high level and then I’m gonna try to bring it down to, to more tangible is this concept of intelligent and trust. [00:20:19] Jose Gomez Cueto: So on the intelligence, what, what we’re, what we’re trying to say here is that your AI is not just generic stuff that you just prompt and you get like anything that is on the web, but there’s contextual. Data, and, and that’s what we do, uh, with what you might be familiar with, which is the iq. Uh, so we have, um, iq, uh, also in Foundry and on our different data products. [00:20:40] Jose Gomez Cueto: So basically bringing the context of your work, of your contacts, of the people you interact, uh, of the meetings of, of the emails, of the SharePoint files, but also important connectors that are in line of business applications that you can bring to copilot. And then. That intelligence, uh, is relevant and that that basically increases innovation. [00:21:01] Jose Gomez Cueto: And the part about trust, uh, uh, not exactly in cybersecurity, but, but related is basically, uh, agent 365. Uh, can I see, show of hands, who’s aware of Agent 365? Maybe like [00:21:14] Vince Menzione: in the front two rows, [00:21:15] Jose Gomez Cueto: 20%? Yeah. So, um, that is basically, uh, an, an amazing opportunity for our MSP channel because it gives you opportunity to. [00:21:25] Jose Gomez Cueto: Basically observe, uh, govern and apply security to the, the agent activity that is happening. So we think in the context of ai, I think that that’s a, a, a super important, uh, aspect to mitigate any risk of, of what can happen if there’s not, uh, the right, uh, posture. Uh, and then, uh, on, on, on the other part of security, I would say that something, I mentioned something about offers. [00:21:51] Jose Gomez Cueto: We brought the capabilities of the enterprise, uh, SKUs and solutions into these add-ons to N 365. So I would say that with, uh, defender for business, uh, plan two, and sorry to go into the SKU language, uh, it, it is important to, to understand that you have those advanced capabilities. And then another one that we’re pushing, uh, hard and, and is had great receptionist, um, uh, purview, uh, and purview. [00:22:15] Jose Gomez Cueto: What allows you is just to really do everything related to data. Data security policies of what data should be prompted by the model, what information to stay or, or, or not stay. Uh, and I think that’s, that’s also a good opportunity that we’re seeing to bring those, uh, advanced capabilities into the SMBs. [00:22:33] Jose Gomez Cueto: The challenge that we have is how do we get them faster, uh, to everyone, especially when there’s, uh, you know, competing, uh, so solutions around it. [00:22:44] Vince Menzione: We have one more question, and I think we’re probably gonna have to break after that. I know we’re over time already and you’ve got a busy rest of your day. I got, well, we got one back there and we’ve got a mic up here, so, so we have two questions. [00:22:57] Vince Menzione: Yeah. We’ll do Tim first and then we’ll get the [00:22:59] Vince Menzione: mic up. I’ll go for the first 30 minutes and we’ll go from there. Yes. Long time listener. Great to see you again. Jose. Um, business premium, we did E seven. We talked about getting a voice from the MSP space. To build out a business premium, like additional offering. [00:23:13] Vince Menzione: Is there any context to that you have any vision in your crystal ball for October? [00:23:17] Jose Gomez Cueto: Uh, I cannot say or, or deny. Uh, but yeah, I think that what, what you I love it in, in all seriousness team. Uh, thanks for the question. Uh, I think that what you should expect is, uh, I call it product truth, uh, more, uh, SMB built purpose built for solutions. [00:23:35] Jose Gomez Cueto: So an equivalent of, of any seven as well. Yeah. [00:23:40] Vince Menzione: You still have, we have another question in the back? Yeah. Yeah. Okay. [00:23:43] Guest: Yeah. Uh, Jeremy here with Integral, um, there’s this kind of idea going around that while CSP has been very successful for many of us as MSPs and, and since the beginning, it’s been a great program that was focused on s and b and it’s come up now. [00:23:57] Guest: There’s this kind of shift saying, and CSPs and you think about being marketplace companies where CSP is, the plumbing and marketplace is, is the lead. If that is true, or maybe you comment on that, that idea. How does marketplace strategy playing into kind of, I guess I’m plugging serials piece now from behind, but how does marketplace strategy then play into the s and b market if CSPs are focused on that marketplace mechanism? [00:24:21] Guest: Where CSPs now are and the, and the modern work and all the things that we’ve been doing really well for a long time become, maybe plumbing is too far down the stack, but really marketplace being a focus, is that a strategy piece that we should be thinking about for CS p strategy overall? [00:24:36] Jose Gomez Cueto: Yeah, it’s a great question and I’ll try to keep it brief. [00:24:39] Jose Gomez Cueto: Uh, I think you need my v The vision that we have is we’re doing both. Uh, we’re empowering, uh, and customers to find what they need. Uh, in the marketplace. Uh, zero talked about also the opportunity for resellers to get enrolled and start to add services and other things. There’s also, another part of the is, is multifacet, uh, to work with ISVs to make it easier and recruit them to bring the right offers. [00:25:03] Jose Gomez Cueto: For SMBI would say that the feedback that we need is to make sure that the right SA ISVs are the ones that serving SMV are represented. Then from another front, I would accept that yes, we have some, uh, plumbing work to do because right now, uh, some part of the billing is not really that nimble for a two tier model if you’re working through a distributor. [00:25:23] Jose Gomez Cueto: So we made some great progress. Uh, we, we, uh, have, uh, announced something and Ignite, if you missed it, I think we might talk about it, uh, soon. Uh, but we have that, that connection via APIs with, uh, the four largest, uh, global distributors. So we’re making progress towards something that will be seamless. Uh, but I think that the biggest opportunity that we have is, is to crack the code, uh, for marketplace. [00:25:46] Jose Gomez Cueto: And, and, you know, if I might say something that is confidential, avid Vince, uh, to be quite honest, please, uh, by definition a marketplace is eliminating intermediaries. So that’s the dilemma. How do you bring the channel in between to help you expand, [00:26:02] Vince Menzione: right? [00:26:03] Jose Gomez Cueto: That that is really the, the, the, the, the holy grail, if I may use those words. [00:26:07] Jose Gomez Cueto: Uh, but that’s something that, that we’re working towards. And I think, uh, we have a great opportunity ahead and, and you should expect, uh, more announcements as we head into the summer events on how we’re gonna make that more seamless. [00:26:19] Vince Menzione: And REO really lit up the channel Yeah. In, in a big way. ’cause that a hundred percent, that was a blocker before. [00:26:24] Jose Gomez Cueto: Yeah. [00:26:24] Vince Menzione: Yeah. But CSP is also an incredible opportunity if it, you know, I know, I know there’s other sessions and conversations around it. And it does feel, and I’ve heard this before, like I wanna buy from my MSP because they’re the ones I trust. [00:26:37] Jose Gomez Cueto: Yes. [00:26:37] Vince Menzione: But yet I go, I have to go around the system in order to transact my Microsoft licenses. [00:26:43] Vince Menzione: Right. Yeah. And that’s, [00:26:45] Jose Gomez Cueto: I think the scenario getting the gentleman was mentioning is related to marketplace. But yeah. Vince, uh, uh, I just wanted to perhaps close, uh, please. Because I think we’re outta time, right? Yeah, we [00:26:54] Vince Menzione: are. [00:26:54] Jose Gomez Cueto: Yeah. Uh, just in terms of what to expect, uh, we are continuing to be, uh, partner centric. [00:27:01] Jose Gomez Cueto: You should expect as we go into the next fiscal year, uh, more refinement into the customer subsegmentation, we have this concept of above 300 and below 300, uh, working even closer with our distributors to help us scale and amplify the efforts that we do around recruitment, scaling, go to market, uh, co-sell, et cetera. [00:27:22] Jose Gomez Cueto: Uh, and then, uh, obviously expect, uh, we, we, a call to action that I have for everyone is become customer zero. Vince, I’m gonna put you on the spot here. How many agents did you use today? [00:27:37] Vince Menzione: None. [00:27:37] Jose Gomez Cueto: Okay. [00:27:38] Vince Menzione: I, I’ve been in the room leading the room today, [00:27:41] Jose Gomez Cueto: even with more reason. [00:27:42] Vince Menzione: No, I, in, I need to do [00:27:43] Jose Gomez Cueto: more. Put your autonomous agents. [00:27:44] Vince Menzione: I do. [00:27:45] Jose Gomez Cueto: I’m not kidding you and I didn’t, I need to be [00:27:47] Vince Menzione: more of [00:27:47] Jose Gomez Cueto: a frontier for myself. The answer I get usually is like one hand raiser, by the way. Uh, but, but, uh, jokes aside, uh, I think. Becoming customer zero is critical. We cannot be deploying and selling what we’re not using. Uh, we have, uh, great tooling for low-code scenarios, uh, in, in, in, now, I don’t wanna say like in a few months now we have no one, uh, people that have zero knowledge and coding already developing and deploying agents into a secure environment. [00:28:19] Jose Gomez Cueto: It is happening. [00:28:20] Vince Menzione: Yeah. [00:28:20] Jose Gomez Cueto: So, uh, then, uh. Copilot. It is not a competitor charge, GVP or cloud. It is a platform we have both included. [00:28:29] Vince Menzione: Yes. [00:28:29] Jose Gomez Cueto: Do we have multimodal, we have iq. That is everything, uh, closed in terms of, uh, your intelligence. It is secure by default. Uh, and then allowing you to, to do, um, agents and then agents 365 to manage it. [00:28:41] Jose Gomez Cueto: So basically those three stages, customer zero. Uh, copilot agents and Agents 365 as your tool to, to manage them [00:28:50] Vince Menzione: and don’t go rogue and start doing your own things with anthropic and setting up your own instances because you’re gonna compromise your, your instance in your environment. [00:28:59] Jose Gomez Cueto: Well, actually, uh, if you do it in the copilot interface [00:29:02] Vince Menzione: Oh, well, I’m saying do it. [00:29:03] Vince Menzione: Yeah. I’m, I’m at RO going off, off, off, uh, [00:29:06] Jose Gomez Cueto: off. Yeah. Yeah, [00:29:07] Vince Menzione: yeah. Great. Well, thank you, sir. Appreciate you. Thank you. Thanks for listening to the Ultimate Partner Podcast. If today’s conversation resonated, share it with a partner leader in your network. Subscribe where you listen, and head over to the Ultimate partner.com for show notes related content and the resources for this episode. [00:29:29] Vince Menzione: And if you haven’t already, now’s the time to register for the Ultimate Partner Live event in Reston, Virginia, October 26th through October 28th. Until next time. Keep showing up in the rooms that matter because being in the room changes everything.

Hay un debate que lleva tiempo flotando en toda la comunidad del desarrollo web, y en la de WordPress en particular: ¿tiene sentido seguir apostando por una plataforma tradicional cuando una IA es capaz de generar páginas enteras, con código personalizado e interfaces, en cuestión de segundos? Es una sensación perfectamente comprensible. Cuando ves lo que la inteligencia artificial puede hacer al vuelo, es natural preguntarse qué espacio le queda a WordPress. Pero, bajo mi punto de vista, el valor de WordPress no ha desaparecido: simplemente ha cambiado de lugar. En lugar de competir contra la IA, WordPress se está consolidando como el motor de ejecución y la base de datos sobre la que esa misma inteligencia artificial opera. Propiedad y soberanía de los datos La gran diferencia entre las soluciones no-code basadas en IA (o las plataformas SaaS empaquetadas que te alquilan su infraestructura) y WordPress es el control total. Con WordPress, la base de datos, los usuarios, los pedidos y los contenidos son 100% tuyos o de tu cliente. No hay locking. Ese concepto que antes se aplicaba a ciertos temas cerrados hoy lo trasladamos a la IA: no dependes de cambios drásticos en los precios de las APIs de empresas como ChatGPT o Claude, ni del cierre repentino de un servicio. La soberanía digital es un activo cada vez más crítico. Hay empresas que despidieron a mucha gente pensando que la IA lo haría todo, y muchas han terminado quebrando por eso. La IA necesita un backend confiable La inteligencia artificial es extraordinaria generando código y diseñando interfaces al vuelo, pero necesita una estructura sólida detrás para funcionar en el mundo real: la lógica de negocio, la persistencia, la gestión de usuarios y roles, las pasarelas de pago, las suscripciones y los flujos complejos. Un ejemplo real: la web de vozcaster.com la desarrollé entera con IA. Registré el dominio, la alojé en Cloudflare y le pedí a la IA que me creara un HTML estático muy mono, con colores bonitos y una tabla de precios dinámica con su selector anual. Todo rápido y precioso. Pero cuando llega el momento de la verdad —los usuarios, la seguridad, la gestión de suscripciones—, esa tabla enlaza a potencia.pro, donde todo lo gestiona WordPress mediante un plugin. La IA te resuelve la parte estática; la lógica de negocio y la persistencia en condiciones te las da una plataforma madura. Infraestructura lista para usar En lugar de construir desde cero un sistema de autenticación, una base de datos y un panel de administración para cada proyecto generado por IA, WordPress te ofrece toda esa arquitectura probada en batalla de forma nativa. Además, WordPress ya no es el simple generador de plantillas HTML o el gestor de blogs de antaño. Hoy cuenta con: APIs robustas gracias a su REST API y a soluciones como GraphQL. Un funcionamiento excelente como CMS headless, donde la IA alimenta los datos mientras la capa de presentación se gestiona externamente. Integración nativa con agentes de IA gracias al protocolo MCP, que permite conectar un sitio WordPress con modelos de inteligencia artificial para generar contenido, automatizar soporte o procesar datos de forma mucho más sencilla, precisamente por la madurez de la plataforma. Los flecos y los casos de borde Crear un prototipo visual con IA lleva minutos. Pero resolver los flecos finales —el cumplimiento del RGPD, las integraciones con ERP locales, la facturación compleja, los flujos de trabajo específicos— sigue requiriendo un sistema maduro. El repositorio de plugins de WordPress aporta esa flexibilidad para cubrir ese 1% de necesidades complejas que una herramienta automática suele pasar por alto. Es curioso: la IA arranca a toda velocidad, pero al llegar a cierto punto se frena, porque necesita hacer cosas que WordPress ya resuelve de forma simple por estar tan implementado. En una carrera de fondo, WordPress se come a la IA. Pasa igual que con los constructores visuales: Elementor es maravilloso y rapidísimo para la primera versión de una web presencial de cliente, pero cuando quieres implementar cosas más complejas a veces te toca irte a temas como GeneratePress. Son esos casos de borde que WordPress tiene más que superados. El rol del profesional no desaparece: se transforma Es como el agricultor que pasa del arado al tractor: sabe lo mismo, pero lo hace más rápido. El trabajo pesado ya no es picar CSS ni maquetar desde cero. El valor del profesional con experiencia en WordPress ha migrado hacia: La arquitectura de la información y la estrategia. El criterio técnico y la seguridad: saber evaluar si el código generado por la IA es seguro, optimizado y mantenible. La capacidad de conectar el ecosistema de WordPress con flujos de IA para aportar valor real, no solo un sitio estático. Otro ejemplo propio: he desarrollado un bot de Telegram que se conecta con WordPress para publicar capítulos de podcast (de hecho, este capítulo lo estoy publicando con ese sistema porque no tenía ganas de editar). Si yo no tuviera el criterio adquirido a lo largo de más de 15 años, no podría usar la IA de forma eficiente. Sé dónde está la identificación de usuario, cómo se gestiona una suscripción, dónde están los datos, y conozco a fondo plugins como PowerPress. Ese conocimiento profundo es el que me permite establecer la arquitectura y la estrategia adecuadas. No cualquiera puede desarrollar así. En resumen La IA ha democratizado la creación de webs sencillas: cualquiera puede pedirle una página que le diga lo guapo y lo alto que es, y quedará preciosa. Pero, precisamente por eso, las arquitecturas abiertas y extensibles como WordPress se han vuelto aún más valiosas como núcleo de proyectos digitales complejos: gestión de bases de datos, arquitectura, estrategia y capa de negocio. Cuando mezclamos ambos mundos —lo que ya conocemos de WordPress con la inteligencia artificial— aparecen cosas maravillosas. Así que no, la IA no nos va a quitar el trabajo: nos facilita avanzar de forma eficiente. Pero hay que usarla con criterio, y ese criterio es justamente lo que los desarrolladores del mundo WordPress hemos acumulado durante años. La IA sin criterio no sirve de nada. 🤖 El contenido de este post ha sido generado automáticamente con inteligencia artificial a partir de la transcripción del audio. Puede contener errores o imprecisiones. 🎙️ Publicado con VozCaster, el bot de Telegram que convierte tu voz en un episodio de podcast publicado. Pruébalo gratis. ¿Te ha gustado el episodio? Si quieres que sigamos experimentando con bots, protocolos y empanadillas polacas, no olvides suscribirte y dejarnos tu valoración. ¡Nos escuchamos en el próximo capítulo! Métodos de contacto Enviadnos vuestras preguntas al grupo de Telegram. Apuntaos al canal de Youtube del podcast https://www.youtube.com/potenciapro Si nos queréis decir algo directamente lo podéis hacer a @potenciapro , @materron, @mpc, o en el grupo de Telegram Y si eres muy muy muy fan del podcast Echa un vistazo a cómo nos puedes ayudar en https://potencia.pro/se-prosperoso/

Alexandra Lato, VP of Product at Stack Overflow, joins Scott to talk about how Stack Overflow is evolving for the age of AI agents. They explore the new "Stack Overflow for Agents" feature, where a single prompt bootstraps an agent with all the knowledge and APIs of Stack Overflow, and what it means for how developers, agents, and communities share and verify knowledge together. https://agents.stackoverflow.com/

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

“Sports has caught up,” says Andy Abramson, CEO of Comunicano. “I think there's a big opportunity.” In this Technology Reseller News podcast, Doug Green speaks with longtime communications strategist and entrepreneur Andy Abramson about Comunicano's return to the sports industry and the growing intersection of sports, technology, media, telecom and business. Abramson began working in professional sports as a teenager, promoting the Philadelphia Wings and later helping build Hockey Central, an early fan-development organization associated with the Philadelphia Flyers. He went on to create programs that introduced thousands of young people to hockey and helped establish initiatives that continue decades later. Comunicano eventually shifted its focus almost entirely to technology. Since then, Abramson says the agency's work has contributed to 64 exits representing approximately $9.5 billion in value for founders, investors and executives. Now, after nearly three decades focused primarily on technology and telecom, Abramson believes sports and technology are finally ready to come together in a more meaningful way. “I think I took a 27-year retirement,” says Abramson. “I kind of feel like Gordie Howe coming out of retirement.” The move has led to the launch of Comunicano Sports and DevLocker, a database containing hundreds of application programming interfaces (APIs), Model Context Protocol (MCP) servers and other development resources. Abramson says these tools can help connect sports organizations, venues, technology companies and emerging AI applications. The podcast also introduces Abramson's role as the sports technology contributor to the new Technology Reseller Sunday Report. His coverage does not focus on scores, standings or betting odds. Instead, it examines the broader business of sports, including infrastructure, ticketing, media, retail, fan engagement, transportation, data and venue technology. “Sports is more than the athletic event,” says Green. “There are a hundred industries wrapped into sports.” One example is Abramson's analysis of the changing economics of stadium and arena parking. Parking has historically generated millions of dollars for teams and venue operators, but ride-sharing services, autonomous vehicles and new transportation habits could substantially reduce that revenue. “The parking lot is getting fired,” says Abramson. For fans, ride-sharing can eliminate long walks, traffic congestion and concerns about driving after drinking. For sports organizations, however, fewer parked cars may mean the loss of a significant and dependable revenue stream. Abramson argues that teams must begin developing new ways to monetize arrivals, departures and the surrounding venue experience. His broader message is that sports organizations cannot focus only on individual games, championships or short-term moments. They must build technology, revenue systems and operational infrastructure that deliver sustainable results. “Systems matter and moments expire,” says Abramson. “It's about how you build infrastructure to win consistently on the floor, on the ice and on the court—but most importantly, at the bank.” Comunicano Sports will focus on the business, marketing and technology of sports, while TR Publications and Abramson plan to continue the conversation through a new series of sports technology podcasts. Learn more and subscribe to Abramson's coverage at Comunicano.substack.com.  

In Episode #676 of the New Media Show, host and Podcast Hall of Famer Rob Greenlee welcomes Steve Wilson, Chief Strategy Officer at Daylight Media and QCODE, for a wide-ranging conversation about the shift from producing a successful podcast to building a sustainable creator-led media company. Creators no longer operate in a simple world of publishing one show to ONE place and relying mainly on advertising. The strongest creators are building direct relationships with audiences across podcasts, video, short-form clips, newsletters, communities, subscriptions, live events, products, and services. Podcasting remains one of the most effective long-form formats for building trust and repeated audience connection. It is now one part of a larger media strategy shaped by video distribution, platform algorithms, artificial intelligence, audience ownership, and the growing power of individual personalities. Steve brings a valuable perspective to this discussion. Before joining QCODE, he spent years working with Apple Podcasts and iTunes. At QCODE, he helped develop and distribute premium scripted audio series featuring talent including Matthew McConaughey, Demi Moore, and Rami Malek. QCODE has since expanded through Daylight Media, which provides distribution, monetization, technology, marketing, and business support for a broader network of creators. Daylight currently works with roughly 70 creators and shows representing approximately 300 million monthly audio downloads and video views. Across that network, video now accounts for a significant share of consumption, although the balance between audio and video differs considerably from show to show. That difference is important. Some creators have audiences that strongly favor YouTube and Spotify video. Other shows continue to perform primarily through audio. For some programs, video may be valuable mainly as a discovery and social marketing layer rather than as the central product. Steve argues that creators should evaluate their format, audience, resources, and business goals before committing to a major video expansion. Rob and Steve discuss Apple's HLS video support and why it may become an important addition to the podcast distribution ecosystem. HLS can give creators greater flexibility in delivering video while maintaining many of the ownership and control principles associated with RSS. The larger issue is whether creators retain access to their audience data, revenue information, advertising options, and the ability to move their shows between services. The conversation also examines what makes a show work in both audio and video. A strong video presentation may include thoughtful set design, lighting, visual storytelling, archival material, graphics, and physical objects that add context. The underlying audio still needs to stand on its own. Listeners should not feel as though they are receiving an incomplete version of a show because they cannot see what is happening on screen. Steve points to television, documentary production, and the work of filmmakers such as Ken Burns as useful references. A show can begin with excellent audio storytelling and add visuals that deepen the experience without making the audio audience feel secondary. There are also cases where the audio and video products should remain different. Scripted fiction uses sound to create a theater of the mind, allowing listeners to imagine the world and its characters. Turning that same property into a visual production may require a separate creative and business decision rather than a simple video version of the podcast. We also explore the potential for long-form content on Instagram, the renewed interest in vertical video, and the growing market for microdramas. They also revisit Quibi and consider whether some earlier media ideas failed because the technology, audience behavior, or distribution environment was not yet ready for them. The discussion then turns to one of the most important distinctions between podcasting and YouTube. YouTube can generate large spikes around individual videos, but performance may vary widely from episode to episode. Traditional RSS-based podcast audiences are often more consistent. That stability can make an audio podcast especially valuable to advertisers because future episode delivery and revenue are easier to forecast. YouTube is highly effective for discovery. Podcast feeds are often stronger at developing recurring habits around a series. Successful media companies will increasingly need to understand the value of both. Catalog discovery is another major opportunity. Podcasts and long-form videos can remain useful for years, but most platforms continue to prioritize newly released material. Steve argues that the industry needs better ways to help audiences find relevant episodes from existing catalogs based on subject, location, interest, and current context. Daylight recently acquired Podcast Review, a long-running editorial publication focused on podcast reviews, interviews, recommendations, and lists. Steve sees independent editorial guidance as an important part of helping audiences find strong shows beyond what algorithms automatically recommend. Podcast Review continues to publish consumer-focused coverage while operating as a Daylight Media property with stated editorial standards and disclosures.  Rob and Steve discuss whether the audience now has more influence over what qualifies as a podcast. Listeners and viewers increasingly use the word to describe a style of recurring, personality-led media rather than a specific technical delivery method. Steve is less concerned with rigid format definitions. He compares the current debate to earlier questions about whether a streaming series released all at once should still be called television. Distribution formats change. Audience behavior changes. The most useful question is whether the content delivers the experience people expect from the show. The second half of the episode focuses on creators becoming media companies. Daylight works with creators at different stages, from established personalities such as Jon Stewart to growing shows entering advertising for the first time. Some partners need only ad sales support. Others are developing subscriptions, merchandise, tours, live events, networks, and additional shows. Steve describes Daylight as an infrastructure layer that often works behind the scenes. The goal is to help the creator's brand remain visible while providing the operational systems needed to grow. Daylight was launched as an expansion of QCODE's creator-support work, with services covering distribution, audience development, marketing, technology, products, and monetization.  Creator-first structure differs from an older media model in which the studio or network brand often controlled the relationship between talent and audience. Steve believes the next era will give creators more leverage, flexibility, and ownership. The companies that support them will need to prove their value without taking over the show’s identity. Artificial intelligence is already affecting that work, although adoption varies widely. Some creators are using AI for research, transcription, artwork, editing, and production support. Others remain strongly opposed to it. Steve does not see the choice as a complete acceptance or rejection of AI. Each use case needs to be evaluated separately. Voice dubbing may help a creator correct a short line without having to re-record an entire session. Transcription can reduce production time. Synthetic performances or undisclosed manipulation may violate the audience’s expectations and trust. Both agree that growing volumes of synthetic content may also increase the value of live, in-person, and clearly human experiences.  Podcast tours, creator conferences, community gatherings, retreats, and interactive events allow audiences to participate in ways that digital platforms cannot fully reproduce. The opportunity may extend beyond recording a regular episode on stage. Future creator events could include audience conversations, workshops, performances, shared activities, and experiences built around a particular community or interest. This episode offers a clear view of where creator media is heading. A show can remain the center of the relationship, but the greater opportunity lies in building a flexible media business around the audience, protecting creator ownership, and selecting formats that serve the content rather than following every platform trend. Topics Covered in This Episode – Why successful creators are becoming media companies – QCODE's expansion into Daylight Media – Audio and video consumption across the Daylight network – Apple HLS video and creator-controlled distribution – Why every show needs a different audio and video strategy – Set design and visual storytelling for video shows – Preserving a complete experience for audio listeners – Scripted fiction, video adaptations, and intellectual property – Instagram, vertical video, and microdramas – YouTube discovery compared with RSS audience stability – Evergreen content and podcast-catalog discovery – Apple Podcasts editorial discovery – Daylight Media's acquisition of Podcast Review – Whether audiences now define what a podcast is – Revenue beyond advertising – Subscriptions, merchandise, networks, touring, and events – Daylight as a behind-the-scenes creator infrastructure company – RSS, APIs, alternate enclosures, and platform control – Creator access to audience and revenue data – AI production tools and audience trust – The growing value of live and human experiences Chapter Time Stamp Markers: 00:00 Welcome to The New Media Show Episode 676 00:36 Creators are building full media businesses 02:24 Introducing Steve Wilson of Daylight Media and QCODE 03:05 How QCODE expanded into Daylight Media 04:23 From premium audio fiction to a creator network 05:16 Video now drives much of the network's consumption 05:59 Apple HLS video and creator flexibility 08:27 Why there is no single audio-video strategy 09:03 What makes a show work in video? 10:44 Keeping audio listeners at the center 12:09 Higher production values and video set design 14:34 Theater of the mind and the value of audio 15:37 Short-form video and social-first podcasts 16:00 Could Instagram become a long-form video platform? 17:33 Microdramas, vertical storytelling, and the Quibi lesson 18:27 Audio-first creators and video anxiety 19:24 YouTube volatility compared with RSS stability 21:01 Tentpole episodes, long-form content, and audience habits 22:10 Evergreen catalog discovery 23:26 Apple Podcasts editorial discovery 25:00 Daylight Media acquires Podcast Review 25:46 Is the audience taking control of media definitions? 27:52 Does the technical definition of a podcast still matter? 31:03 Moving from a show to a media company 32:14 Daylight's creator network and growth spectrum 32:54 Subscriptions, merchandise, tours, and new revenue 33:35 Daylight as a creator infrastructure layer 34:07 Why Daylight works behind the scenes 36:12 The future of video distribution and RSS 37:17 Alternate enclosures and location-based discovery 38:24 Creator ownership beyond RSS purity 39:59 Audio ads, video ads, platforms, and advertiser results 41:07 Independent creators gain more media power 42:07 How creators are using AI 43:02 Where AI can damage audience trust 45:05 AI voice dubbing and practical production uses 45:34 The return of live and human experiences 47:18 Podcast tours, conferences, and creator communities 49:18 Building more interactive creator events 50:51 Final thoughts on Daylight Media and QCODE 51:31 Where to find Daylight and QCODE shows 52:44 End of episode Guest Links: Steve Wilson, Chief Strategy Officer, Daylight Media and QCODE Daylight Media: https://daylightmedia.com QCODE Media: https://qcodemedia.com Podcast Review: https://podcastreview.org Steve Wilson on LinkedIn: https://www.linkedin.com/in/stephenwilson7 Steve Wilson on Instagram: https://www.instagram.com/wilsonomics Steve Wilson is currently Chief Strategy Officer at Daylight Media, with his work continuing across the QCODE and Daylight organizations. Host, Rob Greenlee and New Media Show Links New Media Show: https://newmediashow.com New Media Show on YouTube: https://youtube.com/@TheNewMediaShow New Media Show Audio on Apple Podcasts: https://podcasts.apple.com/us/podcast/new-media-show-audio/id392545649 Rob Greenlee Website: https://robgreenlee.com Rob Greenlee on LinkedIn: https://www.linkedin.com/in/robgreenlee Rob Greenlee on YouTube: https://youtube.com/@RobGreenlee Podcast Hall of Fame: https://podcasthall.com About Trust Factor Lab Trust Factor Lab helps creators, media leaders, executives, and brands build stronger audience trust across AI, video, podcasts, and creator-led media. Founded by Rob Greenlee, it focuses on content strategy, authority development, media positioning, AI transparency, audience growth, and business outcomes built through credibility. AI Disclosure Note: I used AI tools to help organize and edit this episode description and create chapter markers from the episode transcript. The editorial direction, final review, industry perspective, and responsibility for the published content remain mine. The discussion reflects the views expressed by my guest and me during the recorded conversation.The post Creators Become Media | Steve Wilson, Daylight Media and QCODE #676 first appeared on New Media Show.

¿Todavía usando awk para extraer información de ps aux o df? En 2026 hay herramientas mucho mejores. En este episodio te presento tres herramientas que forman un tridente imbatible para trabajar con información del sistema en formato JSON: jc, jq y gron.jc es un conversor de comandos Linux a JSON. Se instala con pip y un simple pipe convierte la salida de ps, df, free, ss, systemctl, lsblk y hasta 60 comandos más en JSON estructurado. Olvídate de awk y de los scripts frágiles que se rompen cuando cambia el orden de las columnas. Con jc, el JSON no depende del formato de salida. Tiene parsers específicos para cada comando, incluyendo crontab, last, lsof, pip list, lsmod, date y más. Si no encuentra el parser que necesitas, puedes crear el tuyo. Está escrito en Python y tiene licencia MIT.jq es la navaja suiza de los JSON. Te permite filtrar, ordenar, agrupar, seleccionar y transformar cualquier JSON con una sintaxis potente. Está escrito en Go y es maduro, estable y rapidísimo. Combinado con jc, puedes listar los procesos que más RAM consumen, los discos por encima del 80% de ocupación o los servicios que han fallado, todo en una sola línea. Si la sintaxis te parece liosa, puedes pedirle a cualquier modelo de lenguaje que te genere la expresión que necesitas.gron es el menos conocido pero igual de útil. Aplana un JSON convirtiendo cada valor en una línea independiente con su ruta completa. ¿Para qué sirve? Para poder usar grep directamente sobre un JSON. Si alguna vez has hecho un curl a una API y has intentado hacer grep sobre el resultado, sabes que no funciona porque todo está en una línea. Con gron, cada valor tiene su propia línea y puedes buscar con grep. Además permite la operación inversa con --ungron: modificas el JSON aplanado con sed y lo reconstruyes.En el episodio presento sysreport.py, un script en Python que junta toda la información del sistema en un solo JSON usando jc y luego te permite hacer preguntas en lenguaje natural usando Llama 3.2 con Ollama. Le preguntas qué procesos consumen más RAM, qué servicios están caídos o si hay algún disco lleno, y él te responde en lenguaje natural. Todo corriendo en local, sin gastar un euro en APIs.El script se puede usar como API local, se combina con watch para monitorización en tiempo real y con notify-send para notificaciones en el escritorio. Además se integra directamente con el nightly-runner del episodio 815 para incluir el estado del sistema en el resumen matutino.Capítulos:0:00 - Introducción: el problema de la salida en texto plano2:30 - jc: convierte comandos Linux a JSON5:00 - jq: la navaja suiza de los JSON8:00 - gron: haz greppable cualquier JSON10:30 - Combinando jc y jq para consultas del sistema13:00 - sysreport.py: el script que lo junta todo16:00 - Preguntando al sistema en lenguaje natural19:00 - Monitorización con watch y notificaciones21:00 - Ventajas: local, sin coste y sin dependencias23:00 - Cierre: el tridente JSONMás información y enlaces en las notas del episodio

Infrastructure readiness has become the real bottleneck for agentic AI in healthcare, as enterprises confront the shift from systems that generate content to systems that execute tasks across complex, regulated workflows.   In this episode, Alex Tyrrell, SVP and CTO of Health at Wolters Kluwer, examines how agentic AI changes operational demands for healthcare organizations in conversation with host Matthew DeMello, highlighting the need for domain‑adapted reasoning, granular APIs, and stronger observability as agents drive higher‑volume system interaction.   He underscores the practical implications for leaders: preparing backend systems for agent‑driven load, adapting models to real‑world workflows, and avoiding monolithic architectures that limit safe, scalable deployment.   Learn how to evaluate AI vendors by assessing leadership expertise, and why funding benchmarks can signal product maturity and stability, download our free PDF report, "5 Ways to Select the Right AI Vendor," at emerj.com/aiv1  

Spotify is shifting from a traditional audio platform into a powerful multi-format ad engine driven by advanced data targeting and transparent, value-exchange sponsorship models. Through innovative automated tools and natural language API plugins, the streaming giant is eliminating traditional production barriers so brands of all sizes can easily deploy high-ROAS campaign creative. Key Highlights

Bun quitte Zig pour Rust en 11 jours à coups de Claude Code, pour 165 000$ payés par Anthropic : la réaction du créateur de Zig ne se fait pas attendre. TypeScript 7 débarque, réécrit en Go, 8 à 12x plus rapide. Entre les deux, Vidocq réimplémente Jakarta EE en souverain, le COBOL met un uppercut aux microservices, et un CTO demande à son équipe combien de temps il lui faudrait pour revenir à sa vélocité antérieure sans Claude Code. De quoi réfléchir avant le prochain rewrite. Enregistré le 17 juillet 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-342.mp3 ou en vidéo sur YouTube. News Langages Est-ce qu'on peut aussi utiliser des double, des longs, ou autre pour gérer les montants monétaires en Java ? https://blog.frankel.ch/bigdecimal-vs-double/ double (IEEE 754) Usage : Calculs scientifiques, métriques, statistiques. Avantages : Très performant (matériel), idéal pour l'approximatif. Risques : Erreurs d'accumulation, égalité (==) trompeuse, NaN / -0.0. Bonnes pratiques : Utiliser une tolérance (epsilon ou ULP) pour comparer ; utiliser des algorithmes de sommation compensée (Kahan/Neumaier) pour la précision. BigDecimal Usage : Finance, comptabilité, fiscalité (précision décimale stricte). Avantages : Contrôle total des arrondis et de l'échelle. Risques : Lent (allocations), immutabilité (risque de mauvaise réaffectation), confusion equals() vs compareTo(). Bonnes pratiques : Initialiser via String ou valueOf() ; utiliser compareTo pour l'égalité. Point fixe (long) Usage : Trading, systèmes haute performance, paiements. Avantages : Très rapide, déterministe, zéro allocation. Risques : Gestion manuelle de l'échelle et des débordements (Math.addExact). Points de vigilance en production Sérialisation (JSON) : Préférer les String pour BigDecimal pour éviter la perte d'échelle. Atomicité : double n'est pas atomique ; utiliser volatile ou DoubleAdder (pour les compteurs). Tests : Toujours définir un delta ou Offset pour les tests de flottants. Bibliothèques recommandées Moneta (JSR 354) : Standard bancaire complet. decimal4j : Optimisé pour le point fixe haute performance. Apache Commons Numbers : Outils robustes pour la précision et les sommations. Typescript 7 est de sortie devblogs.microsoft.com/typescript/announcing-typescript-7-0 Performance majeure : Portage natif en Go offrant des gains de vitesse de 8x à 12x et une consommation mémoire réduite. Architecture optimisée : Utilisation du multithreading (mémoire partagée) et parallélisation native (analyse, vérification de types,émission). Nouvelles options de contrôle : Introduction des flags –checkers, –builders (parallélisation) et –singleThreaded (mode mono-cœur). Nouvel observateur de fichiers : Passage à une solution basée sur @parcel/watcher pour une meilleure réactivité et stabilité du mode –watch. Compatibilité et transition : Compatible avec les bases de code TypeScript 6.0. Utilisation du package @typescript/typescript6 recommandée pour maintenir des outils dépendants de l'ancienne API. Changements de configuration : Durcissement des défauts (ex: strict activé par défaut) et suppression de nombreuses options obsolètes (target: es5, baseUrl, etc.). Amélioration de l'expérience éditeur : Serveur de langage (LSP) plus stable avec une réduction de 80 % des erreurs et 60 % des crashs. Limitations actuelles : Support incomplet pour les frameworks utilisant des plugins de langage (Vue, Svelte, Astro, Angular) en attendant une API stable. "Java, the documentary" est sur YouTube, retraçant l'histoire du langage youtube.com/watch?v=… La vidéo n'était pas encore disponible à l'heure de l'enregistrement. Sortie officielle le 17 juillet. Avec des interviews de James Gosling, Brian Goetz, Venkat Subramaniam, et bien d'autres. Librairies What's New in 8.0 - Hibernate docs.hibernate.org/orm/8.0/whats-new L'intégration de Jakarta Persistence 4.0 apporte des nouveautés majeures comme EntityAgent (qui standardise la StatelessSession), les mappings de result set en SQL natif, et de nouvelles options de configuration de session et de requêtes (Session Creation Options, Query Options). Le support de Jakarta Data 1.1 est ajouté pour les Hibernate Data Repositories, incluant l'intégration avec les requêtes statiques JPA4, les projections @Select, et les repositories asynchrones via Jakarta Concurrency ou Hibernate Reactive. L'introduction du Graph-based Flushing remplace l'ancienne approche basée sur des heuristiques par un modèle de dépendances utilisant les contraintes relationnelles, afin d'améliorer la fiabilité des tris, la gestion des batchs et les performances globales (bien que l'ancienne méthode reste temporairement disponible). L'API ProcedureCall a été améliorée pour faciliter le casting des résultats (asResultSetOutput) et permettre la déclaration paresseuse (lazy) du mapping des ResultSet. Hibernate supporte désormais la sécurité au niveau de la ligne (Row-Level Security) de manière native pour les bases de données compatibles (PostgreSQL, Db2, SQL Server, CockroachDB) afin de gérer la visibilité en contexte multi-tenant. Une nouvelle méthode getReference() permet dorénavant de récupérer la référence d'une entité directement à partir de son natural id. Le mode Safe Mode Validator (hibernate.query.safe_mode_enabled=true) fait son apparition pour bloquer les opérations risquées comme sql(), function() ou column() dans les requêtes HQL et Criteria, ce qui est particulièrement utile pour les applications exposées aux LLMs. La gestion des associations bidirectionnelles lors de la phase de flush peut maintenant être prise en charge automatiquement par Hibernate (hibernate.bidirectionality_management=true), synchronisant la référence côté inverse de l'association. Le Subselect Fetching est considérablement amélioré, supportant dorénavant les associations "to-one" pour le bulk select fetching (au lieu de se limiter aux collections) et devenant une option de premier ordre via FetchMethod.BY_SUBQUERY. Un des papas de Cucumber et Gherkin lance Var, une alternative pour le test et le BDD var.oselvar.com Lancement de Vár : Nouvel outil de test créé pour pallier les défauts de Cucumber. Limites de Cucumber : Syntaxe Gherkin trop rigide, intégration difficile avec les exécuteurs de tests et support éditeur limité. Usage avec l'IA : Conçu spécifiquement pour vérifier que les agents IA respectent les intentions et spécifications de l'utilisateur. Fonctionnement : Utilisation du Markdown plutôt que du Gherkin ; sert à la fois de guide et d'outil de vérification. Développement assisté : Code et documentation générés en grande partie par Claude sous supervision humaine. Appel aux retours : Projet ouvert aux tests et aux critiques de la communauté. Web Une nouvelle méthode HTTP : QUERY https://kreya.app/blog/new-http-query-method-explained/ Méthode HTTP QUERY (RFC 10008) pour les recherches complexes. Problème : GET (limité par l'URL) vs POST (sémantique inadaptée). Avantages : Permet un corps de requête, sûr, idempotent et cacheable. Limites : Support infrastructurel faible, non partageable par lien, cache complexe. Usage : À réserver aux requêtes complexes si l'environnement le permet. Comment je fais du design en tant que dev backend eventuallycoding.com/p/comment-je-fais-du-design-en-tant-que-dev-backend Hugo Lassiège retrace l'évolution de son workflow de création d'interfaces en tant que développeur backend, depuis ses débuts avec Bootstrap jusqu'à l'ère de l'intelligence artificielle. L'article explique comment la structuration des éléments visuels a progressé grâce à l'Atomic Design, l'émergence des design systems et l'adoption des design tokens via un framework comme Tailwind. L'auteur détaille son processus actuel qui s'appuie fortement sur Claude Design pour générer et itérer sur des maquettes à partir d'un brief, d'un screenshot ou d'un design system de référence. Il aborde également le risque de slopification et de standardisation extrême apporté par ces outils, rappelant que si l'IA simplifie la technique, il reste crucial d'injecter de l'identité et de l'originalité pour éviter un web trop aseptisé. Data et Intelligence Artificielle De l'utilisation de SKILL.md et de "loop engineering" pour augmenter sa productivité glaforge.dev/posts/…/of-skills-and-loops-with-ai-assistance Les skills permettent d'encoder une procédure de manière répétable et automatisable Le loop engineering enlève l'humain de la boucle afin que l'agent atteigne un objectif donné de façon plus autonome Pour écrire des Codelabs (sorte de tutoriel guidé pas à pas) Guillaume a transformé une séance de création de codelab avec son agent préféré (Antigravity) en skill réutilisable pour l'écriture de ses prochains codelabs Il a également utilisé l'approche de "loop engineering" à la mode en ce moment pour que son agent IA compile, exécute, teste les instructions et le code de son codelab, pour qu'il soit complètement fonctionnel Gain estimé : passer de 2 jours de travail à moins de 2 heures ! Redeploying Claude Fable 5 anthropic.com/news/redeploying-fable-5 Anthropic a annoncé le rétablissement de l'accès à ses modèles Claude Fable 5 et Mythos 5, qui avaient été suspendus suite à des restrictions d'exportation imposées par le gouvernement américain le 12 juin 2026. Cette suspension faisait suite à un rapport d'Amazon démontrant une méthode pour contourner les garde-fous de Fable 5, lui permettant d'identifier et d'exploiter une vulnérabilité logicielle (un jailbreak). Pour y remédier, Anthropic a renforcé ses mécanismes de sécurité en déployant un nouveau classifieur capable de bloquer cette technique spécifique dans plus de 99 % des cas, acceptant en contrepartie une augmentation des faux positifs sur des requêtes bénignes. Face à l'absence de consensus sur l'évaluation des jailbreaks, Anthropic s'associe à Amazon, Microsoft, Google et d'autres partenaires pour développer un standard industriel évaluant la sévérité de ces failles selon quatre critères : gain de capacité, étendue du gain, facilité d'arsenalisation et découvrabilité. L'entreprise s'engage également à approfondir sa collaboration avec le gouvernement américain, notamment via des évaluations pré-déploiement, un partage rapide d'informations sur les failles, et des ressources dédiées à la recherche conjointe sur la sécurité de l'IA. Outillage La réécriture de Bun en Rust et la réaction du créateur de Zig bun.com/blog/bun-in-rust et andrewkelley.me/post/my-thoughts-bun-rust-rewrite.html Bun, le runtime JavaScript et TypeScript écrit à l'origine en Zig, a été entièrement réécrit en Rust pour des raisons de stabilité et de gestion de la mémoire. Cette migration massive d'un demi-million de lignes de code a été bouclée en seulement 11 jours grâce à l'utilisation intensive de Claude Code fonctionnant en parallèle, pour un coût d'API estimé à 165 000 dollars financé par Anthropic. Andrew Kelley, le créateur de Zig, a réagi publiquement en qualifiant l'ancienne base de code de Bun de "slop" remplie de hacks et de fuites mémoire accumulées par une course aux fonctionnalités. Kelley exprime son soulagement face à ce départ, expliquant que les plantages incessants de Bun devenaient un passif réputationnel toxique pour le langage Zig et sa fondation. Le rachat de Bun par Anthropic fin 2025 avait déjà mis fin aux donations financières de Bun envers la Zig Software Foundation, facilitant cette séparation. La nouvelle version Rust de Bun passe désormais la quasi-totalité des tests, réduit la taille du binaire et est déjà déployée de manière transparente en production dans Claude Code. Nouveautés de Git 2.55 github.blog/open-source/git/highlights-from-git-2-55 Support natif de FSMonitor sous Linux via inotify pour accélérer les commandes comme git status sur les grands dépôts Intégration de la compaction incrémentale MIDX (multi-pack index) dans git repack pour optimiser la réécriture des métadonnées Amélioration drastique des performances de génération des bitmaps et des pseudo-merge bitmaps lors des tâches de maintenance Nouvelle commande expérimentale git history fixup pour intégrer facilement des modifications locales dans un commit antérieur Possibilité d'exécuter des hooks configurés en parallèle pour optimiser le temps de build et de validation Utilisation d'un autostash automatique lors d'un git checkout -m en cas de conflit de fusion pour éviter de bloquer l'espace de travail Nouvelle commande git format-rev permettant de formater rapidement des commits reçus via l'entrée standard (stdin) Support du push simultané vers un groupe de remotes configuré Protection contre l'exécution de séquences de contrôle de terminal malveillantes via les flux de progression distants Vidocq, une réimplémentation souveraine et sans dépendance de Jakarta EE et Microprofile vidocq.dev/posts/vidocq-a-sovereign-jakarta-ee-and-microprofile-runtime Lancement de Vidocq : Runtime Java open source complet, compatible Jakarta EE Core Profile et Souveraineté numérique : Projet européen hébergé sur Codeberg, sous licences EUPL 1.2, EPL 2 et GPL 2.0. Standardisation totale : Implémentation fidèle des spécifications (CDI, REST, JSON, etc.), validée par 5 650 tests TCK officiels. Sécurité radicale : Zéro dépendance externe et aucune bibliothèque tierce. Aucune manipulation de bytecode à l'exécution (« magie » générée à la compilation via JDK 25). Compatible JPMS, AOT, GraalVM et Leyden CDS. Disponibilité : Projet en phase alpha, code et documentation accessibles sur vidocq.dev. Article complémentaire qui revient sur la genèse de Vidocq, en utilisant l'IA et les TCKs pour driver l'aspect spec-driven development vidocq.dev/posts/the-story-of-vidocq Le "selfware" : Guillaume s'est fait plais' en vibe-codant son propre éditeur de texte glaforge.dev/posts/…/selfware-building-my-own-text-editor-without-knowing-swift Concept de « Selfware » : création de logiciels conçus exclusivement pour soi-même, sans monétisation ni contraintes liées aux utilisateurs tiers. Le rôle de l'IA : les agents de programmation (comme Antigravity) suppriment la barrière technique de l'apprentissage des langages (Swift, APIs) pour les non-développeurs. Développement minimaliste : privilégier la performance et l'utilité directe (démarrage instantané, interface native) au détriment des fonctionnalités complexes (plugins, télémétrie, gestion de comptes). Absence de pression : libération des contraintes liées à la compatibilité, à la maintenance logicielle et aux retours utilisateurs ; le logiciel n'a besoin d'être « assez bon » que pour ses propres besoins. Incitation à l'autonomie : encourager la création d'outils sur mesure pour résoudre les frictions quotidiennes plutôt que de subir les limitations des logiciels commerciaux. Architecture Le cobol a donné un uppercut au microservices https://freedium-mirror.cfd/@maahisoft20/your-microservices-lost-to-cobol-let-that-sink-in-8ce2e236d007 Retour d'expérience sur la migration d'un système COBOL vers des microservices cloud-native qui s'est soldée par un retour en arrière après avoir constaté que le traitement batch initial était plus rapide, moins cher et plus fiable Là où le batch COBOL traitait 2.4 millions d'enregistrements en 11 minutes, le système distribué modernisé à base de message queues, retries et Kubernetes prenait 47 minutes et tombait sous la charge COBOL brille par ses caractéristiques conçues spécifiquement pour la finance comme le calcul décimal précis sans floating point errors et l'absence totale d'overhead réseau, de conteneurs ou de cold starts Rappel que distribuer un système multiplie les points de défaillance silencieux et complexifie la gestion de la cohérence transactionnelle par rapport à une exécution locale séquentielle Une invitation à se demander si les projets de décomposition en microservices apportent réellement un gain de performance de bout en bout pour l'utilisateur final ou s'ils optimisent seulement le diagramme d'architecture Méthodologies Ma meilleure question d'entretien Spring beaufume.fr/articles/spring-interview Florian beaufumé partage sa question d'entretien favorite pour évaluer des développeurs Spring de niveau intermédiaire à avancé : "Que pouvez-vous me dire sur le paramètre spring.jpa.open-in-view ?". Ce paramètre détermine l'activation du pattern Open Session In View (OSIV) qui, lorsqu'il est à true (la valeur par défaut dans Spring Boot), maintient l'un EntityManager JPA ouvert durant toute la requête HTTP. Si l'OSIV facilite le développement en évitant les fameuses LazyInitializationException lors de la sérialisation des entités en JSON, il pose d'importants problèmes de performance en provoquant des requêtes SQL non maîtrisées (comme le problème du N+1 select) en dehors de la couche service. Maintenir l'OSIV actif augmente également le temps de rétention des connexions au sein du pool de la base de données, limitant la scalabilité de l'application. La recommandation est de désactiver ce comportement en le positionnant à false, et de gérer explicitement le chargement des données requises au sein des transactions (via des DTOs, des requêtes JOIN FETCH ou des Entity Graphs) pour garder le contrôle sur les accès à la base de données. 10 points à retenir du rapport AI Engineering 2026 : The Acceleration Whiplash faros.ai/blog/ai-acceleration-whiplash-takeaways L'IA a franchi un cap et est devenue l'auteur principal du code : le taux d'acceptation du code généré est passé de 20% à 60% dans les équipes étudiées par Faros AI. La vélocité métier est bien réelle, avec une augmentation de 66% des epics livrées et une hausse de 33,7% du throughput des tâches par développeur. Ce volume cache un code churn massif (+861%), ce qui signifie qu'une quantité énorme de code est supprimée ou remplacée peu après avoir été ajoutée. La qualité en aval se dégrade fortement : les bugs par développeur ont augmenté de 54% et le nombre d'incidents par pull request a explosé de 242,7%. Le processus de code review est complètement saturé, entraînant un temps médian de relecture multiplié par cinq et une augmentation de 31,3% des PRs mergées sans aucune revue. Le système repose de plus en plus sur les développeurs seniors qui subissent une "senior engineer tax", devant relire un volume insoutenable de code à l'apparence correcte mais structurellement fragile. Contrairement à certaines hypothèses récentes de DORA, une forte maturité DevOps ne protège pas les entreprises contre cette détérioration ; le "Acceleration Whiplash" frappe de la même manière les équipes très performantes. En résumé, les outils d'IA inondent les pipelines de livraison avec un volume de code pensé pour un rythme machine, alors que les systèmes de vérification reposent toujours sur un rythme de validation humain. Loi, société et organisation Le coût d'une equipe d'engineering qui ne sait plus ce qu'elle fait dans un contexte d'augmentation de coût des coding agents https://freedium-mirror.cfd/@developer_programmer/i-spent-47-000-on-claude-code-in-90-[…]-asked-me-one-question-and-i-couldnt-answer-it-af3b203f81bb Une équipe de 8 ingénieurs a vu sa vélocité de développement exploser en utilisant Claude Code de manière intensive, jusqu'à recevoir une facture d'API salée de 47 213 $ pour seulement trois mois d'utilisation. Face à cette dépense, la question piège du CTO n'était pas sur le montant, mais sur la dépendance : "Si nous arrêtions Claude Code demain, combien de temps faudrait-il pour que notre vélocité revienne à son niveau initial ?". L'auteur s'est rendu compte qu'il était incapable de répondre car son équipe, en particulier les profils juniors, avait commencé à perdre l'habitude de concevoir et d'implémenter des fonctionnalités complexes sans l'aide permanente d'un agent. Le deuxième risque stratégique soulevé est celui de la dépendance tarifaire et du vendor lock-in : si l'outil devient une infrastructure indispensable au quotidien, l'entreprise perd tout pouvoir de négociation face aux augmentations de prix de l'éditeur d'IA. Pour éviter que l'IA ne devienne une béquille qui atrophie les compétences de l'équipe, l'article suggère de poser des limites budgétaires strictes, d'organiser régulièrement des sprints sans IA ("AI-free sprints") et de concevoir des processus de développement portables. Retour de Nicolas Delsaux sur jqwik qui donne une perspective plus complète concernant jqwik, il me semble que vous oubliez (comme tous les gens qui parlent de LLM dans "l'industrie") que l'auteur n'a pas fait ça juste pour faire chier le monde, mais parce que ces outils ont des externalités incroyablement négatives, ce dont l'auteur s'explique dans son blog (blog.johanneslink.net/2026/06/09/the-jqwik-anti-ai-affair) Vous oubliez également de signaler que le ticket (github.com/jqwik-team/jqwik/issues/708) par lequel un utilisateur se plaint de cette fonctionnalité a été écrit par un agent. N'oubliez pas non plus que l'enthousiasme pour ces technologies n'est en fait pas universel, et que ces technologies sont loin d'être inévitables (les gains de vitesse ne sont, d'après circle CI - circleci.com/resources/2026-state-of-software-delivery, pas des gains de productivité ) OkHttp, Okio, Retrofit et SQLDelight rejoignent Commonhaus ! commonhaus.org/activity/315.html La fondation Commonhaus, via une publication de Andres Almiray, annonce l'arrivée de quatre projets majeurs de l'écosystème Java et Kotlin : OkHttp, Okio, Retrofit et SQLDelight. Ces projets, initialement créés chez Square (devenu Block), sont désormais regroupés et gérés sous la bannière lysine.dev au sein de la fondation. Jesse Wilson et Jake Wharton, créateurs et mainteneurs historiques de ces outils, rejoignent Commonhaus en tant que leaders de lysine.dev. Suite à leur départ de Block, ils expliquent avoir choisi Commonhaus pour offrir à leur immense communauté d'utilisateurs un cadre de gouvernance pérenne, stable et digne de confiance. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 28-30 août 2026 : State of the Map - Champs-sur-Marne (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 10-11 septembre 2026 : Nantes Craft - Nantes (France) 17 septembre 2026 : dotAI - Paris (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 18 septembre 2026 : dotJS - Paris (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 22 septembre 2026 : Salon Data 2026 - Nantes (France) 22-23 septembre 2026 : Agile en Seine & IA 2026 - Paris (France) 24 septembre 2026 : aMP Day Montpellier 2026 - Montpellier (France) 24 septembre 2026 : OWASP AppSec Days France 2026 - Paris (France) 24 septembre 2026 : PlatformCon Paris - Paris (France) 24 septembre 2026 : React Native Connection 2026 - Paris (France) 24-26 septembre 2026 : Paris Web 2026 - Paris (France) 25 septembre 2026 : SAP Inside Track Paris 2026 - Paris (France) 28-29 septembre 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 2 octobre 2026 : DevFest Perros-Guirec 2026 - Perros-Guirec (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) 8-9 octobre 2026 : Forum PHP 2026 - Marne-la-Vallée (France) 12 octobre 2026 : Dev With AI - Paris (France) 22-23 octobre 2026 : Agile Tour Bordeaux 2026 - Bordeaux (France) 26 octobre 2026 : Agile Tour Montpellier - Montpellier (France) 27-29 octobre 2026 : Directions EMEA 2026 - Paris (France) 29-30 octobre 2026 : Campus Agile Grenoble - Grenoble (France) 29-30 octobre 2026 : BDX I/O 2026 - Bordeaux (France) 29-30 octobre 2026 : Agile Tour Nantais 2026 - Nantes (France) 29 octobre 2026-1 novembre 2026 : Pycon FR - Biarritz (France) 30 octobre 2026 : Cloud Nord 2026 - Lille (France) 4-5 novembre 2026 : Devoxx Morocco - Casablanca (Morocco) 14-15 novembre 2026 : Capitole du Libre - Toulouse (France) 19 novembre 2026 : DevFest Toulouse 2026 - Toulouse (France) 19 novembre 2026 : Agile Laval 2026 - Laval (France) 19 novembre 2026 : OVHcloud Summit - Paris (France) 19 novembre 2026 : Codeurs en Seine - Rouen (France) 27 novembre 2026 : DevFest Paris 2026 - Paris (France) 1-3 décembre 2026 : Apidays Paris - Paris (France) 2-3 décembre 2026 : Cloud Native AI Summit Europe - Paris (France) 4 décembre 2026 : DevFest Lyon 2026 - Lyon (France) 4 décembre 2026 : DevFest Dijon 2026 - Dijon (France) 9-10 décembre 2026 : OpenSource Expérience - Paris (France) 9-10 décembre 2026 : DevOps REX - Paris (France) 10 décembre 2026 : KCD Provence - Aix-en-Provence (France) 10 décembre 2026 : DevCon 28 : sécurité | post-quantique | hacking édition 2027 - Paris (France) 14-16 janvier 2027 : SnowCamp 2027 - Grenoble (France) 7-9 avril 2027 : Devoxx France 2027 - Paris (France) 3 juin 2027 : Cloud Native Days France 2027 - Paris (France) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

Send a text to Melissa and she'll answer it on the next episode. A pink, gritty, light-sensitive eye can derail a whole day—especially when it races through a classroom or leaps from one sibling to the next. We take the panic out of conjunctivitis by mapping the most common patterns you'll actually see at home and pairing them with precise homeopathic remedies that calm burning, reduce discharge, and help the eyes recover smoothly without harsh drops.We start by demystifying what conjunctivitis is and how it spreads, then sort the big three: viral pink eye with profuse tearing, bacterial cases with thick yellow or green discharge, and allergic flare-ups marked by relentless itch and seasonal triggers. From there, we share a clean, practical remedy guide: Euphrasia for watery, burning, light-sensitive eyes; Pulsatilla for crusted lashes and thick discharge, especially in children who crave fresh air; Apis for puffiness and stinging that loves cold compresses; Hepar sulf for painful, touch-sensitive eyes that want warmth; Argentum nitricum for stringy, rope-like mucus; Sulfur for recurrent, night-worse irritation; and Belladonna for sudden, bright-red intensity. You'll hear our simple dosing philosophy—choose one well-matched remedy and give it time—plus when to reassess and re-match as the picture shifts.We also dig into supportive care that actually helps: hygiene habits to slow the spread, warm or cool compresses for quick comfort, when to ditch contacts for glasses, and why skipping eye makeup can speed relief. We discuss susceptibility and recurrence, explaining how patterns point to smarter remedy choices and gentle prevention. By the end, you'll have a straightforward roadmap to navigate pink eye with confidence, reduce household transmission, and feel equipped to act early the next time eyes turn red and irritated.If this guide helps, subscribe, share with a friend who needs it, and leave a quick review to help others find the show. Want more hands-on learning or acute support? Visit melissacringle.com for the Inner Healing Circle and our acute consultation options.You may also gain Access to my Fullscript dispensary and save 30% by going to: https://us.fullscript.com/welcome/mcrenshaw FIND ME!

In this episode, we dive into how interactive visual experiences drive online sales and lower backend costs. Angelo Coletta, founder and CEO of Zakeke, shares how his plug-and-play platform helps brands stand out with 3D product customization, virtual try-ons, and brand-safe AI automation. He also reveals strategies for launching quickly without complex development cycles. Topics discussed in this episode:  How visual interaction layers improve customer loyalty.What customization tools do to lower backend operational costs.How automated club uniform ordering eliminates manual mistakes.What 3D visualization adds to enterprise sofa configuration.Why AI-driven image generation boosts catalog visual variety.How computer vision algorithms prevent costly manufacturing errors.Why brand safety guardrails prevent AI prompt hallucinations.What 3D catalogs do to reduce global sampling costs.How prompt-driven onboarding speeds up setup processes.Why flexible public APIs simplify complex stack integrations.Links & ResourcesWebsite: https://www.zakeke.com/LinkedIn: https://www.linkedin.com/company/zakeke/Instagram: https://www.instagram.com/zakeke.official/Facebook: https://www.facebook.com/zakekeofficial/YouTube: https://www.youtube.com/channel/UCZPd_oA-Hjq6CE4G1vSUM1QGet access to more free resources by visiting the show notes at https://tinyurl.com/3jxf4djhI'd love your feedback. Tap the the link to send me a text. ______________________________________________________LOVE THE SHOW? HERE ARE THE NEXT STEPS!Follow the podcast to get every bonus episode. Tap follow now and don't miss out!   Rate & Review: Help others discover the show by rating the show on Apple Podcasts at https://tinyurl.com/ecb-apple-podcasts   Join our Free Newsletter: https://newsletter.ecommercecoffeebreak.com/   Support The Show On Patreon: https://www.patreon.com/EcommerceCoffeeBreak   Partner with us: https://ecommercecoffeebreak.com/partner-with-us/

¿Cansado de perder 15 minutos cada mañana revisando el tiempo, las noticias, las ofertas y el estado de tu servidor? En este episodio te muestro cómo automatizar todo ese proceso con un script en Python, un timer de systemd y un modelo de lenguaje local. Sin n8n, sin agentes, sin servicios externos de pago.Mucha gente piensa que para automatizar cualquier cosa necesitas un agente con montones de herramientas MCP, skills y configuración. Pero la realidad es que para muchas tareas cotidianas, un agente es como usar un lanzamisiles para matar una mosca. Consume demasiado contexto, demasiados recursos y al final no es la solución más eficiente.En este episodio te presento el patrón de las tres capas: un script que hace el trabajo, un timer que lo ejecuta a una hora determinada y un sistema de notificaciones que te envía el resultado. Con esto puedes automatizar cualquier cosa de forma sencilla, eficiente y completamente bajo tu control.Te explico cómo he creado el nightly-runner, un script en Python que cada madrugada recopila información de cuatro fuentes distintas. Primero consulta el tiempo en wttr.in, que te devuelve un JSON con la temperatura, el viento, la humedad y los rayos ultravioleta. Luego hace scraping con IA de tus fuentes de noticias favoritas, extrayendo titulares y valorando su relevancia. Después busca ofertas de zapatillas de running en varias tiendas, comparando los precios con los del día anterior. Y por último recoge información del sistema con df, free, uptime y ps aux para saber si tu disco se está llenando o te estás quedando sin RAM.Toda esa información se guarda en archivos JSON y luego se pasa por un modelo de lenguaje local, Llama 3.2 con Ollama, que genera un resumen en lenguaje natural. El resultado es un mensaje de Telegram con un tono cercano que te da los buenos días, te cuenta el tiempo que va a hacer, te destaca las noticias importantes, te avisa si hay una oferta que no puedes dejar pasar y te informa del estado de tu servidor. Todo en un solo mensaje.El timer de systemd con Persistent=true se asegura de que si tu equipo estaba apagado a las 4 de la mañana, el script se ejecute en cuanto se encienda. Y cada capa es tolerante a fallos: si wttr.in está caído, el script simplemente omite el tiempo y el resumen dice que no hay información meteorológica disponible. Si no hay ofertas nuevas, no las menciona. Si Ollama no responde, envía el resumen sin procesar.Lo mejor de todo es que no necesitas saber Python para montar esto. Puedes usar Open Code o Gemini para que te genere el script con solo explicarle lo que quieres. Y para ejecutarlo, Llama 3.2 en local es más que suficiente. Sin gastar un euro en APIs.Capítulos:0:00 - Crítica a los agentes como solución universal2:00 - El problema: 15 minutos perdidos cada mañana4:00 - La solución: tres capas (script, timer, notificación)5:30 - wttr.in: el tiempo en JSON con un curl7:30 - Noticias: scraping con IA para extraer titulares9:30 - Zapatillas: comparativa de precios contra caché11:00 - Sistema: df, free, uptime y ps aux13:00 - El resumen: todos los JSONs pasan por Llama 3.216:00 - Systemd timer con Persistent=true18:00 - Notificaciones a Telegram y notify-send20:00 - Tolerancia a fallos en cada capa21:30 - Genera el script con IA aunque no sepas Python23:00 - Comparación con Hermes: menos es másEste podcast pertenece a la red de Sospechosos Habituales. Más información en atareao.esMás información y enlaces en las notas del episodio

Don’t Fade and Die in AI Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ Matt Yanchyshyn, VP AWS Marketplace, Rekha Thangelapalita, Elastic GSI Leaders; Allison McFadden, Accenture AWS Leader; and James Kang of Nvidia join Ultimate Partner. In this panel discussion, leaders from Elastic, Accenture, Nvidia, and AWS dissect the urgent shifts in the ecosystem, emphasizing that partners must adapt to AI and agentic co-selling or risk fading away completely. The conversation explores the necessity of deep co-engineering, the power of multi-product solutions in the AWS marketplace, and how automated agents are now replacing traditional human sales pipeline progression. By embracing data readiness and strategic collaboration, organizations can survive the “token maxing” era, effectively scale their enterprise opportunities, and align with NVIDIA’s five-layer strategy to dominate the new cloud landscape. https://youtu.be/zUkL4Wqsa68 Key Takeaways AI agents will automate the majority of AWS partner co-selling attachments and opportunity progressions this year. Partners who fail to embrace agentic workflows and automated governance face the existential risk of fading into obsolescence. Successful multi-product offerings require a “blood to all organs” approach that benefits the client, the ISV, the GSI, and the hyperscaler simultaneously. Nvidia’s “five-layer cake” model emphasizes that successful outcomes at the application layer automatically drive growth for all underlying infrastructure. The “token maxing” phenomenon is forcing enterprises to seek cost-effective, open-model alternatives to scale their generative AI securely. Integrating GSIs and ISVs on the AWS marketplace significantly increases enterprise deal sizes and long-term customer renewal rates. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags strategic collaboration agreement, data readiness engine, agentic co-sell, semantic layer, token maxing, five layer cake, accelerated computing platform, open models, cloud consumption, multi-product solutions, partner central agents, propensity data, automated opportunity progression, generative AI governance Transcript Matt Y and Panel Audio Podcast [00:00:00] Vince Menzione: You have a choice. You can embrace them and figure it out and get governance and, and make your data available. Um, use the partner, central agent, move to Agen Co-sell, or you can fade and die. [00:00:11] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way Hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. [00:00:22] Vince Menzione: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi. Own your host. And each week I sit down with leaders at the intersection of technology, partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:44] Vince Menzione: It is the strategy because [00:00:46] Vince Menzione: being in the room changes everything. Let’s start. [00:00:51] Vince Menzione: We’ve got some amazing leaders joining us. So I think probably for a little bit of context, maybe just start with Rika. You can introduce yourself, your role and, uh, what, what you’ve been doing at Elastic. Yeah. [00:01:03] Rekha Thangellapalli: Yeah, sounds great. [00:01:04] Rekha Thangellapalli: Hi everyone. I’m Reka and I lead GSI Alliances at Elastic. Um, for the past 14 years, I’ve had the pleasure of building different kinds of partner ecosystems across companies such as SAP. MuleSoft, Salesforce, Coupa, and now Elastic. Um, I wanna thank Ultimate partner and Vince for having us here today. Thank you and the panel of these incredible speakers for joining me on stage. [00:01:31] Rekha Thangellapalli: Um, very excited for the conversation today. [00:01:33] Vince Menzione: We love Elastic, and you’ve had some of your other leaders on stage at other events. As such, the quality of your leadership team is amazing. Thank you. [00:01:42] Rekha Thangellapalli: I wholeheartedly agree. [00:01:45] Allison McFadden: Excellent. Um, hello everyone. Allison McFadden. I lead our North America AWS practice at Accenture. [00:01:52] Allison McFadden: Uh, I’ve been there for five years, and truth be told, it was my first partnership role, my first formal partnership role. Uh, so I can take some tips from all of you in the room here today. Prior to that, I was 21 years with IBM, and I got into partnerships because my last role at IBM was actually trying to build. [00:02:14] Allison McFadden: Linux business on the mainframe, and I had to have partners. I had to have partners to help me with workloads to run there. So I kind of learned, uh, trial by fire. But I’m excited for the conversation today. Excited to be in this room and excited to talk about what we’re doing with, uh, elastic. Thank you. [00:02:34] James Kang: Uh, my name is James Kang. Nice to see and meet everyone here. Vince, thank you for the opportunity. Thank you [00:02:38] Vince Menzione: for being here. [00:02:39] James Kang: Um, I’m with Nvidia, so I help manage the AWS partnership at Nvidia all up. Um, I guess fun fact, I’m former AWS and so I see a lot of very familiar faces here in the front row. Uh, former colleagues and then current friends. [00:02:56] James Kang: And so, uh, looking forward to the conversation. [00:02:59] Vince Menzione: Great. Well, we’ll start with an easy tia. Matt. This is not directed to you, directed to the others. So what does a successful AWS partnership look like from your C? So we’ll start with Eureka. [00:03:09] Rekha Thangellapalli: Sure. So from an ISV perspective, I think we really are looking at three things. [00:03:15] Rekha Thangellapalli: Uh, mutual investment building together. And scaling together. So when we talk about mutual investment, elastic recently signed a five-year SCA or strategic collaboration agreement with AWS. And while that is a significant milestone in our partnership, for us, what matters more is what it represents, and that is really a long-term commitment from both companies. [00:03:39] Rekha Thangellapalli: Towards product engineering, um, and joint go to market initiatives to deliver value to customers over time. And that’s what we see is that the best partnerships really compound and they build upon each other every year. Um, they don’t necessarily kind of reset every year. Um, next we talk about building together. [00:03:59] Rekha Thangellapalli: So, um. When we talk about joint solutions, we want to deliver solutions that are better together and the customers have to see us that way. And so whether it’s search, observability, or security, we’re looking at taking to market solutions that we can’t or necessarily don’t wanna take on our own. And finally we talk about scaling together. [00:04:22] Rekha Thangellapalli: And this is where marketplace, for instance, plays a big role, um, when customers can draw down on their cloud commitments, transact online and go from, you know, pilot to enterprise scale adoption in hours, not days. Um, this is when really everyone wins. Um, and this is also where partners like Accenture play a critical role. [00:04:47] Rekha Thangellapalli: Um, you know, the incredible amount of expertise that they bring, uh, the managed services capabilities and, um, their data assets actually play a huge role in having our customers realize that value faster. And, um, like Vince mentioned, at the end of the day, best partnerships are all all about creating kind of that. [00:05:07] Rekha Thangellapalli: Self-sustaining flywheel. And so it starts with investing together, building something unique, and having the customers realize that success faster because that success is really the only thing that’s gonna keep that flywheel going for everyone involved. I [00:05:26] Vince Menzione: absolutely. [00:05:26] Allison McFadden: Okay, amazing. I’m gonna riff off a few things Ika said, but from a GSI perspective. [00:05:32] Allison McFadden: A relationship with a WSA successful relationship with AWS looks slightly different. Um, so I think the first thing that we think of in the GSI Community common thread is that the client outcome and delivering value for clients is what we, what we’re striving for. Um, and so the partnership with AWS in that case, um, um, it has to, it has to. [00:06:01] Allison McFadden: Look like one team in front of our clients. So we have to show up indistinguishable, and that’s with AWS and with an ISV partner, it has to look like one solution in front of the client, especially moments that matter. So board meetings, um, you know, the time we’re gonna sign a deal, like we have to look like one team, uh, and keep our our client outcome, um, first and foremost in mind. [00:06:24] Allison McFadden: The second thing, and this is I think where the magic of all the people in this room comes into play. We can have as many discussions at a CEO level as we want. And if our client teams on the ground are not working together, it falls apart. Falls apart directly in front of the client. Yes. And that is a really hard thing to do. [00:06:45] Allison McFadden: So I’m passionate about the alliance work because that that work is what makes it happen at the corporate level. [00:06:53] James Kang: Cool. Um. I’ll start here. So in Nvidia is a accelerated computing platform company. Um, if you asked. Anyone on the, on the street about a year ago, what is ai? A lot of times they would say AI is, is open ai, or it’s philanthropic. [00:07:12] James Kang: Um, Jensen and I’ll, I’ll reference Jensen a lot today, um, because he is our leader, um, but he also sets the strategy in the direction for Nvidia. He talks a lot about AI in the metaphor of a five layer cake. And in terms of the five layer cake, you start off with the foundational bottom layer being power and energy, which sustains. [00:07:32] James Kang: All of our data centers, you move up the stack in terms of chips. So things think of Foxconn, think of TSMC. Next you have the infrastructure layer. So obvious choice is AWS, and then you get to the models where you do have the philanthropics and the open ais. But finally in at the precipice, you have the application layer. [00:07:53] James Kang: Ultimately, the reason why I mentioned all different stacks of the layers, the five layer cake, is the fact that the application layer is the most important. And so when you think about. Partners like Elastic or ServiceNow Trend, ai, CrowdStrike. Every time you pull from the application layer and you see a success, it pulls all five different components of that layer up. [00:08:13] James Kang: And so ultimately, as I think about success, it’s it’s being able to develop these co-sell wins at the application layer and really demonstrating that through extreme co-engineering and co-design with all the different application. Infrastructure, power and energy layers in mind. Um, Jensen also likes to think of himself not only as the CEO and founder, but also as the, the chief Marketing Officer. [00:08:35] James Kang: We are a very event driven company, and so at our big events like GTC or at big industry events like CES or Computex, he likes to show up on the biggest stage, biggest stages and showcase the partnerships with not only ISVs and GSIs, but also with end customers. And so that’s what I think about when I think of SA success. [00:08:56] Vince Menzione: That’s a really good point. You talked about, Allison, you talked about having an alliance strategy, or at least you teed it up, so I thought maybe we would go there for a second. Right? Like, what does a great alliance strategy look like and why is it important to the success of the partnership? [00:09:11] Allison McFadden: Man, I, uh, I have so many opinions on this. [00:09:13] Allison McFadden: We could probably be up here all day. That’s [00:09:15] Vince Menzione: okay. [00:09:16] Allison McFadden: Um, no, I think. Uh, there, there are a couple things, and the first one that comes to mind is focus. We cannot be all things to all people. Um, so when it comes to think about some of the, the work we’re doing with Elastic, we have a very, very clear point of view on what client problem we’re solving, what clients we want to talk to. [00:09:38] Allison McFadden: It helps if, um, from an ISV perspective, if there’s a very clear fit in. The Accenture portfolio or whatever, you know, SI consulting partner. You’re working with a very clear fit in the portfolio and we know what we’re not gonna go after, what we’re not gonna spend our time on because we have, we have this tendency, there’s millions of people. [00:10:00] Allison McFadden: The ecosystem chart that, you know, Vince, you showed up there, there’s so many connections. There’s probably more connections there than there are atoms in the universe, right? So, um. Defining what we do together and what we don’t do together is the first thing that pops to my mind. [00:10:19] Vince Menzione: Reka, do you have a perspective on it since we’re gonna, we’re gonna talk next about what you’ve done together, but, and I also wanna get mass perspective as a hyperscaler partner here as well. [00:10:29] Rekha Thangellapalli: Yeah, I mean from my perspective, I, I’m gonna, you know, kinda echo what Allison said is to be just maniacally focused. Yep. Um, because, especially from my perspective, so Elastic has three different solutions, right? We’ve got search, we’ve got observability, we’ve got security that map to completely different business units within Accenture. [00:10:47] Rekha Thangellapalli: And of course Accenture does a lot of things. And so, you know, when we first came together it was like. Okay, what are we gonna focus on? What industries are we gonna go after? Which segments are we gonna go after? Which customers, you know, um, outcomes are we trying to solve? And I think that sort of maniacal focus is the number one contributing factor to, to the fact that I’m like, up here on stage today. [00:11:12] Rekha Thangellapalli: Great. [00:11:14] Vince Menzione: Matt? Perspective? [00:11:16] Matt Yanchyshyn: Yeah, I, I, I guess I was trying to. To add something, uh, additional from an AWS perspective, uh, when it comes to, you know, what does a great alliance look like? Uh, AWS is obsessed with data, you know, in data we trust. And, and so the best, um, and, and this goes sales business problem, and it’s not just the engineering teams. [00:11:34] Matt Yanchyshyn: And so, uh, you know, Accenture does a good job of this elastic, definitely. And if you can come to the table with, um, quantifiable proof of the value of customer outcomes and partnerships. Um, you’ll win all the time and it’ll be a durable relationship with AWS ’cause we really are this data obsessed company and, and even the most senior sales leaders. [00:11:54] Matt Yanchyshyn: Uh, and so what I mean by that specifically is like if you, if you can show like your a RR to land an a RR conversion ratio, like in in numerical format, it’ll light up our sales leaders and, and they’ll be all, and they will co-sell with you all day long. If you can show the, I mentioned this earlier, like the AWS service, uh, whether you’re consulting company or, um, elastic and, and how the shape of customer accounts change positively when we work together. [00:12:15] Matt Yanchyshyn: That type of sort of quantifiable data works particularly well from an alliance perspective. With AWS as a partner, we, we really are like this data in sort of results out company. Um, so I, yeah, that’s just adding to the great points that were already made. I would say specific to AWS that that’s key. [00:12:30] Matt Yanchyshyn: Yeah. And I’m gonna bring up one more thing. I want to dive in on the, the joint value proposition, but you mentioned something that made a lot of sense and resonated to me about the organizations once you get out of partner, the partner world that we all know and love. Mm-hmm. Once you get down into a field organization or account management organization. [00:12:49] Matt Yanchyshyn: Not as much understanding and really organizations do a bad job here, honestly, in terms of enabling the field organizations. Do you agree? [00:12:58] Allison McFadden: I agree because I, I agree. And, um, you know, I think that’s one of the things, and, and I, I, when I joined Accenture, what we had was a lot of wicked smart architects delivering programs to clients in the field. [00:13:15] Allison McFadden: Very smart, very deep in AWS knowledge. Um, and that was awesome for the 10 clients they were staffed on and to get that understanding of how AWS works and I dream about lar, right? Like, this is a good, you know, but that takes real effort and real work. Yeah. And it’s, it’s um, almost like being a language translator. [00:13:37] Allison McFadden: Yes. For me. Yeah. So, you know, I had to deeply learn AWS so that I could. [00:13:42] Rekha Thangellapalli: Sure. [00:13:42] Allison McFadden: Teach my account teams. My account teams are really smart. They know who they’re selling to. They know their customers. They know what their customers need. They do not know what AWS has to offer always because they’ve got 20 partners lining up to try to tell their stories. [00:13:57] Allison McFadden: Um, they don’t know how to ask of the AWS team or the elastic team or the Nvidia team. Yeah. What they need [00:14:02] Vince Menzione: this co-selling piece. Yeah. [00:14:04] Allison McFadden: And so that is where, um. We had to build that muscle even around our AWS practice, which was a huge practice at Accenture, but we didn’t necessarily surround it with that kind of enablement and um, almost deal coaching layer. [00:14:21] Vince Menzione: So Elastic and Accenture came together. I dunno which one of you wants to lead this part of the conversation, but you will, right? Yeah. So tell us about the genesis of this and why. And a lot of people dunno what Elastic does, but you do some really incredible work. Like I, somebody told me one day was like, oh, you know, Uber, like, that’s elastic, powering all that. [00:14:41] Vince Menzione: Like, we don’t think about that. That the engines that you have and the, the backend to the customers, huge customers. [00:14:48] Rekha Thangellapalli: Yeah, absolutely. Um, so when AWS launched this feature last, um, reinvent where basically it allowed, you know, channel partners such as Accenture to be able to bundle up their services, their data assets with an ISV solution and put it on marketplace, um, you know, Accenture and Elastic immediately saw an opportunity. [00:15:09] Rekha Thangellapalli: Um, at the time most customers were doing gen ai. But they were running into the same challenge, which was that their data just was not ready. And by the way, this is a problem we were solving. Outside of marketplace. I think the, the feature that you guys launched just gave us a way to package it up and to be able to create this repeatable solution, which we call data readiness engine for gen ai and put it on marketplace. [00:15:40] Rekha Thangellapalli: And, um, this to me was a success because. Each company had a clear reason to invest. Um, so for Accenture, they were able to, you know, create a very differentiated services led offering. Uh, for Elastic, we were able to expand on our AI story. And for AWS, um, you know, it drives marketplace adoption, increases cloud consumption, all of that great stuff. [00:16:07] Rekha Thangellapalli: And customers, of course get. A solution to a very real problem that, that they were having. Um, and you know, the surprising part for me going through that journey was that, um. The pitching, the idea, getting the budget, getting the executive sponsorship was actually the easy part. The hard part was getting all three companies to come together, uh, to go from idea to launch in a very ambitious timeline of six weeks. [00:16:37] Rekha Thangellapalli: Nice. And so, you know, this was very much like. Doesn’t matter your title. We’re rolling up our sleeves and we are on this outcome together. Um, and so we literally built a RACI matrix, a project plan, and you know, we had daily standup calls for six weeks where literally. At least one person from each three of these companies called in, you know, got rid of any blockers and we made sure we were on target for that timeline. [00:17:07] Rekha Thangellapalli: Um, and you know, at the end we had a successful launch. But I think my favorite part about the story is the impact that we’re having and, um. My favorite story comes from a global pharmaceutical company that, you know, had basically nine petabytes of data spread across six different continents. Wow. And by working with Accenture and Elastic, they were able to build that trusted foundation that their AI and their agents can, you know, kind of safely tap into and be accessible at scale. [00:17:41] Rekha Thangellapalli: Um, so that’s my version. Allison. [00:17:44] Allison McFadden: Yeah. Well, I don’t have a lot to add. I just, I would say this is a good example of a couple of principles, right? One is having a forcing function is never a bad idea. Sign up for a big event, sign up. I’m like, I’m here with my, you know, Nvidia guys saying, sign up for the event. [00:17:58] Allison McFadden: It’ll make you move quick, right? [00:18:00] Audience Member: Yes. [00:18:00] Allison McFadden: Um, so that is one, but two, one of my mentors once told me, when you’re designing any kind of, you know, offering go to market motion, it has to get blood to all organs. If it does not get blood to all organs, it does not go [00:18:14] Vince Menzione: nice. [00:18:14] Allison McFadden: Um, [00:18:14] Vince Menzione: I love that analogy. [00:18:15] Allison McFadden: Oh, I love it. And I can talk all day. [00:18:17] Allison McFadden: That guy was brilliant. I love him. But, um, no, and, and so Elastic did a really nice job of bringing the tech to the table. Um, our team has to trust in that technology and its ability to scale, right? Um, because at Accenture we have to be able to deploy across 700,000 consultants. Um. And yeah, so I think those are the two, two things that really worked well here is we had, uh, trust in the technology solved a customer need. [00:18:50] Allison McFadden: Um, it drives, we don’t even talk about, like, yes, it drives marketplace revenue, but it unlocks work that we do that drives even more revenue to our AWS Friends. Right. So this is a, this is a, um, product that’s getting your data ready for AG agentic. It’s a messy problem that everyone’s dealing with, and it removes blockers for clients and it unlocks more, you know, ag agentic work on top of that. [00:19:15] Allison McFadden: So, blood to all organs. [00:19:17] Vince Menzione: So, was that the proposal going forward to say we need to have, we need to have trust in the solution. We need to drive significant revenue. It needs to be something all of our, you know, seven, 700,000 people. Can be a part of and help drive? Is that how you think about? [00:19:32] Allison McFadden: Yeah, and for us right now, um, it’s an interesting time for Accenture. [00:19:36] Allison McFadden: Our clients are asking a lot of us, and what it does is it having some of these accelerators helps us deliver cheaper, better, faster to our clients, which is what they’re demanding of us right now. Um, so it’s an accelerator to client outcomes. [00:19:55] Vince Menzione: James, what is NVIDIA’s role and how do, how do you enter the equation here? [00:20:00] James Kang: Yeah, it’s, um, it’s a good question. Um, I, I would say that Nvidia is probably one of the most misunderstood organizations in the world. Um, despite the, uh, the market capitalization in the valuation of the company, we have a very tiny organization. Um, what I mean by that is, um, if you think about. [00:20:20] James Kang: Salesforces and field sales organizations. Um, we’ll take Salesforce as the account or the customer. As an example, we have one account manager at NVIDIA that no, not only covers and is responsible for the relationship with Salesforce, um, but also manages. Automation Anywhere as well as DocuSign. Whereas at AWS, in contrast, like there are full armies and teams Yeah. [00:20:45] James Kang: That are supporting the Salesforce relationship. And so as you think about partnering and working with Nvidia, the focus has to be on really. Extreme co-design, but also being very prescriptive in terms of what are the very specific customer outcomes that we are solving for. And the guidance that I would give is bring in Nvidia into that equation and that conversation as early as possible because that [00:21:10] James Kang: co-engineering and co-design needs to be part of the foundational building blocks in order for you to come out with a end solution that checks all those different requirements. [00:21:20] James Kang: And so I think. Again, like going back to Nvidia, um, we like to talk about two different types of brains. A brain one and a brain two. Uh, brain One you think about the next quarter and making sure that you’re hitting the revenue targets for the next quarter. Brain two, you think about a long-term goals and potentials looking around corners and being very strategic. [00:21:41] James Kang: The saying internally is without Brain one, there is no oxygen, but without brain two, there is no future. And everyone at NVIDIA is trained to think in that brain two mentality. [00:21:52] Vince Menzione: Wow, Matt. [00:21:54] Matt Yanchyshyn: Yeah, I, I was just thinking I love the blood doll organs. Uh, and so just on, on that note, um, and, and, you know, the multi-product solutions that, that you, you built together, uh, that is a really good example of blood do organs because like we all know, that’s how customers buy. [00:22:07] Matt Yanchyshyn: They, they buy solutions and increasingly they’re looking for combinations of ISV, sometimes multiple products from multiple ISVs with services. Uh, often they’re buying it through a resell motion. You know, and they, and, and so that from a customer perspective, they want a single place to go. And so that’s the multi-product solution. [00:22:24] Matt Yanchyshyn: They wanna find everything they need, they need Accenture, they need Elastic to solve a specific solution. And I think where that’s headed is even more specific listings, like with AI powered listing experience, like, you know, elastic Plus Accenture for, I’ll make something up like a manufacturing workload. [00:22:37] Matt Yanchyshyn: And so this solution based. Uh, sort of buying is, is very customer centric. It’s what customers want. We all know that. But that’s, that’s the customer sort of organ, I guess. Um, but then, you know, you all have SCAs and those SCAs have marketplace commits. It helps if that gets transacted through marketplace helps the AWS relationship, you know that that’s an organ. [00:22:55] Matt Yanchyshyn: It’s the relationship. It’s, it’s the commercial construct and that you have, uh, that that’s another organ. You’re marketing people. They, that’s another organ. They don’t wanna land, uh, leads on a static marketing page. They wanna land a lead on a, a storefront with a multi-product solution that can actually convert and that you can actually buy it through that. [00:23:12] Matt Yanchyshyn: So the marketing person’s happy because they, they have less churn. Uh, and then, you know, our reps are happy ’cause guess how they get paid? They retire quota when they sell Marketplace. And they, we also, Jay McMain will tell you, that’s another organ called Jay or on, on you now. Um, [00:23:27] Matt Yanchyshyn: he’ll like that. I’ll call him up and tell him that. [00:23:29] Matt Yanchyshyn: Yeah, [00:23:30] Matt Yanchyshyn: but he, he’ll tell you, you know, don’t believe me. Obviously, never believe Matt, believe, believe the, the data and, and his data shows that. Those deals will close faster and larger if you use marketplace. So that’s, that’s a lot of organs. That’s the whole body. Um, but you know, when you have your customer happy ’cause that’s how they wanna buy your field happy. [00:23:45] Matt Yanchyshyn: Um, and, you know, the relationship happy and you know, your marketing team happy. Uh, and, and Jay happy. Um, and, and you know, I think that multi-product construct and, and the way you kind of use it to model a partnership and the way buyers ultimately wanna buy is, is really powerful. And so I, I think it’s, you know, it’s really a manifestation of how. [00:24:04] Matt Yanchyshyn: We kind of intend and to go to market anyway. Uh, so I think, you know, and thanks for leading the way, by the way. You’re, you’re amongst the very first, so that’s great to see. [00:24:11] Matt Yanchyshyn: So these storefronts are really helping this drive, drive this. Well, [00:24:13] Matt Yanchyshyn: that’s the next evolution. Like we’re talking about the multiproduct solution. [00:24:16] Allison McFadden: I’m JJ Accenture storefront. [00:24:17] Vince Menzione: Yeah. Oh, there you go. I mean, j and j Accenture storefront. [00:24:20] Allison McFadden: We’re gonna talk about that. [00:24:20] Matt Yanchyshyn: Yeah. I mean, [00:24:21] Matt Yanchyshyn: Accenture also leading the way yet again with storefronts. And so I think the combination of. You know, again, I was talking a lot about conversion. Yeah. And you know, buyers know sometimes they know what they wanna buy and, but if you really wanna convert that lead, you wanna land them again, something that combines, you know, elastic Accenture’s services plus software, but in a storefront that is, you know, surrounding with just the solutions they want so they don’t need to kind of go searching. [00:24:42] Matt Yanchyshyn: So, you know, ultimately reducing that time to close, I guess, really ’cause meeting the customer where they are with what they need. [00:24:51] Matt Yanchyshyn: So we talk about co-selling a little bit. We, Jay and I talk about this all the time. We gotta keep looping Jay in here, even though he is not even in town this week, but Reko, um, what does co-sell look like inside Elastic? [00:25:02] Matt Yanchyshyn: You’ve got, we talked about an incredible leadership team. I’ve gotten meet some of your leaders. Seems like you drive, you do a good job internally driving that. Let’s talk a little bit about it. [00:25:11] Rekha Thangellapalli: Yeah, and this is something I’m, I’m personally very passionate about. Um, co-sell is. Very much a journey, not a destination. [00:25:20] Rekha Thangellapalli: And I think step one for us is recognizing the different partner types that we have. Because at Elastic we work with, you know, OEMs, MSPs, resale distributors, GSIs, um, and they all bring something very unique. To the customer lifecycle and they all contribute very differently within, you know, our own sales cycle and sales process. [00:25:45] Rekha Thangellapalli: And so, you know, figuring out what is the unique benefit they bring, how do we enable them? So training and enablement is a huge piece of it, and so is making sure we’ve got the right metrics to measure success. Um, I know a lot of companies look at partner sourced as the north star, and that’s great, right? [00:26:06] Rekha Thangellapalli: Because that is undeniable. You can say, Hey, that would not exist if it wasn’t for my partner team. Um, but we’ve also noticed that when we bring in GSIs, it actually increases renewal rates. It significantly increases. Um, a RR over time. Um, it expands deal sizes and so these are very real metrics that we can point to, um, beyond just the co-sell and the partner sourced number. [00:26:32] Rekha Thangellapalli: Um, so for us it’s looking at it from a very holistic perspective, but also catering it towards that unique partner and making sure we’re doing everything we can to set them up for success and setting up the partnership for success. [00:26:47] Vince Menzione: So clo close win ratios, deal size and renewal rates? [00:26:52] Rekha Thangellapalli: Yes. For specifically for geos size. [00:26:54] Rekha Thangellapalli: Yeah. [00:26:55] Vince Menzione: Very interesting. Allison, uh, what had to change internally to produce these co-selling? We talked a little bit about the field organization and enabling a, a group of, and, you know, account sellers that are very customer focused and enabling them on the co-sell side. What had to change internally to drive that? [00:27:13] Vince Menzione: Yeah. [00:27:14] Allison McFadden: I, I might have already alluded to this a little bit in a previous answer, but, um, creating the capacity to develop, build, and sell these solutions, um, inside of a large GSI, where billable hours is kind of the number one metric on the table. Um. Is part of the investment that we had to make within Accenture to get this done? [00:27:36] Audience Member: Yeah, [00:27:36] Allison McFadden: so expert technology time. So we have technologists that understand the elastic technology. We do similar with Nvidia, by the way, we. We released some of their time to go co-develop the solution because it has to hold technical water, right? It can’t just be a marketing pitch. It can’t just be, it has to be a real, um, what’s the there, there. [00:27:59] Allison McFadden: So in order to actually do proper co-sell, we had to release some of that time. Um, to invest in those partnerships. Um, we’ve also done similar with some industry aligned business development leaders recently, so we have freed their time up to go. Uh. Open new conversations, educate client, account teams, go to clients, have conversations. [00:28:26] Allison McFadden: Um, so that, that’s a new motion that we, uh, have just kind of recently made, um, to allow them, I love this brain one, brain two also, right? So to allow them to focus on brain two, because a lot of our time. Typically spent delivery issues, you know, getting my hours, where am I charging my time? And so just freeing up a little of that capacity to do this work, um, helps get us in this brain two mode where we’re not just living to survive. [00:28:56] Vince Menzione: I. So, Matt, you’ve removed a lot. I mean, one of the things I admire, I admire AWS for being first to market and removing the most friction in marketplace of any of the vendors. Really, truly that. You talked about some of the announcements. How does some of, how does some of this tie PC central agents propensity sales plays, MCP, how does some of this tie to how, how you’re thinking about the future? [00:29:18] Vince Menzione: And how to enable more motions like this. [00:29:20] Matt Yanchyshyn: Yeah. Well, I, I think if you know my boss, UBA Borno, uh, you’ll know that she has a maniacal focus on automation. Yeah. Um, and, uh, co-sell is increasingly automated. You know, you were asking earlier about propensity data. You can get that propensity data in addition to sales plays and, uh, opportunity scores through the partner central agents. [00:29:38] Matt Yanchyshyn: So things that used to require multiple calls to A PDM, if you’re lucky to have one. Yeah. Or a p sm. Uh, you, you can now get through, through these agents, you know, uh, tech Systems, TGS, they, they manage what, over 5,500 customer opportunities with agents that they built on top of our partner Central APIs. [00:29:55] Matt Yanchyshyn: Um, and work Span has built a whole product and business that’s right on leveraging, uh, our APIs, our capabilities to sort of tie into your CRM. So, majority of all opportunities will be progressed and managed by agents. This year at AWS, we already have a majority of all customer opportunities, all app have a partner attached and I, I took a personal goal for a majority of those partner attachments, not to happen from a human. [00:30:22] Matt Yanchyshyn: But from our solution matching engine. And how do you get recommended by that solution? Matching engine, having a healthy ACE pipeline, thanks to partner central agents and the integrations you’re doing. And in addition to being the specializations and doing things like multi-product solutions and ultimately closing opportunities, you dream of LAR and so LAR will help that. [00:30:40] Allison McFadden: It’s more like a nightmare. [00:30:41] Vince Menzione: And so, you know, [00:30:42] Allison McFadden: it’s more like a nightmare, but [00:30:44] Vince Menzione: nightmare. Well, it’s, it’s, yeah. Nightmare of Laura and, and. Nice dreams of PRM, but the, um, but that’s the loop, right? I, I think, uh, increasingly co-sell for us, and in my mind, is largely a hundred percent automated. Yeah. Except for what matters most, those most largest, most strategic, most complex deals. [00:31:01] Vince Menzione: Where our highly paid and very skilled salespeople are most effectively used. [00:31:05] Vince Menzione: Yeah. [00:31:05] Vince Menzione: You know, the days of, you know, this person with 20 years experience selling, clicking, progressing opportunities through a pipeline, uh, should be over. Uh, and, and we need those people out, out selling and, and co-selling. And so that for me. [00:31:19] Vince Menzione: Yeah. That, you know, we talk a lot about co-sell, but I, I’m obsessed with automating as much of the co-sell as possible. [00:31:24] Vince Menzione: I remember going back to the ex Excel spreadsheets and, and that, that seems to be be Viva became spreadsheet jockeys. [00:31:31] Vince Menzione: Yeah. [00:31:32] Vince Menzione: And, and they stopped selling. They forgot how to sell. [00:31:34] Vince Menzione: Yeah. And people spend all this time doing lunch and learns and things like that. [00:31:36] Vince Menzione: And then, you know. Then the salespeople rotate out after 18 months and, and it, that’s, that’s the old days. Uh, you know, the new days are, are AI powered matching algorithms, uh, ag agentic co-sell, using the partner essential agents to get your data and, and putting that data to use automatically and, and what sounded like magic. [00:31:51] Vince Menzione: 12 months ago is being done, you know, by partners at massive scale across thousands of opportunities. You can do it today. And you know, I, there’s a guy named another Mike, right? Mike another Mike who they have, there’s like a guy who’s doing all this and I’m picking on Mike ’cause I, I know their system really well and I know the guy Mike grew easily built it for them. [00:32:08] Vince Menzione: Um, but, you know, I think, yeah, again, in the days of having 10 people sort of doing lunch and learn could be replaced by one or two people, building agents, uh, managing a massive pipeline. And, and that’s the future. [00:32:18] Vince Menzione: Exactly. James, your perspective on what breaks with co-selling? [00:32:22] James Kang: Oh, what breaks co-sell? Um, I would say. [00:32:25] James Kang: It, it starts and finishes with just misalignment and a loss of trust with the customer, especially when you have multiple partners or stakeholders involved. If you’re trying to do a three-way deal with a end customer and you’re not on the same page, you’re not gonna get to a successful outcome on, on the backend. [00:32:44] James Kang: Uh, the fix is a much more complicated story. I would say that to take a step back, um. We’ve talked about the five layer cake. We’ve talked about where NVIDIA kind of fits within the equation. We are invested in the ecosystem and so as different players and application organizations win and see these outcomes for end customers, we celebrate that success. [00:33:07] James Kang: Um, and as part of that kind of ethos of where NVIDIA fits within the ecosystem, we wanna make sure that not only. Our customers, but our partners like ISVs and GSIs are set up for success. Um, we do not as Nvidia sell hardware or GPUs directly to customers We use. Hyperscalers like AWS as kind of our force multiplier. [00:33:31] James Kang: And similarly we think of ISVs and GSIs as the force multipliers in terms of our extensions of how we, we kind of leverage the relationships and build the trust with our end customers. And so going back to kind of the question, Vince, I would say that it all comes back to trust and being able to build that mutual trust. [00:33:48] James Kang: Um, a lot of what we do when we co-sell with AWS is really on the software layer. Um, we actually have more software engineers at NVIDIA than we have hardware engineers, which is a weird thing to say, um, because everyone knows us for our GPUs. But because of that fact, we are heavily invested in Cuda and making sure that Cuda becomes the foundational layer for how not only our ISVs and GSIs, but also our end customers are building. [00:34:12] Vince Menzione: Very cool. So Reiki, you and James together on this production. Versus pilot with the Gentech ai. Tell us a little bit more about that. Where, where are you in the process? [00:34:24] Rekha Thangellapalli: Yeah. So I mean, in general, what we’re seeing out in the market in, in relation to sort of AI and, and customer’s journeys is that, um, at least from an elastic perspective, um, we’re seeing people very much in production when it comes to, you know, kind of AI assistant co-pilot use cases. [00:34:42] Rekha Thangellapalli: So, you know, things like, um, software development, customer support is a big one. Um, any sort of employee productivity use cases where there’s. Still a human in the loop somewhere. Um, and there’s a very like, clear path to value. And so we see the customers being in production excelling there. Um, no problem. [00:35:01] Rekha Thangellapalli: Where we’re seeing people still kind of in the pilot phase is those fully autonomous workflows where there is no human involved. The agent is reasoning on its own. Um, accessing multiple systems and taking an action on the user’s behalf. And what we’re seeing is that it’s not the intelligence of the agent that’s holding it back. [00:35:26] Rekha Thangellapalli: It’s more about giving the right context to the agent and having the right. Security kind of governance controls in place for the company to feel comfortable in putting these fully autonomous workflows into production. And that’s really the conversation we’re having is all right, what are the controls you need in place? [00:35:47] Rekha Thangellapalli: For you to release this to your business unit. Um, and what is the context that the agent is needed before we can comfortably let the agent make the decision on the user’s behalf? Um, James, I’d be interested to hear what you’re, what you’re seeing in the market [00:36:03] James Kang: plus one on all things context. I, I would even go so far as to say, um. [00:36:09] James Kang: H how many folks in the audience have heard of token maxing? Like this new term? [00:36:13] Rekha Thangellapalli: Yeah. Yeah. [00:36:14] James Kang: Um, I’ll, I’ll give a very specific example of, of Uber that went public. With the example of Claude, like they allowed all of their employees to use as many tokens as possible, and within the span of four months, they exhausted their full budget for the year, and so they had to pull back, and now there’s a cap on every employee. [00:36:33] James Kang: I think the number that’s circulating is $1,500 per month per employee, and so I think that is at least. In this multi-phase evolution of where we’re going to be and where we’re today, cost has become kind of the prohibitive force in terms of agentic AI at scale. Um, I think we are working on some very creative solutions in-house and Nvidia. [00:36:55] James Kang: Um. And we saw some really dynamic announcements this week when it comes to all things agent core, um, where we want to focus on very nimble ways for customers to be able to execute and go to market. And one extreme example of that is our investment within our open model strategy. So Nvidia, not only, again, providing GPUs, we actually offer our own op open models, which we call our Nitron models. [00:37:21] James Kang: And through our Nitron models, we are allowing customers to really develop and fine tune their own proprietary models in a cost effective manner. So right alongside the frontier models like OpenAI and Anthropic. It’s not a if then, it’s not an either or statement. It’s a, it’s a permutation, it’s an and So we’re giving you a cost effective alternative to not only bring your AgTech applications at scale by training on Nibo tron, which is open source, but then once you’ve kind of finished and fine tuned that specific training job to be able to. [00:37:53] James Kang: Go ahead and utilize your frontier models, whether it be OpenAI or Claude. And I know there’s other partners here that are providing those kind of different model capabilities. And so I think for us it’s, it’s a matter of choice. We know that this market is dynamic. It’s gonna be evolving over the next coming months as well as the next coming years. [00:38:10] James Kang: Uh, but we believe that we are positioned for a really unique dynamic expansion of AgTech use cases over the, at least the next three to six months. [00:38:20] Vince Menzione: Allison, for the partners in the room who are glazed over right now going, what do I, what do I do over the next 12 months? [00:38:26] Allison McFadden: Should I wake everybody up by saying, yeah, please. [00:38:27] Allison McFadden: Say go hurricanes. [00:38:28] Vince Menzione: Yes. [00:38:29] Allison McFadden: Is there anyone, anybody? Everyone’s like, boo. I get to leave the parade today to go home to parade. I live in Raleigh, so we’ve got our parade on Saturday. Nice. [00:38:39] Vince Menzione: Nice. [00:38:40] Allison McFadden: All right. Wake up. Um, all right. So for the $50 million partners in the room, um. $50 million is not small. You have something that works. [00:38:50] Allison McFadden: Right. This is great. What I would be thinking about is, you know, we’ve talked about focus before, but really doubling down on, you know, what is, what is your industry, what is your client like, ideal client that you serve. And build, um, almost that kind of community. You know, the, the clients we have move from firm to firm to firm. [00:39:17] Allison McFadden: And if you’ve done good work at one, you’re gonna follow ’em to the next. Um, so build that client demand in a specific place or specific client profile that is just like really knocking it out out of the park for you. Um. Scale with marketplace, right? So if you, I, I love some of the data that you were sharing in your talk earlier, um, because it’s like no overhead scaling mechanism. [00:39:45] Allison McFadden: I mean, it’s, it’s fantastic. Um, Accenture, other GSIs like us, we are investing in marketplace. So we’re investing in resources, um, to help us. Use marketplace more with our clients and we’re gonna capture, right, those storefronts. And if you’re present on marketplace, you’re gonna be able to catch, uh, yourself in that wheel. [00:40:09] Allison McFadden: So I think those are the, the kind of couple of things I would say is focus, focus, focus to drive that client demand and use scaling mechanisms like marketplace to really kind of, uh, accelerate. [00:40:24] Vince Menzione: Matt, anything to add there on the. [00:40:26] Vince Menzione: Well just, you know, Ja, James, you, I love the token maxing reference in Uber and it reminds me, you remember when cloud came out and everyone was like, oh, all these people are, are gonna use the cloud and costs are outta control and. [00:40:39] Vince Menzione: Um, a lot of people pulled back from the cloud and, and a lot of those companies no longer exist. And it’s similar with, with, uh, token maxing, like, oh, these agents are outta control. You have a choice. You can embrace them and figure it out and get governance and, and make your data available. Um, use the partner, central agent, move to agent to co-sell, or you can fade and die. [00:40:58] Vince Menzione: And, and that’s, that’s where we’re at. Uh, is, is the, the companies sitting here today embraced the cloud years ago and won. Uh, and and there’s a set of companies here today who are gonna embrace agents in the, for both buyers and sellers, and will win. And there are those who won’t and they won’t win. And so for me, it’s like we’re, we’re at a, we’re at a crossroads. [00:41:18] Vince Menzione: And, and if you’re gonna win, you gotta leap into that, you know? I love it. And, uh, and, and, and it’s, it means the cost of experimentation is so much lower now. Development and, and even business development or software development is, is agent enabled. And so you can take risks, you can experiment and, and you have to, it’s, it’s an existential moment. [00:41:37] Vince Menzione: Agreed. We’ve got a couple minutes left over for any questions. What do you think? Sure. Are there any here. I think there are a couple. Yeah, we’ve got, we’ve got a co-sell question I’m sure coming up here. [00:41:51] Audience Member: Um, I’m Cassandra, I’m the CEO of Partner Tap. And one of the questions I had was, I think, you know, the co-selling between the sellers is where things get. Really, really hard when you’re multi-partner. And so when I was listening, um, with, you know, the Accenture and Elastic together, you talked about how you had, you, you had to get these BD business development people. [00:42:22] Audience Member: Um, is this a new team that is over the client team? And how do these teams interact like with the elastic sellers? Are you doing a lot of coaching to the field and then with if AWS sellers are, are involved, like what is that whole picture? What does look like, [00:42:43] Allison McFadden: like [00:42:44] Audience Member: on the ground? I mean, that is the hardest part, I think, and that’s what we hear. [00:42:48] Allison McFadden: It’s so, it’s so, it’s so tough. Um, and I will, I’ll just say, so our business development leaders that we now have kind of. Expanded their capacity. They have always been, they have always been there. Um, but they have not been well resourced. They haven’t, they haven’t had very clear kind of job description. [00:43:12] Allison McFadden: I’m gonna say I, in the past they have been kind of focused on partner relationship. And so like more like an alliance manager and maybe working on some of the data. Right? So when I say I have nightmares about Lars, because we’re always trying to increase the LAR for Accenture and, and they were focused like in those detailed weeds of like trying to pass ACE and trying to call the PDM and all this stuff. [00:43:39] Allison McFadden: What we are doing is really pivoting them to be proper sales, business development focused on client outcomes and focused on. Technical skills to be able to describe what this solution is to the field. So, um, and because we need, I have many, many questions about, I gotta get agents to work with Eurogen co-sell so that that part somehow goes away. [00:44:05] Allison McFadden: So that’s a, that’s the thing we gotta solve still, but, um, so we’re pivoting them to be kind of driving. More of that co-sell enablement with the field, um, and taking that message to the field rather than being there, waiting for questions to come in from the field, waiting for like our field teams to discover, oh, I saw something that we’re doing with Elastic, like on a press release on LinkedIn. [00:44:30] Allison McFadden: Right. So we’re kind of trying to pivot them to be more proactive. [00:44:33] Vince Menzione: Very cool. [00:44:34] Rekha Thangellapalli: Yeah. And uh, Cassandra, that’s an excellent question because I think. Multi-party, you know, sort of tri-party offerings. The hardest part is operationalizing it at scale, right? Yeah. And so for this particular offering, we are basically having three routes to market. [00:44:51] Rekha Thangellapalli: So one is seeing how this offering fits into our existing elastic go to market. And so I am constantly enabling our field sellers to say, okay, within our three field sales place, here’s exactly where this fits in. Here are, you know, uh. Keywords that you hear in customer conversations where you bring up this offering and here’s a process of how it works. [00:45:14] Rekha Thangellapalli: Um, exactly At what sales stage do I bring in Accenture, how, you know, what are the roles and expectations? Right? So that’s on the elastic side. We’re doing the same thing on the Accenture side. So we’re doing a ton of training enablement and lunch and learns, and we’re also looking at how do we fit into. [00:45:31] Rekha Thangellapalli: Uh, Accenture’s AI transformation projects, we are the semantic layer, right, of their enterprise brain. And so it’s a whole different sales motion, um, and, you know, having the right assets, having the right process again to make sure that that goes smoothly. And then finally, we’re going directly to the customer. [00:45:49] Rekha Thangellapalli: So we are launching multiple external campaigns where, you know, if the customer raises their hand. We will, we will line up immediately. Right. Um, and so, [00:46:01] Allison McFadden: I mean, I can’t, I can’t, I can’t say how important that third leg of the stool is. ’cause the second part, she talked about getting into our catalog is the first thing. [00:46:09] Allison McFadden: ’cause my BU business development leaders have the catalog. Right. And that’s what they’re selling. So what Elastic has done has gotten into one of those offerings and then. If we have a customer that asks for it, that is the fastest way to alignment. That is like the number one thing that we respond to [00:46:26] Vince Menzione: customer at the center. [00:46:27] Vince Menzione: This is great. Well, I think we’re up to time. This was a great session. I want to thank you. This is what a great, what a great group. [00:46:34] Vince Menzione: Thanks for listening to the Ultimate Partner Podcast. If today’s conversation resonated, share it with a partner leader in your network. Subscribe where [00:46:43] Vince Menzione: you listen, and head over to the ultimate partner.com. [00:46:47] Vince Menzione: For show notes related content and the resources for this episode. And if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, October 26th through October 28th. Until next time, keep showing up in the rooms that matter because being in the room changes everything [00:47:09] I.

First Man (2017) is a “documentary” with an interesting relationship to scientific facts and a disturbing view of human nature. It follows human evolution through four reconstructed ancestors: an early beta cuck ape from Spain; a Tate stan basal hominin from Chad; an ancestral alpha mogger from South Africa; and a brainmaxxing sigma arsonist from China. Join us as we break down the silly walks, unnecessarily blurred monkey genitals, and (content warning!) graphic sexual assault. Links Pierolapithecus Sahelanthropus Gigantopithecus Sivapithecus/Ramapithecus Paranthropus Graecopithecus Peking Man (Homo erectus) Harbin/Dragon Man Our episode on Skullduggery (1970) Our episode on Sasquatch Sunset (2024) Grieving chimpanzee carries dead baby for months Zhoukoudian The myth of the alpha wolf Parsimony Boma predator deterrent Gao et al. (2017) Evidence of hominin use and maintenance of fire at Zhoukoudian Laughter in animals Pusceddu et al. (2025) Animal medical systems from Apis to apes: history, recent advances and future perspectives Ventro-ventro position (doggy is not the only style) Man vs. horse races (horses have won) Persistence hunting Aiello and Wheeler (1995) The Expensive-tissue hypothesis: The brain and the digestive system in human and primate evolution Wrangham (2009) Catching Fire: How Cooking Made us Human Contact Website Bluesky Facebook Letterboxd Email ArchPodNet APN Website: https://www.archpodnet.com APN on Facebook: https://www.facebook.com/archpodnet APN on Twitter: https://www.twitter.com/archpodnet APN on Instagram: https://www.instagram.com/archpodnet APN Store Affiliates Motion Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us Fan MailMost security teams keep buying tools and still feel behind. That's not because you picked the “wrong” scanner or missed the latest AI feature. It's because many organizations are trying to secure software like it's a one-time project instead of a repeatable manufacturing process. I'm joined by Darius Radford, founder and CEO of Knights Watch Cyber, to make that idea concrete and practical for any software-driven business. Darius breaks down why applications, APIs, CI/CD pipelines, cloud platforms, open source dependencies, and identity are the real battlefield now. He shares the core insight behind what he calls the Secure Software Factory: software isn't “written” anymore, it's manufactured. When you adopt a factory mindset, you build in quality control, governance, automation, standards, metrics, and feedback loops so secure software development becomes consistent instead of heroic. We also walk through the four capabilities he sees as non-negotiable: orchestrated delivery, developer-centric application security tooling, supply chain and artifact management governance, and unified risk correlation that turns endless security data into real context. We go straight at the AI era too. Developers and AI will build the next generation of products together, which makes AI security governance and testing even more urgent. Darius gives practical guardrails for AI-generated code, including input validation, session management, authentication and authorization checks, and permission modeling. We also talk about how a mature DevSecOps approach can help you win deals by proving security by design with repeatable metrics and alignment to common risk frameworks like OWASP. If you build software, lead engineering, or sell into security-conscious customers, this conversation is for you. Subscribe, share this with a builder on your team, and leave a review with your biggest question about secure software development, what are you struggling to make repeatable?Thanks for tuning in to this episode of Follow The Brand! We hope you enjoyed learning about the latest trends and strategies in Personal Branding, Business and Career Development, Financial Empowerment, Technology Innovation, and Executive Presence. To keep up with the latest insights and updates, visit 5starbdm.com.And don't miss Grant McGaugh's new book, First Light — a powerful guide to igniting your purpose and building a BRAVE brand that stands out in a changing world. - https://5starbdm.com/brave-masterclass/See you next time on Follow The Brand!

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president could still fire commissioners, as the Court now permits, but if those firings broke quorum, the agency would be unable to proceed until replacements were confirmed. The guardrail would

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.There was an issue with this only going to paid subscribers, so sending it again. Apologies to those who get it twice. I appreciate being paid so feel free to upgrade if you enjoy TWTW.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president

Every career has an origin story, and for longtime CMS leader Lorraine Doo, it begins with the Attachments Rule on her very first day at the agency. In this episode, Lorraine reflects on the evolution of healthcare interoperability—from EDI transactions to APIs, FHIR, and today's digital health ecosystem—sharing behind-the-scenes insights into the policy decisions, collaborations, and regulatory milestones that have shaped modern health IT, along with her perspective on the challenges and opportunities that still lie ahead.

Today, we are dropping our final episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.In our final episode, we are joined by Shayne Higdon, Wallarm CEO, who closes the series by examining what the accountability moment demands from enterprise leaders, what a mature AI governance model needs to prove rather than promise, and what the next 12 to 24 months look like for organizations that get this right.QuestionsWhy is now the accountability moment for enterprise AI?What has changed between the early days of AI experimentation and today's enterprise AI deployments that makes accountability such a pressing issue?When we talk about AI accountability, what does that actually mean in practical terms? Are we talking about visibility, auditability, enforcement, ownership—or all of the above?As organizations race to deploy AI, how should CIOs balance the speed of transformation with the responsibility to govern it effectively?Why are traditional governance and security models struggling to keep pace with the way AI is being adopted across the enterprise?Given those challenges, how should boards and executive teams evaluate whether their organizations are truly ready to scale AI safely and responsibly?And once an organization believes it's ready, what does a mature AI governance model actually need to prove - not just promise?From an operational standpoint, how do capabilities like discovery, runtime monitoring, and enforcement come together to create a closed-loop approach to AI accountability?Stepping back and looking across this entire conversation, what's the one mindset shift every enterprise leader needs to make when it comes to AI security and accountability?And finally, as listeners think about what's ahead, what should they expect the future of AI security and accountability to look like over the next 6, 12, or even 24 months?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/shaynehigdon/Full AbstractAbstract: Join Shayne Higdon, Wallarm CEO, for this episode, which closes the series by examining what the accountability moment demands from enterprise leaders, what a mature AI governance model needs to prove rather than promise, and what the next 12 to 24 months look like for organizations that get this right.AI deployment is not waiting for governance to catch up. Across most enterprises, the gap between how fast AI is being adopted and how well it is being governed is widening every quarter. CIOs and CISOs are not debating whether to govern AI. They are trying to figure out how, under real organizational pressure, with tools and frameworks that were built for a different threat model.That pressure is coming from every direction at once. Boards want AI transformation to move fast. Regulators want documented evidence that it is under control. Security teams want runtime visibility and enforcement capabilities that most of their current tools do not provide. And the AI systems themselves are not waiting: they are accessing data, calling external services, and making decisions continuously, in ways that after-the-fact governance cannot meaningfully constrain.This is the accountability moment. Not because the risk is new, but because the consequences of undermanaged AI are now concrete enough to land on a board agenda, an audit report, and a regulatory deadline at the same time. What accountability actually requires in practice is the full AI control loop: knowing what AI is running across the enterprise, seeing what it is doing at runtime, enforcing policy before damage compounds, and generating continuous evidence that the governance is real and not retroactive. Organizations that can demonstrate all four are in a fundamentally different position than those still assembling audit evidence from spreadsheets the week before a review.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://cash.app* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Small teams with smart AI can solve problems no one imagined before. In this episode, I spoke with Zhen Lu, CEO of Runpod, about the rapidly evolving AI landscape and the future of software development. Zhen Lu shared how Runpod is empowering engineers to train AI models tailored to specific business needs, improve efficiency, and reduce waste. We also discussed his founder journey, the power of co-founder alignment, global innovation outside traditional tech hubs, and the importance of human accountability in AI-driven organizations. Here are the highlights: ● Runpod builds AI developer infrastructure. The platform supports tailored AI workloads, enabling businesses to efficiently train models specific to their needs. ● AI is changing how software is built. Engineers are challenged to rethink software design, not just accelerate existing processes, creating new opportunities for innovation. ● Efficiency and sustainability matter. Fine-tuning smaller, focused models reduces resource waste, energy consumption, and operational costs compared to massive off-the-shelf models. ● Co-founder alignment drives success. Implicit trust, complementary skills, and low ego between Zhen and his co-founder have accelerated decision-making and execution. ●     Global networks and bootstrapping foster innovation. Scarcity encourages creativity, and building strong relationships outside Silicon Valley has enabled Runpod to grow and support AI developers worldwide.   About the guest: Zhen Lu and co-founder Pardeep Singh started by running crypto mining rigs out of their New Jersey basements. When Ethereum's "The Merge" threatened to make that obsolete, they pivoted,  converting the rigs into AI servers. As corporate developers at Comcast building ML projects, they saw firsthand that the GPU developer experience was, in Zhen's words, "just hot garbage." That insight became Runpod.   Launched in early 2022, Runpod offers fast, developer-friendly GPU infrastructure: clean APIs, CLI tools, serverless options, and easy configuration. Rather than the traditional VC route, they debuted on Reddit with "Hey Reddit, give us your worst" — and got "please take my money" in return. They went on to earn validation from Hugging Face co-founder Julien Chaumond and a seed round led by Dell Technologies Capital, hitting $120M ARR, 10 billion serverless requests, and serves ~900,000 developers across 31 global regions.   Connect with Zhen Lu: LinkedIn: https://www.linkedin.com/in/zeen/ Website: https://www.runpod.io/ Connect with Allison: Feedspot has named Disruptive CEO Nation as one of the Top 25 CEO Podcasts on the web. LinkedIn: https://www.linkedin.com/in/allisonsummerschicago/ Website: https://www.disruptiveceonation.com/    #CEO #leadership #startup #founder #business #businesspodcast Learn more about your ad choices. Visit megaphone.fm/adchoices

Small teams with smart AI can solve problems no one imagined before. In this episode, I spoke with Zhen Lu, CEO of Runpod, about the rapidly evolving AI landscape and the future of software development. Zhen Lu shared how Runpod is empowering engineers to train AI models tailored to specific business needs, improve efficiency, and reduce waste. We also discussed his founder journey, the power of co-founder alignment, global innovation outside traditional tech hubs, and the importance of human accountability in AI-driven organizations. Here are the highlights: ● Runpod builds AI developer infrastructure. The platform supports tailored AI workloads, enabling businesses to efficiently train models specific to their needs. ● AI is changing how software is built. Engineers are challenged to rethink software design, not just accelerate existing processes, creating new opportunities for innovation. ● Efficiency and sustainability matter. Fine-tuning smaller, focused models reduces resource waste, energy consumption, and operational costs compared to massive off-the-shelf models. ● Co-founder alignment drives success. Implicit trust, complementary skills, and low ego between Zhen and his co-founder have accelerated decision-making and execution. ● Global networks and bootstrapping foster innovation. Scarcity encourages creativity, and building strong relationships outside Silicon Valley has enabled Runpod to grow and support AI developers worldwide. About the guest: Zhen Lu and co-founder Pardeep Singh started by running crypto mining rigs out of their New Jersey basements. When Ethereum's "The Merge" threatened to make that obsolete, they pivoted, converting the rigs into AI servers. As corporate developers at Comcast building ML projects, they saw firsthand that the GPU developer experience was, in Zhen's words, "just hot garbage." That insight became Runpod. Launched in early 2022, Runpod offers fast, developer-friendly GPU infrastructure: clean APIs, CLI tools, serverless options, and easy configuration. Rather than the traditional VC route, they debuted on Reddit with "Hey Reddit, give us your worst" — and got "please take my money" in return. They went on to earn validation from Hugging Face co-founder Julien Chaumond and a seed round led by Dell Technologies Capital, hitting $120M ARR, 10 billion serverless requests, and serves ~900,000 developers across 31 global regions. Connect with Zhen Lu: LinkedIn: https://www.linkedin.com/in/zeen/ Website: https://www.runpod.io/ Connect with Allison: Feedspot has named Disruptive CEO Nation as one of the Top 25 CEO Podcasts on the web. LinkedIn: https://www.linkedin.com/in/allisonsummerschicago/ Website: https://www.disruptiveceonation.com/ #CEO #leadership #startup #founder #business #businesspodcast Learn more about your ad choices. Visit megaphone.fm/adchoices

In Episode 674 of the New Media Show, Podcast Hall of Fame Host Rob Greenlee welcomes Rox Codes, CEO and co-founder of Flightcast.com.  For a deep conversation about video-first podcasting, YouTube growth, AI-powered analytics, creator tools, and where podcast publishing is heading next. Rox Codes has spent years building tools for creators, including YouTube optimization, thumbnail and title creation, A/B testing, and creator growth systems. Flightcast was co-founded with Steven Bartlett of The Diary of a CEO. Rox is building a platform centered on a core shift in the market: serious shows are no longer just audio-first with a bonus video version. Many of the fastest-moving creators now think about YouTube first, then audio, clips, Spotify, Apple Podcasts, newsletters, social platforms, and every other surface where the audience may discover the show. Has video-first podcasting fully arrived, and is it now equal to audio, or is it becoming even more important for growth? Rox explains that Flightcast came from a very different starting point than traditional podcast hosting. Instead of beginning with RSS, downloads, and audio workflow, the platform was built from a YouTube creator mindset.  YouTube has long been the clearest growth platform for creators because it combines publishing, discovery, audience development, monetization, and measurable performance into a single system. Podcasting, by contrast, has often separated those pieces across hosting platforms, apps, ad systems, analytics dashboards, and RSS-based distribution. The episode explores why this matters now. A modern show can become a long-form YouTube episode, an audio podcast, Spotify video, Apple video, short-form clips, newsletter content, social posts, community discussion, and brand inventory. Rox describes podcasts as a powerful format because a single strong two-hour conversation can yield many different media assets across multiple platforms. That creates opportunity, but it also creates complexity. They discuss Apple's HLS video support, Spotify video, YouTube, RSS, 4K video, thumbnails in feeds, Netflix, Roku, FAST channels, Prime Video, and the growing need for creators to publish into more places without needing to understand every technical layer underneath. Rox argues that creators should not have to care about acronyms like HLS, VAST, RSS, or 301 redirects unless the technology directly affects their business. The software should handle the complexity so creators can focus on the show, the audience, and the growth strategy. A major theme of the episode is that video success is not just about uploading an MP4 file. Rox makes a strong case that the real shift is in mindset. On YouTube, titles, thumbnails, intros, pacing, retention, curiosity gaps, promise, progress, payoff, packaging, and audience behavior all matter. Podcasting has historically treated episode art and titles as secondary. YouTube treats them as the front door to the content. Rob and Rox spend significant time on thumbnails and titles, including why creators need to understand the psychology behind a click without reducing the work to empty clickbait. Rox explains that a thumbnail should create a question the viewer wants answered, while the episode itself must deliver enough value to earn attention and retention. The best creators do not copy blindly. They study what works, understand why it works, and apply that structure in their own voice, to their audience, and through their creative point of view. The conversation also moves into AI. Rox does not describe Flightcast as an AI-first platform, but AI is an important layer inside the system. He sees major value in AI analytics, back-catalog analysis, clip testing, title suggestions, descriptions, chapters, transcripts, and pattern recognition. His larger ambition is to bake more of the YouTube strategist and producer mindset directly into the software so creators can see what is working, what is not working, and where new growth opportunities may exist. Rob and Rox also discuss monetization. As video moves deeper into podcast platforms, host-read ads, dynamic ad insertion, video ad formats, brand partnerships, affiliate models, and creator-controlled advertising may begin to converge. Rox explains Flightcast's ability to support programmatic, dynamic sponsorships and bring-your-own-programmatic monetization, while keeping the platform focused on growth and creator support rather than solely on ad sales. The episode closes with a look at the future of podcast hosting itself. Rox argues that basic hosting has become a commodity. The next layer is growth, analytics, experimentation, distribution, monetization support, and creator intelligence. In a world where AI can make software easier to build and copy, the real advantage may come from insight, speed, taste, data interpretation, and the ability to help serious creators make better decisions faster. For creators, publishers, networks, and podcast platforms, Episode 674 is a clear look at the next stage of podcasting: video-first, data-aware, AI-supported, YouTube-influenced, and increasingly built around shows that can travel everywhere. Chapter Topic Time Stamp Markers: 00:00 Welcome to The New Media Show Episode 674 00:19 Has video-first podcasting arrived? 01:21 Introducing Rox Codes and Flightcast 02:32 Building tools for YouTubers before podcasting 03:18 Why podcast hosting needed a YouTube-first mindset 04:13 Building the Flightcast playbook around video-first shows 05:31 Bringing the Diary of a CEO playbook into software 06:54 Can a platform support data-driven production? 08:27 AI, automation, and the future of show creation 09:13 LLMs and AI tools in creator workflows 10:23 AI as a podcast data consultant 11:19 Publishing, analytics, and experimentation 13:41 Could Flightcast become a creator operating system? 14:17 Podcasts as long-form source material for every platform 16:39 Is YouTube success the strongest growth signal? 17:19 Growth, retention, and why YouTube has the advantage 19:44 Going from YouTube to audio vs audio to video 20:49 Who Flightcast is built for 21:28 Apple HLS, Spotify video, and platform complexity 22:21 Can Apple or Spotify compete with YouTube video? 23:43 Video as human connection 24:24 Apple Podcasts video and the return of native video distribution 25:51 Netflix, podcasts, and low-cost creator TV 27:22 Why creator video can work across phone, laptop, car, and TV 28:20 Apple TV, 4K, and the quality question 29:16 Why creators should not need to understand HLS or RSS 31:55 The podcast industry still has too much technical friction 33:46 Alternate enclosure, iHeart, HLS, and RSS thumbnails 34:27 Why episode artwork and thumbnails now matter more 35:10 The thumbnail and title are the packaging 37:14 AI thumbnail scoring and creative judgment 38:08 Thumbnail psychology and curiosity gaps 38:52 Retention editing, strong intros, and promise-progress-payoff 40:10 Why better videos can multiply results 41:19 The real workload behind YouTube-quality video 42:37 Legacy media skills vs YouTube creator skills 44:50 Faster testing cycles changed creator media 45:32 Why video can be ten times harder when done right 45:53 How AI can bake strategy into software 47:00 Where Flightcast differs from early-stage creator tools 47:32 YouTube algorithm changes and podcaster anxiety 49:19 HLS, video ads, and creator-controlled ad insertion 50:22 Dynamic ads across YouTube and podcast platforms 51:33 Platform trust and monetization value 52:01 YouTube brand connections and affiliate models 53:18 Right brand, right creator, right price 54:35 360 campaigns, affiliates, and creator investments 57:17 Organic product mentions and brand relationships 58:14 What podcast hosting becomes next 58:46 Hosting as a commodity and growth as the real business 59:37 APIs, AI coding, and software moats 1:00:30 Where is the moat in creator software? 1:01:07 Staying ahead through insight and experimentation 1:03:32 Advanced analytics as the future advantage 1:04:41 Every creator has a different data pattern 1:05:14 MrBeastification, AI, and creator sameness 1:06:10 Outlier analysis and creative inspiration 1:07:15 Why outlier theory works when used correctly 1:08:44 Packaging the topic before recording 1:09:32 Does this conflict with podcasting culture? 1:11:01 Why data-driven formats do not have to kill creativity 1:12:23 Ryan Trahan and creative twists on proven formats 1:14:17 Packaging as the price of audience attention 1:15:04 Craft, creativity, and changing the playbook 1:16:51 Podcasting's radio roots and YouTube's different rules 1:17:27 Flightcast demo: stop guessing what content works 1:18:00 Publishing video everywhere with unified analytics 1:18:51 Measurement standards and the 30-second vs 60-second debate 1:20:21 AI analytics, clips, and test channels 1:21:35 YouTube API limits and thumbnail A/B testing 1:22:51 Flightcast monetization and bring-your-own programmatic 1:23:55 Transcripts, AI titles, descriptions, and chapters 1:24:21 Flightcast pricing and plans 1:25:41 HLS across all plans and video storage differences 1:26:32 Future pricing credits for more clips 1:26:48 Closing thoughts and where to find the episode Guest Links: Rox Codes, CEO and Co-Founder, Flightcast Flightcast: https://flightcast.com Rox Codes Website: https://roxcodes.com Rox Codes on LinkedIn: https://www.linkedin.com/in/roxcodes Rox Codes on X: https://x.com/RoxCodes Rox Codes on YouTube: https://www.youtube.com/channel/UChg53qPBdY1FF8gjOlB9zkg Rob Greenlee and New Media Show Links Rob Greenlee Website: https://robgreenlee.com New Media Show: https://newmediashow.com New Media Show Audio on Apple Podcasts: https://podcasts.apple.com/us/podcast/new-media-show-audio/id392545649 New Media Show on YouTube: https://youtube.com/@TheNewMediaShow Rob Greenlee on YouTube: https://youtube.com/@RobGreenlee Podcast Hall of Fame: https://podcasthall.com Personal / AI Disclosure Note: I used AI tools to help organize and edit this episode description and generate show notes from the episode transcript. The views, clarifications, responsibility, and industry perspective are mine and my guest's. This article reflects my editorial direction and the substance of the conversation.The post Video-First Podcasting Has Arrived | Rox Codes, Flightcast #674 first appeared on New Media Show.

Hoje o papo é sobre o SERP, o Sistema Eletrônico dos Registros Públicos! Neste episódio, conversamos sobre como a plataforma Meu Registro pretende simplificar o acesso aos serviços de cartório e integrar diferentes tipos de registros públicos. O papo também passa por interoperabilidade, APIs, segurança, assinaturas digitais, modernização dos cartórios e os desafios de conectar sistemas antigos em uma única porta de entrada. Vem ver quem participou desse papo: Paulo Silveira, o host que usa a palavra governança Vinny Neves, cohost, dev e professor na Alura Fabrício Carraro, co-host do IA Sob Controle, Program Manager da Alura, autor de IA e host do podcast Carreira Sem Fronteiras Rodrigo Gonçalves de Souza, juiz auxiliar da Corregedoria Nacional de Justiça, no CNJ Rodrigo Pinho, gerente de projetos de tecnologia no Operador Nacional de Registro de Títulos e Documentos e de Pessoas Jurídicas (ON-RTDPJ)  Links:  Sistema Eletrônico dos Registros Públicos (SERP) Meu Registro Conselho Nacional de Justiça (CNJ) Corregedoria Nacional de Justiça Operador Nacional de Registro de Títulos e Documentos e de Pessoas Jurídicas (ON-RTDPJ) Operador Nacional do Registro Civil de Pessoas Naturais (ON-RCPN) Portal Oficial do Registro Civil Operador Nacional do Sistema de Registro Eletrônico de Imóveis (ONR) Lei nº 14.382/2022 Central Nacional de Registro de Títulos e Documentos e de Pessoas Jurídicas Atos normativos do Conselho Nacional de Justiça Infraestrutura de Chaves Públicas Brasileira (ICP-Brasil) Plataforma Digital do Poder Judiciário Brasileiro (PDPJ-Br) Toda revolução tecnológica começa com quem antecipa o futuro e transforma ideias em soluções de alto impacto. Conheça os cursos da Alura + FIAP Skills & Go: Agentic Engineering, Building AI Products, e AI Data Strategy. Saiba mais sobre o Skills & Go. Vá para o Vale do Silício com Paulo Silveira, Marcell Almeida, Fabrício Carraro e Marcus Mendes na “Imersão IA Sob Controle e Alura no Vale do Silício“! Vagas limitadas, corra para reservar a sua. TechGuide.sh, um mapeamento das principais tecnologias demandadas pelo mercado para diferentes carreiras, com nossas sugestões e opiniões. #7DaysOfCode: Coloque em prática os seus conhecimentos de programação em desafios diários e gratuitos. Acesse https://7daysofcode.io/ Produção e conteúdo: Alura Cursos de Tecnologia – https://www.alura.com.br Edição e sonorização: Rede Gigahertz de Podcasts

Mike & Tommy tackle whether AI agents can solve what the Power BI admin portal can't—giving you a true tenant-level, workspace-free view of who can access what in Fabric. They break down Taylor B.'s real-world agent that stitches together audit logs, model APIs, grants, direct links, and app audiences into a central governance store, and weigh in on whether Microsoft's native governance story is ready or still a DIY puzzle.From Purview's role to the risks of letting agents reason over permissions, they explore what it would take to make data governance in Fabric actually feel complete—and what builders like Taylor should ship now versus wait for.Mailbag question from Taylor B.: OneLake Architectural Guidance | Fabric Task Flow Studio | Chicagoland Power BI MeetupGet in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

Show DescriptionHow do websites handle advertising embeds, thinking DND thoughts about team topologies, does knowing UX help get jobs in web dev, using Elena for building progressive web components, and dealing with buttons in web components. Listen on WebsiteLinks BuySellAds Carbon Ads It needs to map back to a role – Eric Bailey Elena | Progressive Web Components Ariel Salminen David Darnes SponsorsNotionWrite custom tools for Notion Agents that generate assets, query live data, and hit any API. Listen for incoming webhooks from any app, then run workflows with Notion Agents, pages, databases, and external APIs. All of this, on a hosted runtime. Workers are isolated sandboxes managed by Notion, so the code behind your syncs, tools, and workflows runs on our infra instead of your servers.

Payments are getting faster and more automated, but the hardest part still isn't the tech. It's trust. We talk with Rachel Costello, VP of Platform Growth at Maverick, about why the future of payments is human and how leaders can keep empathy, judgment, and real partnership at the center while AI and automation reshape everything around us.Rachel shares her career path into fintech, from early work in ad tech and e-commerce to revenue operations and growth roles, and why payments became the perfect place to combine strategy, go-to-market, and customer impact. We also dig into what she's building today: strategic partnerships, embedded payment strategy, and the infrastructure that helps software platforms deliver seamless commerce experiences.A big theme is embedded payments and embedded finance, and what it actually means when a software platform becomes a commerce platform. We break down the customer journey, preferred payment methods, and the practical outcomes businesses want: less friction, faster scaling, and simpler ways to get paid. Along the way, Rachel makes the case for fundamentals, customers don't buy buzzwords like AI or APIs, they buy the results those tools deliver.We wrap with leadership lessons, mentorship, and advice for the next generation of women in payments: raise your hand before you feel ready, get comfortable being uncomfortable, and choose a culture that fits how you work and grow.

Don’t let the AI wave crush you. Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ Dive into the seismic shifts happening within the AWS Marketplace and discover how AI, self-service product-led growth (PLG), and advanced co-selling strategies are redefining partner success. Matt Yanchyshyn, VP of Marketplace at AWS breaks down the recent announcements from the summit, illustrating how agility and adaptation are crucial to surviving the new agentic future. From lowering professional services fees to the explosion of business applications like ServiceNow, this conversation reveals the hidden mechanics of modern cloud procurement and how you can position your organization to capture massive enterprise opportunities before your competitors do. https://youtu.be/gaWxU1kgCLk Key Takeaways Adapting to the new agentic future requires agility rather than fighting the influx of AI tools. Lowering the listing fee for professional services from 2.5% to 0.5% drastically improves partner economics. Organizations without a self-service or PLG motion on the marketplace are literally leaving money on the table. Millennial buyers increasingly initiate complex enterprise procurements through self-service and AI-driven research. New AI-powered opportunity scoring empowers partners to prove their value internally and to AWS. Marketplace success hinges on optimizing metadata for AI agents, not just traditional SEO. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags: AWS Marketplace, agentic workflow, med pick scoring, phoenix.ai, Cara Cloud, branded storefronts, product-led growth strategy, intrinsic value boost, SaaS evolution, self-service motion, Databricks credit model, Trend Micro companion app, MCP servers, opportunity score tracking, PPA drawdown, concurrent agreements, AAMI structural debt, CXML procurement Transcript: Matt Y Audio Podcast [00:00:00] Matt Y: The ability to adapt with change and kind of roll with punches. ’cause a lot of people are saying like, agents are gonna destroy everything. And, and the opposite has been true. [00:00:08] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. [00:00:19] Vince Menzione: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi, own your host. And each week I sit down with leaders at the intersection of technology, partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:42] Vince Menzione: It is the strategy because being in the room changes everything. [00:00:46] Matt Y: Let’s start. [00:00:50] Vince Menzione: And now on to the really important stuff. So, Matt, I don’t wanna butcher it ’cause I, a couple people have told me how to pronounce your last name and they said use the word magician and you’ll get close to it. But I’m just gonna introduce you as Matt Wy and I’m gonna ask you to pronounce your name on stage, but I want to have you join us. [00:01:08] Vince Menzione: So excited to have Matt wy. After a super busy day and night last night, come over from Brooklyn and join us today. Matt, so great to have you. Thanks. Thank you so much. Thank you so much. Alright, so pronounce your name for us. [00:01:23] Matt Y: Anyone wanna guess? Ian’s? It’s like magician. [00:01:27] Vince Menzione: It’s not that hard, [00:01:28] Matt Y: it’s not that [00:01:28] bad, [00:01:28] Vince Menzione: but I don’t wanna butcher. [00:01:29] Vince Menzione: I wanted to let you do it. Good. [00:01:30] Matt Y: What calls me Matt White. [00:01:31] Vince Menzione: That’s great. [00:01:32] Matt Y: Yeah. [00:01:32] Vince Menzione: So 13 years. [00:01:34] Matt Y: Four coming up on 14 next month. Yeah. [00:01:36] Vince Menzione: Wow. Congratulations. Yeah. So you’ve been there, you’ve been there since the early days. And we, we had a conversation. I had some Microsoft, former Microsoft colleagues. Uh, Theresa Carlson, for those of you who knew the public sector business. [00:01:48] Vince Menzione: Yeah. Who started, I mean, Andy came out, it was so funny because I was there and she was hosting Andy for a dinner and with all the CIOs of the federal government. [00:01:57] Matt Y: Yeah. [00:01:58] Vince Menzione: And she was still at Microsoft and it was actually kind of an interesting time. And she came over and did a lot of great things for a number of years. [00:02:04] Matt Y: Yeah. She [00:02:05] Vince Menzione: and a lot of great [00:02:05] Matt Y: business. [00:02:06] Vince Menzione: Yeah. She really like, it went from employee number one to 7,000. [00:02:09] Matt Y: Yeah. [00:02:09] Vince Menzione: And you, you were, you’ve been there all that whole time. Pretty much. [00:02:12] Matt Y: Yeah, I guess when I started in New York, just down the road, we were, uh, in a Regis facility. There were like 11 of us in, uh, just sitting around a table and we had to speak quietly sometimes because there was a, um. [00:02:21] Matt Y: Some type of a financial services organization down the hall and they’d listen to try and get stock tips on Amazon. Yeah, [00:02:28] Vince Menzione: I love it. [00:02:29] Matt Y: Never leaked. That’s [00:02:29] Vince Menzione: good. I love it. [00:02:30] Matt Y: Yeah, [00:02:30] Vince Menzione: you probably got some great stories and, um, we won’t have time for today ’cause I wanna leave some room for conversations on marketplace end questions. [00:02:38] Matt Y: Yeah. [00:02:38] Vince Menzione: But I would love to invite you back for a real, like, in-depth podcast and I would love to get the whole genesis story. [00:02:44] Matt Y: Let’s do it. [00:02:45] Vince Menzione: We’ll do it. Okay, so let’s talk about, let’s talk about yesterday for you. Uh, some, some really big announcements as well. I thought maybe you could recap a little bit of what’s been going on in the marketplace business and it’s an, it’s been an exciting time. [00:02:58] Matt Y: Yeah. Yeah. You know what’s, I think what was really nice yesterday is it was sort of the combination of bringing, uh, our partner services like Partner Central and all those other services together closer to marketplace. We’ve been doing that over, over several years. So Marketplace has some of its own. [00:03:12] Matt Y: Big announcements, like, uh, we have a, we formalized our list and sell initiative. For example. We have a new, so it we essentially reducing the cost, uh, to list on marketplace through a partner program. [00:03:22] Vince Menzione: Yep. [00:03:22] Matt Y: And incentives associated with that. We have a new AI powered listing experience, which I think is particularly important ’cause I think many of you are like me and watching your SEO numbers go down and watching your agent traffic go up. [00:03:33] Matt Y: And so having, uh, an AI assistance in marketplace to optimize your listings for not just to, you know, retain what you can of your SEO, but prepare for the newent future and improve your GEO as we’re calling it. So that, [00:03:45] Vince Menzione: so it’s GEO now? [00:03:46] Matt Y: Yeah. You know, there’s a little debate right now in the acronym Moral A A EO versus GO I’m going, I’m on the G team, so, yeah. [00:03:52] Vince Menzione: Alright. GEO [00:03:54] Matt Y: It’s like the, the, yeah, they’re gonna win. They’re like the Knicks, but the, um, [00:03:57] Vince Menzione: yeah, yeah, exactly. [00:03:57] Matt Y: But yeah, so AI assisted, uh, I mean, making. The most of, like, essentially marketplace is an excellent conversion engine. And so using AI to help improve that conversion engine in the form of your PDPs for both humans and agents. [00:04:08] Matt Y: So that was an exciting launch. Um, I got the most applause when I announced that. We lowered, we made the economics better for, uh, consulting offers professional services, nice to marketplace. We lowered the listing fee from 2.5 to, to 0.5% and wow, it goes even lower in certain circumstances. So just improving the economics. [00:04:24] Matt Y: I’m really excited to. Really partner with a lot of you to reinvent services through, through the marketplace like we did with SAS and other areas. Uh, and we’re doing with agents right now. So that was a big one. And then a whole series of announcements around, um, how we’re making it easier and more cost effective and more efficient to partner with AWS. [00:04:41] Matt Y: So using AI to, uh, using med pick scoring to automatically progress opportunities so you don’t have to kind of wait on a human. To, to click and progress, you know, that that can take days. And, uh, if you, if you wanna have an opportunity and have that be cos sold with AWS, that can be through a mix of agents for the long tail and with humans in the, in the sort of top end and more complex. [00:05:00] Matt Y: And allowing AI to help all the partners improve their opportunity quality so that we can better co-sell together. So. Yeah, I said AI a lot intentionally. Um, [00:05:09] Audience Guest: yeah, [00:05:10] Matt Y: AI sort of in the whole cycle for buyers, for sellers, uh, for operational efficiency, cost of sales. So a lot of announcements. I think I hit the big ones, so yeah. [00:05:18] Matt Y: I’m Might have missed something there. There we go. [00:05:21] Vince Menzione: George. [00:05:21] Matt Y: Oh, and storefront. Yeah. Thanks George. See, I look at George to see what I missed. Uh, we, we acquired a great company called phoenix.ai late last year. Okay. And you, you actually were said Caresoft and Yeah. Be down. Uh, [00:05:30] Vince Menzione: yeah. [00:05:30] Matt Y: So if you’re familiar with Cara Cloud, they have a procurement portal. [00:05:33] Matt Y: It’s heavy use by the US government, and they, um. Uh, we, we acquired them, uh, the really great growth company. They have over 70 logos now, and they help you build a branded storefront on marketplace, which obviously is important in the government space. If you’re procuring on a certain contract with a certain reseller, um, you know, there’s a certain set of products you’re allowed to buy. [00:05:51] Matt Y: But what we’re finding is even down on Wall Street, you hear, um, enterprises are, are using storefronts for internal procurement and they wanna have a curated collection of, of partner products and, and your own ecosystems internally. So we’re selling to both customers. And also to channel partners to build custom storefronts, branded storefronts for, and [00:06:07] Vince Menzione: it makes total sense, right? [00:06:08] Vince Menzione: Yeah, because you wanna li you wanna limit the, the viewing and, uh, and get, because I mean, how many different listings do we have? Like over 30,000? [00:06:16] Matt Y: Yeah. Yeah. There’s, I think the official numbers over th we have over 36,000. I was checking from over 6,000 vendors. Um, it’s a lot. And, and that’s gonna explode with the AI powered, uh, listing, uh, experience that we launched. [00:06:26] Matt Y: We’re gonna make it easier. And I guess what I’ve been telling partners is. You know, customers aren’t clicking through categories anymore. They’re using AI to search. And so it doesn’t matter how big our catalog is, what matters is being found. And what matters is converting that buyer. So if you have a. [00:06:39] Matt Y: If you’re running a demand gen campaign for say, like, you know, life sciences in, in Jersey and there’s a specific buyer at j and j, you wanna capture, that person doesn’t wanna be just dropped onto a generic marketplace, 30,000 listings. They wanna be dropped in a very specific place where they’re seeing like life sciences offers from Accenture, for example, coupled with a life sciences power thing with Elastic, you know, like, but a solution. [00:07:00] Matt Y: And that they want to land in a curated place where that highly intention buyer can be converted effectively. So that, that’s what we’re doing with all this. [00:07:06] Vince Menzione: And that’s where the GEO comes in because [00:07:09] Matt Y: Yeah. ’cause that buyer might be an agent That’s right. With, and that agent has is even more fickle, honestly. [00:07:14] Matt Y: And you know, what used to be milliseconds for the human before they kind of click away is, is now perhaps microseconds. Yeah. And so, uh, you know, having the right metadata and, and the right positioning, uh, the right story that an agent or a human can pick up to ultimately. Uh, complete their product research and choose your product is, is critical. [00:07:30] Vince Menzione: Very cool. Very cool. So before I, I, I’ve been asked to ask you this because I, I’ve had this con, people have brought come to me and said, you gotta ask Matt about music. He’s a big music guy. And, uh, so what are your favorite bands? [00:07:49] Matt Y: So, I mean, the, the real answer is, uh. I, I go to about a show about every week. [00:07:54] Matt Y: As, as Mike Trill knows, uh, we heard a show last night. Um, we were, uh, just a few hours ago, really? And, uh, um, favorite band, uh, well, I’ll tell, I’ll tell a story. I, I had a side hustle with MTV for years. Um, I used to run a music website. Um, oh, that’s cool. I didn’t know that. It got, it got kind of popular. It got sponsored by, if, if anyone’s into like early hip hop. [00:08:16] Matt Y: It got sponsored by a group called Jurassic Five. ’cause he, one of them reached out to me and said, nice. Hey, uh, you know, I’ve been, I like your website. And he ended up paying for a web, hosting a Dream host, if you remember, of cost back then. [00:08:26] Vince Menzione: Oh, Jesus. [00:08:26] Matt Y: Because I was broke and couldn’t afford it. And then, uh, and then this band sent me like a, a single and said, Hey, you know, trying to get the word out about our little band, can you help us out? [00:08:35] Matt Y: And I put their, uh, I put their, you know, single up on my, on my website and it blew up. And that band is Vampire Weekend. So they’re kind of big now. Wow. Yeah. Um, and uh, that got picked up by like Vanity Fair and all these other guys. And then I got sponsored by MTV to essentially write. Music reviews for years on the side. [00:08:51] Matt Y: So I was working for the Associated Press, laying cable in sports and war and, and, uh, yeah. So Vampire Weekend was good to me that, that they, they kind of paved a way to go to a lot of free shows over the years and a lot of bands and see a lot of great music. But yeah. [00:09:03] Vince Menzione: That is very cool. And that, and how did that get your day? [00:09:05] Vince Menzione: WS It was just a, it was just the technology path that was like, [00:09:09] Matt Y: I mean, it’s a, it’s a, I guess it’s a bit of a long story, but, um, the. There’s many versions of this story. I’ll tell the, tell the one quickly. I was living for free in a Fulbright scholarship house in West Africa. You, we can talk about how that happened another time. [00:09:23] Matt Y: And, uh, a guy had sort of fallen down on the floor ’cause he’d had too much to drink. And I, I sort of lay down beside and be like, Hey man, are you all right? And, um, he, uh. He worked, he, he worked for the Associated Press and next day I had the job, um, being West Africa, head of technology for West Africa. [00:09:37] Matt Y: And because of that, um, and as I learned years later, the AP didn’t have dr they had no disaster recovery. Yeah. And I, I can tell you that now ’cause um, you know, 16 years since I worked there, but they, uh, I put the DR in, um, on AWS and we’re talking like, yeah, 16, 17 years ago. This is early. It was early days. [00:09:56] Matt Y: And I, I swear to God, I paid for. Uh, our AWS bill using, um, taxi receipts, fake taxi receipts that I bought in on Nigerian market, um, because there was no budget and so, you know, it was like 30 bucks. [00:10:08] Vince Menzione: I was gonna say swipe a credit card, but they didn’t [00:10:09] Matt Y: knew that this is the entire press this before. [00:10:11] Vince Menzione: This is before, yeah. [00:10:12] Matt Y: Yeah, like the entire ap. Um, and, uh, so AWS called me like, who are you? Like, why, why are you paying on like this like low limit credit card for like the ap? Like, who are you? And, uh. Next day I had the job. Well, a week later I had the job with aw WS. That so cool. So that’s the story’s [00:10:29] Vince Menzione: cool thing. [00:10:29] Matt Y: Yeah. [00:10:30] Vince Menzione: Very cool. [00:10:31] Vince Menzione: Uh, sports teams. So Knicks fan. [00:10:34] Matt Y: Yeah, I mean, I like the Knicks. Um, they’re h hockey, I’m not allowed to say anything different. No. I appreciate them. Uh, I’m a Raptors fan. I grew up in Toronto mostly. Yeah, yeah. Uh, so, and you know, when they won, uh, that was very exciting as well. So no, Nicks are great. I like the Knicks. [00:10:49] Matt Y: Nothing against the Knicks. Um. They’re fine. Yeah. [00:10:54] Vince Menzione: Hockey, hockey fan. Favorite hockey teams? [00:10:56] Matt Y: Oh yeah. Itron. Maple leaf. Maple leaf. Yeah. They’re gonna, they’re gonna win. Of course. Of course. Yeah. Um, like every year they’re actually, we [00:11:02] Vince Menzione: have some Canadians laughing in the sand. [00:11:03] Matt Y: Well, the leaf are, are, are the Knicks of hockey? [00:11:05] Matt Y: Like Yes, they are. You know, it’s 67 years out, coming up on 68 since they won, so That’s crazy. 53 is nothing. I know. Pain. So. Yeah, definitely the least. Yeah. [00:11:15] Vince Menzione: I love it. I love it. It’s so cool. Yeah. So what was the, uh, what was the, what was the last concert you went to? [00:11:22] Matt Y: Well, literally last night. Oh, it was last, [00:11:23] Vince Menzione: oh, that [00:11:24] Matt Y: was actually concert were my favorite bar in the world. [00:11:26] Matt Y: This place called Sunny’s. Uh, it’s, you know, I, I took Mike and, and Matt from, from Texas and from TGS down there to sort of see my neighborhood and they’re like, where are we? And I’m like, yeah, I live here. Uh, sort of an industrial part of Brooklyn. And, and we went to see, um, I dunno what you would call it, like. [00:11:40] Matt Y: I guess it’d be like roots music. There was a woman with an accordion and a guy with a big cowboy hat. Yeah, it was, it was fun. Yeah. [00:11:47] Vince Menzione: That is so funny. Alright, we’re gonna shift back years. Um, important time right now for partners. What, what should partners be looking out for the most? What would you say to them in terms of what’s the, what’s their real headline for them? [00:11:59] Matt Y: Well, I, I, you know, to borrow from you actually, you know, I liked, uh, the, the principles you had up there and, and with agility, um, you know, there’s a lot of fud flying around right now. You know, people. People were like, oh, it’s the demise of sis with the arrival of ai, you know, everyone’s gonna be using agents. [00:12:13] Matt Y: And then it turns out it’s been a huge boon for most, uh, you know, system integrators and consulting companies that I work with. They all have, you know, the, the good ones especially have vibrant consulting practices now, and everyone is deploying fds, uh, you know, um, the new, the new cool acronym. But it’s, it’s essentially created a huge opportunity for the consulting space. [00:12:31] Matt Y: Uh, and similarly, uh, you know, there there’s this narrative around the sa sa apocalypse, which I really hate, you know, ’cause it was, uh, premature and kind of a trigger reaction from the stock market. And, you know, just look, look what Snowflake did. And, you know, they did what a lot of SaaS companies are doing, but they, they added a nice sort of glaze of positioning and, and, you know, their stock popped and they did pretty well. [00:12:50] Matt Y: And so I think the ability to adapt with change and kind of roll with the punches. ’cause a lot of people are saying like, agents are gonna destroy everything. And, and the opposite has been true. For the more successful consulting companies and software companies who have become agentic. But SaaS hasn’t gone away, you know? [00:13:05] Matt Y: No. Look at our own marketplace. We have this agent marketplace, but people aren’t buying atomic agents at scale. They’re buying ified SaaS solutions with sort of agent sidecars, which has created new opportunities for candidly additional licenses, [00:13:16] Vince Menzione: right? [00:13:16] Matt Y: Um, as customers sort of want to consume more AI services on top of their. [00:13:20] Matt Y: On top of their SaaS solutions. So I think being agile, you know, you see like ServiceNow as part of our billionaires club. Yes. They’re not going anywhere. They’re, yeah. They’re gentrifying. You know, Salesforce has pivoted to this headless model, um, along with Asian Force and using sort of Slack as the operating system. [00:13:34] Matt Y: And, you know, you said like a lot of companies from the seventies aren’t around anymore. They’re gonna be winners and losers. Yeah. Um, but the winners are gonna win even more. And so I, I think what’s so important right now for partners is to not, not bite too hard at the, the latest trend. You know, models are changing and everyone’s like, oh, you know, philanthropics really in the world and they’re wonderful, great to work with, amazing technology. [00:13:55] Matt Y: That’s what people are saying about open AI six months ago. That’s right. And before that, you know, and it, I, I was with Fireworks AI yesterday, a great company and they have some really cool stuff with sort of, um, they believe in more cost effective, uh, open source models essentially, that you can find tune. [00:14:08] Matt Y: Maybe that’s gonna win. I don’t know. Um, is it gonna be sort of domain specific models? Is it gonna be highly capable LLMs? Are LLMs gonna level off as soon as Fable and Mythos are allowed to launch? Maybe. I, I don’t think anyone can predict the future right now. So you have to be agile and you have to kind of seize the opportunities and take a couple punches. [00:14:25] Vince Menzione: Yeah. [00:14:26] Matt Y: You know, and marketplace too, like we’re, you have to be unafraid to experiment right now. Um, you know, that’s hard if your stock’s taking a beating. Um, but this is, it’s a, it is a disruptive time, uh, but it’s creating actually enormous opportunities for growth for partners and, and we really see that, you know, in marketplace specifically within AWS. [00:14:45] Vince Menzione: It, it, it does still feel like the deer in the headlights moment. Right. Would you agree? Like you’re probably taking a lot of meetings and, and calls from ISVs specifically? [00:14:54] Matt Y: Well, [00:14:54] Vince Menzione: that are still trying to figure it out. [00:14:56] Matt Y: Yeah. But it’s everyone. Yeah. I think what’s really interesting, I had a meeting [00:14:58] Vince Menzione: with, it’s not just one. [00:14:59] Matt Y: Yeah. I, well, I had a meeting with one of the leading AI companies, like one of the biggest ones. And they, uh, they demonstrated how they work and they were really proud. They were like, you know, look at our agentic workflow. And I came out at me. I’m like, that’s it. Ours is way better. Like really like, you know, ’cause we we’re, we’re using quick desktop with MCP servers and connectors and all this, and you know, we, we have our own sort of ecosystem of partners, a mix of homegrown software and third party. [00:15:20] Matt Y: And I kinda walked out there and, and looked at, you know, my phone, which has been populated by agents this morning with all the, and I was like, I have a way better agent workflow than this world’s leading supposedly AI company. And I think, um, that really, so during, I, I would, during the headlights, you can call it deer in the headlights, I call it chaos. [00:15:36] Matt Y: And in times of chaos there are people who create. Opportunity again. And so, yeah, there are some people who are stuck and who don’t know what to do, who are over worried about token costs, um, who are not experimenting. But there are a lot of companies, uh, taking this opportunity to kind of pivot their business. [00:15:53] Matt Y: Um, I think, I think we’re in a moment and, uh, yeah, I, I candidly I see more of the latter. I see more experimenting. [00:15:59] Vince Menzione: You mentioned ServiceNow. Any other great examples of that? Organizations that really embraced it? [00:16:04] Matt Y: Uh, yeah. Well, you know, ServiceNow is part of this business applications category, as we call it, in marketplace. [00:16:09] Matt Y: That outside of AI, I think is the fastest growing category in marketplace, which is wild when you think about it. ’cause we’ve historically been an infrastructure partner marketplace with security and data and analytics and, you know, security with channel partners, et cetera. But Salesforce, ServiceNow, Workday, Adobe, you know, I could go on. [00:16:23] Matt Y: They, they are actually. You know, our fastest growing category and yeah, ServiceNow, obviously reinventing itself for ai, Salesforce, but Workday, you know, the workday’s done some, who knows if it’s gonna work, but they, they’re experimenting with essentially like a Databricks, uh, credit style model for like, units of work, uh, which I think is fascinating. [00:16:41] Matt Y: Like everyone’s talking about value-based, outcome-based pricing and meter. And, and you have companies that are ERP companies, you know, like traditional business applications, experimenting with effectively like a metered pay as you go, value based credit model. Again, like who knows if it’s gonna work. [00:16:54] Matt Y: But I think that’s really amazing to see and we need more ISVs experimenting. I, I was talking about trend ai and I know they’re, they’re, they’re one of the sponsors yesterday. You know, many of you know them as Trend Micro back in the day. They’ve successfully reinvented themselves. They built that companion app. [00:17:10] Matt Y: Um, you know, that I think we’re seeing. Just a ton of experimentation in the market across categories. Uh, I could go on and on about partners. Um, yeah, there, I I wouldn’t pick a winner right now. Yeah. [00:17:24] Vince Menzione: You, you, we’ve talked about ai. We’ve talked, talk more about the buying journey and how that’s changing, because again, it feels, it feels like that’s also [00:17:33] Matt Y: Yeah. [00:17:33] Matt Y: So, you know, one of, one of the core, uh, strategic objectives, or we’ll say like the philosophy marketplace is that. Um, financial incentives are important, you know, EDP or PPA drawdown, uh, credits. Like we need to act as an efficient and effective vehicle for allowing buyers to exercise their discounts for, and, and sort of partners to exercise their credits, et cetera. [00:17:55] Matt Y: That, that’s actually important. But what, what a lot of people over rotate on that, and we’re really, one of the things we say a lot inside at Amazon or at AWS marketplace is we want to continue to boost the intrinsic value of marketplace beyond the financial incentives. And well over a quarter of all private offers, private pricing, private, uh, custom terms, et cetera. [00:18:14] Matt Y: Um, begin with a self-service or PLG motion. And partners who don’t have a PLG or self-service motion are literally leaving money on the table. Like if you look at like a Databricks for example, and they did a good job integrating buy with a WS within their SaaS application. They have free trials, they have really strong pego and, and, uh, and PLG motion. [00:18:33] Matt Y: They’re making, I can’t share their numbers obviously, but they’re making a ton of money. On purely self-service motions. And importantly, they’re acquiring new business, new logos that they nurture, you know, really like not just leads but closed opportunities, right? That they lead, they’re growing, uh, at a reasonable conversion rate or or success rate into the next big logos. [00:18:50] Matt Y: And these are over multi-year horizons. They’re patient, you know, they bring in these new logos with PLG, and they’re also bringing banking, a lot of large enterprises. Through self-service. I, I was with data Mask. There’s this great little startup from New Zealand. They’re a New Zealand based company. Um, super nice guy. [00:19:06] Matt Y: And, and, uh, they, they got huge logos. I think they got, what was it? A DP and some huge American logos. Okay. And this like logo in, I think it was Chile, or no, it was Peru. They’ve never been to Peru. They don’t have sales in Peru. Um, and they. Buyers were discovering them self-service and they, they, I think they got something like 13 logos entirely through a self-service motion. [00:19:26] Matt Y: One password will tell you the same thing. I was just with them in Toronto and companies big and small startups and the largest are getting enterprise wins in addition to net new small logos through that PLG. Buyer motion. And that’s because you have a whole generation of CFOs, CTOs, CROs, whatever. The C is [00:19:43] Vince Menzione: millennial [00:19:43] Matt Y: who grew up on their phones. [00:19:45] Vince Menzione: Yeah. [00:19:45] Matt Y: And, and it sounds like, you know, hyperbole, but it’s true. They, they want immediate apps, immediate access. And that actually, you’re like, oh, that never translates to business applications. Turns out it does. It does. And they might not be buying on their phone, but what they are doing is researching and we see the numbers, the amount of customers who are doing their research, and then eventually landing on the page from chat, GPT. [00:20:06] Matt Y: From major financial, like Fortune 500 companies is extremely high. Yeah. Uh, you have procurement team, sourcing team, uh, developers who are starting the research increasingly, like in clawed in chat, GPT, and then, you know, building a proposal and then handing it to their enterprise procurement team. Yeah. [00:20:22] Matt Y: Which is still largely unchanged. So buyer behavior is on the front end, on the research side is really changing. So the [00:20:29] Vince Menzione: discovery is happening through PLG. [00:20:32] Matt Y: Yeah. [00:20:32] Vince Menzione: And then the backend work on private offers and things like that sometimes still happens the old way. [00:20:36] Matt Y: Yeah. Well, and so, you know, it’s [00:20:37] Vince Menzione: fax machine, [00:20:38] Matt Y: some people Yeah, sure. [00:20:39] Matt Y: They’re bringing the deal directly to Marketplace last minute. But even if that deal goes direct, sometimes they’re still beginning their research journey and increasingly using Marketplace as a research vehicle, which is why we launched Agent Mode, um, to help you sort of help you and agents do research. [00:20:51] Matt Y: But that I think if, if I have one piece of device for any partner consulting or ISV is. Don’t leave those leads and that money on the table by not having a PLG self-service strategy like you’re fooling yourself. Uh, and it’s, it’s a huge, it’s a huge, huge business for us. The, the majority of all customers by far on marketplace don’t even have a PPA, uh, and a huge percentage of even those with PPA spend beyond the p. [00:21:17] Matt Y: And so if you’re just think if you’re just using marketplaces as like BPA retirement, you are literally losing money. [00:21:22] Vince Menzione: Yeah. [00:21:22] Matt Y: Yeah. [00:21:23] Vince Menzione: We have a session with Vinod. We’re gonna talk a little bit about that right after. Great. So good. Um, so I, yeah, I think, um. We talked about, we talked about agents, we’ve talked about the millennial buyer, the change in buying behavior. [00:21:40] Vince Menzione: What other, what other areas of aspect I, I, I, I do wanna think about like opening it up though for a second. I think that maybe with maybe nine minutes left. Sure. I just want to get a read from the people in the room. People have questions for Matt that we weren’t able to ask them. Yeah, I think, I think we probably have a few of those. [00:21:57] Vince Menzione: I think that would probably be great. [00:21:58] Matt Y: I can sense the hardball coming. [00:22:00] Vince Menzione: You’ve known each other [00:22:00] Matt Y: a long time. [00:22:01] Vince Menzione: Yeah. No, no. Hardball. We have a mic back here. Okay. I’ll just, we’ll, we’ll, we’ll get you a mic as we are recording. So good. Thank you. [00:22:11] Audience Guest: Uh, Boris Geller with a, a Click PLG is near and dear to my heart. [00:22:17] Audience Guest: We’ve been doing a lot of business in marketplace and I’m still struggling to sell my vision internally on, on, uh, on PLG. Uh, I think. Ag Agent AI is gonna be one of the drivers, and we are already on, uh, agent Marketplace, but I would appreciate guidance on, uh, best practices. How do we kind of, uh, operationalize it? [00:22:41] Audience Guest: It’s, it’s on us, not on you. [00:22:43] Matt Y: Well, no, I think it’s on both of us. You know, we, uh. One thing that we’re trying to do is give you more data to, to sell to your internal stakeholders in your executive suite. The value of co-sell with AWS all up, like finally with what we launched at, uh, the summit yesterday, you now get an opportunity score. [00:23:02] Matt Y: You, you get a number. People have been asking for this for years, so, so you can say when we do this and we, when we give AWS this information. The score goes up and we have a higher propensity to be cos sold by humans or agents before you had to kind of, it was like this mystery you had to guess. And similarly with marketplace, um, we, we have new dashboards that you can use to sort of, you used to have to sit down with us and go through spreadsheets to trace sort of lead to trace the funnel to sort of a close opportunity. [00:23:28] Matt Y: And we’re gonna continue to launch more there. But you now have more data that you can show. You can be like, listen, these are our inbound leads, this how’s converting, and now we have PRM, the partner revenue measurement where we can say like, this is what it’s translating into in terms of. AWS service revenue driven by our product. [00:23:41] Matt Y: And so that being able to tie from that inbound lead from your demand gen campaign through to a converted opportunity to what you actually drive from an AWS impact perspective, so you can, and then what your opportunity score is that data you can use to sell. Not only internally, but to us as well. Yeah, to a skeptical sales team or whatever who’s not maybe, you know, hype on partners in the, in the US West. [00:24:03] Matt Y: You can be like, listen, I don’t care what you think about my business. This is what I’m gonna drive for you with your quarter retirement from an AWS perspective, and this is how the shape of your customer accounts are gonna change. And this is why you should pay attention to my opportunities. ’cause my opportunity score is, is crazy high and I’m giving you insights into business that AWS would not otherwise have. [00:24:19] Vince Menzione: That’s your brand story we’re talking about. [00:24:21] Matt Y: Yeah. [00:24:22] Vince Menzione: Building your story up with within [00:24:25] Matt Y: So it’s, it’s about the data, I guess. And, and you should, you know, you should all actually be [00:24:28] Vince Menzione: Yeah. [00:24:29] Matt Y: Asking me for more data, so, you know, and tell me like, what do you need to sell to your internal stakeholders? ’cause if I can draw a clear line. [00:24:35] Matt Y: From your demand chain campaign that lands on a marketplace, which I know is a conversion machine, it has way better than industry levels of, of conversion rates. And then you can show, hey, if we have a PLG strategy and we land those leads on marketplace, we will convert them with high efficiency, low cost of sales and, and, and have sort of a bifurcated where we can close some through self service, some through express private offers and some through private offers, depending on deal size. [00:24:57] Matt Y: Like you tell A CFO that, and they’re my number one customer now and they love it ’cause they see cost of sales going down, cost of operations going down and business going up. Um, so I think we have more data than we used to use that data. And let me know what other data do you need to make that pitch and make that pitch to the CFO go around the head of sales, all those other people. [00:25:15] Matt Y: Honestly, the CFO is where we get the best leverage. [00:25:18] Vince Menzione: Awesome. Great question. [00:25:23] Matt Y: Gonna bring your mic. [00:25:23] Vince Menzione: We’re, we’re gonna get your mic here. There you go. Oh, [00:25:25] Audience Guest: thank you. So my name’s Jody Cheval and I’m a consultant now, but I was at Workday during when they adopted AWS and it, a sales organization needs propensity to buy data. [00:25:34] Audience Guest: To really drive the sales team to realize the opportunity kind of makes them visualize it. We didn’t struggle, but it was challenging to get that data because at that time we’re getting spreadsheets. So does AWS have a vision of making that API based data that our client, my clients, can get at and bring into a tool to start building account hypothesis based on that data? [00:25:57] Audience Guest: ’cause it really is important to an enterprise sales guy to have the sense that OAWS can help me close this deal. [00:26:03] Matt Y: Yeah. I mean. Part of that. So we, we launched, we’ve been launching part of that in stages and we’re not done. There’s, there’s more coming. Um, part of that is embedded really within the new, uh, partner agent workflows. [00:26:13] Matt Y: We are giving sort of more, uh, information back to you, not just about like what funding programs you’re eligible for, but like, you know, and when, when we will co-sell this deal with you, which is effectively a signal like we, we see this as a high value opportunity, that you have a likelihood of winning internally. [00:26:28] Matt Y: We, we have this solution matching engine that we’re using and we announced. That, that that ties you the partner to a customer specific opportunity that you have a high propensity or the partner has a high propensity to assist with and ultimately win. And now we’ve tied that to our express private offers, which we announced this week. [00:26:44] Matt Y: So it’s an indirect answer to what you’re asking, but a rep can essentially say. Send a private priced offer to the customer on behalf of the partner without having to ring up the partner because they have a high propensity to win this deal with the customer. So we’re progressively launching features like that. [00:26:59] Matt Y: In addition to the propensity to buy data that we do now share. It used to be kind of, again, manual magic depending on who you knew we could share. Now we do share that programmatically, and there’s more to come specifically in that space. Uh, I’d say watch that space. In the next few months, there’s gonna be more data coming away, but we do have the APIs, we have the agent. [00:27:16] Matt Y: We have things like express private office solution matching, and we have been sort of in that space progressively launching features over the last six to 12 months. And, and you should expect to see some more there soon, not just from us or from our partners. [00:27:27] Vince Menzione: Nice. Any announcement dates? [00:27:30] Matt Y: I can’t commit to a date or else my engineers will get mad at me. [00:27:33] Vince Menzione: It looks like we Another question number. Is the mic still back there? Okay. There’s a gentleman over here [00:27:40] Audience Guest: first Go leaves. Um, it’s awesome. I’m right next to. I was right next. [00:27:46] Vince Menzione: We’ve got a lot of great plants here, so, [00:27:49] Audience Guest: um, so this may be a little bit myopic or, or a challenge that we run into, but I love a lot of the innovation that’s looking forward and all the future things that we’re doing. [00:28:00] Audience Guest: One of the things that we’re struggling with is a little bit of almost like tech or structural debt. How do you think about bringing flexibility to the core pieces that underpin all of the innovation, which is. We are self-hosted. So one of our listings is an a MI. You can’t amend an a MI, you have to cancel and start over. [00:28:18] Audience Guest: So a lot of the building blocks, when you think about PLG, if somebody wants to add to that in an a MI listing, it’s, it’s sort of broken. So how are you thinking about taking all of the, the rapidly changing buyer behavior and then looking back at the structural foundation that underpins all of those things, like offers and, and amendments and changes and all of that? [00:28:39] Matt Y: Yeah. I, I promise I didn’t seed that question, but that, that’s a great one. Um, so not to get too in the weeds, but fundamentally, marketplace was built up, um, a bit like AWS like a set, a series of services somewhat independently. And each product type was effectively its own service, SaaS, server images, ais. [00:28:59] Matt Y: Um, what we’ve done recently is now we, we have, we got rid of product types basically on the backend. You, you don’t see it, but what that means, for example, like another thing AAMIs don’t support today, future data agreements. Um, or concurrent agreements, uh, they will all be supported by amis before the end of the year. [00:29:14] Matt Y: ’cause what we’re doing, this fundamental thing that you won’t even see called product offer decoupling. Uh, and it’s a fundamental piece of things that we need to unwind. ’cause we built up, we were moving very quickly over the years. We had a distributed engineering model and we built each product type independently. [00:29:28] Matt Y: And so yeah, if you’re a seller and you’re selling containers, agents, SaaS, amies, um, we’re breaking down the silos between those so that each of them will get the same benefits. And, and by the way, we’re taking the same approach to international. Hopefully you’ve noticed now that. It’s not like a feature launches in the US only and then takes five years to launch in either public sector or another country. [00:29:48] Matt Y: We, we’ve taken a global approach to feature launch and increasingly a product type neutral approach to feature launches. Uh, that’ll be largely resolved before the year’s out. We’re working on it right now. So again, it’s, it should be transparent to you, like you shouldn’t actually see any difference in the, in the experience. [00:30:05] Matt Y: Except that all of those features will be available. So, so that is, uh, actively under work. And that’s actually something if you’d like to try, um, you’re, you’re welcome to. So, yeah, [00:30:17] Vince Menzione: we have time for maybe one more question and we we’re actually gonna have you up here with a couple partners. [00:30:24] Matt Y: Sounds [00:30:24] Vince Menzione: good. Kind of fun. [00:30:32] Audience Guest: Hey, Matt, uh, met Natasha from Dondo. Uh, quick. So great announcements. And you know, you talked about the million, multi-billion dollar, uh, club, and, uh, that’s all great. Uh, in terms of the. Propensity data. I think that’s coming at the center of a lot of things, right? You know, for enterprises, oh, there’s an investment and you tap into that investment. [00:30:53] Audience Guest: But also there’s the other side of the procurement where a lot of customers, sometimes we work with, they’re like, they still wanna go direct for whatever reason, right? So I think there’s an education piece there, but also trying to understand like how we can work together to, you know, get some of that side of the things sorted out as well. [00:31:11] Audience Guest: You know? ’cause a lot of times it’s not about. Just, you know, retiring the, uh, the, the spend comets, but also like, Hey, I’m used, I’m already used that for something else. So maybe that’s not an, uh, something that applies here. And in also in tying that the PLG motion, uh, you know, for the customers you said, you talked about, you know, if there is. [00:31:34] Audience Guest: Leads on the TA table, like where the, it’s not the enterprise, but you know, the others. Um, I feel like it’s more to do, changing the business model at some times. Like with the enterprises, you have the revenue stream coming through, say large deals, right? And all of a sudden you tap into this, you know, PayGo. [00:31:51] Audience Guest: Where it flips the whole equation with, you know, the financing and the, and the, and the revenue measurement. So I think there’s two aspects of how do you kind of cons reconcile those things in terms of, you know, the revenue measurements going forward. [00:32:05] Matt Y: Yeah. So, so two things real quick on the procurement. [00:32:07] Matt Y: Um, yeah, like, yeah, I sort of alluded to this earlier, but, uh. Procurement is a bit late to the AI ag agentic transformation. They’re trying, and there’s a lot of great new incumbents in this space. And the big leaders like, you know, Coupa and Ariba and Oracle are, are, are evolving their products, albeit a bit slowly. [00:32:26] Matt Y: Um, but the, I think, uh, it’s still the long pole in the tent. You know this. And so like, there are two reasons why deals tend to go direct, because it kind of hits a wall of. Legal, uh, you know, procurement, governance, like all that kind of after the selection’s been made, et cetera, or, or they’re, you know, we can’t change. [00:32:44] Matt Y: People are gonna optimize for, for finance, you know, they’re, they’re going to, if they’re getting big discounts. I mean, that is life. I always say it’s like sellers at the most agented company are still gonna chase quota no matter how, you know, crazy. Uh, your, your company is, and it’s the same with, um, with the chief, uh, financial officer and chief procurement officer. [00:33:01] Matt Y: They are going to, they’re literally. Paid to find discounts. And so we’re not, we’re not gonna get rid of financial engineering. That’s a, that’s a thing. What we can do is reduce the friction for procurement. So we launched, for example, like mandatory purchase orders. That was a big thing. We, we have buyer notifications, now we’re making other procure to pay enhancements. [00:33:17] Matt Y: I mean, procurement systems still use like CXML. It’s like, that was, that was cool when I worked for the ap. And like I, I have teenagers that are old, like older than, so they, I, I think, um. Procurement needs to evolve and we’re gonna help it evolve. We’re gonna push it forward and, and we need to make it more seamless for procurement teams so that we remove those objections. [00:33:38] Matt Y: Uh, I can’t remove the financial engineering objection, like, you know, that’s just life. Um, but I can make it irresponsible not to use marketplace ’cause it’s so easy to use. And, uh, that, that’s kind of the approach we’re taking on, on the front end. Uh, you, you know, I think you, you, again, I didn’t see this question. [00:33:52] Matt Y: You, you stepped into a trap. Un unwittingly, um, PLG is not just is for enterprise. And, and PLG doesn’t necessarily mean pego or self-service. Uh, doesn’t necessarily like, uh, most of our self-service efforts are actually focused on private offers. And not necessarily for pego. Uh, when, when I say self-service and, and PLG, uh, it, it can mean all kinds of things like it. [00:34:14] Matt Y: We have requested private offer, requested demo call to actions, buttons that you can put on your listing. For example, you don’t necessarily need a free trial or a metered pay as you go listing to take advantage of those inbound self-service leads. So, and those inbound self-service leads are often massive enterprise deals, like I mentioned specifically, uh, the data mask. [00:34:31] Matt Y: Those giant enterprise deals that they launched came from an enterprise like Fortune 1000 Enterprise in the US that organically discovered their solution on the marketplace using our AI search. And that was a massive enterprise. And so I, I think yes, there is the long tail, you wanna capture a new logo acquisition, but you should think of your product like growth in your self-service strategy as a way to, um, acquire all kinds of leads, including large enterprise. [00:34:54] Matt Y: And so when I say leave money on the table, I’m not just talking about things that are gonna mature over two years or tiny little deals. These could be massive deals. Uh, and, and you’ll accelerate those deals by accelerating their discovery and, and research so that I think that, so, and my advice is don’t, you don’t have to go all in if you don’t have, if you don’t have metering, if you don’t have PayGo, that’s cool. [00:35:13] Matt Y: Start with something simple. Start with a public listing, with a request to private offer like that. That is a, a huge step. That doesn’t take much, and, and it kind of blows my mind still that a lot of companies aren’t doing that yet. [00:35:25] Vince Menzione: Great answer. Well, it’s now time we’re gonna bring, we’re gonna bring, it’s time. [00:35:29] Vince Menzione: We, we’ve got some great partners coming up here, Nvidia Elastic, Accenture gonna all join us for a conversation. Great. And I’m glad that you’re gonna stay with us. And let’s, let’s, well, let’s thank Matt, by the way, for that session. [00:35:41] Matt Y: Thanks. [00:35:42] Vince Menzione: And [00:35:42] Matt Y: thanks for listening to the Ultimate [00:35:44] Vince Menzione: Partner Podcast. If today’s conversation resonated, share it with a partner leader in your network. [00:35:51] Vince Menzione: Subscribe where you listen. And head over to the ultimate partner.com. For show notes related content and the resources for this episode. And if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, October 26th through October 28th. Until next time, keep showing up in the rooms that matter because being in the room changes everything.

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Today, we are dropping another episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.In today's episode, Craig Thomas, Sr. Solutions Engineer at Wallarm, returns to the show to dive into why runtime behavior is the critical blind spot, and what CISOs should demand if they want to move from policy to control.QuestionsSecurity teams are used to detecting incidents and responding after the fact. Why is that model becoming insufficient for AI-driven systems?Building on that, when we talk about response today, enforcement often means actions like restarting pods, rotating credentials, or shutting down services. Why can those measures come too late in an AI environment?So if traditional response isn't enough, why does AI behavior require controls that operate much closer to runtime?And when people hear "runtime enforcement," they may think of existing security controls. What changes when enforcement happens at the kernel level rather than only at the network, identity, or application layer?Can you make that tangible for us? What does it actually mean to revoke or contain a compromised AI session without disrupting the broader deployment?How does that kind of real-time containment change the risk equation for AI agents that have access to sensitive data, external services, or production workflows?With that in mind, what are some examples of AI behaviors that organizations should be able to stop immediately?Of course, security teams also don't want to become a bottleneck. How do organizations balance strong enforcement with the need to keep AI development and deployment moving quickly?And once organizations have the ability to discover, observe, and enforce AI behavior in real time, how does that change accountability at the enterprise level? What does good governance look like from there?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/cu-craigthomas/Full AbstractThis episode examines what is actually missing in AI security today. Craig Thomas, Sr. Solutions Engineer at Wallarm, dives into why runtime behavior is the critical blind spot, and what CISOs should demand if they want to move from policy to control.CIOs and CISOs have moved past debating whether AI security matters. The question now is what to actually do about it, and most organizations are finding that their existing tools answer a different question than the one AI is asking.Traditional security tools were built around access: who can reach a system, what credentials they present, what traffic looks like at the perimeter. AI shifts the problem to execution: what a system does once it has access, whether that behavior matches what the business intended, and how you know when it doesn't. Most current tooling has no answer for that. It can tell you what is deployed and what is configured. It cannot tell you what your AI is actually doing at runtime, on whose behalf, or whether any of it violates the policies you thought were in place.That gap is where most AI security programs stall. There is no shortage of governance frameworks, compliance checklists, and vendor claims. What is missing is operational control: the ability to see AI behavior as it happens, enforce policy at runtime, and produce evidence that holds up when an auditor or a board asks for it. The four capabilities that define a closed AI control loop, discover, observe, enforce, govern, are well understood as a category. Getting all four working together in production is where the real work begins.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://cash.app* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li

Seema Amble, Steven Sinofsky, and Elena Burger unpack one of the biggest questions facing enterprise software: what happens when AI agents become the primary users of software instead of humans? The conversation explores the rise of "headless" software, why APIs and agentic workflows are reshaping enterprise applications, and whether traditional SaaS products are becoming systems of record rather than systems of engagement. They discuss Salesforce's Headless 360 announcement, MCP, enterprise software architecture, and why AI may fundamentally change how businesses interact with their data. Along the way, they examine what actually makes enterprise software sticky, why replacing systems like SAP and Salesforce is harder than it appears, and where startups have the greatest opportunity as AI reshapes the software stack.   Resources: Follow Seema Amble on X: https://x.com/seema_amble Follow Steven Sinofsky on X: https://x.com/stevesi Follow Elena Burger on X: https://x.com/VirtualElena Related Reading Is Software Losing Its Head?https://a16z.com/is-software-losing-its-head/ The Death of Software? Nah.https://a16z.com/death-of-software-nah/ Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.