Podcasts about Apis

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.

The European Commission has mandated binding antitrust measures that require Google to grant access to essential Android system features for competing AI assistants and to share its extensive search data with rival search engines.This significant ruling, implemented under the Digital Markets Act (DMA) of the European Union, seeks to create a more equitable environment and prevent Google from exploiting its dominant Android ecosystem to monopolize the swiftly advancing artificial intelligence industry.Google is obligated to provide access to 11 specific system-level features for competing artificial intelligence models, such as OpenAI's ChatGPT and Anthropic's Claude.Competing AI agents must be permitted to achieve the same level of system integration as Google's own Gemini, which includes enabling comprehensive voice activation and the ability to perform background tasks, such as making restaurant reservations through third-party applications.Additionally, to dismantle Google's near-monopoly on data, the company is required to start sharing anonymized search data with qualifying rival search engines by January 2027.Noncompliance with these binding directives could lead to substantial fines of up to 10% of Alphabet's global annual revenue.In the meantime, Kent Walker, Google's President of Global Affairs, has strongly criticized the ruling. He expressed that compelling the company to disclose search metrics to "unfamiliar companies" poses significant privacy risks, data protection concerns, and cybersecurity threats to European citizens.EU Competition Commissioner Teresa Ribera defended the action, emphasizing that society is experiencing a significant digital transformation that necessitates robust legal protections for fairness and consumer choice.The vigorous implementation of the Digital Markets Act continues to provoke criticism from U.S. political leaders, who contend that European regulators are unjustly targeting and undermining American technology giants.

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

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.

Send us Fan MailAre traditional websites dying, or are they just changing shapes? Hosts Greg Sterling and Mike Blumenthal catch up with serial entrepreneur and Mozilla VP of Product Raj Singh to discuss how AI search friction is changing the local web. Learn why small businesses must lean into "trust artifacts," how APIs and the Model Context Protocol (MCP) will surface local businesses to AI agents, and why human-in-the-loop authenticity is your best defense against algorithmic spam.Subscribe to our newsletters and other content at https://www.nearmedia.co/subscribe/

A set of principles to enhance testing APIs created inside R, and the engine behind automatic reloading of rendered Shiny apps becomes available for new use cases.Episode Links This week's curator: Jon Calder - @jonmcalder@fosstodon.org (Mastodon) & @jonmcalder (X/Twitter)Testing Your Plumber APIs from Rwatcher 0.2.0: filesystem watching for R, and the engine behind Shiny auto-reloadEntire issue available at rweekly.org/2026-W28Supplement Resources{plumber2}: https://plumber2.posit.co/{httr2}: https://httr2.r-lib.org/index.html{mirai}: https://mirai.r-lib.org/index.html{nanonext}: https://nanonext.r-lib.org/index.htmlroxygen2 @exampleIF tag https://roxygen2.r-lib.org/articles/rd-functions.html#examples{watcher}: https://watcher.r-lib.org/fswatch https://emcrisostomo.github.io/fswatch/Supporting the showUse the contact page at https://serve.podhome.fm/custompage/r-weekly-highlights/contact to send us your feedbackR-Weekly Highlights on the Podcastindex.org - You can send a boost into the show directly in the Podcast Index. First, top-up with Alby, and then head over to the R-Weekly Highlights podcast entry on the index.A new way to think about value: https://value4value.infoGet in touch with us on social mediaEric Nantz: @rpodcast@podcastindex.social (Mastodon), @rpodcast.bsky.social (BlueSky) and @theRcast (X/Twitter) Mike Thomas: @mike_thomas@fosstodon.org (Mastodon), @mike-thomas.bsky.social (BlueSky), and @mike_ketchbrook (X/Twitter) Music credits powered by OCRemixRelm's Weepy Loop - Final Fantasy 6 - MkVaff - https://ocremix.org/remix/OCR00178

Jake and Michael discuss all the latest Laravel releases, tutorials, and happenings in the community.Show linksRoute Metadata Support in Laravel 13.17Worker Metrics on the WorkerStopping Event in Laravel 13.18Help make Filament faster!Clonio CLI: Clone Production Databases With Anonymized DataA Copy/Paste Detector CLI for PHP 8.5+EnvKit: A Local Development Stack for Laravel on Windows and macOSMonitor and Control Schedules, Queues, and Errors in Laravel with WatchtowerYammi Audit Log: Track Who Really Made a Change Across Jobs and QueuesUSAIGE: Track Token Usage and Costs for Laravel AI SDK RequestsTurn PHP Attributes Into Docs With SignalLaravel WhatsApp: Two Backends Behind One Facade Laravel AI Tasks: An AI Orchestration Package for Queues, Logging, and Cost ControlTutorialsShip AI with Laravel: Failover, Queues, and Middleware for AI AgentsShip AI with Laravel: Test Your AI System with Zero API Calls

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.

In "Private Fleets Rescue Freight Brokers in 2026", Joe Lynch and Russell Jones, CEO & Co-founder of Private Fleet Net Zero, discuss how unlocking empty private backhauls provides brokers with discounted, high-quality capacity to combat fraud and skyrocketing liability. About Russ Jones Russell Jones co-founded Private Fleet Net Zero to help the 45% of trucks that are in Private Fleets with usually empty backhauls find loads from $50B+ of 3PL freight spend, leveraging his leadership of Cargo Chief, which enables 1,200+ 3PL buyers with $8B+ of spend to buy transportation capacity more profitably. Previously, Mr. Jones co-founded and led two cloud-based physical security firms.  He was also the founding CEO of Clearvox Communications, which pioneered the market for cellular phone headsets, which he sold to Plantronics.  Beforehand at Adaptec, Mr. Jones doubled a $50M channel products business to $100M. Mr. Jones has been awarded 10 patents, and holds a BSBA with highest honors from Boston University and an MBA from the Harvard Business School. About Private Fleet Net Zero Private Fleet Net Zero, PFNZ, is uniquely aggregating 10,000s of trucks with 1,000s of lanes of underutilized, underpriced, theft-free and superior private and dedicated fleet trucking capacity and matching via multi patent-pending technologies and artificial intelligence to $10Bs of freight spend registered on our cloud-based platform, while generating a compelling client ROI. Our network is quickly and efficiently growing both fleets and 3PLs on PFNZ, which is on a path to save 30M+ tree equivalents. Key Takeaways: Private Fleets Rescue Freight Brokers in 2026 In "Private Fleets Rescue Freight Brokers in 2026", Joe Lynch and Russell Jones, CEO & Co-founder of Private Fleet Net Zero, discuss how unlocking empty private backhauls provides brokers with discounted, high-quality capacity to combat fraud and skyrocketing liability. Massive Fleet Scale: Private Fleet Net Zero (PFNZ) has rapidly aggregated 80,000 trucks and 40,000 lanes of coverage, using patent-pending AI to match this massive pool of underutilized capacity with tens of billions of dollars in registered freight spend. The $150B Backhaul Waste: Private fleets (where the cargo owner owns the asset, like Walmart or Sherwin-Williams) make up 45% of all trucks on the highway, yet they run empty 80% of the time on their backhauls, leaving a $150 billion pool of premium capacity sitting idle. Pure Profit for Fleets: Because the primary "front haul" already covers the driver's salary, equipment, insurance, and core fuel costs, any backhaul revenue captured through PFNZ represents a 95% pure profit margin for the fleet owner. Quadrupled Broker Margins: Brokers can secure this premium capacity at a 25% discount to the market rate. In a tight market where a typical gross profit might only be $150 on an $1,150 load, cutting carrier costs from $1,000 to $750 can effectively triple or quadruple a broker's net margins. Eliminating Fraud and Liability: Shifting to private fleets bypasses the modern plague of cargo theft, cyber fraud, and "chameleon carriers" who hide bad histories under new DOT numbers. Furthermore, because private fleets have newer equipment and a third less accidents, they shield brokers from catastrophic multi-million dollar "nuclear verdicts" tied to carrier safety under the recent Montgomery ruling. Combating the 2026 Capacity Crunch: Massive federal enforcement of English language proficiency rules is projected to strip 25% of for-hire drivers (400,000 to 600,000 drivers) off the road. PFNZ rescues brokers by giving them an automated "outsourced recruiting team" to tap into stable private capacity that was previously heavily monopolized by the top 10 mega-brokerages. Seamless Integration & Sustainability: PFNZ connects to a broker's TMS within weeks via APIs, reports, or an AI bot to automatically map buying patterns. By eliminating empty miles, the platform is on track to save over 45 million tree equivalents in CO2, giving public companies and shippers a verifiable decarbonization story for SEC and board reporting. Learn More About Private Fleets Rescue Freight Brokers in 2026 Russ Jones | Linkedin Private Fleet Net Zero | Linkedin Private Fleet Net Zero Private Fleet Net Zero: The Deadhead is Dead with Russ Jones The Broken Safety System Threatening Shippers and Brokers with Chris Burroughs What is Blue Ocean Strategy | About Blue Ocean Strategy The Logistics of Logistics Podcast If you enjoy the podcast, please leave a positive review, subscribe, and share it with your friends and colleagues. The Logistics of Logistics Podcast: Google, Apple, Castbox, Spotify, Stitcher, PlayerFM, Tunein, Podbean, Owltail, Libsyn, Overcast Check out The Logistics of Logistics on Youtube

In this episode of the Altium OnTrack Podcast, host Zach Peterson sits down with Rob Barton, Head of Platform API at Altium, for a deep dive into how programmatic access is transforming PCB design and electronics development. Rob traces the evolution of Altium's API—from the early disconnected SDKs and the launch of Nexar, through Octopart supply data, all the way to the new Platform API that exposes design data, supply chain intelligence, and manufacturing services through a single, federated GraphQL schema. If you've ever wanted to connect Altium 365 and Altium Designer data directly into your own systems, this conversation maps out exactly where the technology is heading. Through two live demos, Rob shows how to query live Octopart supply data—pricing, availability, RoHS compliance, and BOM resolution—then navigates the Platform API down to individual PCB layers, nets, and track coordinates. The discussion also explores API-first design philosophy, why discoverable APIs now matter for AI agents and MCP servers, and the upcoming Altium Developer Center that will open this platform to engineers, enterprises, and third-party developers. Whether you're a procurement professional, a PCB designer, or building AI-enabled tools on top of electronics data, this episode is a clear look at the future of open, programmatic hardware design.

What is an AI agent, really? Strip away the hype, and it's a model with access - to tools, APIs, databases, email, anything that lets it take real action instead of just generating text. That access is exactly where the risk lives, and Devvret Rishi, GM of AI at Rubrik, and former co-founder & CEO of Predibase, joins Craig Smith with a string of real-world incidents that make the case concrete: AWS reporting four major outages in 90 days after deploying coding agents, a Meta-related agent that deleted someone's emails while they were actively asking it to stop, and Rubrik's own internal pilot catching incidents that, without governance in place, would have gone unnoticed. The conversation lays out the impossible choice most enterprises are facing right now - block AI agents and forfeit the ROI boards are demanding, or grant access and hope nothing breaks - and walks through how Rubrik's approach uses small, fine-tuned AI models to enforce plain-English security policies on every single agent action in real time. It closes on one of the most underexamined risks ahead: as agents increasingly talk to other agents to get work done, a layer of activity is forming that no human is watching, and the question of who's accountable when something goes wrong in that layer is only getting more urgent. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

In this episode of Building Better Developers, Jim Hodapp and Bob Belderbos discuss why Rust continues to gain momentum among experienced developers. The conversation explores software craftsmanship, memory safety, AI-assisted development, and why language choice is becoming less important than understanding how software actually works. Key Discussion Points Why Rust attracted both systems programmers and Python developers The relationship between AI coding tools and strongly typed languages How Rust improves software reliability The importance of understanding software fundamentals Why developer growth often requires embracing discomfort The Rust Developer Mindset is not really about Rust. That may sound strange coming from two developers actively teaching the language, but one of the strongest themes from the discussion with Jim Hodapp and Bob Belderbos was that successful software development starts with understanding systems, not syntax. As AI generates code faster than ever, developers who understand architecture, performance, and reliability are becoming increasingly valuable. Rust simply happens to be one of the best environments for developing those skills. About our Guests Jim Hodapp Jim Hodapp is a veteran software engineer, engineering leader, and technical coach with deep roots in systems programming. His background spans C, C++, Linux, embedded systems, software architecture, and engineering management. In recent years, he has become a recognized Rust advocate, helping developers transition from traditional systems languages into modern, memory-safe development practices. Through RefactorCoach and his Rust training initiatives, Jim focuses on improving engineering effectiveness, software quality, and developer growth. Follow Jim on LinkedIn: https://www.linkedin.com/in/jim-hodapp/ Bob Belderbos Bob Belderbos is a software developer, educator, coach, and co-founder of PyBites. Originally coming from a finance background, Bob transitioned into software through automation, scripting, and Python development. He has spent years helping developers improve their coding skills through practical challenges, mentoring, and community-based learning. More recently, Bob has expanded his focus into Rust, combining his Python expertise with modern systems programming practices to help developers build faster, safer, and more maintainable software. Follow Bob on LinkedIn: https://www.linkedin.com/in/bbelderbos/ Why the Rust Developer Mindset Starts with Fundamentals Many developers begin their careers with languages that allow rapid progress. Python is an excellent example. Developers can create useful applications quickly, automate repetitive work, and see results almost immediately. That accessibility explains much of Python's popularity. The challenge appears later. The Rust Developer Mindset encourages developers to move beyond writing code that works and toward building systems that remain reliable over time. Great developers eventually become students of systems, not just programming languages. How Rust Forces Better Engineering Habits One reason both guests spoke so positively about Rust is that the language encourages deliberate thinking. Rust's ownership model, compiler checks, and strict type system often prevent entire categories of bugs before software ever runs. For developers accustomed to highly dynamic environments, this can feel restrictive at first. Eventually, however, the restrictions become guardrails. Instead of discovering issues in production, developers discover them during compilation. That shift changes how software gets built. The language rewards planning, understanding data flow, and thinking carefully about how components interact. Those are valuable skills regardless of which language a developer uses professionally. Rust Developer Mindset in the Age of AI One of the most interesting topics from the episode was AI-assisted development. A common assumption is that AI reduces the importance of programming expertise. The opposite may be true. Modern AI tools can generate large amounts of code rapidly. However, generated code still requires evaluation, validation, testing, and architectural oversight. Strongly typed languages create an interesting advantage. When AI generates imperfect code, the compiler immediately becomes part of the feedback loop. The compiler identifies errors, exposes assumptions, and forces corrections. This creates a collaborative cycle between the developer, AI, and compiler that often produces more reliable outcomes. The Rust Developer Mindset embraces this reality by treating AI as a productivity multiplier rather than a replacement for engineering judgment. Faster code generation does not eliminate the need for software design expertise. Learning Through Productive Friction Bob described his transition from Python to Rust as a challenge. That challenge turned out to be valuable. Many developers plateau because they remain inside familiar environments. They become highly productive but stop expanding their understanding. Learning Rust introduces concepts that many scripting languages intentionally hide: Ownership Borrowing Memory management Concurrency considerations Compiler-guided design These concepts can initially feel uncomfortable. Yet that discomfort often signals growth. Developers gain a deeper appreciation for what their software is doing beneath the surface. The result is not merely Rust knowledge. It is a broader engineering capability. Why Performance Still Matters The conversation also highlighted a topic that often gets overlooked in modern development. Performance still matters. Cloud resources may be abundant, but inefficient software still creates costs. Applications that consume excessive memory, waste CPU cycles, or scale poorly eventually impact users and businesses. Rust provides developers with low-level control while maintaining modern safety guarantees. This combination helps engineers build software that remains efficient without sacrificing maintainability. The Rust Developer Mindset recognizes that performance is not about optimization for its own sake. It is about creating software that respects resources and scales effectively. Identify one application you currently maintain and investigate where performance bottlenecks originate before attempting optimization. The Future Belongs to Software Engineers The strongest takeaway from the episode is that language debates are becoming less important. AI can help generate syntax. Documentation can explain APIs. Tutorials can teach frameworks. What remains difficult is understanding how systems behave. Developers who can reason about architecture, reliability, performance, and maintainability will continue to stand out regardless of tooling trends. That is ultimately what Rust helps reinforce. The future belongs to engineers who understand systems deeply enough to guide both AI and software toward better outcomes. Conclusion The Rust Developer Mindset is not simply about adopting a new language. It is about developing a stronger understanding of software itself. By encouraging developers to think more carefully about correctness, performance, and system behavior, Rust creates opportunities for long-term growth that extend far beyond any individual technology stack. Stay Connected: Join the Developreneur Community

Learn how Vercel's "self-driving infrastructure" vision pairs with AWS databases to eliminate backend friction, securely cutting Aurora Serverless creation time from minutes to seconds.Topics Include:Hedieh Zandi (Vercel) and Manbeen Kohli (AWS) introduce prompt-to-production sessionVercel powers 18 million developers, maintains Next.js and AI SDKVercel's agentic infrastructure runs on AWS Lambda, CloudFront, and S3AI now generates frontend, APIs, and workflows for small teamsBackend friction remains: credentials, provisioning, database configuration still hardVercel envisions "self-driving infrastructure" that adapts automatically to appsNew AWS partnership brings native Aurora DSQL and Postgres integrationManbeen explains databases now built into Vercel Marketplace and v0Aurora Serverless database creation sped up from minutes to secondsAurora Postgres, DynamoDB, and DSQL scale prototypes without rewritesPre-configured templates help builders start RAG or shopping AI appsDatabase security uses OIDC and IAM tokens, no stored passwordsAWS chosen for agents: low latency, autonomy, one-click simplicityskills.sh gives agents reusable instructions, mirrors AWS Kiro's "powers"v0 lets users build full-stack apps using natural language promptsv0 uses Bedrock models and deploys directly on Vercel infrastructureLive demo: v0 builds restaurant app, provisions database, adds Stripe checkoutDemo ends at AWS console; Rauch quote and hackathon close sessionParticipants:Hedieh Zandi - Product Lead, VercelManbeen Kohli - Director of Product Management, Aurora and RDS Databases, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Garnet Heraman, co-founder and GP at Aperture Venture Capital, joins Ian Bergman to explain how investors can separate real technology shifts from hype. They explore vertical AI, enterprise adoption, quantum infrastructure, and why historical patterns still matter in fast-moving markets.Garnet shares how his journey from startup operator to venture investor shaped the way he evaluates emerging technologies. He breaks down why generic enterprise AI may struggle, why vertical AI can create deeper value in industries like healthcare, insurance, and finance, and how adoption often lags behind aspiration.The conversation also looks ahead to quantum computing, including why AI utilization may drive demand for quantum infrastructure, why software could be the next major opportunity in the quantum stack, and how quantum could transform asset management, climate risk, and financial services.For founders, operators, and innovation leaders, this episode offers a practical way to think about technology cycles, enterprise behavior, and where the next wave of opportunity may emerge.Key Topics

Dan Adams is a 13-year Microsoft 365 and SharePoint veteran who joined Andrew to talk about the often misunderstood world of SharePoint, the shift to Microsoft Graph, and his custom PowerShell module built which can assess and benchmark M365 environments. Dan breaks down why SharePoint gets such a bad reputation (spoiler: it's usually about who built it, not the platform), explains why "everything in M365 is SharePoint" isn't just a meme, and digs into how the Microsoft Graph API is changing the way admins interact with the platform. He also shares his experience going from longtime podcast listener to first-time guest, and closes with some honest advice about reaching out to people in the community. Key Takeaways: SharePoint's bad reputation often comes from poor initial architecture, not the platform itself. Getting the information structure right at the start has downstream effects on everything from Copilot to Purview to legal compliance, and PowerShell automation is only as useful as the metadata you've set up to work with. The Microsoft Graph API is consolidating how everything in M365 is accessed. Where older APIs like the SharePoint client-side object model might count a batch of 100 items as 100 separate API calls, Graph treats that same batch as a single call, which is a meaningful difference for performance and rate limiting. Dan's custom PowerShell module (SP'r Smash Bros Automation) automates M365 permission extraction to produce security assessments against CIS benchmarks and Microsoft Secure Score. Built as a practical tool for consultants and admins, it delivers a fast, standardized baseline of a tenant's security posture, featuring an optional AI sidecar to instantly generate personalized remediation plans. Guest Bio: Dan Adams is a Microsoft 365 and SharePoint Architect with 13 years of M365 consulting experience. Leveraging deep SharePoint and strategic information architecture expertise, he has led teams to deliver over 40 Fortune 500 intranets across industries ranging from healthcare to the NFL. He is the architect behind multiple award-winning portals, including two Ragan "Best Overall Intranet" winners. Dan is also an AI enthusiast, an advanced PowerShell expert, and the creator of the custom "SP'r Smash Bros Automation" module for Microsoft 365. Resource Links: Dan Adams on LinkedIn: https://www.linkedin.com/in/dan-adams-10887650/ Dan Adams on Github: https://github.com/sprsmashbrosautomation Connect with Andrew: https://andrewpla.tech/links PnP PowerShell: https://pnp.github.io/powershell/ PDQ Discord Community: https://discord.gg/pdq The PowerShell Podcast on YouTube: https://youtu.be/OLsTaKC9GmM PowerShell Wednesday Playlist: https://www.youtube.com/watch?v=XQT8lrn8hhU&list=PL1mL90yFExsix-L0havb8SbZXoYRPol0B

In this episode, we're joined by Matt DeBergalis, CTO and Co-Founder of Apollo GraphQL, to explore what happens when AI agents start interacting with enterprise systems that were never designed for them.We dive into the collision between APIs, MCP, GraphQL, and agentic AI, and why traditional assumptions about trust, permissions, and security are breaking down. Matt argues that AI agents should be treated as untrusted actors by default, and explains why giving agents access to enterprise data creates entirely new challenges around governance, access control, and risk management.Along the way, we discuss semantic APIs, enterprise data silos, citizen developers, agent permissions, security boundaries, and how GraphQL and MCP can work together to make enterprise systems more accessible to both humans and AI. The conversation also explores why companies are racing to deploy agents despite the risks, and what the future of enterprise software might look like when AI becomes the primary consumer of APIs.Apollo GraphQL: https://www.apollographql.comMatt DeBergalis: https://www.linkedin.com/in/debergalisAlex Salkever: https://www.linkedin.com/in/alexsalkeverTimestamps:[00:00] AI, APIs, and Trust[01:16] MCP API Lessons[06:16] GraphQL and MCP Integration[12:55] API Security for MCP[16:10] Linux Kernel Security Concerns[19:09] API Design and Controls[21:52] Trust in Autonomous Systems[25:06] MCP GraphQL Wish List[27:13] API Access Patterns[28:44] GraphQL API Perspective

In this episode of Agentic Conversations, we're joined by Shaun Smith, software engineer, open source advocate, and contributor at Hugging Face, to explore how AI coding has changed almost overnight.We dive into reinforcement learning, MCP (Model Context Protocol), Fast Agent, Claude Code, open source AI, and why today's language models have become so capable that many traditional software libraries are becoming "liquefied." Shaun explains how reinforcement learning unlocked long-running autonomous agents, why ideas are becoming more valuable than code, and how developers should think about building software in an era where AI can generate entire applications.Along the way, we discuss Hugging Face's MCP server, Fast Agent, AI-powered developer tools, multimodal applications, MCP Apps, context windows, coding assistants, Rust, Python, TypeScript, open-weight models, software architecture, and what the future of programming looks like when humans increasingly focus on design instead of implementation.Shaun Smith: https://www.linkedin.com/in/smithshaunDemetrios: https://www.linkedin.com/in/dpbrinkmHugging Face: https://huggingface.co⏱️ Timestamps[00:00] Introduction[01:56] The State of Open Source AI[05:18] Reinforcement Learning Changed Everything[07:50] Fast Agent Explained[10:18] Fast Agent as an MCP Reference Platform[12:20] Building Smarter AI Tools at Hugging Face[15:17] Natural Language Search Instead of APIs[17:46] Why MCP Apps Matter[20:06] The Evolution of MCP Apps[23:05] Building AI-Native User Interfaces[26:12] Context Is the New Programming Language[28:00] The End of Code Libraries[29:50] Why Developers Aren't Writing Code[31:25] AI Changes Software Engineering[33:05] The Future of Open Source AI[35:43] Claude Skills That Save Hours[38:02] Training Models with AI[39:05] Building Your Own AI Tools[40:50] MCP for Consumers, Enterprises, and Developers[43:42] Why Shell Access Makes Agents Smarter[45:18] Secure Agent Workflows[46:08] The Future of AI Interfaces[47:02] Outro#HuggingFace #MCP #OpenSourceAI

Unlocking billions in cloud marketplace revenue. Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ This powerful panel discussion featuring leaders from Google, Tackle, and dbt Labs dives deep into the explosive growth of cloud marketplaces and the radical shift toward AI-driven go-to-market strategies. With hyperscaler backlogs nearing half a trillion dollars, the conversation unpacks how top-tier organizations are transforming their compensation models, aligning executive buy-in, and navigating the complexities of co-selling to capture committed customer budgets. From the rise of AI agents acting as metered SaaS to the essential operational investments required to scale marketplace revenue from 10% to over 50%, this session provides an actionable roadmap for software companies ready to dominate the 2026 partner ecosystem. https://youtu.be/LSj49f5FEII Key Takeaways Hyperscaler backlog commitments represent a massive, nearly half-trillion-dollar addressable market that completely changes the budgeting conversation. Successful marketplace selling requires complete executive alignment, right down to the CFO, and strategic adjustments like spiffing sales teams for marketplace transactions. The AI category is experiencing staggering 18x year-over-year growth, forcing companies to pivot toward an “agent-first” go-to-market model. Shifting from traditional channels to cloud go-to-market demands a multi-year, intentional investment in operations, people, and technology. System integrators are evolving into software companies as they build orchestration agents to manage fragmented, end-to-end workflows. Leveraging cloud commitments bypasses standard 12-15 month budget cycles, allowing for significantly faster deal closures and larger initial lands. 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: Google Cloud Marketplace, hyperscaler backlog, cloud commitments, co-selling strategies, AI agents, metered SaaS, product-led growth, rev ops, B2B sales transformation, ecosystem shift, channel strategy, system integrators, Deal registration, private offer APIs, digital transformation, software procurement. Transcript: Insight to Revenue- The State of Cloud GTM [00:00:00] Dai Vu: These are all things everyone has to do to get to that first five to 10 deals, and then 10, 20, 30% of your business through Marketplace. [00:00:09] 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. [00:00:21] 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. [00:00:43] Vince Menzione: It is the strategy because being in the room changes [00:00:46] John Janke: everything. Let’s start. [00:00:52] Vince Menzione: And we have an incredible session. The way that we wanted today to, to, to start the day up was like, let’s talk about what’s happening right now and let’s get three leaders in this space to come up and talk about the world and how it’s a rapidly evolving. So I want to invite to the stage dvu from Google is a great friend of Ultimate Partner. [00:01:14] Vince Menzione: Are you guys ready? Are you guys micd up already? Okay, good. Good. John Yanke, the CEO and Founder of Tackle, and Sean Todo, who is an incredible leader with DBT, but also an old friend of mine. We worked together on Microsoft Days. Good to see you gentlemen. Thanks Sean. Great to have you with us. [00:01:37] John Janke: They stuck me on the side ’cause they said I’d block the screen if I sat in the middle. [00:01:41] Shawn Toldo: You still block it a little bit. [00:01:42] John Janke: And that picture’s from like 1985. I, I, we do have to get that. I had way darker hair. It was, uh, 10 year, 10 years at a startup. Makes you turn white. [00:01:52] Shawn Toldo: Mine’s the exact same right now. So it’s all good. [00:01:55] Shawn Toldo: Mine’s AI generated. Yeah. [00:01:57] Vince Menzione: Well, you know, guys, I just took it all off at that point, you know, it’s like good. Yeah, but you lose enough of it. You pull it out over the years. Yeah. So, uh, some really exciting times. Uh, you, we gotta spend some time at you at our breakfast. That’s right. A couple weeks ago. [00:02:13] Dai Vu: A lot of folks here, too. [00:02:14] Vince Menzione: A lot of folks that are here were at that breakfast, and I thought we’d spend a few moments with you talking about all the exciting things that have been happening at, at Google. I mean the, yeah, the businesses just to, first of all, the numbers were house. Outstanding. Congratulations. [00:02:28] Dai Vu: That’s right. [00:02:28] Vince Menzione: Yep. [00:02:28] Vince Menzione: Really, some really great numbers. Commitments are off the charts. [00:02:32] Dai Vu: Yes. [00:02:32] Vince Menzione: Crazy off the charts. [00:02:33] Dai Vu: Yes. [00:02:34] Vince Menzione: Yes. Uh, and then there’s a lot happening in this little world called ai, which makes a ton of sense. Yep. I was critical about Google in the beginning because you had all the assets, but Microsoft leaned in first. [00:02:45] Vince Menzione: Uh, but now it’s like things have evolved, uh, quite a bit since those first days. Absolutely. In, in November of 2022. So, uh, take us through a little bit. Let’s, let’s go through [00:02:56] Dai Vu: it. Yeah. I could talk for quite a bit of time because obviously we came out next, yeah. At the end of April, and then we had our earnings announced, but shortly thereafter. [00:03:03] Dai Vu: But, but real quick on next, uh, for folks who attended, uh, you know, the way they framed, uh, the discussion was they showed this AI integrated stack, and that’s how they frame the keynote because we position ourselves as being the only vendor that provides this. Fully integrated stack from custom silicon all the way to the apps and agents. [00:03:23] Dai Vu: And a lot of the announcements were, were focused in those areas. Um, uh, I won’t go through the, the long list, but I think the big ones coming out of next were, uh, certainly the eighth generation TPU we announced, so we actually split this into two specialized chips for training and inference. Uh, so that’s, uh, that was a big piece. [00:03:41] Dai Vu: Uh, but the big one that we announced was this, uh, Gemini Enterprise. Uh, agent platform. So think of it as the comprehensive platform for companies to basically build scale, govern and optimize their agents. And of course, once they have that, they can bring that into, uh, what we call a Gen Gemini enterprise app, which is really the front door for AI for. [00:04:03] Dai Vu: All customers and all employees to manage a mix of agents, um, as part of their daily workflow. And, uh, and a big part of it is, you know, certainly they’ll have some custom agents, but we think a lot of the agents will come from the ecosystem. And obviously there was a big announcement around what we’re doing there. [00:04:21] Dai Vu: Um, and in fact, one of the things that’s interesting is this shows the evolution of, of marketplace in our, in our partnership, which is we’ve taken a lot of the marketplace experience. And brought it into Gemini exp uh, Gemini Enterprise app, right? So search, discovery, uh, the ability to invoke agents, uh, in context. [00:04:39] Dai Vu: I think that’s gonna be very powerful as we think about the evolution, uh, of, of go to market. And then the last thing maybe I’ll highlight is this, um, is. 750 million, uh, investment fund that we’re gonna drive with the broad partnership. So this cuts across all partner types, global system integrators, uh, uh, you know, AI, pure plays, uh, ISVs, uh, the big management consultants as well, uh, because we recognize that partners are gonna be critical to drive business transformation with our end customers. [00:05:08] Dai Vu: So we’re investing around things like. Technical enablement, access to our product teams, access to our FDE for deployment engineers, and then a lot of incentives to drive usage and deployment. So, um, so a lot of, a lot of activity and obviously the ecosystem’s gonna be very critical for us to drive that impact’s. [00:05:25] Dai Vu: Fine. And the last thing, I know we’ve going on and on fine, but the last thing I’ll just mention is just on the earnings announcement, uh, Vince touched on the backlog, so people have been tracking Yeah. Two quarters ago. We were 155 billion on the backlog, and then a quarter later we were 240 billion. And then in the last quarter, just recently, 462 billion. [00:05:46] Dai Vu: So obviously that’s a, a massive signal of customer intent, but more importantly, it’s a, it’s, it’s a addressable market for this ecosystem to go after as well. [00:05:54] Vince Menzione: Yeah. Almost a half a trillion dollars. Yes. In commitment. So a lot, a lot of reason why we should be on the marketplace. [00:06:01] Dai Vu: Absolutely. Absolutely. [00:06:02] Vince Menzione: Um, each of these gentlemen have some things to talk about as well, about their companies and the exciting things that have been happening. [00:06:08] Vince Menzione: I’m gonna start, John, I’m gonna start with you because Tackle has, has transformed quite a bit since the last time you were on stage with us. I thought maybe introduce the company. Take us through the transformation and then we’re gonna do the same thing with Sean with his organization. [00:06:21] John Janke: Yeah. Thanks. Uh, thanks Vince. [00:06:23] John Janke: Great to see everybody. Uh, John Yanke, GM of Tackle at App Direct. So the big news there is Tackle was acquired in Q4 by a company called App Direct, and I think the why behind this app, direct Powers, marketplaces, they run 400 marketplaces around the world for telcos, for ISVs, for system integrators, channel partners. [00:06:42] John Janke: And we were talk like, when you build a marketplace and diagnose this, stocking the shelves is actually really hard. Uh, and we were talking to them about how could we connect the dots between the hyperscaler marketplaces, the iscs we support, and these additional routes to market. Uh, and that became more strategic and we ended up joining forces in December. [00:07:00] John Janke: And since then, the other part that’s really hard when you build a marketplace is how do you generate demand? Uh, so four weeks ago we acquired a company called Partner Stack. And Partner Stack does affiliate content. They have an affiliate content platform that allows you to connect with 150,000 content providers to be able to start to tell your story to drive leads to. [00:07:23] John Janke: Marketplace. So we think there is a tremendous opportunity to continue. We’re in the earliest days. I think the, you know, Jay, I was with Jay at Channel Partners a few weeks ago and he is like, we under called it, he didn’t say this on stage yesterday, but he is like, uh, the 82% growth. He’s like, we totally under called it. [00:07:40] John Janke: Uh, and I think just listening to dies commit level increase mm-hmm. Reinforces the fact that we’ve under called it. But I also think we’re at this tipping point in the market where all of the new capabilities coming out, we have to all rethink our better together stories. So I think the challenge to all partner leaders, it’s like, how do we. [00:07:58] John Janke: Figure that out. So it’s, it’s a, it’s a fun time. As we transform the way we worked. We wrote the first helping people kind of list, launch and sell through the marketplaces. And now to be able to take that to the next level to hopefully unlock the next a hundred billion of marketplace throughput. [00:08:13] Vince Menzione: And are we at a hundred billion? [00:08:15] Vince Menzione: ’cause that was the number, right? [00:08:16] John Janke: I mean that’s, that’s, that’s the number that’s talked about. I mean, we’re seeing the data signals we see, I mean, we will process 20 billion plus this year. Uh, and that number’s growing faster than Jay’s stated number. So I think we’re excited to see where this year lands. [00:08:30] Vince Menzione: We’ve come a long way from three years ago and we all got on stage and talked about marketplaces together. Right. It’s been, it’s been amazing. And then Sean, let’s talk about DBT. You’ve had some excitement. I know some things maybe we can’t even talk about yet on stage. [00:08:43] Shawn Toldo: Uh, yeah, go ahead. [00:08:44] Vince Menzione: No, I was saying I, I could, I’ll pre-announce things, but No, I’m just, uh, tell, tell us about DBT for those who don’t know in the room, sure. [00:08:49] Vince Menzione: Mean Yeah, that might help. [00:08:51] Shawn Toldo: So, uh, Sean Todo, I lead the partner business at DBT. I’ve been here about 18 months. Um, DBT really started as an open source tool. That help data engineers be successful in SQL transformation with cloud data warehouses? Right. And so back even to the Redshift days now into what I would call more the BigQuery, snowflake, Databricks fabric led days, um, DBT is the tool of choice amongst the data engineering community in terms of how they wanna drive SQL transformation. [00:09:21] Shawn Toldo: And so more recently, we kind of jumped into this kind of paid world. Which is why we needed to bring in additional experience leadership around go to market product, sales, et cetera. And so when I walked in the door, one of the things I noticed really quickly was we were running on AWS, which was great. [00:09:40] Shawn Toldo: We were doing some AWS marketplace stuff. We were running on Azure in Europe only. And one of my first strategies was we have to be everywhere, right customer. We have to meet customers where they are. And so we, uh, made some major investments to be on Google Cloud platform to then be able to really take advantage of marketplace, to then really be able to take advantage of the co-sell opportunities that exist in the field from a day, day-to-day AI perspective with Google. [00:10:07] Shawn Toldo: And it has been a hell of a ride. We launched on, uh, Google Marketplace in July of last year. We went to Google next and we were Google Partner of the Year. Wow. For data and analytics in a very rapid way. We’re now in three, uh, data centers around the, the world. So we’re here in the us, we’re in Frankfurt, we’re in uh, uh, UK as well. [00:10:30] Shawn Toldo: And so it’s been a pleasure to work with D and the broader team. Because the enablement we’ve had and the support we’ve had from that group has really helped our growth be up and to the right. The data point I would give is that when I walked in the door, we were 10% of our business from an A RR perspective was transacting through marketplace. [00:10:48] Shawn Toldo: Last quarter we cracked 40%. Whoa. We will be at north of 50, uh, next quarter. [00:10:53] Dai Vu: Wow. [00:10:54] Shawn Toldo: The other piece that Vince was talking about is we’re getting ready to merge with a company called Five Tran. And so there will be a new company name at some point down the road. Uh, pay attention on June 1st for a public announcement around that merger. [00:11:06] Shawn Toldo: Uh, but we’re really looking forward to what we’re gonna be able to do with folks like DI and the Google team as well as others in the ecosystem. Um, ’cause I think in this data world that we’ve played for so long. This trusted foundational element of data and what it’s gonna mean to context in the AI world. [00:11:23] Shawn Toldo: We’re in a very interesting place to really continue our growth rate at a high level. [00:11:28] John Janke: Yeah, that maybe just a comment something there. Start there. I think we, we used to hear people say we wanted to be strategic with cloud, go to market and get to say 10 or 20% of revenue. I think this like 40, 50%. Yeah. Th that’s where people are setting the bar these days. [00:11:43] John Janke: Yeah. So the numbers are getting really crazy. Yeah. Uh, and people are showing up and being like, I have to go big. Mm-hmm. So a huge change over the last few years. [00:11:52] Vince Menzione: Yep. What’s the experience you’re seeing as well? I mean, it, it was a huge amount of buzz at next. [00:11:57] Dai Vu: Yeah. I mean, so interestingly, um, you know, typically when, when people get started on the, on the marketplace in Cosal journey, I always try to caution them and say, this is, uh, this is like a multi-year. [00:12:07] Dai Vu: Yeah. Uh, process. You have to be very intentional. You have to invest. It’s not gonna be a thing where you just list and, and, and, and, and, and sort of this channel opens up. So in some ways, Sean is describing an acceleration that is not common, right? Uh, so they’ve done, we’ve done some amazing things together and we hope to keep that acceleration going. [00:12:22] Vince Menzione: What does that require, by the way? Is it engineering resource? I mean, there’s, I talk about executive commitment and maniacal focus. Yeah. But it’s all those things, right? [00:12:29] Shawn Toldo: Well, all of it. But we went to a QBR in Austin, and I put up a slide and I said, we have to do this. And everybody in our ETE agreed. So when you have a chief financial officer that’s bought into the partner business. [00:12:43] Shawn Toldo: Yeah. And I guess qualifying coming into this role at this company, I qualified the C-level staff. Uh, like are they really serious about partner or not? And it’s one of the reasons I took the role. So I think executive commitment was one thing. I think the second thing is we were really well supported, um, by the Google team across the board, right? [00:13:02] Shawn Toldo: Yeah. So folks, Indy’s team that we would work with regularly on, these are the things you need to do to have an effective marketplace offering. Here’s what you need to do operationally with folks like John and team and others that are in the market, right? That helped us a ton to be able to scale. And then the other thing that we did is we changed comp. [00:13:20] Shawn Toldo: So from our VP of sales levels down, we have a 5% kicker for everything that goes through marketplace. [00:13:26] Vince Menzione: Hear [00:13:26] Shawn Toldo: that everyone. So as soon as we incented the sales team, I love that, right? We, we created the foundation on the partner side, but then from top down on the sales side, they were all in. And as a result of that, the question would become, okay, which marketplace stage two sales cycle are we gonna go use? [00:13:42] Vince Menzione: Yeah. [00:13:43] Shawn Toldo: Who’s the right partner to go partner with? And then my team is reaching out to make sure that co-sell connection happens. [00:13:48] Vince Menzione: That is such a best practice, Sean, to, because there is, as a seller out in the field and we talk about, you talk to John, talks about rev ops all the time. But getting rev ops eng getting the field engaged in the right way. [00:14:01] Vince Menzione: ’cause it feels like it’s more work for them. ’cause they have to think, they have to have more conversations with their customer about their cloud commitments and things like that. Mm-hmm. And then getting them incentive to do the right things. The right behavior. [00:14:12] John Janke: Yeah. It’s a strategy process. People, technology problem. [00:14:17] John Janke: Yeah. It’s not just some flip API automation, go list something if you don’t like that top down view. I think the other thing. Like there’s a, there’s a theme in startups where VCs fund second time founders. I think Sean and team have done this before and they took a lot of learnings over the years and reapplied them, which I think helps them go faster. [00:14:36] John Janke: It’s like that second time. Yeah. Second time cloud go to market Founder theme. [00:14:41] Vince Menzione: Yeah. Yeah. Um, so we could talk about the platform and all the changes there on the. The, the commitments and everything. Mm-hmm. Uh, what separates ISPs generating real incremental revenue on your, in your marketplace? What, what do you see? [00:14:58] Dai Vu: Yeah, so I mean, I, I think there are a couple things. Number one is, uh, the, the foundation has to be, uh, this better together story, uh, with Google Cloud. Um, so this idea that what, you know, what do you bring, what does the Google platform bring and how does that drive impact with customers? And I think this is the reason why Sean and DBT Labs has been very effective. [00:15:16] Dai Vu: ’cause our field recognized they, they can recognize that better together story and communicate it to their customers. So I think that’s the foundation. For everything. Right. And I think as you get started, uh, you know, we do tell partners that they probably need to lean in a little bit, uh, in terms of focus, uh, you know, pick a vertical, a customer segment, um, you know, a geography where they’re particularly strong and, you know, get that momentum going. [00:15:39] Dai Vu: And once you do that, the field knows about it and starts to pull you into deals. Um, so I think that’s the other big opportunity. And then the other thing I just mentioned. Which, uh, the panel already touched on, which is be very intentional around all the things you need to do to invest. Whether it’s like, uh, you know, the business functional alignment, uh, the policies around like, uh, pricing and, and comp, uh, making sure you have the operational capabilities. [00:16:02] Dai Vu: These are all things everyone has to do to get to that. First five to 10 deals, and then 10, 20, 30% of your business through marketplace. And not to, not to top you Sean, but our very top partners are driving 80 to 90% of their business on marketplace. And in fact, some of these partners are actually only marketplace first, uh, uh, because they started out that way. [00:16:21] Dai Vu: Obviously it’s the bigger challenge if you have an existing channel, you’re trying to shift that. But, uh, the aspiration to be more marketplace focus, uh, is up there. [00:16:28] Shawn Toldo: So I just set a new goal for the business plan for me. So that’s exciting. I love it. Looking forward to seeing you in six months on that. [00:16:35] Shawn Toldo: It’s good. [00:16:36] Vince Menzione: I love [00:16:37] Dai Vu: it. Work together on that. [00:16:38] Vince Menzione: Well, di I’m just gonna add, add this because I, I got to see operationally with some of the things you do. Mm-hmm. You, you have an overlay organization. [00:16:45] Dai Vu: Yes. Yes. [00:16:46] Vince Menzione: And so you put accelerants in place within your own organization Yeah. To drive the ISVs into the, into the lines of business. [00:16:54] Vince Menzione: Right. You have, you, you do some of that to accelerate. [00:16:57] Dai Vu: Yeah, I mean, I think, I think this is somewhat unique. I don’t, I don’t wanna speak to the other [00:17:00] Shawn Toldo: hyperscalers, [00:17:01] Dai Vu: but we do have, um, uh, you gotta know the field roles, right? [00:17:04] Shawn Toldo: Yeah. So [00:17:04] Dai Vu: obviously at Google Cloud in the regions, we have, uh, ISV sales specialists who are effectively quoted on marketplace revenue, right? [00:17:12] Dai Vu: So they’re a hundred percent focused on that. And, uh, in addition to that, uh, we also have these, uh, co-sell teams, partner teams where, you know, opportunistically if there’s an opportunity, uh, in a, in a, in a particular area. This team is responsible for connecting the regional sales leadership, uh, the regional, uh, sales teams with, with the partner on the opportunity. [00:17:32] Dai Vu: So there’s a lot of things we’re doing to sort of accelerate that. And of course, the foundation for all this is, you know, our, our, you know, registering deals. And as you definitely get started on that, it’s very important to be very mindful around when you register deals. Uh, be very clear around what the ask and the engagement is with the field reps. [00:17:51] Dai Vu: But once you have that going and get the right rhythm, it becomes sort of a natural way to sort of register all your deals and get that engagement. And then, um, and then maybe the last thing I would say is it isn’t always the sales specialists. It’s, you know, the FSR, our field sales rep as well as our customer engineers are also very motivated. [00:18:08] Dai Vu: To work, uh, with, uh, with our partners because they know that this, you know, whether it be solution completeness or it’s part of a bigger workload or helps unlock greenfield opportunity, they really are motivated to engage with the partners. [00:18:21] Vince Menzione: Nice. [00:18:22] Shawn Toldo: Yeah. I’ll just add, I’ll just add to that statement too. I think, um, it’s one thing to have a story as it relates to. [00:18:30] Shawn Toldo: Google Cloud and what you do with marketplace. It’s another thing to have a story in terms of how you impact data and analytics in our world. And there’s a set of specialist sellers inside of Google mm-hmm. That really care about us because we drive a lot faster consumption of big query. And our ability to tell that story across the world effectively has really created a pull now. [00:18:54] Shawn Toldo: And so I, I would say it’s almost, you know, back to, you know, being 12 years at Microsoft and watching kind of that. Phase and how that went. As we went to the cloud and we picked specialty areas, um, Google is doing that as well and they’re doing it extremely fast in a very, very productive way with partners. [00:19:12] Shawn Toldo: And so, you know, I’ll get comments from like Levi who runs west in north region for us, and he’s a, he was at Google next and he was like, I, I gotta, I, I just gotta go to bed. I’m tired. Like we wore him out over two days with their sales team and gave him a host of follow ups and actions related to specific sales areas as well as specific accounts. [00:19:34] Shawn Toldo: And I think that’s the other thing that, um, Google’s done a good job of, but we’ve pushed and we’ve had to work really hard to earn that seat at the table. To help make those people successful from a comp perspective inside of Google as well. [00:19:45] John Janke: Yeah, and this is a huge failure zone for partners with the clouds because they think enablement’s a one and done thing. [00:19:51] John Janke: Like I did a training for the field and I told them the better together story. That doesn’t work. Like you have to literally. Have consistency around this message every day. Oftentimes you need experts who can partner with your reps to give them the confidence. ’cause they may be able to ask the first line question, but someone asks a follow up and they fold up ’cause they know your product. [00:20:11] John Janke: That’s right. They don’s don’t understand all of the nuances of Google and the clouds and the questions that may come back. But if you do that well, it is a huge unlock. [00:20:20] Vince Menzione: Talk about the coaching you provided on the tackle side of that as well and kind of helping. Through this maturity model? [00:20:26] John Janke: Yeah. I mean we, we, over the years, I mean we started as a pure SaaS company and over the years our customers would consistently ask us for more help and we would struggle to figure out how to do that, and we had to invest in services and we actually acquired a company. [00:20:42] John Janke: Five years ago now, that was the foundation. Aaron Feiger, who’s in the room. The core consulting was the foundation of our services business. And that continues to evolve with us. And you know, we see customers at scale saying, I wanna operate my cloud, go-to market really consistently, and I want you to do all the backend operations so my teams can be outselling our products, selling the better together value with Google and others, and not have to figure out how to run the machinery. [00:21:09] John Janke: So we’ve invested a lot there. We have services around strategy, like how to help people think about their business strategy and translate it into a better together story and able to get executive buy-in. And then we have coaching, which is really a phone, a friend, because I think these things get complicated. [00:21:24] John Janke: And I had a customer who was doing the largest deal in their company history. It was the end of the quarter and it was Friday, and they’re like, this is going to be the most complex transaction we’ve ever done and we have no idea how to do it. Our team gets on the phone with them, they work through, what are you selling? [00:21:40] John Janke: How are you selling it? Is your listing set up the right way? Can we actually create all the offers? In a way you have confidence to execute. ’cause those are failure modes. You try to build a cloud, go to market business, and you mess up the largest deal in the company. On the last day of the quarter, uh, that’s something you can’t recover from. [00:21:55] John Janke: So we try to really wrap support around our customers to help them have the confidence to grow. [00:22:02] Vince Menzione: Di you’ve seen tremendous growth in marketplace. Mm-hmm. We don’t publish the numbers specifically. Yeah. We kind of try to figure it out on the back end, but [00:22:09] Dai Vu: Yep. [00:22:09] Vince Menzione: I know you’re accelerated. Your, your marketplace numbers are astounding. [00:22:13] Dai Vu: Yes. I can share some numbers, if that’s [00:22:15] Vince Menzione: okay. Please. Yeah, let’s go. [00:22:18] Dai Vu: So, um. I would say that for a few years now, we’ve been talking about growth. So we’ve been consistently, uh, you know, north of a hundred percent year over year growth. Uh, for the last few years we’ve been processing, uh, what I say, uh, billions of dollars, uh, annually and, uh, uh, millions of transactions. [00:22:36] Dai Vu: And again, that’s for a few years now. Now for 24 to 25, that full year we also doubled. Wow. Uh, which is, uh, which is amazing when you think about the scale in which we operate. But more importantly, if you look at specific category areas, right? So, you know, historically, marketplace has always cater to, uh, those solution pillars that are tied to cloud migrations, like, uh, like security and data and analytics. [00:22:59] Dai Vu: And those continue to be very strong areas for us. But the biggest growth area is, uh, is in the areas of business app. So obviously, you know, the, the ServiceNow workday, uh, Salesforce of the world, as well as the AI category. So one number that we threw out next was 18 x. Year over year growth for the AI category. [00:23:17] Dai Vu: Wow. So in one year now, a lot of it is models, right? So foundational models with our, with our ecosystem. But a lot of that is around agents. So this whole agent go to market model is gonna be, continue to grow and it’s gonna be a huge focus area for, for the coming years. [00:23:32] Vince Menzione: Fantastic. Yeah. Fantastic growth. [00:23:34] Shawn Toldo: Yeah, and, and I’ll add, Diane and I talked about this at Google next. This is a. Very complex thing for DBT, where today we sell seats. [00:23:42] Vince Menzione: Mm-hmm. Yeah. [00:23:43] Shawn Toldo: To data engineers. [00:23:44] Yeah. [00:23:44] Shawn Toldo: And now we have all these agentic things that are hitting our engine. And di and I are talking and we’re like, okay, so how does this work in an ag agentic marketplace? [00:23:54] Shawn Toldo: Yeah. Kind of a scenario. And what should we build? Where should we play it? ’cause we’re gonna spin the meter in a different way, so to speak. [00:24:01] Dai Vu: Yep. [00:24:01] Shawn Toldo: And so candidly, we got stuff to figure out related to that. Um, I think what’s been fascinating for DBT is our partner ecosystem changed overnight. So now it’s like I talked to x.ai on Monday. [00:24:15] Shawn Toldo: Mm-hmm. We got time with open AI on Thursday and we have a call with Anthropic and our, uh, CEO and co-founder and uh, chief Product Officer next week. [00:24:26] Vince Menzione: Mm. [00:24:27] Shawn Toldo: We don’t have anybody managing those partners. [00:24:29] Vince Menzione: Right. [00:24:30] Shawn Toldo: Today our focus is on managing the large, uh, hyperscalers plus Snowflake and, uh, Databricks. [00:24:36] Vince Menzione: Mm-hmm. [00:24:36] Shawn Toldo: And then the SI ecosystem and some tech partners. So we’re having to like, to your point on Agile yesterday. Yeah. Mm-hmm. Like we’re having to change our strategy, operating model and organizational model to support that. And candidly, we don’t have all the answers yet, so we have a lot of things to figure out fast, which is a little bit scary. [00:24:54] Shawn Toldo: And challenging, but it’s also a huge opportunity we have to kind of embrace and get into. Yeah. [00:24:59] Vince Menzione: And they’re figuring out as well. ’cause they’re, they’re new to partnering as well. Yeah. As organizations [00:25:03] John Janke: and these AI agents. I think to demystify for a lot of people, and what Sean said is totally right. [00:25:08] John Janke: They’re disrupting everyone’s business model. But in reality from a marketplace standpoint, they’re metered SaaS. This is a thing that’s existed for a long time. Yeah. They look like product-led growth products. There is a lot of patterns around how product-led growth products work in marketplace. Mm-hmm. [00:25:24] John Janke: But you have to bring your business strategy, your product and pricing strategy to those two categories. Metered SaaS and product-led growth. Put that all together to get cross-functional alignment. So we are seeing like. A lot of people get tripped up here and it really does go back to more of the company strategy, product strategy questions, and a lot of partner leaders are not in the room for those conversations. [00:25:48] John Janke: So I think at, at this point in time, as you see big pivots with the partners to go all in on agents, you have to go elevate. Those discussions to be like, what is our plan here? ’cause I, I mean, pricing and packaging will be the thing that trips almost everyone up. [00:26:02] Dai Vu: If I could, if I just build on what John John mentioned, um, so I do agree. [00:26:06] Dai Vu: P it looks a lot like POG, but, uh, but the difference I think is POG has. More historically been in like the data and developer space, now it’s like the general business user, right? So this idea that you want a business user to be able to search and discover, um, agents that could actually be part of their like everyday workflow is going to be very critical. [00:26:26] Dai Vu: And uh, you know, I do think that when we think about the ecosystem building agents. Uh, you know, a lot of the ISV partners aren’t necessarily gonna own end-to-end workflows, right? They’ll, they’ll have a very specific, uh, domain and scope area, but you have to enable yourself to be orchestrated and managed by, you know, orchestration agents or, or, or meta agents that are gonna span end, end workflows. [00:26:49] Dai Vu: And sometimes that includes system integrators and, and others who can stitch that, that automation. So I think, I think that’s, that’s one piece of it. But the other area that I think is gonna be different is, um. There’s going to be a lot of agents. I mean, literally you’re gonna have a very fragmented set of, uh, uh, of players, right? [00:27:07] Dai Vu: It’s not just gonna be the incumbents, it’s gonna be a lot of disruptors and, and, and, and startups. And so the, uh, for the incumbents in the room, it is a mandate that you need to, to innovate because if you do not identify and go to like an agent first, go to market model. Uh, you’re gonna be, you know, disintermediated. [00:27:25] Dai Vu: Somebody’s gonna go build an agent that’s going to leverage you as a dumb database. Um, and they’re gonna own the workflow. So you have to, you have to push the, the, the, the limits here. And I think it’s creates a big opportunity for everyone in this room. [00:27:39] John Janke: I’m going off script. I’m curious. Let’s do it. I’m curious on your take on the system integrators. [00:27:44] John Janke: ’cause I think this, this puts like they’re all, a lot of them are creating agents for people and I think that’s turning them almost more into software companies than they’ve ever been. [00:27:53] Dai Vu: They are, and I think they’re, you know, obviously they’re being, uh, impacted from like, you know, typical like, you know, SOW you know, time and materials type type business models. [00:28:02] Dai Vu: But I do think they play a big role because a lot of the system integrators are bringing, um, you know, vertical and business process expertise. And, um, like I said, I said before, a lot of the ISVs are not gonna necessarily have big enough scope in their area to own end-to-end workflows. And that’s really the promise of agents, right? [00:28:20] Dai Vu: You really need. This cognitive, you know, reasoning, planning, executing across end to end workflows. And I think, you know, the system integrators are gonna bring that capability either, either through, you know, these custom, uh, orchestration or meta agents or if they’re able to productize that and bring that to a model, they can also sort of go through the marketplace model as well. [00:28:41] Dai Vu: So who knows is how it’s gonna evolve. But you know, we’ve always been talking about. Marketplace being a broader opportunity for all partner business models. And I think that will extend to not only, uh, you know, traditional sort of, uh, sell and services partners, but also some of these system integrators as well. [00:28:58] Shawn Toldo: If I could comment on that, please. Yeah. I, I was in London two weeks ago and we did an SI partner day. Mm-hmm. We had 25 sis in a room, probably about 50 people. We had no, um, hyperscalers or cloud data warehouse providers. And when we started talking about open data infrastructure. The role that they can play. [00:29:17] Vince Menzione: Mm-hmm. [00:29:18] Shawn Toldo: Cross platform in a cost efficient manner for customers and the advisory orientation of that. They all leaned in and we, we stopped talking and they started talking. [00:29:28] Vince Menzione: Right. [00:29:28] Shawn Toldo: So they’re all facing this kind of same problem, which is actually causing a little bit of a shift, I think, in how they think about, I’m a Databricks partner. [00:29:38] Shawn Toldo: Uh, you sure you wanna do that? [00:29:39] Vince Menzione: Yeah. [00:29:40] Shawn Toldo: So this, this whole thing that’s kind of evolved in the last six to 12 months, when you kind of pick one horse to ride, I, I would tell you be cautious about what that means. You may pick a horse to lead with mm-hmm. But you’re gonna have to flank yourself a bit in terms of other providers that can help you be successful with that, that that partner you’re gonna roll with. [00:30:00] Vince Menzione: So you’re suggesting data vendor agnostic. [00:30:04] Shawn Toldo: I’m suggesting you really have to think about your strategy. Yeah. Because I think the AI, AI disruption is gonna make you think about that strategy. [00:30:13] John Janke: Yeah, I mean there’s, someone mentioned anthropics First Partner Summit. I was not there, but I’ve heard from a bunch of people were there. [00:30:20] John Janke: You know, they had a hundred partners in the room. 95 of them were system integrators. Five were technology companies, the three Clouds, Databricks and Snowflake. Like if you just think about the, the one of the major disruptors in ai, ISVs, were not in the mix. So I, I think, are they trying to disrupt all of us? [00:30:40] John Janke: Uh, do they need us? And they haven’t figured out how to work with us. I, I think. It’s, it’s, [00:30:44] Vince Menzione: and I’ve heard they only have five people in their partner organization, so I just, it’s, [00:30:49] Shawn Toldo: it’s 11 now, but it’s 11, [00:30:51] Vince Menzione: so it was five [00:30:51] Shawn Toldo: last growing fast in the, in the new company I have 50. So like, to put it in perspective, they have to make some pretty big priority. [00:30:59] John Janke: Yeah. And everyone’s been there a hot second, [00:31:00] Vince Menzione: like, right, exactly. Yeah, they, well, we will talk about the learnings we’ve had over the years, getting to where they need to get to. It’s exciting times. We got a lot to talk about here. Um, I, you know, we have about 15 minutes. I I, I want to kind of gauge, ’cause we could talk, we, we have a few things we could talk about, I could ask about, but I want to see if there’s an, like, an interest in opening up to the room for questions. [00:31:25] Vince Menzione: ’cause I feel like we’ve got a very interesting group here. [00:31:28] Shawn Toldo: You got a hand here? [00:31:29] Vince Menzione: Uh, are there hands that wanna Yeah, there’s some people that wanna ask some questions. So Yeah. We have a mic? Yeah, [00:31:37] Dai Vu: we have [00:31:37] Shawn Toldo: a mic. We, [00:31:37] Vince Menzione: we [00:31:38] Shawn Toldo: got one here. [00:31:38] Vince Menzione: We got one here. One here. Thank you. Sorry we went off script, but [00:31:44] Shawn Toldo: that’s fine. [00:31:45] Vince Menzione: It’s fine. [00:31:45] Dai Vu: Off [00:31:45] Vince Menzione: script. Better is good. [00:31:46] Shawn Toldo: I’m sure you planted the questions outta anyway. It’s okay. We [00:31:48] Vince Menzione: did, we did. [00:31:55] Audience Guest: Okay. All Eva, Sean Lightner, quick question to your, uh, increase on the marketplace, and you said you spiff the salespeople by fifth percent. 5%. Mm-hmm. So, and that obviously drives a very large adoption of, uh, marketplace transactions. How are you accounting for the margin you’re losing on, uh, you know, going through the marketplace? [00:32:14] Audience Guest: And also have you done analysis? I’m sure you have, how much is, uh, shape shifting or shifting from existing versus incremental? [00:32:22] Shawn Toldo: Yeah, it’s a great question. Um, um, lemme make three points. Number one, the backlog statement makes the margin statement not matter. So do you wanna play in that space where a customer’s already bought or not? [00:32:36] Shawn Toldo: Yeah. Or do you wanna force a budget conversation that you have to drive on your own in a direct model? That to me, I think it was 484 4 62 [00:32:43] Dai Vu: 4 6 [00:32:44] Shawn Toldo: 2. [00:32:44] Vince Menzione: That’s new Tam available to you? [00:32:46] Shawn Toldo: Yeah. That, that’s just with one. Right. And we are, we are, uh, running on four marketplaces. So that just increases our tam and makes our, our sellers lives easier. [00:32:55] Shawn Toldo: So on that piece, yes, there’s an expense, but we believe it’s right for growth. So there’s a balance there. Um, I think the, and then the second part of your question again. Sorry, [00:33:05] Vince Menzione: shapeshift. [00:33:05] Shawn Toldo: Oh, shift. We, we actually don’t think we would’ve won the business. So if I go back to our Q4 and I can probably point to three or four deals that went, um, Google Marketplace, we would not have won those deals because we couldn’t have created the budget cycle and that quarter. [00:33:23] Shawn Toldo: To make it happen. Generally a budget cycle is gonna take anywhere from 12 to 15 months. Bingo. Because of the spend that was available to us, we were able to close it in that quarter, and we had the largest Q4 in company history. [00:33:35] Vince Menzione: That is such an important point. I’m sorry. [00:33:37] Dai Vu: Okay. [00:33:38] Vince Menzione: But I, I just wanna, that is such an important point of the budget cycle. [00:33:42] Dai Vu: Yeah. [00:33:43] Vince Menzione: Being a year to a year and a half versus being able to tap into a commitment that’s already been made. Yeah, so I just emphasize that [00:33:51] Dai Vu: I was, I was just gonna add real quick, even, even when we see sort of a, uh, a channel shift renewal, which is, you know, it’s on partner paper and it moves to marketplace as part of the renewals, we do consistently see that the, uh, renewal rates on marketplace and the incremental a CB on the expansion and new opportunities tend to be better when it’s on the platform marketplace than than offline. [00:34:12] Dai Vu: And that’s why partners choose to continue to drive renewals on marketplace at a reduced to rev share. But uh, because they see that that growth, [00:34:20] John Janke: we, we, sorry. [00:34:22] Shawn Toldo: We see that as well. Yeah. And I would also make the statement on our land business, when we go through marketplace, we are two x higher across marketplaces. [00:34:30] Shawn Toldo: We’re three x higher with them. [00:34:32] John Janke: Yeah, I think separate new from renewals and then instrument deeply. [00:34:37] Shawn Toldo: Yeah, [00:34:38] John Janke: go proactively talk to your CFO and your head of rev ops to understand their mindset. Because I was with a billion dollar seller a couple weeks ago, their CFO still creates friction in the process, even though they’re selling a billion dollars through these channels. [00:34:52] John Janke: But when they broke it down, their deals are three times bigger. They do them faster. They use more components of the product, which I thought was a really cool one. So customers who buy this platform, many component platforms through a marketplace, end up using six components of the product. Versus a normal land customer who uses two increases gross in net retention. [00:35:12] John Janke: So you have to get to the point where you have the data and you can tell that story real really clearly to your finance team to get support ’cause that they will trip you up if you don’t get them on board. [00:35:23] Vince Menzione: And you’re saying there’s friction in that company. I’m just kind of curious ’cause a billion dollar company. [00:35:27] John Janke: There’s a billion dollar marketplace seller [00:35:29] Vince Menzione: market marketplace company. That’s what I meant. Yeah. But, but the fact that this, their CFO friction, like, is it, is it because they’re not doing a good enough job or? [00:35:37] John Janke: Uh, in, of educating, I, the root of the question is from this person is, would they win without it? [00:35:44] Vince Menzione: Yeah. [00:35:45] Shawn Toldo: Oh, and is it worth the three points? [00:35:46] John Janke: Right. It’s, it is And, and I think some pe like to me, it’s the cheapest channel in the world. Yeah. Like with committed budget and people to support you winning. Like the, that formula, the math is so simple. [00:35:57] Shawn Toldo: Yeah. For, for a company of our size to go to like the classic resell ecosystem, I gotta walk in with 30 points. [00:36:02] John Janke: Yeah. [00:36:03] Vince Menzione: Yeah. [00:36:03] Shawn Toldo: It, it’s an illogical conversation. Outside of public sector and growth, you know, geos around the world. And so I, I’ve been lucky to have a CFO that I haven’t had that challenge with, at least at DBTI should say. [00:36:19] Vince Menzione: Really great insights. I think we have, we have another hand up here. [00:36:28] Audience Guest: Yeah. Thanks Susan. The question is for Dai. Uh, my name is Latif Hamani. I’m the founder of Partner System ai. Um, so what we’ve done is we’ve built a, a co-sell AI agent mm-hmm. That your partners can use to Yeah. Reduce all the friction in the co-sell with you. Uh, the questions that I have is, I guess I should back up, so XAWS Madison with a very large alliances, and then I worked, went on the other side. [00:36:55] Audience Guest: For software companies, and even though I had an operational team, I was spending two to three hours on on the keyboard, right? Mm-hmm. Deal registration, emails that can’t be automated, et cetera. So the question that I have for you is, I’d love for you to validate that. You know, unless you are one of the big companies, one of the big enterprises, if you go to the lower end of the enterprise or the mid market, uh, would you validate that there is a challenge? [00:37:20] Audience Guest: There’s a lot of friction for a smaller company. Mm-hmm. Uh, ’cause these marketplaces are complex. Yeah. The cosell is complex. Uh, that there’s an opportunity to really break down that friction with some automation and ai. [00:37:33] Dai Vu: Yeah, absolutely. So, um, we have already been, uh, part of the journey to remove some of the, uh, the friction as part of that selling and purchasing journey. [00:37:43] Dai Vu: Uh. We’re not quite there yet. But, uh, we’ve done things like we have, uh, you know, private offer APIs. We, uh, we have co-sell, uh, registration automation. Um, you know, we have tools like, uh, propensity to buy, tooling to help, uh, partners do, uh, more targeted efforts. Um, but the a i piece is still coming. Um, so I think, uh, the idea here is that we have launched a number of agents as part of our, um. [00:38:08] Dai Vu: Uh, part of our, uh, Google Cloud Partner network, partner hub. Uh, so these are, uh, agents that are gonna do a bunch of things to help partners as part of their workflow, but we’re gonna extend this to the marketplace and ISV area as well. Uh, so I think there’s a lot of opportunity. So, uh, I know there’s probably a lot of feedback in friction, uh, in, in certain parts. [00:38:29] Dai Vu: So we can, we can go tackle together. [00:38:32] Vince Menzione: Hey. There you go. There was a little [00:38:34] Dai Vu: plug [00:38:34] Shawn Toldo: there for tackle. Exactly. [00:38:37] Dai Vu: Uh, and I wanted, and just to be clear, I want to take a look at it from the end to end, uh, uh, flow, right? It shouldn’t just be just marketplace. It should be all the way from like, you know, top of the funnel, demand generation, all the way to like post transaction follow up. [00:38:51] Dai Vu: So we really need to take a look at, at the, the end, end flows and figure out a way we can remove some of that friction [00:38:56] Vince Menzione: three sense. [00:38:57] Dai Vu: Yeah. [00:38:59] Vince Menzione: Any more questions [00:39:00] Audience Guest: back here? Hey. Hey guys. This, this is a really good discussion. Uh, di this question’s primarily, uh, from, I’m interested in the hyperscaler response. [00:39:09] Audience Guest: Yep. Uh, but all of you, uh, can you talk about the patterns or say more about the patterns between. Um, the consumption of just platform capabilities versus industry workflows. Mm-hmm. And how industry where I, I mean, I, I, my sense is that industry workflows are becoming more [00:39:27] Dai Vu: Yeah. [00:39:28] Audience Guest: Uh, the easier thing for enterprises and SMBs to buy. [00:39:33] Audience Guest: Yeah. Especially SMBs, I think. Um, but say more about those patterns that you’re seeing develop and kind of what is. Uh, who are, where, where are those kind of, where is the demand being driven? Is it, is it, yeah. The search and discover in the marketplace, or is it being led by field sales of mm-hmm. Either GCP or partners? [00:39:55] Dai Vu: Yeah, so let me, I’ll mention a couple, a couple areas where, where it’s growing. So I think number one I mentioned before about some of these large horizontal business apps that we’re partnering with, right? Um, and, uh, and of course the fact that we’re, we’re, we’re transacting them through marketplace is, is a huge. [00:40:14] Dai Vu: Evolution from a few years ago. So who would’ve thought you would be buying like, you know, a hundred million dollars a CB deals, uh, through, through marketplace with like a Salesforce or a ServiceNow workday. But it’s happening now. And to be clear, all these. Horizontal business app. They’re not doing this in a very, you know, opportunistic, transactional way. [00:40:32] Dai Vu: They basically see marketplace and cloud go to market as a strategic growth lever for them. So that’s one big area. So from just a pure large deal perspective. Okay. Then you mentioned before around sort of corporate and SMB. Well, we find that a lot of the big opportunities are mostly around as they scale their business, uh, they’re not necessarily looking for things in the traditional sort of infrastructure space, but they’re looking for, you know, full SaaS applications to help scale their business, right? [00:40:58] Dai Vu: So it would be CRM, finance, hr, these types of solutions to become very attractive for some of this, uh, downstream market. And then lastly, as I mentioned before, which is, uh, when we think about this gentrification and owning, um. Uh, driving, uh, this business process and vertical, the ISVs become very important along with the services partners who bring that domain expertise to drive the end to end workflow. [00:41:25] Dai Vu: So I think that’s gonna be increasingly important. So those are three areas I think we need to watch out for. We. Okay. [00:41:30] John Janke: Maybe one thing, like as the cloud commit grows inside of companies, it’s shifted from being an engineering department, IT department budget line item to a corporate finance budget line item. [00:41:40] John Janke: Typically one of the top five to 10 expenses in a company. So that has shifted. Who is thinking about optimizing? The cloud commit with marketplace contracts. And that opens, that’s really opened up the avenue in addition to like these biz apps, vertical apps players. Yeah. Like having success. So I, I do think even inside your own company, evaluating where your cloud commits are, who owns them and are they thinking about the intersection of marketplace? [00:42:06] John Janke: ’cause I, I think it’s smaller companies, they’re still figuring it out. I run into engineering leaders who still own the commits, uh, but in medium to large companies. Very different. [00:42:16] Vince Menzione: Really good point. Because it, you know this, the optics change dramatically, right? This large commitment is now at the board level, [00:42:23] John Janke: right? [00:42:23] John Janke: And then you do have to teach your sellers as a vertical or business application player how to ask that question. ’cause the first resistance everybody says is, oh my, my person, my stakeholder, we. Manufacturing vertical application provider talking at an event last week, and they’re like, the shop floor manufacturing owner doesn’t know anything about the cloud commit. [00:42:43] John Janke: But if they ask the question, be like, Hey, do you guys have a strategic relationship with Google? Would it be easier to buy our product on the bill? Eight out of 10 times they get a yes. So [00:42:52] Vince Menzione: which is why the 5% comes in And that really accelerates the conversation happening. Yeah. We’ve got three more minutes. [00:43:01] Vince Menzione: Um, if we don’t have any other questions, I ha I have one for each of you really about the maturity model and partners are in the room that are not committed yet, right? We’ve talked about some very significant DBTs doing some incredible things, right? So we, there’s maybe a sense that like we, you, you are working with the be the biggest and the best out there, but what about everyone else that’s in the room that maybe isn’t committed yet? [00:43:23] Vince Menzione: And maybe they’re in motion, but they need some help and advice on what to go do next. What? What would you say die first? [00:43:30] Dai Vu: So they’re early stage, [00:43:31] Vince Menzione: early, early stage or not, they’re not on board yet. They’re not, yeah. They’re not with you yet. [00:43:35] Dai Vu: Yeah. So I’ll, I’ll go back to my earlier comment, which is that as you go into the journey, just be very intentional about what you need to do from an operational, investment people, uh, technology perspective. [00:43:47] Dai Vu: Uh, because it could be, it could be a multi-year journey. Um, uh, so I’d say go into it with the right expectations as opposed to thinking it’s going to be some accelerated six month thing that Sean has been driving here. It’s, he’s the outlier. [00:43:59] Shawn Toldo: But, but the reason for the outlier, [00:44:00] Dai Vu: yeah. [00:44:01] Shawn Toldo: And just to add to the intentional point Yeah. [00:44:02] Shawn Toldo: Is, you know, hire the right people. Right. So, somebody told me a long time ago, uh, hire slow, fire fast. That’s a really, really, really good principle that I take. Mm-hmm. I don’t like the fire part, obviously, but just for context, I, I am very lucky to have a great set of leaders that we were able to add people in. [00:44:24] Shawn Toldo: When I walked in the door, we had a person that was leading the Snowflake and AWS partnership. I had nobody on GCPI had nobody on Microsoft. I had nobody on Databricks. And then we made prioritization decisions on where we’re gonna go next. And so we hired people that had the experience and could drive the outcome in the right way. [00:44:43] Shawn Toldo: But we were very thoughtful about when we made those decisions on a quarterly basis, not a daily basis. So who you’re gonna bet on and then who you’re gonna put in the seat to make that bet come to life, I think is a really important thing as well. [00:44:58] John Janke: Yeah. [00:44:58] Vince Menzione: John, you worked with the be biggest and the best out there, so Yeah, sorry. [00:45:01] John Janke: Well, I think there’s the, like there’s the bottoms up and the tops down. Like seven years ago, this was all bottoms up. It was a partner leader who thought launching a marketplace would be good and they would go figure out how to do some deals and then sell their way up. Today there’s a lot more top down where people get it. [00:45:17] John Janke: But you can evaluate top down pretty fast. ’cause if you go talk to your CEO, you talk to your head of product, you talk to your CFO, and they have an allergic reaction to these concepts. You know, you have to go bottoms up. But there also are success story examples in every single ISV category that exists. [00:45:33] John Janke: Like this is not just security and data and DevOp like the, I think the ServiceNow. Salesforce workday. Examples are really great, like the marketing tech examples, more and more business of vertical apps every day. So I do think you can look at those people who’ve been successful. Maybe they’re your competitors, maybe they’re people you aspire to be and reference them as you’re trying to figure out how to do top down. [00:45:55] John Janke: But like you need both. You can’t win long term unless you get top down and bottom up aligned. [00:46:01] Shawn Toldo: And, and when I, when I would go ask for resourcing, I would always get the question, do, could you go faster with more? And I’d say, no. Gimme the one or two humans here, let me go prove it out and I’ll come back. [00:46:13] Shawn Toldo: So there’s a little bit of a strategy in doing that, that you’re gonna get more over time when you’re, you know, very measured in how you go ask for investment and resource. And so I would just add that point also. [00:46:27] Vince Menzione: Was, was hiring a significant component of your executive commitment, Sean? I mean, [00:46:33] Shawn Toldo: yes. So when I walked in the door at DBT, we had eight people in the partner organization. [00:46:38] Shawn Toldo: Today we have 25, and that was 18 months ago. But that did not happen. I didn’t go in and ask for, you know, that 16 people. Right. I asked over time in a very measured way with, you know, the programs and strategy team, like, what can we also support? You don’t want to bring somebody in to go do something and you don’t have the programs and operations side to support it ’cause they’ll fail. [00:47:01] Shawn Toldo: So we’ve been very thoughtful about how we’ve done that as well. [00:47:04] Vince Menzione: Die from you. I know you had something. [00:47:06] Dai Vu: No, no, no. I, I was good. [00:47:08] Vince Menzione: What is the one thing that people in this room need to go better and differently? Is there one, is there one specific thing other than what we’ve already discussed, did we miss anything? [00:47:16] Dai Vu: No, I would just, the whole identification. So obviously, uh, identifying this is not just like slapping a chat bot, but more around thinking all the things we talked about, product commercials, but also go to market where it’s agent first, where you can surface your agent in a workflow like Gemini Enterprise app. [00:47:34] Dai Vu: That’s gonna drive high alignment with how we work and go to market with Google. [00:47:38] Vince Menzione: Awesome. [00:47:38] Dai Vu: Yeah. [00:47:40] Vince Menzione: Wow. Good stuff. Yeah. Very good session. [00:47:44] Dai Vu: Thank [00:47:44] Vince Menzione: you guys. What do you think? Everyone? Thank you very much. [00:47:47] Shawn Toldo: Thanks for listening to the Ultimate Partner Podcast. [00:47:50] Vince Menzione: If today’s conversation resonated, share it with a partner leader in your network. [00:47:55] 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, [00:48:11] John Janke: October 26th through October 28th. [00:48:14] Vince Menzione: Until next time, keep showing up in the rooms that matter because being in the room changes everything [00:48:22] I.

As heat waves and storms reshape daily life, see how localized weather tech and smart notifications are becoming essential tools for staying safe, planning ahead, and getting your forecast with a side of fun. Apple Weather app evolution from basic utility to Dark Sky integration Localized weather data, alerts, and hourly forecasting explained Shortcut automations for Weather: capabilities and app integration Changes in third-party weather app APIs post-Dark Sky acquisition CARROT Weather: customizable data sources, achievements, and unique personality (Not Boring) Weather: design-driven, interactive weather visualization Met Office Weather Forecast: official UK alerts and specialized app benefits Mercury Weather: cross-platform support, trip planning, and rain forecasting Acme Weather: US-only, feature-rich app from Dark Sky creators App Caps: Spigen TapZip MagSafe organizer and ESR CryoBoost charging station Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsors: blackhat.com/us-26 and use code TWIT NetSuite.AI/IOS

As heat waves and storms reshape daily life, see how localized weather tech and smart notifications are becoming essential tools for staying safe, planning ahead, and getting your forecast with a side of fun. Apple Weather app evolution from basic utility to Dark Sky integration Localized weather data, alerts, and hourly forecasting explained Shortcut automations for Weather: capabilities and app integration Changes in third-party weather app APIs post-Dark Sky acquisition CARROT Weather: customizable data sources, achievements, and unique personality (Not Boring) Weather: design-driven, interactive weather visualization Met Office Weather Forecast: official UK alerts and specialized app benefits Mercury Weather: cross-platform support, trip planning, and rain forecasting Acme Weather: US-only, feature-rich app from Dark Sky creators App Caps: Spigen TapZip MagSafe organizer and ESR CryoBoost charging station Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsors: blackhat.com/us-26 and use code TWIT NetSuite.AI/IOS

As heat waves and storms reshape daily life, see how localized weather tech and smart notifications are becoming essential tools for staying safe, planning ahead, and getting your forecast with a side of fun. Apple Weather app evolution from basic utility to Dark Sky integration Localized weather data, alerts, and hourly forecasting explained Shortcut automations for Weather: capabilities and app integration Changes in third-party weather app APIs post-Dark Sky acquisition CARROT Weather: customizable data sources, achievements, and unique personality (Not Boring) Weather: design-driven, interactive weather visualization Met Office Weather Forecast: official UK alerts and specialized app benefits Mercury Weather: cross-platform support, trip planning, and rain forecasting Acme Weather: US-only, feature-rich app from Dark Sky creators App Caps: Spigen TapZip MagSafe organizer and ESR CryoBoost charging station Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsors: blackhat.com/us-26 and use code TWIT NetSuite.AI/IOS

As heat waves and storms reshape daily life, see how localized weather tech and smart notifications are becoming essential tools for staying safe, planning ahead, and getting your forecast with a side of fun. Apple Weather app evolution from basic utility to Dark Sky integration Localized weather data, alerts, and hourly forecasting explained Shortcut automations for Weather: capabilities and app integration Changes in third-party weather app APIs post-Dark Sky acquisition CARROT Weather: customizable data sources, achievements, and unique personality (Not Boring) Weather: design-driven, interactive weather visualization Met Office Weather Forecast: official UK alerts and specialized app benefits Mercury Weather: cross-platform support, trip planning, and rain forecasting Acme Weather: US-only, feature-rich app from Dark Sky creators App Caps: Spigen TapZip MagSafe organizer and ESR CryoBoost charging station Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsors: blackhat.com/us-26 and use code TWIT NetSuite.AI/IOS

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 his follow up appearance on the Code Story podcast, Tim Ebbers, Field CTO at Wallarm, discusses why detection alone is insufficient for AI-driven systems, what real enforcement looks like at the runtime level, and what accountability becomes possible once all four stages are in place.QuestionsSecurity teams are used to detecting incidents and responding after the fact. Why is that model insufficient for AI-driven systems?What does “enforcement” usually mean today, and why can actions like restarting pods or rotating credentials come too late?Why does AI behavior require controls that operate closer to runtime?What changes when enforcement happens at the kernel level rather than only at the network, identity, or application layer?Can you explain what it means to revoke or contain a compromised AI session without touching the broader deployment?How does real-time blocking change the risk equation for AI agents that access sensitive data, external services, or production workflows?What kinds of AI behaviors should organizations be able to stop immediately?How do teams balance strong enforcement with the need to avoid slowing down AI development and deployment? Once organizations can discover, observe, and enforce AI behavior, what does accountability look like at the enterprise level?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/tebbers/Full AbstractTim Ebbers, Field CTO at Wallarm, discusses why detection alone is insufficient for AI-driven systems, what real enforcement looks like at the runtime level, and what accountability becomes possible once all four stages are in place.Detection tells you what happened. It does not stop it. For most security incidents, that tradeoff is manageable. For AI systems that can access sensitive data, call external services, and trigger downstream actions at machine speed, the gap between detection and response is where the damage happens.The enforcement model most security teams operate today was built for a slower threat. Restarting pods, rotating credentials, and updating policies are all responses to something that has already occurred. Against an AI agent that can exfiltrate data, invoke a production workflow, or violate a compliance boundary in the time it takes to page an on-call engineer, that response model is not enforcement. It's cleanup.Closing that gap requires controls that operate at the layer where AI behavior actually executes, not at the perimeter, not at the identity layer, not at the application boundary. Kernel-level enforcement changes what is possible: a compromised session can be revoked by user identity or trace ID, connections can be terminated at the workload level, and enforcement can happen without a pod restart, a deploy cycle, or any impact to the broader environment. That is what it means to complete the AI control loop. Discover what is running, observe what it is doing, enforce what it should not be doing, and govern with evidence that the enforcement worked. Organizations that can only do the first two are solving half the problem.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

What keeps the digital world running safely behind the scenes, and why are so many organizations only now discovering that one of their most important security foundations is becoming increasingly difficult to manage? In this episode, I sit down with Lakshmi Hanspal, Chief Trust Officer at DigiCert, to demystify Public Key Infrastructure (PKI) and explain why it quietly powers almost every secure digital interaction we rely on each day. From online banking and cloud services to connected devices, APIs and AI systems, PKI is the technology that verifies identities, protects communications and helps ensure the information we exchange can be trusted. Our conversation is inspired by DigiCert's latest PKI Modernization research, which surveyed senior IT and security leaders to understand how organisations are managing digital trust in an increasingly connected world. The findings reveal a growing gap between awareness and execution. While most leaders recognize the importance of digital trust, only 34% report having full visibility into their digital certificates, leaving many vulnerable to outages, operational disruptions, and unnecessary risk. Lakshmi explains why certificate management has become so fragmented over the years, how machine identities now outnumber human identities by a significant margin, and why AI is adding another layer of complexity as autonomous systems communicate at unprecedented scale. We discuss why spreadsheets and disconnected management tools are no longer sufficient, and why many organizations are moving towards centralized PKI management with greater automation and policy enforcement. We also look ahead to two challenges that are rapidly moving up the boardroom agenda. The first is establishing trust in AI-generated content through stronger verification, digital signatures, and cryptographic provenance. The second is preparing for a future in which quantum computing could eventually undermine today's encryption standards, making cryptographic agility an important business capability rather than simply a technical consideration. If you've ever wondered what lies behind the secure websites, applications, and digital services you use every day, this conversation offers a practical introduction to one of the internet's most important foundations while explaining why modernizing digital trust has become a business priority rather than simply an IT project. How prepared is your organization for the next generation of digital trust? I'd love to hear your thoughts and whether PKI modernization is already part of your security strategy.

Spirent: 6G Is Coming Sooner Than Expected, Podcast, Pre-commercial 6G trials could begin in 2028, with commercial deployments arriving as early as 2029 “Six G leaders are now going to be judged on how resilient the network is, how adaptive, how quickly it can recover from an issue,” says Stephen Douglas of Spirent, a Keysight company. In this Technology Reseller News podcast, Doug Green speaks with Douglas about the accelerating 6G timeline and why service providers may need to begin planning for 6G business models sooner than many expected. For years, 2030 was widely viewed as the point when the industry would begin looking seriously at 6G. Douglas says that assumption is changing. Pre-commercial 6G demonstration systems are now being discussed for 2028, with the first commercial 6G equipment and operator deployments potentially emerging in the mid-to-late 2029 timeframe.   The shift is being driven by rapid movement in standards, including 3GPP work on 6G radio, core network architecture, security, APIs, application enablement, management, orchestration and AI-based operational support. Douglas says this creates a compressed window for operators, vendors and ecosystem partners to test, validate and prepare for a new network generation that may arrive sooner than expected. The podcast also looks at how 6G may differ from previous mobile generations. Rather than treating AI as an add-on, Douglas describes 6G as an AI-native network architecture, where AI is built into operations, orchestration, security, APIs and even the radio layer itself. “What you're seeing is a big shift, and AI is at the heart of that,” Douglas says. “That big shift is moving from an AI-assisted network to an AI-native network.” That could allow networks to predict congestion, reconfigure network slices, detect threats, expose capabilities to third parties in real time and support new AI-driven services at the edge. A major theme of the discussion is monetization. The 5G era delivered important technology advances, but many operators struggled to translate those advances into new revenue. Douglas argues that 6G gives service providers an opportunity to avoid repeating that pattern by developing new services, ecosystems and pricing models during the deployment phase, rather than waiting until after the network is built. The conversation highlights several emerging 6G opportunity areas, including AI and communication, integrated sensing and communication, ubiquitous connectivity, immersive communication, massive communication and hyper-reliable low-latency communications. Douglas points in particular to integrated sensing, where the network could support services that use radio infrastructure to understand objects, environments and movement while also carrying traditional communications traffic. Douglas also discusses the growing role of edge AI. As more AI inference moves from centralized data centers toward edge locations, devices and regional infrastructure, operators may have an opportunity to participate more directly in the AI economy. Instead of acting only as transport providers for AI traffic, service providers could support sovereign AI, low-latency inference, industrial computer vision and other AI-as-a-service models. The key message for operators is that they do not need to wait for 6G to begin preparing. Douglas says 5G Advanced can serve as a bridge, allowing operators to test business models around sensing, edge compute, AI services and network-based awareness today, while building a roadmap toward 6G. The winners in the 6G era, Douglas says, may not simply be the providers with the fastest networks. Success may be defined by intelligence, sensing and resilience — and by the ability to turn 6G capabilities into services that customers are willing to pay for from day one. Learn more at keysight.com.

Watch the YouTube version of this episode HEREIn this Maximum Lawyer episode, Tyson Mutrux sits down with longtime friend of the show, automation and AI expert Kelsey Bratcher of Hired Gun Solutions to talk about the future of intake, phone answering, and legal tech. The conversation starts from a simple Facebook post about hiring an AI answering service and turns into a brutally honest breakdown of why most voice AI products are expensive, over‑engineered, and still not better than a well‑built DIY solution.Kelsey explains how tools like Retell let law firm owners build their own AI receptionist for a fraction of the cost of vendor setups, using the same documentation they already give Smith, Ruby, or Lex Reception. You'll hear practical guidance on scope, latency, and call flow, why “simple beats fancy,” and how to use AI plus APIs to replace low‑value data‑entry work while protecting the human parts of client communication. Tyson and Kelsey also zoom out to the bigger picture: migrations between case management systems, why legal tech pricing is broken, and how AI will reshape non‑lawyer roles inside law firms.What you'll learn:Why most AI answering services are overpriced and still average at intake.How to use Retell to build your own AI receptionist with your existing scripts.The importance of keeping scope narrow and latency low so calls feel natural.When AI can replace virtual receptionists and when you still need a human.Smart call‑flow tweaks (using caller ID, fewer confirmations) that boost conversion.Where outbound AI is risky and when it works for expected, simple calls.How Kelsey uses AI to build one‑off tools and migrations in hours, not days.Why legal tech pricing is broken and which non‑lawyer roles are most exposed.Highlights00:01 – How a Facebook post on AI receptionists sparked this episode.01:28 – Kelsey's “build your own on Retell” philosophy and cost breakdown.04:27 – Retell explained: connect language models and voice APIs without coding.06:34 – Do AI receptionists lose leads? Why scope and consistency matter.10:34 – Latency: the real make‑or‑break factor for voice AI.12:45 – Fixing annoying call flows: stop over‑confirming names and emails.14:12 – Why Kelsey avoids outbound AI for provider calls and sensitive data.17:18 – Using AI to build tools that talk to APIs instead of “agents clicking around.”22:25 – Five‑minute Codex app that fixed a multi‑hour data‑entry mistake.26:07 – Inside a multiplaintiff lawsuit tool tied into major CRMs.29:19 – Faster, cheaper case‑management migrations using AI‑built scripts.31:33 – Moving away from Zapier/Make in favor of AI‑built micro‑apps.33:32 – “Legal tech is going to get rock and rolled” and why.36:29 – The flat‑fee AI trap and token consumption.39:18 – Which non‑lawyer roles AI eats first and which survive.44:37 – First 30‑day steps to test voice AI in your firm.Connect with KelseyWebsite InstagramFacebook

Most API testing stops at the happy path. The problem is that the bugs that actually hurt you in production are sitting in everything many testers skip, like the boundary values, the oversized payloads, the missing tokens, the security headers, the inputs that make no sense at all. In this episode, Joe sits down with Liudas Jankauskas, who has spent almost twenty years breaking software and testing APIs since 2008. Liudas demonstrates Rentgen, his free and open-source API testing tool, live on screen. You'll watch him take a single request from a real app, map it in seconds, and generate dozens of tests covering security, boundaries, performance, and load—all from one click. You'll learn: How to discover APIs hiding under the hood of any application, even when there is zero documentation Why happy path testing leaves you exposed How to run a fast hygiene check before your real automation ever starts Liudas also explains why Rentgen runs completely locally with no server and no data leaving your machine, making it safe for banking, healthcare, and other regulated environments. Plus, he demonstrates the killer Copy Bug Report feature that drops a standards-based ticket straight into Jira or Trello. In This Episode You'll Discover How to find and test undocumented internal APIs using the browser DevTools Network tab Why happy path-only testing misses the bugs that matter most How Rentgen turns one request into security, boundary, performance, and load tests automatically Where Rentgen fits in your workflow as a pre-automation hygiene layer—not a Postman replacement How to use it for regression by comparing results across environments The one piece of advice Liudas gives every tester to level up their API testing Try Rentgen, free and open source, at Rentgen.io. Connect with Liudas Jankauskas on LinkedIn: https://www.linkedin.com/in/liudas-jankauskas/

Google Search has changed more in the past few weeks than it has in the last 25 years, and every garage door company and home service business needs to understand what comes next. In this episode of Torsion Talk, Ryan shares the latest updates from Google, AI search, local SEO, and digital marketing while breaking down what these changes actually mean for contractors and business owners.Ryan also shares highlights from his recent trip to Miami, attending the Brazil World Cup match with his son, before diving into six major marketing and AI updates that could dramatically impact how customers find your business online.The episode explains Google's rollout of AI Search and how the shift toward conversational search is changing local SEO forever. Ryan discusses why traditional rankings aren't enough anymore, how AI-generated answers are replacing website clicks, and what garage door companies should be doing today to stay visible as search behavior continues to evolve.Ryan also covers Google's new Local Service Ads verification changes, AI Max updates for Google Ads, upcoming campaign deadlines, and why blindly allowing Google's AI to optimize your advertising may not always produce the best results. He explains how agencies and business owners should approach these changes strategically instead of relying entirely on automation.Another major topic is Google's continued crackdown on low-quality AI-generated content. Ryan shares why mass-produced AI blogs and service area pages are being penalized, how Google is detecting AI spam, and what actually works if you want to rank in both traditional search and AI-powered results.The conversation also explores how businesses can improve their visibility inside ChatGPT, Gemini, Claude, and other AI platforms through better content, trust signals, structured data, online mentions, and high-quality local authority.Finally, Ryan previews an upcoming discussion on AI software ownership, APIs, data security, GoHighLevel, automation, and why every business owner should be thinking carefully about who controls their marketing assets and customer data as AI continues transforming the home service industry.If you own a garage door company, HVAC business, plumbing company, electrical company, roofing company, or any local service business, this episode delivers practical strategies to help you adapt to Google's AI revolution, protect your rankings, and prepare for the future of search.Subscribe to Torsion Talk on YouTube, Spotify, and Apple Podcasts for weekly insights on AI, local SEO, Google updates, digital marketing, entrepreneurship, leadership, automation, garage door industry news, and business growth.Find Ryan at:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://garagedooru.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aaronoverheaddoors.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://markinuity.com/⁠Check out our sponsors!Sommer USA - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://sommer-usa.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Surewinder - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://surewinder.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Stealth Hardware - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://quietmydoor.com/⁠

Show DescriptionWhat to do when you have more ideas than time, getting new musical gear, nerding out and getting excited about things, the state of conferences in 2026, Cursor being bought, and Dave's got a minimum viable design system idea. Listen on WebsiteWatch on YouTubeLinks FFConf Figma Config 2026 SpaceX locks in $60 billion Cursor deal Google Antigravity Devon: An open-source pair programmer Introducing Composer 2 @function CSS at-rule A tale of two browsers Google's Prompt API 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.

Want to know how to avoid getting burned by shippers with terrible operations or a track record of late payments? Today, Lance Healy from FreightFacts is back with a game-changing solution: scorecarding shippers! Lance drops some knowledge on how his new venture is bringing much-needed transparency and accountability to the LTL space. We break down the massive gap between the boardroom and the dock, and why having access to a shipper's score—tracking everything from dwell times to damage claims—is priceless information you need before you even start prospecting. If you're ready to level up your operations, gain a competitive advantage, and leverage the power of standardized APIs and automation to scale your freight brokerage, don't miss our conversation!   About Lance Healy Lance is the Co-Founder and CEO of Freight Facts, the industry's first data drivenshipper scorecard relating to LTL shipping location attributes and behaviors. Prior to Freight Facts, Lance served as the VP of Innovations at Optym, that focused on developing applications for LTL Carrier line and P&D optimization. Prior to that, Lance was the Founder and President of Banyan Technology that pioneered API connections to LTL Carriers in the late 1990's. He was awarded a US patent on LTL Carrier Dynamic Pricing in 2017. Lance serves several positions on numerous industry associations including the CSCMP, Center Of Excellence chair of Technology and Awards committees, and serves as a chair of Digital LTL Council workshops defining the next evolution of efficiency through APIs.   Connect with Lance Website: https://freightfacts.io/  Email: lhealy@freightfacts.io     

How to Build Your Own AI VP of Marketing Step-by-Step with SaaStr's Chief AI Officer SaaStr's CAIO Amelia LeRutte built 10K, SaaStr's AI VP of Marketing, live on stage at SaaStr AI Annual 2026 - and you can follow along and build your own right now. 10K started as a simple dashboard in January. Five months later it runs autonomous email campaigns, generates daily marketing ideas grounded in real data, sends attendee newsletters, and acts as a full co-pilot for SaaStr's entire go-to-market. In this session, Amelia walks you through the exact spec, the sample data, and the live build so you can deploy your own version before the video ends. What you'll learn: How to write a spec that gives your agent one clear goal and actually produces useful outputs How to connect Salesforce, your marketing automation platform, social media, and other APIs so your agent has real data to work with The stair-stepping approach: build one agentic workflow at a time instead of trying to automate everything at once How to set guardrails so your agent runs campaigns semi-autonomously without emailing your entire database by accident What 10K does today versus what it could do on day one, and what the realistic 30, 60, and 90-day build looks like Resources from this session: Grab the spec and sample historical data to build your own: saastrannual.com/resources Free Replit credits: use code REPLITSAASTR Read 10K's own take on whether he is a VP of Marketing: saastr.com/is10kavpofmarketing About this session: Recorded live at SaaStr AI Annual 2026 in San Mateo. Part of SaaStr's ongoing series on building and deploying AI agents

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 this episode, Craig Thomas, Sr. Solutions Engineer at Wallarm, examines what rogue AI actually means in practice, where the risk materializes, and what it takes to move from detection to control.QuestionsWhen we say "rogue AI," what do we actually mean? Is it only malicious AI, or can legitimate systems become risky too?What are the most common ways AI systems drift outside intended boundaries? Once an organization understands what rogue AI looks like, where does that loss of control typically begin, and who is responsible for preventing it?How do shadow LLMs, unsanctioned agents, and unmanaged AI workflows create risk even when no attacker is involved? If AI drift often starts with normal business activity, where do shadow AI systems fit into that picture?Why can an AI action look legitimate in isolation but still create serious business, security, or compliance risk when viewed as part of a larger sequence of actions? As these shadow systems become more embedded in everyday workflows, why is it so difficult to recognize risk in real time?How do APIs, integrations, and connected systems amplify the impact of those seemingly legitimate actions? What changes once those actions begin flowing across APIs, business applications, and interconnected systems?What kinds of unexpected outcomes worry CIOs and CISOs most today when AI systems are operating across those interconnected environments? As that connectivity expands, what are security and business leaders most concerned about?And given those concerns, what does meaningful oversight actually look like when AI systems can act at machine speed? How should organizations distinguish between the experimentation they want to encourage and the unmanaged AI behavior they need to control? One challenge is balancing governance with innovation. How do organizations avoid slowing down AI adoption while still maintaining control?We know that many organizations can detect risky AI behavior after the fact. But if they can't stop it in real time, what critical gap still remains? Even with governance programs in place, many organizations are still operating reactively. In closing, what's the key difference between detecting AI risk and actually controlling it?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/cu-craigthomas/Full AbstractIn this episode, Craig Thomas, Sr. Solutions Engineer at Wallarm, examines what rogue AI actually means in practice, where the risk materializes, and what it takes to move from detection to control.Not every AI threat starts with an attacker. Some of the most consequential AI risks organizations face today come from systems that are working exactly as designed, just not quite as intended. An agent that calls an API it was never supposed to reach. A workflow that exposes PII because nobody mapped the data path before deployment. A shadow LLM standing up in an AWS account because a developer needed to move fast and approval processes were slow. None of these require malicious intent to create serious business, security, or compliance exposure.Rogue AI is a broader category than most governance frameworks account for. It includes the unsanctioned, the unmonitored, and the unpredictable: AI systems that drift outside intended boundaries, take actions that look legitimate in isolation but create risk in sequence, and operate at machine speed in ways that make after-the-fact detection feel like a consolation prize. The gap most organizations have is not in detecting that something went wrong. It's closing the loop fast enough to matter.Meaningful AI governance requires more than policy and discovery. It requires the ability to observe AI behavior at runtime, understand what triggered each action and what it touched, and enforce boundaries before consequences compound. That closed AI control loop, from knowing what is running to seeing what it does to stopping what it should not, is the operational standard AI transformation demands. Most organizations are not there yet.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://click.cash.app/ui6m/mt82fpxl #CashAppPod. Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. See terms and conditions at https://cash.app/legal/us/en-us/card-agreement. Cash App Green, overdraft coverage, borrow, cash back offers and promotions provided by Cash App, a Block, Inc. brand. Visit http://cash.app/legal/podcast for full disclosures.* 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

How prepared are businesses for a new wave of attacks targeting the apps, APIs, and AI systems now powering digital growth? In this episode, I speak with Richard Meeus from Akamai Technologies about the latest findings from Akamai's State of the Internet report, with a focus on apps, APIs, and DDoS activity across EMEA. Richard explains why APIs have become such an attractive target for attackers, especially as AI adoption accelerates. We discuss the sharp rise in API abuse, the growing use of automation to industrialize attacks, and why many organizations still lack visibility into the APIs exposing sensitive data. We also examine the rise in layer 7 DDoS attacks, how attackers are combining multiple techniques to distract defenders, and why sectors such as retail and manufacturing are facing growing pressure. Richard also shares his view on the geopolitical forces shaping DDoS activity and why hacktivist groups continue to use these attacks as a public statement. Another major theme is the security risk around AI chatbots. As more organizations deploy chatbots to improve customer service, Richard explains how overly helpful AI systems can expose data, respond to prompt injection attempts, or create new blind spots if the right controls are missing. But this conversation is not all about risk. Richard also explains why AI can help defenders strengthen visibility, improve testing, analyze logs faster, and support more proactive security strategies. So, as businesses race to adopt AI and modern digital services, are they paying enough attention to the APIs and infrastructure sitting underneath it all? Share your thoughts.