Podcasts about Databricks

Immad Akhund, Co-Founder and CEO of Mercury, talks with TITV Host Akash Pasricha about his fintech's multi-product evolution and $200M raise. We also talk with The Information's Kevin McLaughlin about Microsoft's contentious decision to cut off a key Databricks integration, and we get into the realistic market math behind SpaceX's $1.75 trillion valuation with our editors Martin Peers and Meredith Mazzilli.Articles discussed on this episode: https://www.theinformation.com/newsletters/the-briefing/spacex-worth-700-billion-1-75-billionhttps://www.theinformation.com/articles/microsoft-opens-new-front-fight-data-ai-agentsSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - OpenAI Hits $5.7B Q1 Revenue02:39 - Microsoft Triggers New Corporate Data War12:43 - The Right Way to Value SpaceX25:46 - Mercury CEO on Fintech Growth and AI Tools

Hosted by David Cowen | Careers and the Business of Law Everyone's talking about Harvey, Legora, Spellbook, and Ivo. Nobody's talking about what they ride on top of. Tom Baldwin - founder and CEO of Entegrata, former CIO at Foley, Sheppard Mullin, Reed Smith, and Cadwalader - argues the real story is data infrastructure. Without a single source of truth, every AI tool in your firm is working from a partial picture. WHY THIS MATTERS? If your firm is buying AI tools without auditing the data underneath them, this is your warning shot. Tom's framing: toaster ovens need an electrical grid. KEY TAKEAWAYS AI tools work on narrow tasks, not whole-firm intelligence. 50 asset purchase agreements? Great. 200 million documents? No. Pulling documents out of your DMS strips away the metadata that makes them valuable - judge, opposing counsel, area of law, industry. That context is what AI actually needs. Business-of-law use cases (lateral prediction, cross-sell, client attrition, FP&A) are wide open. Practice of law got all the attention. A data lakehouse unifies data across 20-40 systems. Snowflake popularized it; Azure/Databricks/Fabric are the modern stacks.  Cost is roughly the same at 200 lawyers or 2,000 - six figures, ongoing. Compute and storage are cheap; talent is the investment. Firms move from "nice to have" to "must have" after a near-miss. Tom's example: a firm almost fired an associate because their FTE calc didn't account for maternity leave. The chief data officer is becoming a real C-suite role. Sidley's among the early movers. Watch the forward-deployed legal engineer trend. Harvey is hiring practitioners for these roles. PEOPLE MENTIONED David Cowen - Host Tom Baldwin - Entegrata founder & CEO Andrew Sieja- Founder of kCura/Relativity; Entegrata's first angel investor Renee Morris, Katrina Dittmer, Glenn LaForce - Data leaders Tom mentioned COMPANIES AND TOOLS MENTIONED Entegrata - Turnkey data lakehouse in Azure Snowflake, Azure, Databricks, Microsoft Fabric - Data platform stacks Harvey, Legora, Spellbook, Ivo - Practice-of-law AI tools Sidley Austin - Early adopter of the chief data officer role

In this episode, Ben and I walk through the exact conversations we're having with employees at companies preparing to go public. We talk about how to reduce taxes, create liquidity without selling everything, protect against downside risk, and avoid the mistakes we see people make after sudden wealth events.Whether you work at SpaceX, Anthropic, OpenAI, Databricks, Plaid, or another fast-growing private company, this is the framework I'd want you to hear before making any big decisions.-------✅ Financial planning for 30-50 year old entrepreneurs: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.allstreetwealth.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠✅ My personal blog & newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.thomaskopelman.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Disclaimer: None of this should be seen as financial advice. It is just for informational purposes.

Are AI agents silently draining your cloud data budget? With the rise of consumption-based pricing and autonomous AI queries, data teams are facing a perfect storm of skyrocketing costs and operational chaos. In this episode, I sit down with Sanjay Agrawal, CEO and Co-founder of Revefi, to discuss the intersection of data engineering, cloud warehouse optimization, and FinOps in the age of AI.We chat about how legacy on-prem habits are bankrupting modern data platforms, why query optimization is more about ROI than just speed, and how AI agents are changing the landscape of data consumption. Sanjay shares his deep expertise from building world-class databases at Microsoft and ThoughtSpot, revealing how to automate cost management and performance tuning for Snowflake, Databricks, and BigQuery.Key Topics:The evolution of cloud data warehouse pricing and why it breaks traditional budgets.How AI agents are causing massive, unpredictable spikes in compute spend.Real-world horror stories of ""lift and shift"" cloud migrations.Why database benchmarks focus on speed but ignore the actual ROI of data.The future of open table formats (Iceberg) and multi-engine routing.

Performance em vendas não se sustenta apenas com metas batidas. No episódio 217 do DoTheMATH, Flávio Maliatti, Senior Sales Director da Databricks, discute como disciplina, cultura, processo e foco no cliente constroem operações comerciais mais consistentes. “A gente não cria processo para burocratizar. Cria para universalizar a forma como as pessoas executam, performam e conseguem replicar. Isso traz previsibilidade.” A conversa passa por contratação, fit cultural, top performers, vendas enterprise, pipeline qualificado, adoção de produto e o risco de depender de resultados heroicos para sustentar crescimento. Episódio relacionado#23 Quais metodologias ajudam seu time a vender mais e melhor? | Dagoberto HajjarSiga o DoTheMATH no Spotify para acompanhar conversas sobre IA, dados, tecnologia, liderança e negócios com quem está fazendo acontecer no mercado. Novos episódios toda quarta-feira. Apresentação  Marcel Ghiraldini, CSO, MATH  Fabiana Amaral, Brand and Culture Executive Director, MATH  Convidado Flávio Maliatti, Senior Sales Director, Databricks  Capítulos 00:00 – Introdução e apresentação do convidado 01:38 – Do futebol à liderança comercial 05:00 – Performance além da meta 08:21 – Alta performance, cultura e limites 18:29 – Vendas como esporte coletivo 30:12 – Crescimento rápido, pipeline e métricas 40:41 – O KPI que mostra a direção certa  Para ouvir e seguir:    

Ben Miller is the co-founder and CEO of Fundrise, an alternative asset management platform that gives individual investors access to private real estate, private credit, and venture capital. In March 2026, he listed the Fundrise Innovation Fund on the NYSE under the ticker VCX, one of the first publicly traded venture capital funds. VCX gives retail investors direct exposure to private companies like Anthropic, OpenAI, Databricks, and SpaceX. The fund manages over $650 million and has over 100,000 individual investors. Ben is a returning Summation guest.In this episode of Summation, Ben and Auren discuss:Why VCX traded up 700% on day one while Bill Ackman's fund traded down the next weekWhy ETFs fall apart for private markets and closed-end funds are the right structureHow AI will reshape real estate by 2031 and which markets get hit hardestThe hidden truth that SoHo, Wynwood, and Miami Beach were all built by the same personYou can find Auren Hoffman on X at @auren and Ben Miller on X at @benmillerise

What's up everyone, today we have the pleasure of sitting down with Elizabeth Dobbs, AVP of Marketing Technology, Data and Growth at Databricks.(00:00) - Intro (01:18) - In This Episode (01:47) - Sponsor: Knak (02:55) - Sponsor: MoEngage (04:16) - Why Velocity Beats Permanence in Marketing Data Architecture (12:00) - Why Databricks Embedded Data Engineers Inside Marketing (15:02) - Inside Databricks' 3 Marketing Ops Agents (18:56) - How Databricks Built an AI Analyst That Marketing Teams Actually Trust (26:13) - How Agent Tagatha Cut Months of Manual Content Tagging to Hours (30:07) - Sponsor: AttributionApp (31:09) - Sponsor: GrowthLoop (34:48) - How Agent Atlas Replaced the Rules-Based Segmentation Wheel (39:28) - Why Marketers Don't Care Whether You Call It an Agent (43:32) - How to Get Data Warehouse Access When Your Team Doesn't Own It (48:36) - What Databricks Is Actually Testing for in Marketing Hires Now (54:04) - What Gives Liz Energy Outside the Office Summary: Elizabeth Dobbs spent 6 years at Databricks doing something most marketing leaders only talk about: building the data infrastructure before deploying the AI on top of it. She's shipped 3 production agents (Marge, Tagatha, and Atlas) and she'll tell you exactly what broke first and why the team kept going anyway. You'll hear how a marketing lakehouse becomes the foundation that makes every agent actually work, why the agent label debate is a distraction, and what Liz is genuinely testing for in marketing interviews now that AI-polished resumes all look the same in Greenhouse. If your AI ambitions are running ahead of your data foundation, this episode is going to reorder your roadmap.About Elizabeth DobbsElizabeth Dobbs is the AVP of Marketing Technology, Data and Growth at Databricks, where she leads the team responsible for the company's full marketing stack, including data engineers and data scientists embedded directly in marketing. Promoted to AVP in February 2025 after more than 5 years building Databricks' marketing data infrastructure from scratch, she architected the company's marketing lakehouse and deployed 3 production AI agents serving the entire marketing org. Before Databricks, she spent nearly 7 years at Khoros in a series of marketing operations and demand generation leadership roles, including Chief of Staff to the CMO.Why Velocity Beats Permanence in Marketing Data ArchitectureIf you work at a company called Databricks, you assume the marketing data is fine. The word "data" is literally in the name. When Elizabeth Dobbs was interviewing 6 years ago and someone in sales ops told her straight up that the data was a complete mess, she thought they were being politely humble. She took the job. She found out they meant it.What she encountered fit the startup playbook exactly. Agencies hired for agency's sake because headcount was thin. Systems that barely talked to each other. Stacks of what she calls "human middleware," people spending their days manually bridging gaps the infrastructure couldn't close. Databricks was probably no worse than any other high-growth startup at that scale. But fixing it meant accepting something most marketing teams resist: building for permanence is a waste of energy.When Liz and her team sat down to fix things, they made a call that runs against how most marketing orgs are wired. They stopped trying to build the perfect foundation. At 1,000 people, you might get away with it. At 10,000, perfection is a distraction. By the time you finish, the company has changed shape again. So they optimized for velocity. Centralized data imperfectly. Built shared definitions that not everyone followed consistently. Accepted the bubblegum-and-duct-tape reality. And they stayed intentional about exactly 1 thing: knowing which decisions you cannot walk back.The one-way door framework is how they sorted the rest. Some decisions hurt to make but compound over time. A marketing lakehouse, all first-party data in 1 governed and catalogued place, is the example she keeps returning to. There is no SaaS tool you would buy, no agent you would deploy, that wouldn't benefit from having that foundation underneath it. That makes it a no-regret decision even when it's brutal to build. The other category, the rip-and-replace bets, is where you move fast and hedge. Agents might automate an entire workflow in 18 months. They might not be ready. You place smaller bets there and iterate. What you don't do is apply the same level of commitment to decisions that actually shouldn't last.6 years later, the core of Databricks' marketing stack looks a lot like it did when Liz started. LeanData. Familiar prospecting tools. The same basic webinar infrastructure. The vendors who survived are the ones who grew alongside the team, who stayed flexible as Databricks scaled well past what their standard playbook assumed. In a market that treats every tool as disposable, the ones that last are the ones that earned it. The companies that build durable AI systems in marketing will be the ones who made the unsexy architectural call first and let everything else follow from it.Key takeaway: Before committing to any AI agent or new platform, split your roadmap into 2 categories: one-way doors and reversible bets. A centralized, governed marketing data layer goes in the one-way door category. Pour resources into it without condition and treat every setback as a speed bump. For everything else, including which agents you deploy and which tools you layer on top, move fast, hedge small, and iterate. Run that filter on your next planning cycle and you'll stop debating tools and start building the foundation that makes all of them actually work.Why Databricks Embedded Data Engineers Inside MarketingMarketing ops leaders who don't have embedded data engineers spend a lot of time explaining to others why they can't move faster. Liz's team has data engineers and data scientists who report into marketing, not into a central IT org. Most people assume she fought for it. The actual story is less dramatic and more instructive.It came from 2 leaders giving the team room before they could prove the full return. Her CMO Rick and CCIO Mike Hamilton were direct about it: we have our own fires, you know enough to be dangerous, you know where the lines are. File Jira tickets if you need something outside your lane, but otherwise go run. That kind of organizational trust is rare. What made it stick was showing the velocity difference on something concrete. Bring in 1 or 2 data engineers with actual marketing domain experience, and the speed gap becomes obvious. Marketing data has its own rules. MDF means different things to different teams. ROAS has regional variations. Pipeline attribution is a political minefield. Someone who has lived in that domain moves 10 times faster than someone learning it in place.That observation turns out to apply directly to the agents Liz's team built later. You spend months onboarding a new hire with marketing domain context. That person leaves before the investment fully pays off and you start over. Agents don't do that. You train them, you give them the context, they hold it. What Databricks figured out with internal resourcing, they've since encoded into how they think about deploying AI. The parallel is direct and Liz draws it explicitly: the reason domain knowledge matters for people is the same reason it matters when you're configuring an agent.The team that resulted from this structure is part of why Marge, Tagatha, and Atlas were even possible. You can't build a marketing lakehouse without engineers who understand what the data is supposed to represent. You can't deploy an agent ...

In this episode, Ben Lorica talks with Richard Garris and Barry Dauber from Databricks, about what enterprises are actually struggling with as they move AI from demo to deployment. Subscribe to the Gradient Flow Newsletter

https://www.techshowfrankfurt.de/big-data-ai-world Datensouveränität war lange ein Randthema. Jetzt ist sie ganz oben auf der Agenda. In dieser Folge des AI or DIE Newscasts spricht Andreas Wiener mit Carsten Bange über die neue BARC-Studie zur Datensouveränität – und darüber, warum Unternehmen plötzlich sehr genau hinschauen, wo ihre Daten liegen, wer Zugriff darauf hat und wie abhängig sie von Cloud-Anbietern sind. Die Zahlen sind eindeutig: Fast jedes Unternehmen bewertet Datensouveränität inzwischen als wichtig oder sehr wichtig. Besonders regulierte Branchen wie Banken, Versicherungen, Energie und Healthcare spüren den Druck massiv. Doch es geht nicht nur Politik. Es geht Regulierung, Cybersecurity, AI in Kernprozessen, hybride Cloud-Strategien und die Rückkehr von On-Prem. Außerdem werfen Andreas und Carsten einen Blick auf den Data- und AI-Markt: sinkende SaaS-Bewertungen, neue Build-vs.-Buy-Fragen durch AI, weniger M&A-Dynamik und spannende Deals rund SAP, Reltio, ServiceNow, Pyramid, Databricks und ClickHouse. Die klare Botschaft: Wer Datensouveränität heute noch als technisches Detail behandelt, hat die Lage nicht verstanden. ⸻ Timestamps 00:00 – Intro: Datensouveränität als Dauerbrenner 01:17 – Warum das Thema politischer geworden ist 02:11 – Neue BARC-Studie und Big Data & AI World 03:08 – Erste Welle: USA, Cloud und neue Abhängigkeiten 04:00 – Zweite Welle: Zölle, Grönland und geopolitische Unsicherheit 05:21 – Studie: Datensouveränität wird deutlich wichtiger 06:20 – Warum fast jedes Unternehmen betroffen ist 08:05 – Wie Hyperscaler auf europäischen Druck reagieren 09:26 – Treiber: Regulierung, Politik und Cybersecurity 10:52 – Interne Treiber: Daten und AI in Kernprozessen 12:10 – Konkrete Maßnahmen: Security, Hybrid Cloud und On-Prem 13:06 – Repatriation: Daten zurück aus der Cloud 14:08 – Cybersecurity und Schutz sensibler AI-Assets 15:52 – Multicloud und regionale Cloud-Anbieter 17:13 – Stackit und die neue Logik der Schwarz Gruppe 20:18 – M&A-Update: Data- und AI-Markt unter Druck 21:14 – SaaS-Bewertungen und AI als Geschäftsmodellrisiko 22:10 – Vibe Coding verändert Build vs. Buy 23:32 – Konsolidierung im Beratungsmarkt 24:55 – Investments: Qdrant, CircleOne, Databricks, ClickHouse 26:38 – Akquisitionen: SAP/Reltio und ServiceNow/Pyramid 29:42 – Weniger Deals und kältere Marktphase 30:24 – Ausblick: Big Data & AI World und Data Festival 31:13 – Outro

Welcome to "To the Point Cybersecurity Podcast." This week, hosts Rachael Lyon and Jonathan Knepher dive deep into the evolving challenges of security operations with special guest Monzy Merza, CEO and co-founder of Crogl. With a career spanning government research, security innovation at Splunk, and go-to-market leadership at Databricks, Monzy Merza brings a unique perspective on why the security operations problem remains unsolved and why industry solutions often fall short for SOC teams. In this episode, you'll hear about the realities faced by security analysts: data sprawled across countless tools, rising alert volumes, and unrealistic expectations for operator expertise. Monzy Merza shares his eye-opening experience stepping away from executive roles to work directly on SOC teams, the complexity of today's threat landscape, and how AI is changing—sometimes complicating—the security equation. He also discusses pitfalls of centralizing data, the importance of auditable and transparent AI, and how Crogl strives to capture institutional knowledge and empower security teams. For links and resources discussed in this episode, please visit our show notes at https://www.forcepoint.com/govpodcast/e379

Ryan MacDonald, country leader for SAS Canada Recorded on site at SAS Innovate 2026 in Grapevine, Texas, today’s In The Channel features Ryan MacDonald, country leader at SAS Canada, in a wide-ranging conversation about what the week’s major announcements mean for Canadian organizations – and where SAS sees its channel and partner opportunity growing. The conversation opens on the energy at SAS Innovate, which marks the company’s fiftieth anniversary, and what the announcement lineup – including the new SAS AI Navigator for AI governance and the expansion of agentic AI capabilities across the Viya platform – means for the Canadian market specifically. MacDonald describes Canadian enterprise AI maturity as strong in intellectual capital but still building toward consistent economic output, with the governance and trust framework a necessary foundation before organizations can scale. He draws a direct line between Canada’s regulatory environment – OSFI E-21 in particular – and the practical operational pressure organizations are feeling as model validation volumes have grown from two a week to multiple per day. On the competitive landscape, MacDonald addresses the challenge from Microsoft Fabric and Databricks with an argument about SAS’s existing footprint in business-critical decisioning layers – often invisible infrastructure organizations don’t always realize they’re sitting on, and an upgrade path through Viya designed to deliver incremental value rather than a rip-and-replace. The conversation also covers the evolution of SAS’s channel strategy, the managed services opportunity in a data sovereignty environment, and the MCP-based openness that is letting external AI agents call SAS analytics directly. Read Full Transcript Robert Dutt: Hello, and welcome to In The Channel from ChannelBuzz.ca, bringing news and information to the Canadian IT channel for the last 16 years. I’m Robert Dutt, editor of ChannelBuzz.ca, and your host for the show. This week, I’m coming to you from Grapevine, Texas, where I’ve been on the ground at SAS Innovate 2026. It’s a significant week for SAS Institute on a couple of fronts. The company is marking its 50th anniversary this year, and the announcement lineup has been one of the more substantive in recent memory, with major moves in AI governance, agentic AI across the Viya platform, and a meaningful shift in how the platform opens up to external AI agents and frameworks. My guest today is Ryan Macdonald, country manager [CHECK: title recorded as “country manager” – should be “managing director” if you want to punch in] for SAS Canada. Ryan’s been with SAS Canada for about a decade, and has just stepped into a role leading the country this year. He has a front row seat to some significant strategic changes – the move to Viya, the expansion of the partner and channel program, and now what I think is a genuinely important moment as AI governance moves from theoretical concern to practical operational requirement, particularly in Canada’s regulated industries. We cover a lot of ground – what this week’s announcements mean for Canadian organizations, where Canadian enterprise stands on AI maturity right now, the OSFI E-21 story, how SAS is thinking about its channel ecosystem and the mid-market opportunity, and a candid conversation about managed services and data sovereignty. Let’s get right into it. My chat with Ryan Macdonald. [MUSIC] Robert Dutt: Ryan, thanks for taking the time, and what I’m sure is a busy week for you. Ryan MacDonald: Yes, of course. Thanks for having me, Robert. Robert Dutt: You guys turned 50 this year, and it feels like one of the bigger product lineup announcements at Innovate in a while. Curious what you felt from the room. What’s the energy, what’s the vibe that you’re getting from this year at Innovate, especially given that 50 years of SAS framing? Ryan MacDonald: I agree with the energy you’re feeling. Certainly a ton of energy around our 50th and just what we’re seeing in terms of AI tooling and where we fit into that ecosystem. So lots of conversations about the data estate, how that’s evolving, and then just really looking for the reality check on where practical value lives in the new AI ecosystem that’s being framed around, especially for enterprise technology stacks. Robert Dutt: Look at the announcement stack this week. You’ve got Navigator for AI governance. You’ve got the agentic AI expansion in Viya, the various industry solutions. Curious – and I’m sure you’ve seen some of these before they were announced to the public and been following their development – what is kind of activating your Spidey senses in terms of, “ooh, that’s going to play well at home right now.” What are we seeing as sort of the big early day opportunities out of those innovations? Ryan MacDonald: Certainly in Canada, the regulatory domain around model risk management and model management and lineage and explainability is front of mind for everybody. I think that’s the major limiting factor in terms of proliferating cost of AI, in terms of actually calculating a per unit cost of running a model or introducing intelligence to something that was maybe traditionally rules-based. And so I think not only is there a regulatory driver, but people are seeing that as a practical constraint. So a lot in the governance and trust domain is certainly a hot topic. Robert Dutt: And that kind of speaks to where I wanted to go next, actually, which is you guys have been in Canada across verticals for a long time, obviously. Curious how you would describe the overall kind of AI maturity of the Canadian market right now. Are we kind of leading, lagging? Or is there something distinctly Canadian to it? Ryan MacDonald: Yeah, great question. This is close to home. We have the benefit of working with thought leaders in AI, folks like Ajay Agrawal. And just knowing the pedigree of intellectual property around this conversation in Canada, we have so much there. Of course, Geoffrey Hinton and Ilya Sutskever and the folks at U of T have just delivered so much to this community. I think that said, enterprise adoption and converting this into economic output is still something that we’re figuring out. So I think our investments generally, relative to peer groups around the world, we’re still a little behind. I think we’re doing some advanced things. There are some exceptions to this, where use cases are at the forefront of what’s being delivered globally. But generally, I think the data estate and this trust dynamic and the need for establishing a scalable framework for trust and governance – it’s a responsible thing to do. But relative to other geographies, it’s setting a foundation before we really run away with some use cases and deliver. Robert Dutt: One thing we’re tracking – I’m sure a lot of people are – is the idea of AI initiatives that get a start and a lot of fanfare and then fizzle out before hitting production or certainly proving their worth. I’ve heard a lot of the framing of the idea of trust and governance as kind of the growth driver, rather than the compliance tax. How is that hitting in Canada? And is that any different than what you’ve seen in terms of reactions and feeling and overall motion in the states or elsewhere? Ryan MacDonald: I think there are certainly differences in the tone of this conversation. For me, the purview is mostly north and south of the border – the US and Canada. But I think in Canada, we have a regulatory domain that is really prioritizing these things. So it’s not optional for a lot of – especially in a regulated market, this isn’t really a luxury you’d have to say, do I comply with this or not? But I think it’s also putting a per unit cost parameter on this for folks that is important. We’re seeing a huge proliferation of AI. Everything – your microwave, your lawnmower, everything has some sort of AI enablement component to it. Is it necessary? Are you getting the appropriate uplift? And these teams that are validating and pushing these models through the organization – what we’re hearing from them – this went from two a week, to a month, to two a day, five a day, ten a day. And so the systems – it’s not just a luxury or a question really of the ethics. Are we doing the right thing? Is this responsible? It’s a framework that’s required for the validation process, even just table stakes, to really scale through the organization. Robert Dutt: To that point, in Canada we’ve got financial services, and particularly we’ve got OSFI E-21 coming up. That’s pretty scary – things attached to it if you’re not hitting the bar. Are you seeing that create urgency? Or are customers still in a wait and see kind of space around that? Ryan MacDonald: I think the regulatory conversations there are interesting. There’s a lot of assessment of what peers are doing. And I think OSFI, to their credit, really listens to the community. Rather than setting a standard blind lead, just based on their intellectual property and what they see as being a requirement, they really listen to the community and measure from where everybody is, taking stock of that. So I don’t believe there’s a lot of fear and panic. I think organizations – as we did a lot of work around E-21 [CHECK: transcript rendered as “E23” – confirm on playback] specifically in this space – they were really well prepared. They had some ideas on how to make this more efficient, really focus on the materiality of where the risk lives and develop a framework that’s consistent with the risk posture in other domains. And I think that’s really – nobody was suggesting, “hey, this isn’t a good idea. This is too much pressure. This is putting a cost burden on us.” That wasn’t really the dialogue. Robert Dutt: Beyond financial services and other regulated industries especially, what are you seeing in terms of how customers are wrestling with AI governance right now? Ryan MacDonald: I think the scale of maturity across industries just varies so greatly. You have some organizations that are really just getting started, and they’re acknowledging that. In some of the roundtables we’ve had the benefit of participating in, some folks are trying to find their first step in AI. What does this even mean? They’re trying to find the right resources that can guide them. They’re still building their technology estate. And then, conversely, you have folks that are, as we spoke about earlier, leading the world – the global community – in terms of things like automated decisioning frameworks and integrating what were previously siloed processes. We see this in risk and fraud domains merging together. So I think we’re seeing both ends of that spectrum in Canada, certainly. Robert Dutt: Analytics has become a crowded space lately – with Databricks, with Snowflake, with Microsoft Fabric getting in there, all in territory that you guys have been in for a long time. How do you make the case to Canadian organizations that have been told, especially by Microsoft, “hey, you can just have analytics as part of what you already have?” What’s the competitive message there? Ryan MacDonald: Yeah, that’s a regular conversation for us, of course. I think what we really offer institutions, especially given the scale of the organizations we support – and we work in almost every major industry, every major enterprise in Canada – we offer a very different risk posture in moving through this process. So they may have what were traditional analytics with SAS. Maybe we had dabbled in what was previously BI, something like that. But for a lot of institutions, we support business-critical payload. There is a core application to their business that’s being delivered with a component of SAS. And oftentimes, as our relationships diversify across the organization, maybe we have a specific technology sponsor that helped build this alongside their business counterpart. Maybe they’ve moved on. And that decisioning layer is sort of obfuscated. So we spend a lot of time identifying – hey, is this what looks like ETL work potentially, in a report or an assessment that’s performed? Is this really a decisioning layer in your organization? And that’s what we’re really finding is there. And what folks are really interested in is taking that framework – what was previously identified as legacy SAS – and seeing what we offer in terms of Viya. It’s scaling far beyond what the competition can offer in terms of decisioning frameworks and automating process and delivering core value. A lot of the AI discussion is focused now on where are you seeing ROI? How long do we have to wait? What is the roadmap to finally get something out of this? And I think that’s really the core difference. Yes, there’s a lot of tools. It’s a crowded space. The competition is fierce and they can do some very exciting things. I think what we offer organizations is really the opportunity to do those same things and more, and to take your current investments, your current intellectual property, through that framework – which delivers value incrementally rather than a build within a complete new paradigm. Robert Dutt: One of the announcements that really caught my eye this week was the addition of the MCP – in that essentially you guys are opening up the analytics engine to external AI agents like Claude to call it directly. It seems like a pretty significant shift in terms of thinking about openness, thinking about consuming SAS from wherever folks want to consume it. What does that motion mean for the Canadian organization and for your Canadian customers? Ryan MacDonald: I think this is an extrapolation of what we spoke about earlier, in the sense of we are providing these deterministic decision frameworks to these organizations today. And so we talk about this almost in the sense of the Apple/Android paradigm. This was a previously closed ecosystem. The SAS code base was proprietary. The compute infrastructure was proprietary. And the open source motion was the first move here – running Python and R and other code frameworks natively within SAS is something that we’ve supported now for years within Viya. And it’s an extrapolation of this – meeting our customers where they are. SAS did not endeavor to compete directly with the frontier labs and build LLM models. But we certainly see the benefit – this is providing the market the productivity increase, the creativity of use cases, and what this adds to decisioning frameworks. I think the shortcoming is still the deterministic component, where something can be built in a hard and trusted capacity, presented to a regulator with the appropriate lineage. That’s really where we see these worlds coming together. So I don’t think it’s a great strategic decision if SAS were to impose, “we have one specific framework, one partner in this space.” We’re seeing, in addition to the frontier labs, a lot of custom work in this space as well – enterprises that are building more small language models around their data sets. So imposing this integration framework, I think, allows us to really meet customers where they are. Robert Dutt: A few years ago there was a flurry of things going on on the channel side for you guys. You brought on TD SYNNEX as a distributor. I believe it was a worldwide, not Canadian-specific figure that you were going for – 30% of contribution through partners. Where’s the channel scene at for you today? How would you characterize where you’re at against those goals and others? Ryan MacDonald: I think we’re still making progress in that domain. The channel business is still growing very aggressively. It’s a big shift to turn, frankly, in terms of getting the allotment of customers we had when we segmented what work was going to the channel, how that was going to be developed. And we compare ourselves to our peers in the industry – they’ve been at this for a lot longer. So just the maturity continues to develop. I think we’re seeing great progress, great feedback from customers in terms of the way that the channel is able to support them. And we see proliferation of niche players here that have come out of the woodwork that are very industry-specific. So I think that’s really the opportunity – where we had a general technology-based approach for certain industry segments, what we’re seeing is these channel partners can really tie together these business outcome-driven discussions in a way that was much more expensive and difficult for SAS to scale to. Robert Dutt: What does the community look like today in terms of scale, profile of partners, what they’re doing, and where do you see that evolving over the near future? Ryan MacDonald: I think we’re seeing this change very quickly with the advent of AI in terms of what use cases are being prioritized. I think in Canada, a lot of organizations have hit a wall in terms of understanding their data foundations – they’re not necessarily ready to scale them towards all the outcomes they’re seeking to deliver. And so channel partners are that domain. What are our peers doing? And this is GSIs and niche consulting firms and everybody in between. So we’re really seeing those conversations take shape of almost a reset of the roadmap, a reprioritization of how they’re building out their target state ecosystem. And that industry expertise is, I believe, the real differentiator. There’s a lot of competition. It’s a crowded space in that sense. So having an outcomes-focused point of view, whether that’s from SAS directly or a channel partner, is really important. Robert Dutt: Is the changing nature of what you guys are focused on in terms of AI governance and all those kinds of things that we’ve been talking about changing the definition of who you’re working with as a partner? Or is that something that’s likely to happen in the near future? Ryan MacDonald: I don’t think it’ll necessarily change. We might add some things to it, but they’re really part of the same conversation. I don’t think you can have a conversation about scaling AI without a discussion about the governance framework. And in a lot of cases, model inventory work, and just being the core platform of delivering models in this decisioning layer, is something that SAS had a lot of experience and an existing footprint within. So I think it’s really germane to the way we’ve been working with these customers today. Robert Dutt: How does the service mix – how they actually bring this all to market as partners – change as kind of what you’re going after changes? Ryan MacDonald: I think there’s a lot more consultative work right now around these outcome-focused and prioritization discussions. So I think it certainly is changing. And if you’re seeing this sort of increased competition in the technology domain and more commoditization of certain tool sets, it just puts more weight on – how do I really navigate? It crowds the pathway and creates more obstacles in terms of delivering outcomes. And so I think just refocusing on outcome-oriented discussion – and a lot of times these are deep partnerships between a niche consulting vendor, or somebody that now is a channel partner to SAS, and these firms in sectors across Canada. So it’s not necessarily changing the way we’re working with them. It’s changing the prioritization of the discussion, putting consulting maybe ahead of technology. Robert Dutt: Before we sat down to record, just as we were getting to know each other, you mentioned that part of your path through SAS Canada was you had managed services, at least for a while – and I believe that to be internally. How has that shaped, and how does this moment shape, how you think about working with partners who are in that managed services kind of motion? Ryan MacDonald: Yeah, that conversation is changing everywhere in the world. The political landscape, of course, is relevant here – in terms of we’re seeing some location dictate where customers are willing to send or host data. We’re seeing geo-repatriation in that sense. We’re seeing movement to the cloud change the dynamics of the cost model, what folks are seeing in terms of stable applications that don’t necessarily need the scalability or proximity to data. We’re seeing them pull some things back on premises and build clouds internally with OpenShift and other technologies. So I think it’s a cycle like most things in technology, where we’ve had the gold rush of moving everything to the cloud. And I think especially enterprise customers are now deciding not only how do they divide that workload amongst hyperscaler partners, but what is appropriate for internal clouds, which are now growing in popularity. And I think in Canada, we’re not seeing a huge disruption in this space, but we’re seeing a lot more of our business grow in terms of managed services. And as we talk about more outcome-driven engagements – less just providing raw access to the technology – the managed service really bridges the gap in terms of the various integration points that need to be managed along the way. And so it’s not just simply providing the infrastructure and application support. We’re seeing the managed service domain, especially around SAS – where this is not a one-size-fits-all approach – really extrapolate into “can we help you really derive your outcome” with expertise in either transformations of data, or we’re providing models now in terms of a service offering, in addition to consulting work of building models custom to each application. So that’s really evolving quickly. Robert Dutt: One of the trends that we follow a lot is this move across the industry to look at partners less as a direct, straight-through channel and more as an ecosystem – a lot more multi-partner engagements, especially given where you guys sit in the complexity and custom nature of a lot of what customers are asking of you. How are you guys thinking about that ecosystem, multi-partner play? Ryan MacDonald: I think the list of partners is generally growing as we talk about extrapolating into channel and SAS’s ambition to have, as you stated, 30% of our revenue flowing through the channel in Canada. I think the customer really dictates the specific mix. And so customers in large enterprise have a preference of GSI and specific domains. And what we’re seeing more is the introduction of niche players alongside GSIs, where typically that was binary previously. They would typically – let’s say they work with Deloitte or EY, for example – that would be their preference to continue in that direction. And now we’re seeing them want to leverage the scale those organizations offer, but really like the thought leadership and expertise delivered by a niche partner, and want to bring us all together. So we’re seeing a lot more partners enter the conversation, which I think is very healthy for the competitive domain and just in terms of getting to specific outcomes very quickly. Robert Dutt: The traditional sweet spot for SAS has been clearly enterprise, and Canada’s a very SMB-heavy nation, obviously. But a lot of the stuff that’s going on right now between the Viya SaaS model and the stuff going up on GitHub and the move towards managed services suggests that there might be even more of a mid-market play than before. I’m curious what you see in terms of what a Canadian reseller can realistically and credibly pursue right now. Ryan MacDonald: That has been the way the economy has been structured in Canada for decades, of course, and something that I think our channel strategy really celebrates and prioritizes. SAS – it’s hard to work both ends of the spectrum. And so our legacy of working with enterprise customers, to explore some of the topics we’ve covered in the regulatory domain and how that takes shape, the reach to SMB customers has been something that we’ve candidly struggled with at times. The channel is really the resolution to that. So we’re seeing, as we talk about more entities in this space, the mix of consulting partners or partners in general proliferating – that’s really where we’re seeing it, down more towards the SMB segments, less on the enterprise side. Robert Dutt: Acknowledging that there’s going to be a wide range of things here, and it may even depend partner to partner, but looking at the channel as an aggregate – what do you need more of from your partners right now in terms of areas of focus, in terms of opportunities to be going at, in terms of skillsets? Ryan MacDonald: I think because we are trying to aggressively pursue this market in Canada and service this customer base – which, again, the channel is just better suited for, all around – to me, it’s the feedback loop. That’s something that we challenge, of course, our frontline in an enterprise setting. You have a consistent flow of communication that’s bidirectional. We’re getting feedback on what’s important to them, what they are doing with the platform at times in our tool sets. And having that flow through an additional intermediary is an additional step in the process in the channel segment. But I think that’s really important – just to make sure we’re collecting feedback not just from channel partners, but direct from customers – their experience with SAS, how our channel partners feel in terms of support and enablement, pricing and mechanics and the rest of it as well. Robert Dutt: Curious what you see success at SAS Canada looking like over the next 12 to 18 months. What are the conversations you want to be having that you aren’t yet? What are the measurements that you’re looking at? Ryan MacDonald: We have been growing the business – in terms of revenue, of course, is always important to us – but influence in the market, I think, is something else. SAS, having such a – as we celebrate 50 years – our legacy is something we’re incredibly proud of. It’s afforded us the opportunity to build these great partnerships in Canada, all across the country, various enterprises. I think at times the double-edged sword there is they may equate us to the way they had built with SAS previously and don’t necessarily take stock of some of the things you’re seeing us bring to market today and announcing here at Innovate. So I think that is really what we look for – not just in terms of revenue growth and are we delivering more outcomes and scaling the progress with these customers. Are we really – are they delivering within the new framework? Are we changing the narrative in terms of what they see from SAS and who we are to them? Robert Dutt: My last and definitely most important question – how many dinners did you have last night? Ryan MacDonald: I had one dinner. Robert Dutt: One? One dinner. Oh, that’s an accomplishment. I appreciate you taking the time, Ryan. Thanks. Ryan MacDonald: Thank you, Robert. Really, really nice to meet you here today. Thank you, I appreciate your time. Robert Dutt: There you have it – Ryan Macdonald from SAS Canada. I’d like to thank Ryan for his time. This was our first in-person recording with the new setup, and I think you can hear the difference. And thank you for listening. A few things I’m taking away from this one. First – the AI governance story in Canada is moving faster than it might look from the outside. Ryan’s framing stuck with me: the volume of models organizations are pushing through validation has gone from two a week to five to ten a day. The governance framework isn’t a compliance tax – it’s the operational infrastructure that makes any of this scalable. And for Canadian financial services firms, OSFI E-21 isn’t on the horizon anymore – it’s here. Second – SAS’s competitive argument is more interesting than the standard “we’ve been around longer” play. The pitch is that there’s already a business-critical decisioning layer in your organization that’s been built on SAS. And the real question is whether you’re going to upgrade and grow from that investment, or build something new from scratch alongside it. For a lot of Canadian enterprises, that’s a conversation worth having. And third – Ryan was candid that the direct sales model doesn’t reach the SMB, and the channel is the answer. What’s interesting is where the growth is coming from – niche, industry-specific partners alongside the big GSIs, with customers already wanting both in the room. If you’re a Canadian reseller or systems integrator with deep vertical expertise, SAS is worth a conversation. We’ll be back tomorrow with more from on the ground here at SAS Innovate 2026, as we chat with the global channel chief at SAS Institute, John Carey [CHECK: transcript rendered as “John Kerry” – confirm on playback before publishing]. If you found this one useful, follow or subscribe to In The Channel from ChannelBuzz.ca. We’re on Apple Podcasts, Spotify, YouTube, and most of the major directories. Ratings and reviews are always appreciated and genuinely help other people in the channel find the show. Until next time, I’m Robert Dutt for ChannelBuzz.ca, and I’ll see you in the channel.

Mike & Tommy weigh in on whether Databricks and Microsoft Fabric are converging into direct competitors, exploring how Databricks' push into BI with Genie and AI capabilities is closing the gap on Power BI's presentation layer. They question whether "end-to-end" platforms are the future or just feature bloat, discuss where semantic models should live in a modern data stack, and help teams decide when to bet on one platform versus embracing the dual-stack reality.Get 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/

In this episode of Talk Money To Me, we unpack one of the most complex market environments in recent years — where geopolitical risk, inflation pressures and blockbuster IPOs are colliding at the same time.Recorded on 13 April 2026, Felicity Thomas and Candice Bourke break down the latest developments in the Iran conflict, including the breakdown in ceasefire negotiations, the U.S. blockade of the Strait of Hormuz, and what this means for oil prices, inflation and global equity markets.At the same time, markets are preparing for what could be one of the largest IPO cycles in history, with potential listings including SpaceX, Anthropic, Databricks and OpenAI. We explore whether these mega listings will drive the next leg of growth or drain liquidity from an already fragile market.To help unpack it all, we're joined by leading global equities investors:Alex Pollak (Loftus Peak)Nick Griffin (Munro Partners)Together, we cover:What's really happening in the Iran conflict and why markets are reactingOil above US$100 and the implications for inflation and interest ratesWhether we are officially in a market correction phaseThe investment case for SpaceX, Anthropic, and DatabricksHow investors should think about AI, valuations and capital intensityWhy ETFs like the BetaShares Nasdaq 100 ETF NDQ may be a smarter way to gain exposureThis is a market being pulled between fear and future and understanding how to position your portfolio has never been more important.

Want to know how top brands like NASCAR and the PGA Tour are turning massive data sets into instant marketing revenue? Join us as Chris Sell, Co-Founder and Co-CEO of Growthloop, reveals how Generative AI is eliminating the need for SQL and empowering marketers to launch highly targeted campaigns in minutes.   In this episode of Born in Silicon Valley, we dive deep into the evolution of customer data platforms and the rise of the Composable CDP. Chris shares his entrepreneurial journey, from building a pizza delivery app in college to bootstrapping Growthloop to profitability within its first three months.   We explore the critical pivot Growthloop made when they hit a wall with traditional data syncing, leading them to pioneer a zero-copy architecture directly on data clouds like Snowflake, Databricks, and Google BigQuery. If you are a founder, marketer, or data engineer, this conversation provides a masterclass in building a B2B SaaS company brick by brick and leveraging AI to accelerate the path from insight to action.   Discover how Growthloop's latest AI audience studio, Marv, allows marketers to use plain English to generate complex SQL queries, completely transforming the go-to-market strategy for enterprise organizations. Chris also drops invaluable advice for startup founders about the reality of the entrepreneurial journey and the importance of focusing on solving new problems every day instead of obsessing over the final destination.   00:00 Introduction and Welcoming Chris Sell 01:13 Chris's Origin Story: From Pizza Delivery to Google 03:35 The Reality of the Entrepreneurial Path 05:46 Bootstrapping to Profitability in the First 3 Months 08:11 The Core Problem Growthloop Solves 11:18 Composable CDPs vs Traditional Customer Data Platforms 13:37 Enterprise Use Cases and B2B Sales Strategy 15:29 The Aha Moment That Proves Product Value 18:12 The Impact of Generative AI on Marketing 20:48 Adapting the Engineering Team to the AI Era 22:34 The Biggest Strategic Challenges for the Company Today 25:33 The Breakthrough Pivot to Zero-Copy Architecture 28:05 Crucial Advice for Founders: Focus on Problem Solving, Not the Destination 31:21 The Future of Data-Empowered Marketing and Wrap-Up   Host: Jake Aaron Villarreal leads the top AI recruitment firm in Silicon Valley, www.matchrelevant.com, uncovering stories of funded startups and going behind the scenes to tell their founders' journeys. If you are growing an AI startup or have a great story to tell, email us at: jake.villarreal@matchrelevant.com

Arcee is a tiny 26-person U.S. startup that built a high-performing, massive, open source LLM. And it's gaining popularity with OpenClaw users. Also, Matei Zaharia has won the top honor from the Association for Computing Machinery. Now he's working on AI for research and says AGI is simply misunderstood. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Send us Fan MailModern data platforms are evolving—and speed, scale, and efficiency are becoming non‑negotiable.In this episode of Exchanges with Hitachi Solutions, host Matt Volke sits down with Evan Sotos, Engineering Manager for the Empower Data Platform, fresh off his return from NVIDIA GTC. Together, they explore how GPU acceleration is moving beyond AI and machine learning—and into the core of data engineering.The conversation dives into what Evan heard from engineers, partners, and vendors at GTC, why NVIDIA is positioning itself as an algorithms company, and how technologies like NVIDIA RAPIDS are being used to dramatically accelerate analytics and data pipelines without rewriting existing code. What You'll Learn·       Why GPU acceleration is becoming a core capability for modern data platforms, not just AI workloads·       What NVIDIA RAPIDS is and how it enables existing CPU‑based workloads to run on GPUs·       How GPU acceleration can significantly reduce processing time and overall compute costs·       Why “zero code changes” is such a critical advantage for real‑world data teams·       Which types of data workloads benefit most from GPU‑accelerated pipelines From AI Buzz to Real‑World Data Engineering ImpactWhile NVIDIA GTC is often associated with AI and large language models, this conversation highlights a broader shift: GPUs are increasingly being applied to traditional data engineering and analytics workloads.Evan shares how NVIDIA RAPIDS acts as a mapping layer that allows existing Spark and Databricks workloads to take advantage of GPU compute. Rather than forcing teams to refactor complex, production‑grade code, GPU acceleration can be enabled through configuration—making it practical for teams to test, validate, and adopt without disruption.  The result? Faster pipelines, improved cost efficiency, and a shorter path from raw data to actionable insight—especially for large, time‑sensitive workloads. What This Means for Data TeamsFor organizations running large‑scale analytics, predictive models, or operational reporting, time truly is money. Evan explains how accelerating data pipelines can directly impact downstream use cases—from predictive maintenance to real‑time decision‑making—by reducing the lag between data ingestion and insight.Most importantly, this episode emphasizes practicality: GPU acceleration isn't about chasing hype. It's about giving data teams another tool they can turn on, test, and adopt when it makes sense—without introducing risk, rework, or operational complexity. global.hitachi-solutions.com

It's long been said that good data is necessary before you can have good AI. But to an increasing degree, AI is also helping businesses manage, analyze, and generate their data too.With AI code generation already well understood, businesses are also leaning on natural language processing and agentic AI to help their experts such as data engineers and data scientists automate their work more effectively.What does all this mean for businesses looking to adopt AI? And how is the UK AI market maturing?In this episode, recorded on the ground at Databricks AI Days London 2026, Rory speaks to Michael Green, UK&I managing director at Databricks and Richard Shaw, AVP Field Engineering at Databricks, to better understand how data and AI are converging.Read more:What is natural language processing?‘A true vote of confidence': Databricks announces $850m UK investment as firm looks to quadruple London office footprint"We want AI to work for Britain": The UK government wants to upskill 10 million Brits in AI by 2030 – and the courses are free to accessThe UK's AI ambitions depend on channel partnersMicrosoft says fear of falling behind is driving an AI arms race among UK businesses – and it's fueling record adoption ratesDatabricks wants to train 100,000 people in AI across the UK and Ireland – here's how to get involved

"Companies designing for agents, not humans, are going to get a lot of lift."ClickHouse started as an internal tool at Yandex. Today it's the database Anthropic, OpenAI, Meta and Tesla all run on.In this episode, CEO Aaron Katz joins Lukas Biewald to talk about how he turned an open source project into a $15B company, why he acquired LangFuse knowing it could cost him customers, and what he's actually building for the agent era.Snowflake, Datadog and Databricks all come up. He doesn't shy away.Connect with us here:Aaron Katz: https://www.linkedin.com/in/aaron-katz-5762094ClickHouse: https://www.linkedin.com/company/clickhouseinc/Lukas Biewald: https://www.linkedin.com/in/lbiewald/Weights and Biases: https://www.linkedin.com/company/wandb/00:00 Trailer00:57 The Origin Story: From Yandex to ClickHouse Inc.04:43 Building ClickHouse Cloud & Raising $300M10:36 Growing Up Around Xerox PARC12:51 Salesforce, Mark Benioff & the Dot-Com Bust15:32 Cloud Skeptics vs. AI Skeptics | History Repeating18:05 Building a Modern Go-To-Market Playbook21:57 The SaaS Crash, Agents & the Future of Infrastructure27:09 The Datadog Love-Hate Story35:21 Hardest Moments: Russia, SVB & Sleepless Nights43:16 Outro

Databricks Roundtable episode: Operationalizing AI Agents: From Experimentation to Production. Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguideBig shout-out to Databricks for the collaboration!// AbstractThis panel discusses the real-world challenges of deploying AI agents at scale. The conversation explores technical and operational barriers that slow production adoption, including reliability, cost, governance, and security.The panelists also examine how LLMOps, AIOps, and AgentOps differ from traditional MLOps, and why new approaches are required for generative and agent-based systems. Finally, experts define success criteria for GenAI frameworks, with a focus on robust evaluation, observability, and continuous monitoring across development and staging environments.// BioSamraj MoorjaniSamraj is a software engineer working on the Agent Quality team. Previously, Samraj worked at Meta on ads/product classification research and AppLovin on MLOps. Samraj graduated with a BS+MS in Computer Science from UIUC, advised by Professor Hari Sundaram, where he worked on controllable natural language generation to produce appealing, interpretable science to combat the spread of misinformation. He also worked with Professor Wen-mei Hwu on accelerating LLM inference through extreme sparsification.Apurva MisraApurva is an AI Consultant at Sentick, focusing on assisting startups with their AI strategy and building solutions. She leverages her extensive experience in machine learning and a Master's degree from the University of Waterloo, where her research bridged driving and machine learning, to offer valuable insights. Apurva's keen interest in the startup world fuels her passion for helping emerging companies incorporate AI effectively. In her free time, she is learning Spanish, and she also enjoys exploring hidden gem eateries, always eager to hear about new favourite spots!Ben EpsteinBen was the machine learning lead for Splice Machine, leading the development of their MLOps platform and Feature Store. He is now the Co-founder and CTO at GrottoAI, focused on supercharging multifamily teams and reducing vacancy loss with AI-powered guidance for leasing and renewals. Ben also works as an adjunct professor at Washington University in St. Louis, teaching concepts in cloud computing and big data analytics.Hosted by Adam Becker// Related LinksWebsite: https://www.databricks.com/https://mlflow.org/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Samraj on LinkedIn: /samrajmoorjani/Connect with Apurva on LinkedIn: /apurva-misra/Connect with Ben on LinkedIn: /ben-epstein/Connect with Adam on LinkedIn: /adamissimo/Timestamps:[00:00] Introduction[02:30] AI Agents in Operations[04:36] AI Strategy Consulting[05:30] Agent Quality Focus[06:17] AI Agent Expectations[11:44] AI Use Cases Evolution[15:25] Agent Expectations Adjustment[17:41] Agent Quality Monitoring[23:22] Trust in GenAI Systems[33:33] Data Prep vs Product Thinking[40:27] Quality Systems Distinction[44:54] Q & A[1:00:57] Wrap up

Pradeep Mannakkara (CIO) and Ben Mayrides (CISO) of Cvent explain how they govern AI agents at scale across their 5,500-person organization, which now has over 6,000 agents in production. In this fireside chat recorded at a Glean event in NYC, they walk through the AWARE framework developed by Glean's Work AI Institute with Databricks and Palo Alto Networks, and describe the practical tradeoffs of moving fast while managing risk. The conversation covers agent identity, observability, cultural adoption, CIO/CISO dynamics, and what enterprise-grade AI governance looks like in practice.You'll discover:✅ Why traditional IAM and observability controls fail in agentic architectures where agents reason, delegate, and act autonomously✅ How Cvent deliberately encouraged 6,000 agent creations to build AI fluency before layering in moderation and metrics✅ The AWARE framework's five pillars: identity, context, guardrails, risk scoring, and ecosystem observability✅ Why "risk is too high" is never the final answer, only "risk is too high for now"✅ How Cvent filters AI demand through ROI gates before projects reach security review✅ Why replacing gut-feel security objections with shared criteria moves the CISO from gatekeeper to business partner✅ The sandbox-first approach that separates experimentation from production deployment✅ Why SOC 2 control criteria for AI agents are likely within 18 to 24 months⏱️ TIMESTAMPS0:00 Introduction and the AWARE framework0:34 Core challenges of agent governance2:43 What agents do for us and to us4:36 Applying the AWARE framework in practice7:09 Choosing platforms with built-in controls9:25 Making governance a cultural shift11:51 Earning trust through deliberate risk decisions13:49 Replacing gut reactions with shared criteria15:20 Managing the CIO/CISO tension18:54 Shared language for hard tradeoffs22:01 Go/no-go decisions are never one and done24:48 Advice for putting AWARE into practice26:38 Scaling to 6,000 agents

Carlyle's Jeff Currie says even if the Iran war stops now, surging oil prices will impact the economy for months. Databricks CEO Ali Ghodsi on his new cybersecurity product and AI disruption within the space. Plus, Anthropic and the Department of Defense head to court. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The SaaS multiples run was long, but it had to come to an end. Or Had it? Navigation: Intro Setting The Scene The Roots — This Didn’t Happen Overnight The Structural Thesis — Why This Isn’t Just A Sell-Off The Private Market Fallout The Bull Case — Is The Market Wrong? Separating The Wheat From The Chaff — Who Survives? Wrap-Up & Key Takeaways Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Introduction Nuno Goncalves PedroWelcome to Episode 75 of Tech DECIPHERED, the SaaS Apocalypse: Why AI Breaks or has Broken or Broke the Software Business Model. In today’s episode, we will talk about what’s been going on in SaaS. SaaS, also known as Software as a Service, as a sector, has just had its worst month since the 2008 financial crisis. Give or take, around 1 trillion in software stock market cap has evaporated this year, and it was triggered in many ways by the rise of a lot of the things we’re seeing, in particular, agentic AI. We’ll talk about it later.One of the key triggers seems to have been the launch of Claude or Claude Cowork. There’s a lot of fears that the model that is taken as SaaS to be the darling of investors, both VCs, private equity funds, and also retail investors, has now evaporated. The sweetheart industry no longer works. Bertrand, what happened to SaaS? What’s happening? Bertrand SchmittSetting The SceneWe are in the middle of what some are calling the SaaSpocalypse. I think that was a coined term early this year. It’s pretty bad. We are recording that March 13th. Definitely January, February of this year, 2026, were really terrible. There is no question about it. Strangely enough, since the start of the war with Iran, there has been a small rebound, so we will see how it goes. But also to give some context, we are still not worse than what happened in 2022. We are still in a better place so far. I would say the difference, there is clearly a focus in terms of SaaS versus tech in general for that down term. Nuno Goncalves PedroWe’ve seen obviously a lot of things happening, right? A lot of announcements. The iShares expanded Tech-Software ETF down 25% year-to-date. Everyone seems to be running into panic, JPMorgan, Goldman Sachs. Basically, Jefferies, I think, as you said, originally termed this the SaaSpocalypse. But definitely, it seems like everyone’s trying to sell stock and saying, “Hey, SaaS is going to die.” We’ve seen a lot of interesting elements to this, we’ll talk about it later, around AI eats software. Software eats the world. AI now eats software. I guess AI eats the world.But the reality is, we’ll discuss it later in the episode, it might be just a lot of stuff that’s reacting to what’s actually happening in the market, that there was a couple of misses in terms of numbers, that the growth of some of the key SaaS players that are driving a lot of the public stock wasn’t that great recently. That adding to some launches like we mentioned, the Claude Cowork launch, et cetera, has led people to say, “Hey, maybe some entire spaces of SaaS don’t make much sense going forward.” Bertrand SchmittActually, I don’t know if you noticed, but I think it was yesterday, it was announced that the CEO of Adobe just resigned. I was shocked how bad they managed the transition to AI. I guess it’s one of the first victims of what has been happening. From my perspective, and I will go deeper, but there is a bit of an overreaction. Claude is amazing as a tool, but the launch of Claude Cowork, a few plugins decimating the market, I think that’s an overreaction in the sense that many of these SaaS companies will be able to actually benefit from AI as well. Or some of the new AI tools really, really depend on the existence of an underlying SaaS layer that’s controlling some processes, some data. So I think we have to be careful about the extremes.At the same time, what is true, the growth rate has been going down for SaaS. If you look in the 2021 to these days, we move maybe from 30-11%, 12% average growth rate. It’s a dramatic difference in growth rate, and you cannot keep the same valuation when your growth rate has been divided by three. I mean, that’s just not possible.I think that there might be some overreaction about what company like Claude can truly achieve. At the same time, the reality is there that while SaaS companies are usually relatively strong companies, the growth rate has diminished, and as a result, so should the valuation.The Roots — This Didn’t Happen OvernightBut maybe we can move deeper about what happened the past 2 years about SaaS. Nuno Goncalves PedroIndeed. Some things going back as much as 2024 when Salesforce had its worst trading day. By then, in 2 decades, and went down by 20% on a rare revenue miss. So some early people, a lot of analysts, see this as an early warning of what was to come. Late last year, a huge shift as the different labs of a bunch of different players started launching agentic solutions, which in some ways started eating into a lot of the functionality, not just of vertical SaaS, but also of horizontal SaaS. As a distinction for some of our listeners who are not familiar with that distinction, vertical SaaS is normally SaaS that’s very specific to a specific industry or sub-industry or specific arena, whereas horizontal SaaS is normally SaaS that doesn’t require much adaptation to work across industries. A good example of that might be HR management systems.But basically, because of some of the early developments in those labs and a lot of the solutions that we started seeing around agentic tools, the market started being less positive on SaaS players and trying to readjust it. Those are the historic moments, 2024, 2025. Then all of a sudden, we see the growth rates of SaaS companies coming down, because obviously this doesn’t only have manifestations in the public equity markets. This has manifestations in clients.People, at this moment in time, we’ll talk about it later, are reconsidering their options. They’re like, “Why should I have a SaaS tool? Should I buy it from another player? Should I have a more holistic solution or an integration with Claude, for example? Should I develop in-house?” We’ll talk at length on what’s in customers’ minds, but customers started changing their views and stop buying some solutions that were out there from the large players that are public equities today. Bertrand SchmittYeah, it’s clear that there has been also just overall industry-wide tendency to try to cut on the SaaS subscriptions. Maybe there was too much interest buying too many software solutions, not rationalizing enough, not being careful about the spend. It makes sense that this has hurt overall SaaS growth rate. At the same time, there has been a transfer from IT spending from SaaS tools to AI, so we create a smaller budget for buying SaaS software.But going back, when you look at the change in revenue multiples, it’s crazy. In 2021, we were close to 20X EV, enterprise value to revenues. Now we are talking about 6-7X entering 2026, and we will see later on it does crunch even more. Right now, we are at 4X revenues. So from 20 to 6 to 4, and that’s the lowest in terms of multiples since 2016. That’s 10 years ago. P/E multiple for what multiples also comprise from close to 40 to close to 20.Talking about Adobe, Adobe trades at 5-year average of 30X, now at 12X. No wonder the CEO resigned. I don’t want to be mean, but I think it’s clear some CEO were very strong leading their companies into a SaaS paradigm, but were not as strong leading their company to a new AI paradigm. I think the markets are going to be brutal. If you are good at showing that you can transition to AI, you’re an important piece of the puzzle for AI, that’s one thing. But if the markets believe your products have not kept up, then it’s truly big trouble.I mean, they are not the only one. Intuit 34% decline in a month. Atlassian, minus 35 in a week. ServiceNow also down a third. They are not the only one, but definitely companies have to show some proof of either the lack of vulnerability in an AI world or their capacity to really move strong to a brand-new AI world. Nuno Goncalves PedroThe Structural Thesis — Why This Isn’t Just A Sell-OffWhat are the structural issues? Why wasn’t this just a sell-off? Why is this structurally a problem? The first thing is really around monetization and business model. SaaS 1.0 or 2.0, however we want to call it, was based on seat-based licensing. Seat-based licensing was the notion that with more employees and more users on the platform, there would be more revenue for the SaaS company. Very simple, very clear, very lucrative.Now, obviously, AI agents don’t occupy seats. An agent can do the work of 10 people, can do the work of 20 people, 30 people, 100 people, whatever it is. Therefore, if I’m a company, and I’m using agents, and not necessarily a human user, I’m not going to buy 10 licenses for the work of 10. I have one license, and it’s used by an agent that basically has access to that tool. That’s the first issue. The first issue is that the seat-based pricing, assuming humans, assuming a certain degree of productivity, et cetera, all of a sudden is under stress. Bertrand SchmittMaybe to highlight some point, not every SaaS company was focused on per-seat pricing. Me, when I led App Annie, we didn’t have a per-seat licensing or pricing at all, so we were focused on value-based pricing. But that’s true that around us, we have seen that quite a lot of your typical SaaS business was run on a per-seat pricing. Anytime there is a market downturn, you pay a dear price for your per-seat pricing. On top of it, these days, as you said, we have AI. In an AI world, the per-seat pricing model breaks down. Nuno Goncalves PedroIndeed. Now people are asking for other kinds of pricing schema, right? Either flat pricing based on certain usage patterns or, for example, outcome-based pricing. So depending on the outcome of what I’m trying to achieve, is it a booking of a sales call, is it something else? Whatever it is, I pay for that. But I do not pay for seats because that doesn’t work anymore.There have been a lot of movements around these licensing agreements and these basic elements. Some have actually now tried to create agentic licensing agreements. It’s like, “Okay, I have licensing agreements now for your agents, not for your end users.” It used to be end user licensing agreements. It’s now agentic licensing agreements. Obviously, there’s a shift.Part of the shift is, I believe people want to be in a measurement scale that is different. They don’t want just to pay for a seat. They want to pay for either specific outcomes that are very clearly measurable or have flat fees across the board on a variety of things. I think we’ll see the emergence of a couple of these business models and these monetization models more significantly. I do think we’re still to see some innovation around some of these monetization models, which will occur over the next probably few years as people are getting used to it. Okay, now it makes more sense for me to pay by this rather than by that.Again, because it’s a disruption, we’re still getting and nailing down what effectively the new monetization models and business models will look like for some of these players, but it still will be served as a service. We’ll come back to that later as well. Agents can do a lot of stuff and whatever, but it’s like agents and AI are software. AI is software, whatever you want to call it. AI is software at its base and its profound meaning and what it does, et cetera. Bertrand SchmittSeat-based pricing, usage-based pricing, yes, it’s too simple. Yes, it has its flaw. But at the same time, when the industry started, it made a lot of sense. That’s easy to manage, easy to control, at least from the SaaS company perspective. But definitely now that the industry is maturing, I can see that rise and the benefit and value of moving to an outcome-based pricing or to a value-based pricing. What I like with that also, it’s more truly win-win for both sides, for the SaaS companies as well as for the customer of the SaaS company. If you are more win-win, more aligned, I think it’s a better situation, more frictionless. I think it would be a big change.Another interesting piece of the puzzle, obviously, of all the changes we’re seeing is that one of the best assumptions in SaaS was you have 80% to 90% gross margin. If you are below 80%, there were serious questions coming your way in terms of what’s wrong with your business model as a SaaS business. Below 80% was blinking yellow light, below 70, blinking red lights. But now, it’s very different because AI-native companies, you’re expecting more a 50-60% gross margin.Obviously, if you’re SaaS companies, you better move fast to more AI-native tools and services. That will impact your margin. When you decrease so much your margins, of course, it will impact your valuation. There is no other way around that. You cannot value the same way a 90% gross margin business and a 50% gross margin business. That’s simply not reasonable. I think that one is part of the change and part of a different way to value companies. It’s very reasonable. Nuno Goncalves PedroThe first two structural issues is, one, obviously the per-seat pricing piece is potentially dying or at least becoming less pervasive in the market, added to these emerging pricing and monetization models that we just discussed, value-based, outcome-based, some usage-based pricing, some hybrid models that are also out there with some base subscriptions and then other kinds of things and tiers on top of it, either usage or outcome-based.The third big structural shift that we are seeing is, and I already alluded to it earlier, this notion of build-versus-buy. In the past, I think the market went fully into buy. In some ways, even beyond the, “I will buy one” solution that solves all the problems, we went into best in class. We went to unbundled buying: I’ll buy the best solutions for what I need in my corporation and enterprise needs.Now we’re getting a shift back into building: I’ll build my own stuff. I think a lot of it is relating to two things. One, there’s coding agents out there like Claude Code, Codex from OpenAI, and a bunch of other coding agents that have emerged. There’s a lot of solutions out there, like we mentioned already, Claude Cowork, that really managed to have agentic solutions into workflows that are deeply embedded into some of the enterprises.At the end of the day, I think there’s a lot more of this notion of, I have all my data in-house. I want to really leverage all the data I have. I don’t want to just use a third-party solution that has generic data. I want to use my data set, I want to use my stuff, and I want to basically fit that into ongoing improvements in terms of workflow.The other piece, I think, what’s happening with IT departments in some large corporations that’s leading to this build mindset rather than this buy mindset is also the notion of maybe we have too many people. How do we really express our productivity if we don’t have solutions that are at the core of our processes? If we have solutions at the core of the processes that we develop ourselves or that we develop in partnership with integrators, et cetera, but using some of these new AI platforms, we also have more visibility on the people that we can let go.Now, I know this is quite negative, but I think this has also been leading to all the layoffs that we’ve been seeing across industries recently, where people are like, “Well, I can just extract productivity.” We’ve seen some of those very visible ones. We were talking about Amazon and what’s happening at Amazon with the layoffs recently. A significant amount of layoffs recently announced.Then some other issues on the other side where apparently the junior engineers that were still working on stuff using Claude and other tools that they were using internally started breaking platforms and breaking systems. Anyway, definitely there’s a lot of that going into this build mindset. I want to have control. I want to make sure I understand where the productivity enhancements are, and that will give me more visibility on the people that I need to keep and the people that I need to let go. Bertrand SchmittI’m not so convinced about this part of the puzzle. I think that for many, AI is a convenient demand, but I’m more thinking that some companies, Amazon included, Microsoft, truly, truly over-hired in 2020, 2021. Yes, they scaled back a bit, 2022, 2023. But I don’t think they ever scaled back to what was reasonable given their needs. So it’s quite convenient to say, “No, it’s not management mistake of efficiency, it’s something new AI, and we have to adjust to that.”What I believe is true, however, is that you cannot fund both at the same time in the sense of you cannot finance an over-bloated workforce, and two, significant extremely large AI investment. At some point, these companies were faced with a choice, and they took a reasonable decision on this to be more efficient with their workforce.But personally, I think that actually the ability to do so much more with AI will make more companies think more about their teams and building things because when suddenly your engineers can be way more efficient, can build way more, the value increases. So you could argue that there is an opportunity for companies to deliver more, and as a result, I can see if you’re a good engineer, then there will be opportunities to build more value, potentially across more companies.So we might see a shift where you have more growth in software-related jobs outside the core top 10 bigger software companies, but growing more widely across your typical S&P 500 and even SMBs who could never afford to really deliver value with typical software engineering. But now suddenly, software engineering equipped with AI can be more dramatic in terms of value for them. Nuno Goncalves PedroI agree this is a scapegoat. I agreed that there’s a lot of posturing as well. If someone can lay off a significant percentage of their… It’s almost like the percentage of people you can lay off becomes your new pattern as a CEO, your new, “Basically, I’m saying right now to the market, I can cut…” I mean, Block, I think, cut off 40% of their workforce.At this point in time, seems a bit dehumanized. I think the tech companies are the worst cases, in particular because AI also does disrupt them a lot in their own processes internally. But it feels to me right now, it’s a little bit this one-upmanship of, “Okay, I can lay off more people than you can, kind of thing.” It’s precisely all the fears that a lot of people have around AI. It’s like you’re dehumanizing work. It’s like at the end of the day, people are still needed to work, et cetera. Bertrand SchmittBut I think Block might be one of these companies that completely over-hired over the past few years and never took the pill to reoptimize the business. Nuno Goncalves PedroI think we mentioned it at a previous episode that there was an estimate at some point in time that… For example, even Google had more than double the number of engineers they needed at any given point in time. So obviously, they did hoard engineering resources in other capacities. But at this point in time, it feels a little bit like up to you since being a software engineer right now is a kiss of death kind of thing. Which is weird because at the same time, we are seeing tremendous reallocation of capital overall in the industry towards infrastructure and platforms, where hyperscalers are at 660-690 billion in infrastructure CapEx for this year alone, and 75% of that being AI, where we are seeing a lot of movements around how do I budget accordingly if I’m a corporation.To your point, I think you made that point earlier, Bertrand, how if I’m the CIO of a company, do I allocate my resources more clearly, in particular, if I’m taking into account that I need to spend more money on AI and AI tooling and AI platforms. Obviously, at the end of the day, the CFOs are still there, and the CFOs are basically saying, “Hey, guys, we went into an unbundled world. We had all these agreements with all these people. I want more concentration.” At the same time, the CEO is telling me we need AI, “So whatever it is, you guys tell me what it is, but we can’t increase our budget for this stuff. We need to decrease it, and there needs to be AI in it.” Obviously, there’s a lot of reallocation also at a micro level within the corporate world. Bertrand SchmittYes, you cannot say it will be more built versus buy. At the same time, we are going to need less engineers to do the build. You see what I mean? Even with AI helping you, building which still cost you more, require more software engineering than just a buy decision. For me, what’s interesting is that not so many of these stories can be true at the same time. You require a next workforce, but at the same time, you’re going to rebuild your whole software stack from zero just because of the AI God that you just brought in from cloud. This is not reasonable, simply not reasonable. Nuno Goncalves PedroI think the thesis is that your top engineer is I think, in particular, the more senior engineers, can now do the job of 10. Therefore, what I am switching in terms of cost, I’m not saying I’m agreeing with the thesis, but the thesis is that. What I’m reallocating in terms of budget is, I’m reallocating towards spend at infrastructure platform level, on tokens, et cetera. That’s basically, I think, the thesis of what we’re seeing happening right now. Bertrand SchmittYes, but if you were just, quote, unquote, buying software, you’re not building software. You didn’t need software engineering to just buy software. Your software engineer that becomes as valuable as 10, yeah, but you had zero if you were just buying software. You see what I mean? Nuno Goncalves PedroNo, IT departments have always had engineers, the larger corporations. Yeah, for sure. Bertrand SchmittIt’s a very different game if you are moving from buying to building. It’s my point, I guess. Nuno Goncalves PedroIt is. Just to be clear, Bertrand, this whole build-versus-buy, the build is going to be done with a lot of use of outsourcing and a lot of use of service providers and a lot of use of integrators, et cetera. This whole bullshit of build-versus-buy, in effect, it’s a misnomer because at the same time, you’re going to have to hire, to your point, you’re going to have to hire companies, et cetera, to help you do this. It’s not magically that you can do it off the existing IT departments that you have. Bertrand SchmittExactly. The question will also be, is your first priority of business to rebuild Salesforce from scratch so that it better fits your internal need as a corporation because you have rebuilt from scratch with AI? I don’t think so. That for me is total overhyped bullshit. Klarna was big on that, this is total BS, quite frankly. Not only it didn’t work, but it makes zero business sense. Zero business sense. You’re not going to rebuild a CRM just for the fun of it while your software engineering could be focused on your core value proposition as a business. If you’re a company just starting, you have processes from scratch, you still don’t have solution, yeah, maybe you could consider that.But even then, is it really your priority versus building your core value proposition? For me, that’s a big question. But what I would expect, however, is that this overall trend mindset and stuff is going to keep the pressure on two software companies in terms of reducing tiers of cost, in terms of delivering more value, in terms of being more aligned to the business, and in terms of overall growth rates that are simply not the same as they used to be. Nuno Goncalves PedroBefore maybe we move to another topic, I think it’s clear, we’ll come back to that later, that there are a lot of overblown elements in this. You can never disregard a couple of very, very core elements. A lot of these software companies have very deep tooling into significant enterprise customers. You can’t just rebuild it from scratch yourself to your point. Not only does it make sense, but you can’t. It would take you years to do it. Good luck to you.Secondly, they have also distribution. They are pervasive in the market. They have sales forces. They have people that are selling out there. They have go-to-market teams. Again, we’ll talk about that in maybe one of our penultimate sections today. But maybe to move forward, we talked a lot about the public equity markets and how there’s been a reckoning by institutional and retail investors, et cetera.The Private Market FalloutBut also there’s been a private market fallout. The first one is very obvious to understand. Private equity firms loaded themselves with SaaS. Some even went after roll-up strategies in SaaS, like bringing a bunch of companies together and trying to attack a market and really getting a significant part of that. Software accounts for roughly 25% of the private credit market, which is incredible. Just that’s private credit alone, significant again. They’re loaded with a bunch of companies that have nowhere to go. They can’t IPO, nobody else is interested in buying them unless it’s for a huge write-off or write-down. That’s the first problem right now that we’re seeing in this fallout, which is the private equity market itself. Not only the buyout market, but also we saw a lot of growth funds loading themselves with private equity stock, with a rather SaaS stock, private SaaS stock.Right now, there’s nowhere for that to go. They’re stuck between rock and a hard place with a lot of solutions that are not growing at the rates they were growing before, with a public market that’s not really interesting right now to IPO in, because as we were mentioning earlier, the multiples have gone downhill dramatically, so it’s not interesting. Basically, it’s a chicken-and-egg issue. I would love to sell this now, but I can’t because I have awful market. I can’t IPO it either, so what do I do with all these assets? That’s the first issue here. Bertrand SchmittIt’s clear that you have to be pretty delusional to think that what’s happening in the software public markets is not impacting the private markets. We don’t know why it will be in six months. In six months, it could keep getting worse in the public markets. Six months, at some point, maybe there is a recognition it went too far in terms of adjustment. It’s always tough. But at the same time, you have to be prudent. For sure, what it means is that if I’m a private equity investor in a SaaS business, you have to be a very, very, very special SaaS company to get more financing these days at good terms.Sometimes it’s a very simple math. If you fundraise at 20X, even 10X, how do you go to get to another round of financing if now your multiples are at 4X? That simply makes absolutely no sense whatsoever. Or you need to have grown into your valuation enough that it’s not crazy anymore. If you raise at 20X, and now you’re in 4X multiple, then you need to have grown 5X in your revenues so that you simply stay at the same valuation, or maybe you have to accept a different valuation. But again, quite frankly, the tough part would be convincing investors that it make any sense to put money in a SaaS business. Nuno Goncalves PedroJust to rub it in, just to make it even worse, the secondary market, which was a great market for exits or partial liquidations, et cetera, is demanding now huge discounts. There’s no way I’m going to buy into a stock if it’s not growing at the same pace. I’m like, “I’m sorry.” I will buy your stock at a significant discount. In some cases, it might be what would be a lesser price per share than your last round or your last two rounds. Not just, I want a discount on what you think you’re worth, but it’s like, I want a discount on your last round.Because there’s liquidity issues also in some parts of the market, we were talking just about the private equity firms, some of these deals will go through. If all of this wasn’t quite enough, we have what’s happening in venture capital, which is very close to my heart, of course, because that’s where I play. If you come to me, it’s like I’m a SaaS player immediately off the game. I’m like, “Really? You’re a SaaS, tell me more.” I was just talking to a player recently, SaaS play, there was nothing around AI in their pitch.It’s not just because you have AI in your pitch that I’m going to give you money, clear, but if you’re doing a SaaS play and there’s no AI in your pitch, I’m like, “Am I missing something?” If it looks very classic, I’m like, “Oh.” There’s been a huge, huge reduction in confidence in the VC space in investing in SaaS. There’s a tremendous hyper focus on AI, and in AI investing, AI apps, platforms, infrastructure by most VC firms at this moment in time. And so at this point in time, if you’re a non-AI SaaS player trying to raise money, where’s your AI play? I think that’s the question you’re going to get. It’s going to be very difficult to raise, very difficult to raise. Bertrand SchmittI agree with you. Myself, I saw that SaaS startups with absolutely no AI in their deck, and I was so shocked. I was like, “Guys, where are you living? Are you living in a parallel universe? Are you living under a rock? What’s going on?” Then they are like, “Yeah, but we’re preparing something like that, I come back and prepare.”But even then, as you say, it’s not just leaving AI in your deck. It’s what are your proof points? What have you delivered? How do you make sure that it’s truly differentiator? And how does it make sense versus a pure AI native companies? How are you going to find the new cloud tools that are going to get out in a few weeks and more or ChatGPT or whatever? You have to have a very different proof point. There is nothing new in the past. It’s how are you going to survive against Google? How are you going to survive against Salesforce? How are you going to survive against Microsoft? So nothing is new.Software universe is changing. There’s always that big guys that can destroy you in a matter of weeks. So the question is more, how are you going to be smart enough not to be killed too easily and to find your way in a space that’s probably moving faster than ever? That is probably the difference is that it’s weeks after weeks, you have big change. I’m pretty sure it didn’t happen in that space before because I’ve seen there, I’ve seen that, and it’s moving faster than ever. But it’s nothing new that there is this big company potentially destroying your business. You have to be smart.I feel in some ways, maybe it’s the 2020s, but people stopped being smart, quite frankly. They just raised easy at very large valuation and think that you just do something sometimes pretty basic in terms of software development and that’s good enough. Your GTM is traditional, and you think you made it, and you deserve some investment. I think you must have seen some of this. I have seen a lot of this. In some ways, it’s good. The market is becoming more discerning. Nuno Goncalves PedroThe Bull Case — Is The Market Wrong?But is the market wrong? Maybe shifting to that, at least my perspective is it’s wrong. It’s not fully wrong, but it’s wrong. There’s a right sizing of multiples, but maybe 4X is not the right multiple either. This whole 20X on actuals and 40X on forward stuff didn’t make any sense. There is an argumentation to say that the market is oversold. All the banks have come forward. Goldman Sachs, JPMorgan, Jeffries, Morgan Stanley. Everyone’s come forward and said there’s been definitely, Bank of America, whatever, there’s been an overselling of stock, a dramatic overselling of stock. There’s been a panic that wasn’t warranted. The price has gone down too dramatically for some of these key players.I think part of it, in some ways, is what we were alluding to earlier, the fact that some of these players have built really important stacks that are fitting their customers in a significant on core processes. You can’t just rip it off and put something new. Magically, it will work. It will be around building things around it rather than building things that replace it. Will there be over the long term potential disruption of some of these players around CRM and other solutions? For sure, we’ll see it.But definitely, some of the existing players, public companies that are large, are here to stay, and they themselves will buy into these markets. They’ll acquire positions into other service providers into toolmakers, into other platforms that allow them to be fully AI-enabled and to make their platforms more AI-enabled. I do think there was a huge amount of overselling. The second thing we already alluded to as well as go-to-market. If I’m selling something to someone, there’s a salesperson involved or there are a couple of salespeople involved, they’re not going anywhere. So in some ways, that relationship building with CIOs, with their teams, with procurement teams, all of that is still there.And a lot of the large SaaS players have been doing this for decades. So they have the surface of attack and go-to-market that will take a long time to build for even some of these startups that are disrupting, so to speak, the market. My view is there has been too much panic and the modes of the large players that are already public, in some cases, haven’t been considered at all. Bertrand SchmittThere’s definitely some truth in that. Another piece of the puzzle is that if SaaS is not growing as fast as it used to be, it’s still growing. Many companies are still very good cash generation machines. Many of these companies are moving to AI full speed, improving their tools, changing how you can search their data, how you can leverage their data. They are very close to the data, so they know best how to deliver value on this data. They can integrate existing AI tools. There are a lot of ways for them to capture part of the value that native AI companies are claiming they will get. I think it’s definitely going to, and we’ll talk more later on. I think there will be a question around how do you differentiate the best SaaS companies from the worst SaaS companies in that context.But maybe I just felt we moved a bit quickly on one big event that’s shaping the software industry, it’s the current crash in private credit. Do you have some thoughts about that? Because what’s happening there is pretty crazy, to be frank. Nuno Goncalves PedroYeah, we’ve seen a lot of these players like KKR and Apollo getting slaughtered. Basically, Blue Owl, TPG, Ares, KKR all fell double this in one day on private credit exposure fears. Overall, Apollo has fell 7% as the date of as we were recording BlackRock, 5%. These guys were walking on water and all of a sudden, there was like, “What happened?” And what happened was private credit exposure. A lot of the concerns in the market is private credit is super sexy, and for those who don’t understand what it means is I’m giving credit to a private company in exchange for something, either warrants in the company or revenue sharing in the future, or I’ll get your revenues in advance from you, or I’ll take, whatever it is. There’s over exposure.There’s this potential logic that all these guys are scaling, all the companies that they give private credit to are scaling. And now there are concerns that there might be some dramatic credit in the market, that some of these companies are actually going to die, they’re going to implode, or they’re not going to really fulfill their covenants in their private credit agreements. Bertrand SchmittIt was hidden in plain sight, but that some of these private credit funds at 25, 35% exposure to software, IT, and SaaS, so a huge chunk in an industry where you bet on the long term revenues and cash flow to pay back your loans, while at the same time there is a discovery that this business may be at risk in the next three, five years or even one year because of AI.I think that was the first big chink in the armor that suddenly the creditworthiness of these companies might not have been evaluated properly. But two, it looks like there is also fraud that has been happening. I was reading stories how three, four people, accounting companies, were valuing and estimating loans for hundreds of SaaS business. Good luck, this is crazy. It looks like there is another layer to that story. Nuno Goncalves PedroWhen there are industries building a lot of wealth or apparent wealth that’s coming a little bit from out of nowhere, the likelihood that there’s fraud and things that were not properly done is, it sadly increases dramatically or exponentially. I think we’re seeing just maybe the first effects of that. Bertrand SchmittI was reading, for instance, that one of these big funds was no haircut across the portfolio, ever seen value that was 100%, whatever. One quarter after that, one of their clients going out of business and they lost everything. In three months, you move from no haircut to 100% haircut, decent enough part of your portfolio. This is crazy for a credit business. Nuno Goncalves PedroIt’s ostrich syndrome. You just put your head under the ground, and you’re like, “Hey, whatever.” I don’t know. Bertrand SchmittYeah, it’s zero mark-to-market in an industry that should be relatively conservative. This is private credit. This is not VC, this is not startup, this is not equity, this is credit, so pretty scary. Another piece was like, some of them were supposedly senior on the debt, but they were not so senior after all, this is insane. You claim seniority, but you don’t have it.My point, I think what’s happening in private credit is maybe it all started with that what’s going on, a lot of software exposure. It’s risky because of AI, but the more investor dig into it, that’s when they started to realize that maybe there is more than just that software issue. I guess, all of this is going to be an issue for software business because if suddenly you cannot get loans anymore or the loans you add, you have to pay them back or when it’s time to pay them off, you cannot renew the loan. There is nobody else to turn yourself to get another loan to replace it. That’s not going to be fun and that’s going to impact your growth rates. That could potentially also even be worse than that, be dramatic for your own business survival. Nuno Goncalves PedroMaybe now switching back to the positive part for the bull case. We think the market’s wrong, not fully, but wrong. The other side is still things move on. We’ve also had the same issues in credits in several industries in the past when markets imploded and credit came back. In some cases, it took a while. In other cases, it came back relatively quickly. One great analogy on making a bull case on why all of this stock that was sold was oversold, there’s too much stock being sold on SaaS and at prices that don’t make any sense is an analogy, precisely, for example, with retail. Amazon was going to destroy everyone their mother in 2010, and it did not. It was going to destroy Walmart. Walmart passed the $1 trillion market cap. Bertrand SchmittNot too bad. Nuno Goncalves PedroSo what happened? They adapted. They had huge advantages. They had huge advantages in terms of their customer base, presence, relationship with their suppliers, with the offerings they had, et cetera. They had huge advantages of economies of scale, and they leverage those advantages. And those advantages ultimately materialized in tremendous increase in revenue, tremendous increase in market capital as well.Amazon has done really well as well. It’s not like Amazon didn’t do well. Again, I think this notion, people sometimes have this difficulty in separating the notion of disruption from the notion of replacement. Disruption doesn’t mean necessarily full replacement. You can disrupt industries, disrupt players in that industry, and still those players will exist 10, 20 years later, and they’ll be much bigger because they adapted. The ones that don’t adapt may be killed.But the disruption doesn’t necessarily mean replacement or killing. It means just that effectively the rules of the game, the business model, which we already talked about, monetization models, the way that capital flows in that industry, et cetera, all of that shifts. It doesn’t mean that necessarily the existing players are not going to exist tomorrow. In some cases, they will exist and they’ll be even stronger tomorrow. Bertrand SchmittI think what’s happening is truly a disruption of the SaaS business model, of the SaaS valuations, of the SaaS analysis, because now you need a new prism to analyze it. What are the markets doing in the meantime? They are just dumping it, waiting for, “Okay, how do we look at it in a different way? Who are going to be the winners and the losers?” For now, we don’t care, they’re all losers. But I think that the next piece of the puzzle for us in this episode, but for the market is, how are we going to separate the wheat from the chaff? Who is going to survive? Who is going to more than just survive? Who is going to thrive in that new industry. Nuno Goncalves PedroThere I feel the ones that survive, there’s a couple of obvious ones we can go into. Two that immediately come to my mind are data infrastructure, the Snowflakes, Databricks of the world, because this is the underpinning of everything that’s happening around AI. I don’t see the data infrastructure fundamentally shifting right now. It might in the future, but right now I don’t see it fundamentally shift. Those guys have, if anything, tailwinds rather than headwinds.Then the other one that’s very obvious to me is cybersecurity, where I think AI is very additive to it rather than just necessarily replacing everything that exists. In some ways, that already been used for a while, certainly by the top players. Definitely, those are two immediate categories and areas that come to mind that have maybe more headwinds and tailwinds where really AI is adding rather than subtracting to it. Bertrand SchmittNo, I totally agree with you concerning data infrastructure, cybersecurity. You could argue if you take cybersecurity, that with the rise of AI attacks, with AI making it easier than ever to generate attacks, you better build up your security. Nuno Goncalves PedroWith AI? No, but you have to have AI on your side defending as well. The only way to defend AI is AI. Bertrand SchmittThat’s my point. Your cybersecurity vendors will become AI-enabled, will leverage AI at scale in order to defend you, else they won’t be able to defend you, just quite frankly. Nuno Goncalves PedroCorrect. Bertrand SchmittThat’s part of the game. Data infrastructure, no questions. Again, I don’t think you want to redo your infrastructure with brand-new tools, brand-new stuff is the current tools are working great and doing the job. Maybe another piece of the puzzle is that vertical SaaS, domain-specific tools, healthcare, manufacturing, if you have proprietary data, regulatory modes, it will be much harder for AI to disrupt quickly. If you are not disrupted quickly, you have more time to readjust your business model, to adjust your business model, to leverage AI to improve your business model.Again, of course, some companies, we have seen with Adobe, for instance, have not proven great skills at adjusting to AI. Not everyone is going to get out as a winner. I think some categories have better chance to actually not just survive, but potentially thrive. Another piece are systems of record. If you are holding proprietary non-scrapable data that AI needs to function, that you have deep switching costs protecting you, you are not going to disappear right away. I think you will probably survive. If you are smart enough, you might be able to even adjust and leverage AI.But I can see some might just stick to their revenues and hold companies hostage and might not innovate a lot. I guess we’ll do well on the short run, but on the medium to long I would definitely more worried. Nuno Goncalves PedroOne point I would like to make is at the end of the day, there’s more than that. The algorithmic methodologies you should use for specific industries, for specific verticals, for specific use cases could vary. We’re still very early in a lot of the application of some of these AI methodologies. We’re not early in the development of the research around them. They’ve been around for decades, but the application of them is still relatively early. I think that’s one of the advantages why vertical SaaS companies and vertical SaaS solutions right now might have an advantage, because the domain in which you’re operating, even algorithmically, is actually different, and you need to really right purpose it for those environments and for those domains.For me, that’s an important point to make. It’s not just any vertical SaaS. I think vertical SaaS, where there’s algorithmic distinctiveness, definitely has a shot at it. Other might not. We just saw a lot of discussions around legal tech and how legal tech got slaughtered with the launch of Claude Cowork, for example. Definitely, it will depend a little bit on the verticals. Bertrand SchmittTake the legal side. There has been some interesting decision recently where basically, if you use AI for legal advice, then this data, this discussion is not privileged. You are at big risk of discovery. There is a lot of issues that if you are working with real lawyers, will not be there. Your data is not discoverable, your discussion stay private, so it cannot be used against you. I think companies have to be very careful and very worried about how some of these tools are being used because it’s creating new risk. Some of these tools are not going to get privileged in the coming few months, I don’t think so.You could argue most of these companies in the first place claim a right to access your data and leverage it. I think that even in legal, it would be interesting to see how it evolved. AI will be able to claim some privilege at some point? Maybe, I don’t know. But on the short run, I can imagine how the legal profession, for instance, will not let it happen too quickly, and how you have to be very careful. It’s great to move fast, but you have to be careful with what is it that you are getting into. Nuno Goncalves PedroLet me guess, the last company you’re going to say or the last type of companies that you’re going to say are like the survive, thrive are AI-first or AI-native companies. Is that correct? Bertrand SchmittYeah, I guess. Yes. They are going to be less disrupted by AI, given that they’re already AI native. Nuno Goncalves PedroThey are AI. Bertrand SchmittWe are going into another territory. Even if you are AI-native, are you going to still get killed by Claude because you don’t have enough technology or ChatGPT because you don’t have enough technology? You are just that basic rapper around another AI tools. Here my perspective and what I share more and more with some entrepreneurs is you have to be careful if you are just an AI native company, but ultimately you are a very AI light in the sense that, yes, you are a native, but you are just reusing other LLMs and stuff, and you have not built any proprietary tech or moat with your data or in your industry. That’s going to be trouble. That’s going to be trouble.I’m not sure the market discriminated well enough at this stage, but I think there will be quickly some premium around, have you built a real technology mode? Are you really in such a situation that you are not going to get killed by a Claude or ChatGPT in a few weeks? I think there will be some discrimination that’s going to happen. Ai native won’t be enough to save you, basically. Nuno Goncalves PedroI think there’s one thing. One is what you’re saying. Is there fundamental technology differentiation and/or product differentiation that will sustain itself as a moat? The second thing is, even if it’s an AI app at a higher level, the reality is the guys that are in the market today, the OpenAIs, the Googles, the Anthropics, etc., they’re not going to address all use cases. There are places where some use cases will still exist. We saw that in the mobile app economy.In some of these use cases, you’d be like, why hasn’t, for example, Apple addressed the need for this kind of solution, whatever, and maybe it took them a decade to do it. Then, when they did it, they almost killed the market. But you have some of these AI apps that I think will still be in the market that will emerge and will address use cases that for some time, for some reason, OpenAI, Anthropic, etc., won’t go after. To Bertrand’s point, and I think importantly, if you’re an entrepreneur, if you’re writing on a very specific use case, and there’s seemingly a high likelihood that any of these players are going to address at some point, you’re not in a sustainable place. You’re not going to be around very long. Bertrand SchmittOr you have to take that initial leadership position and transform it into a deeper technology mode, a business mode. You have to leverage that first mover advantage, maybe, to something deeper than that, something more defensible. Maybe you pivot also in term of industry. You started in industry A, but you realize industry B is really the good one. You have to really optimize your way and not take anything for granted. Nuno Goncalves PedroBertrand, do you remember when it’s like every release of iOS and whatever, we were like, what industry is Apple going to kill now? What are they integrating? There was a period of time where it was literally like every big release, every major release, the yearly one, you’d be like, what industry are they going to kill now? Bertrand SchmittTotally. Totally. I think the same is happening. Definitely, we say AI, but I think some players have been smart enough to zigzag around that onslaught from Apple, from Google. But some will stay put. We think it’s not going to happen to them. Yes, they got into trouble pretty quickly. I think also what we have seen is that a lot of value could be from players who are simply more neutral and independent vis-à-vis a platform. If you need someone in the middle, your three or four mobile platform, or now your three or four LLMs or AI platforms, there might be value you can extract because companies are not… That’s another piece of the puzzle.You don’t want to just depend on Claude. You don’t know in three months, ChatGPT has a better model. You will want to make sure that whatever you are running can adjust to a change of LLM providers, for instance, or tool providers. I think, for instance, one position could be that mutual player, the one gives you the ability to adjust quickly to different technical AI development. We will see. But I think there are different strategies you can go through to make sure you end up not being killed, and that will require smart entrepreneurs. Nuno Goncalves PedroSeparating The Wheat From The Chaff — Who Survives?We talked about who survives, who doesn’t survive. Let me start with one. Or where I think will be categories that will be incredibly under attack, so a lot of players, I think, will disappear or will become very, very small. One obvious for me is anything that relates to the small, medium business markets, so very SMB-focused SaaS, a lot of regional SaaS stuff that has emerged, copycatting in certain markets because the larger players didn’t want to expand in some of those markets.I think a lot of that stuff gets just replaced because a lot of the SMB markets are price sensitive. A lot of these markets are also best effort-driven. It’s like it doesn’t need to be perfect, it just needs to do the basic stuff. Therefore, I see that market as a market that’s going to get, in all honesty, over the next 3-5 years, slaughtered. It’s not going to be rapid death, but some of them are just going to be totally replaced. Bertrand SchmittI agree with you. If you don’t have a big enough moat, if it’s very shallow, if your clients are moving quickly, you can easily switch based on a small price difference. That’s definitely trouble. Nuno Goncalves PedroI’ll let an anecdote just so people I don’t understand. Because people say, but these regional SaaS solutions normally because of their specificities to the markets and stuff like that, whatever. I literally drafted the other day an agreement, a semi-agreement relating to Portuguese law on Claude in Portuguese, from Portugal, not Brazil and Portuguese. It drafted an agreement from scratch based on my prompting, and it took into account specificities of the Portuguese legal system and taxation. Guys, it’s like, this is a freaking consumer tool. Localization of what? The tax regime and whatever? Who gives a shit? It’s like, again, I think that’s the market that definitely will get a pretty significant beating. Bertrand SchmittAnother market for me, we talk about Adobe, but content creation tools. Here, I think there is a dramatic shift in how you use them. Before you use another Photoshop to replace something in a picture, change a slightly picture stuff. Now, you just say, hey, remove this guy from the picture. Hey, replace. Hey, create that picture from scratch. I have five photo IDs, put these guys in context, put them in your meeting room, and go for it. This is such transformational versus how you used to work before that I think some of this industry is getting destroyed.There will be simply no point of using these tools anymore because something else is just 10X better. That is not even a question. You could argue there is still a niche of professionals doing stuff in an always because it guarantees a bit more higher quality or this or that. Sure. But overall, this is getting disrupted big time and the much bigger business might be totally new and totally AI native. Nuno Goncalves PedroI will do a parochial comment. We have two investments in the content creation space, one more on the marketing side and the other one more on the hardcore content creation side. They’re both AI from inception, so they’re both AI native. One of them is called LetsEnhance, the other one is called blaze.ai. I feel it’s true that there’s going to be a lot of replacement of some of the content creation tools in certain markets like consumer and prosumer, driven by the Nano Bananas of the world and all that stuff.But on the top end and in enterprise and all that stuff, we feel that AI native content creation tools are there to be. It’s actually one of the areas of what I would call use cases or AI apps/platforms where I feel being AI native will give you an advantage. Just being a cross-cut play around the market being Anthropic or OpenAI, whatever, actually won’t solve the problem for some of the markets that need to be served in. Bertrand SchmittMakes sense. I agree with you. Maybe more quickly, some point solutions, relatively high risk. If you have a single function tool, then could be easily replaced potentially by an AI agent. We already talk about it. If you are too SMB-focused, that’s not the best segment of the market, typically. Maybe you can have a single test to check if that company is at risk. If you were to replace that tool, can a $20 a month AI agent do this task? If switch it cost are low, then maybe that’s not a good business opportunity. Maybe you should not invest, or you should sell the stock.Again, maybe you have to focus more on regulated niches, hardware dependent, critical private data, solutions where there is already outcome or value-based pricing in place. You have to put some rules and analysis to help you understand, is this business at risk of significant disruption or not? Not all business are the same. As an investor, that might mean that there would be some good opportunities. SaaS businesses that are going to emerge even stronger right now are at a cheap discount. Nuno Goncalves PedroAbsolutely. I think at the end of the day, certain basic workflow tools that are out there to simplify CRM, some very basic ERP modules, anything that’s very, very simple in terms of if this then that, all those tools are also going to be slaughtered relatively soon, sadly. If you’re in that space, maybe time, as Bertrand was saying earlier, to pivot, to go after some fundamental differentiation, or to do something else. You want to conclude, Bertrand? Bertrand SchmittConclusionSure. I guess we could see that from a trade perspective, from an investor perspective. I think it’s creating quite genuinely some opportunities. Some stocks are in the bargain, some of those are value traps, so you better get your investment skills in order. PE, private credit, definitely a lot of risk, not just from AI, I think from basic fraud as well.Secondary market, as you just say, it’s not an easy one. It’s a canary in the coal mine. I think you will agree, but this is before getting between AI native versus everything else these days, especially if you are more early stage. A more established business, it’s a different thing. But right now, just starting a regular SaaS company, that’s a tough one. From an investor perspective, you need to pivot as fast as you can from seed-based pricing, hybrid, outcome-based, value-based pricing. You have to do the move quickly. You don’t want to be pushed when it’s too late.Build-versus-buy is real, and that will only accelerate as coding agents mature. Vertical specialization, proprietary data are strong moat. They were before as well, so it’s nothing new. But I think the importance of having a true moat is more critical than ever. Lots of companies have received investment with not enough moat, and that’s the one getting destroyed in the private and public market. If you have strong matrix, there is a question of when is a good time to exit? I don’t know if the relations will ever come back. I think it truly depends as well on your business, a strategic fit with acquisition opportunities.Anecdotally, I have seen some businesses who look at exit opportunities and now are finding attractive options. It’s not all that dark, I would say. Maybe to answer to the question, do we have a SaaS apocalypse? Yes and no. Some companies are going to end badly, some companies are going to emerge stronger. I think that’s it for today. Thank you, Nino. Nuno Goncalves PedroThank you, Bertrand.

Ep. 332For decades, the stock market meant public companies. Apple, Microsoft, Amazon — the giants everyone invests in.But something big has changed.More companies are staying private longer, and some of the most valuable businesses in the world — SpaceX, OpenAI, Anthropic, Databricks, Stripe — are not publicly traded.So the question becomes:Are private markets where the real growth is happening now?In this episode, Gabriel Shahin breaks down the shift from public markets to private investing, why billion-dollar companies avoid going public, and what investors need to understand before jumping into private stock opportunities.In this video, we discuss:-Why fewer companies are listed on public exchanges today-Why major companies choose to stay private longer-How SPVs (Special Purpose Vehicles) allow investors to buy private shares-The fees, carry structures, and costs behind private investments-Why governments sometimes push companies to go public-The pros and cons of private markets vs public markets-The importance of operators and leadership in early-stage companies-The risks of hype investments (like NFTs and speculative trends)-Private investments can offer incredible upside — but they also come with less transparency, limited liquidity, and higher risk.As always, the key question remains:Is it a good company solving a real problem — or just a hot trend?

“I don't know if any rational person ever became a billionaire running a disruptive company.” — Keith TeareIs capitalism by permission of democracy, or is democracy by permission of capitalism? That's the question Keith Teare and I have been circling for a while on our weekly tech roundup, and this week it triggered a full-blown discussion of our 21st century economic and political fate.Earlier this week, Vinod Khosla — one of Silicon Valley's most successful venture capitalists — posted on X that “capitalism is by permission of democracy.” Keith agrees. I'm not so sure. My sense is that as AI start-ups approach valuations that rival the GDP of nation states, the old equation inverts. Governments no longer permit capitalism. Capitalism permits government. The Sam Altmans and Elon Musks of the future, running 10 or $15 trillion dollar startups, won't lobby politicians. They'll replace them. Dario Amodei's confrontation with the US government, then, is a sneak preview of the future. Indeed, as what Om Malik calls a “symbolic capitalist”, Amodei is a good example of the type of engaged capitalist who will usurp traditional politicians. That's the good news. The bad news is that other examples of symbolic capitalists include Elon Musk and Peter Thiel. Five Takeaways•       Keith Says OpenAI Will Be Worth $10 Trillion in Five Years: I told him I'd take him to dinner if he's right. He said I'd have to do more than that. His logic: NVIDIA promises $1 trillion in new revenue by the end of next year, Anthropic did $5 billion in new revenue in a single month, and the three expected IPOs — Anthropic, OpenAI, SpaceX — would together raise more money than the entire IPO market of the last decade. The Netscape moment, if it comes, won't be a moment. It'll be an earthquake.•       Fundrise Is the Canary in the Coal Mine: A fund holding private shares in Anthropic, OpenAI, SpaceX, Databricks, and Anduril went public this week at $34 and closed above $100. Retail investors paying three times net asset value for companies that aren't even public yet. Keith says that's not irrational — it's the market pricing the future. I'm less sure. History is littered with futures the market got catastrophically wrong.•       Om Malik Reframes the Entire Debate: His essay on “neo-symbolic capitalism” argues that value in the 21st century derives from symbols, narratives, and reputation rather than products. In that framing, Amodei's fight with the government isn't a miscalculation — it's brand-building. Musk is the master of it. Altman tries to wear every hat simultaneously. Peter Thiel is in Rome talking about the Antichrist. And the billionaires who signed the Giving Pledge now want out.•       Keith and I Disagree on What $10 Trillion Means: Keith says the government retains power regardless of corporate size. Being big doesn't give you political power unless governments are corrupt. I think that's naïve. If AI companies approach valuations that rival the GDP of nation states, the old equation inverts. Government doesn't permit capitalism. Capitalism permits government. The Amodeis and Musks of the future won't lobby politicians. They'll replace them.•       Contrarianism Is at the Very Core of Innovation: The one thing Keith and I agree on this week. Every billionaire is irrational. Musk is on the spectrum. Thiel believes in the Antichrist. Amodei thinks he can fight the US government and win. Keith concedes: no rational person ever became a billionaire running a disruptive company. The question is whether that irrationality is a feature of capitalism or a threat to democracy. We disagree on the answer. About the GuestKeith Teare is a serial entrepreneur, investor, and publisher of That Was The Week, a weekly newsletter on the tech economy. He is co-founder of SignalRank and a regular Saturday guest on Keen On America.References:•       That Was The Week — Keith's editorial on public markets and price outcomes.•       Om Malik on neo-symbolic capitalism — the essay that reframes the Amodei debate.•       Episode 2835: Why Dario Amodei Might Be the 21st Century's First Real Leader — last week's TWTW, where the Amodei debate began.•       Episode 2836: Is Elon Human? — Charles Steel on the curious mind of Elon Musk, referenced in the conversation.•       Fundrise (VCX) — the IPO that triggered this week's discussion, trading at 300% above NAV.About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:(00:00) - Introduction: AI and unreason define the world (01:49) - Markets as prediction machines: NVIDIA's $1 trillion promise (04:42) - The three IPOs that would dwarf a decade of IPOs (05:50) - Fundrise (VCX): retail investors paying 300% premium (09:23) - Keith's prediction: OpenAI at $10 trillion in five years (11:44) - The Anthropic debate continues: tactics vs. morals (14:22) - Silicon Valley's behind-the-scenes support for Amodei (16:42) - What happens when an AI company rivals a nation's GDP? (23:05) - Om Malik on neo-symbolic capitalism (28:10) - Musk as the master of symbolic capitalism (30:08) - Bezos, Project Prometheus, and the Prometheuses of AI (32:07) - Peter Thiel, the Antichrist, and the Giving Pledge collapse (35:27) - Vinod Khosla: capitalism by permission of democracy? (38:23) - Or democracy by permission of capitalism?

What does it really take to build a successful cybersecurity career in today's fast-changing world? In this episode of Life of a CISO, Dr. Eric Cole sits down with Jesse Scott, a cybersecurity leader whose career spans NATO, Ernst & Young, CrowdStrike, Barclays, Amazon, Databricks, and startup leadership. Together, they break down what aspiring CISOs need to know about navigating big companies, fast-moving startups, and even launching a company of your own. Jesse shares lessons from working across seven countries, leading in both enterprise and startup environments, and staying ahead in a world being reshaped by AI, cyber risk, identity security, automation, privacy, ransomware, and nation-state threats. This conversation also dives into how AI is changing security operations, why CISOs must think more like business leaders, and what it means to take control of your own career in cybersecurity. If you are a CISO, cybersecurity leader, security architect, founder, or aspiring executive, this episode is packed with real-world insight on leadership, innovation, risk, and the future of cyber defense. In this episode, you'll learn: How startup experience can accelerate your path to CISO Why every cybersecurity leader should understand business and revenue How AI agents are transforming security teams and attack surfaces What CISOs should know about privacy, automation, and data poisoning Why betting on yourself may be the smartest move in cybersecurity

What happens when AI agents — not humans — become your primary customer? That's not a hypothetical. It's already happening, and the founders who recognize it earliest are rebuilding their entire infrastructure stacks from scratch. In this live episode of Founded & Funded from our IA Summit in Seattle, Madrona Venture Partner Jon Turow sits down with Parag Agrawal, former CEO of Twitter and founder of Parallel Web Systems, and Nikita Shamgunov, who led Neon through a rapid AI pivot before its acquisition by Databricks. What they cover: Why Parag is building a new search index from the ground up — and why existing ones weren't designed for AI agents The moment Nikita realized Replit agents were spinning up databases 4x faster than all human developers combined — and what that forced him to do How to pivot an established company in weeks, not months, when your customer base suddenly changes The "pagers vs. iPhones" framework for knowing when to lean into disruption vs. protect what you have Parag's two-person hiring rubric for teams operating in deep uncertainty Why Nikita added the head of product for ChatGPT to Neon's board — and what that signaled to the market The "two-way door" model for giving agents real autonomy without catastrophic downside Whether you're building infrastructure, running an AI-native startup, or trying to figure out where your product fits in an agent-first world — this conversation will sharpen your thinking. Full Transcript: https://www.madrona.com/twitter-ex-ceo-web-built-for-humans-make-it-work-for-ai-agents-nikita-Shamgunov-parag-agrawal Chapters (00:00) – Introduction (01:52) – Parag Agrawal: Why Parallel Was Built for AI Agents From Day One (03:22) – Why Existing Search Indexes Don't Work for AI Agents (05:08) – Nikita Shamgunov: How Replit Agents Outpaced the Entire World on Neon (08:27) – The Pager-to-iPhone Decision: Lean Into Disruption or Get Left Behind (11:13) – How Neon Built an AI Team in Two Weeks and Launched MCP Before Anyone Else (13:41) – Firing Bullets: Why a 4-Out-of-9 Batting Average Was Good Enough (15:37) – Parag on the Two Types of People You Need to Take Concentrated Risk (21:08) – Building Trust in Agents: Evals, Confidence Scores, and Read-Only Infrastructure (23:32) – Nikita's Two-Way Door Framework for Agent Autonomy (25:35) – Parallel Execution: Fork Environments and Let Agents Compete

Turbopuffer came out of a reading app.In 2022, Simon was helping his friends at Readwise scale their infra for a highly requested feature: article recommendations and semantic search. Readwise was paying ~$5k/month for their relational database and vector search would cost ~$20k/month making the feature too expensive to ship. In 2023 after mulling over the problem from Readwise, Simon decided he wanted to “build a search engine” which became Turbopuffer.We discuss:• Simon's path: Denmark → Shopify infra for nearly a decade → “angel engineering” across startups like Readwise, Replicate, and Causal → turbopuffer almost accidentally becoming a company • The Readwise origin story: building an early recommendation engine right after the ChatGPT moment, seeing it work, then realizing it would cost ~$30k/month for a company spending ~$5k/month total on infra and getting obsessed with fixing that cost structure • Why turbopuffer is “a search engine for unstructured data”: Simon's belief that models can learn to reason, but can't compress the world's knowledge into a few terabytes of weights, so they need to connect to systems that hold truth in full fidelity • The three ingredients for building a great database company: a new workload, a new storage architecture, and the ability to eventually support every query plan customers will want on their data • The architecture bet behind turbopuffer: going all in on object storage and NVMe, avoiding a traditional consensus layer, and building around the cloud primitives that only became possible in the last few years • Why Simon hated operating Elasticsearch at Shopify: years of painful on-call experience shaped his obsession with simplicity, performance, and eliminating state spread across multiple systems • The Cursor story: launching turbopuffer as a scrappy side project, getting an email from Cursor the next day, flying out after a 4am call, and helping cut Cursor's costs by 95% while fixing their per-user economics • The Notion story: buying dark fiber, tuning TCP windows, and eating cross-cloud costs because Simon refused to compromise on architecture just to close a deal faster • Why AI changes the build-vs-buy equation: it's less about whether a company can build search infra internally, and more about whether they have time especially if an external team can feel like an extension of their own • Why RAG isn't dead: coding companies still rely heavily on search, and Simon sees hybrid retrieval semantic, text, regex, SQL-style patterns becoming more important, not less • How agentic workloads are changing search: the old pattern was one retrieval call up front; the new pattern is one agent firing many parallel queries at once, turning search into a highly concurrent tool call • Why turbopuffer is reducing query pricing: agentic systems are dramatically increasing query volume, and Simon expects retrieval infra to adapt to huge bursts of concurrent search rather than a small number of carefully chosen calls • The philosophy of “playing with open cards”: Simon's habit of being radically honest with investors, including telling Lachy Groom he'd return the money if turbopuffer didn't hit PMF by year-end • The “P99 engineer”: Simon's framework for building a talent-dense company, rejecting by default unless someone on the team feels strongly enough to fight for the candidate —Simon Hørup Eskildsen• LinkedIn: https://www.linkedin.com/in/sirupsen• X: https://x.com/Sirupsen• https://sirupsen.com/aboutturbopuffer• https://turbopuffer.com/Full Video PodTimestamps00:00:00 The PMF promise to Lachy Groom00:00:25 Intro and Simon's background00:02:19 What turbopuffer actually is00:06:26 Shopify, Elasticsearch, and the pain behind the company00:10:07 The Readwise experiment that sparked turbopuffer00:12:00 The insight Simon couldn't stop thinking about00:17:00 S3 consistency, NVMe, and the architecture bet00:20:12 The Notion story: latency, dark fiber, and conviction00:25:03 Build vs. buy in the age of AI00:26:00 The Cursor story: early launch to breakout customer00:29:00 Why code search still matters00:32:00 Search in the age of agents00:34:22 Pricing turbopuffer in the AI era00:38:17 Why Simon chose Lachy Groom00:41:28 Becoming a founder on purpose00:44:00 The “P99 engineer” philosophy00:49:30 Bending software to your will00:51:13 The future of turbopuffer00:57:05 Simon's tea obsession00:59:03 Tea kits, X Live, and P99 LiveTranscriptSimon Hørup Eskildsen: I don't think I've said this publicly before, but I just called Lockey and was like, local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you. But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working.So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people. We're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards. Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before.Alessio: Hey everyone, welcome to the Leading Space podcast. This is Celesio Pando, Colonel Laz, and I'm joined by Swix, editor of Leading Space.swyx: Hello. Hello, uh, we're still, uh, recording in the Ker studio for the first time. Very excited. And today we are joined by Simon Eski. Of Turbo Farer welcome.Simon Hørup Eskildsen: Thank you so much for having me.swyx: Turbo Farer has like really gone on a huge tear, and I, I do have to mention that like you're one of, you're not my newest member of the Danish AHU Mafia, where like there's a lot of legendary programmers that have come out of it, like, uh, beyond Trotro, Rasmus, lado Berg and the V eight team and, and Google Maps team.Uh, you're mostly a Canadian now, but isn't that interesting? There's so many, so much like strong Danish presence.Simon Hørup Eskildsen: Yeah, I was writing a post, um, not that long ago about sort of the influences. So I grew up in Denmark, right? I left, I left when, when I was 18 to go to Canada to, to work at Shopify. Um, and so I, like, I've, I would still say that I feel more Danish than, than Canadian.This is also the weird accent. I can't say th because it, this is like, I don't, you know, my wife is also Canadian, um, and I think. I think like one of the things in, in Denmark is just like, there's just such a ruthless pragmatism and there's also a big focus on just aesthetics. Like, they're like very, people really care about like where, what things look like.Um, and like Canada has a lot of attributes, US has, has a lot of attributes, but I think there's been lots of the great things to carry. I don't know what's in the water in Ahu though. Um, and I don't know that I could be considered part of the Mafi mafia quite yet, uh, compared to the phenomenal individuals we just mentioned.Barra OV is also, uh, Danish Canadian. Okay. Yeah. I don't know where he lives now, but, and he's the PHP.swyx: Yeah. And obviously Toby German, but moved to Canada as well. Yes. Like this is like import that, uh, that, that is an interesting, um, talent move.Alessio: I think. I would love to get from you. Definition of Turbo puffer, because I think you could be a Vector db, which is maybe a bad word now in some circles, you could be a search engine.It's like, let, let's just start there and then we'll maybe run through the history of how you got to this point.Simon Hørup Eskildsen: For sure. Yeah. So Turbo Puffer is at this point in time, a search engine, right? We do full text search and we do vector search, and that's really what we're specialized in. If you're trying to do much more than that, like then this might not be the right place yet, but Turbo Buffer is all about search.The other way that I think about it is that we can take all of the world's knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can't compress all of that into a few terabytes of weights, right? Compress into a few terabytes of weights, how to reason with the world, how to make sense of the knowledge.But we have to somehow connect it to something externally that actually holds that like in full fidelity and truth. Um, and that's the thing that we intend to become. Right? That's like a very holier than now kind of phrasing, right? But being the search engine for unstructured, unstructured data is the focus of turbo puffer at this point in time.Alessio: And let's break down. So people might say, well, didn't Elasticsearch already do this? And then some other people might say, is this search on my data, is this like closer to rag than to like a xr, like a public search thing? Like how, how do you segment like the different types of search?Simon Hørup Eskildsen: The way that I generally think about this is like, there's a lot of database companies and I think if you wanna build a really big database company, sort of, you need a couple of ingredients to be in the air.We don't, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database. Multiple times you look at a company like Oracle, right? You will, like, I don't think you can find a company on earth with a digital presence that it not, doesn't somehow have some data in an Oracle database.Right? And I think at this point, that's also true for Snowflake and Databricks, right? 15 years later it's, or even more than that, there's not a company on earth that doesn't, in. Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we're in that kind of moment now, right?I don't think you're gonna find a company over the next few years that doesn't directly or indirectly, um, have all their data available for, for search and connect it to ai. So you need that new workload, like you need something to be happening where there's a new workload that causes that to happen, and that new workload is connecting very large amounts of data to ai.The second thing you need. The second condition to build a big database company is that you need some new underlying change in the storage architecture that is not possible from the databases that have come before you. If you look at Snowflake and Databricks, right, commoditized, like massive fleet of HDDs, like that was not possible in it.It just wasn't in the air in the nineties, right? So you just didn't, we just didn't build these systems. S3 and and and so on was not around. And I think the architecture that is now possible that wasn't possible 15 years ago is to go all in on NVME SSDs. It requires a particular type of architecture for the database that.It's difficult to retrofit onto the databases that are already there, including the ones you just mentioned. The second thing is to go all in on OIC storage, more so than we could have done 15 years ago. Like we don't have a consensus layer, we don't really have anything. In fact, you could turn off all the servers that Turbo Buffer has, and we would not lose any data because we have all completely all in on OIC storage.And this means that our architecture is just so simple. So that's the second condition, right? First being a new workload. That means that every company on earth, either indirectly or directly, is using your database. Second being, there's some new storage architecture. That means that the, the companies that have come before you can do what you're doing.I think the third thing you need to do to build a big database company is that over time you have to implement more or less every Cory plan on the data. What that means is that you. You can't just get stuck in, like, this is the one thing that a database does. It has to be ever evolving because when someone has data in the database, they over time expect to be able to ask it more or less every question.So you have to do that to get the storage architecture to the limit of what, what it's capable of. Those are the three conditions.swyx: I just wanted to get a little bit of like the motivation, right? Like, so you left Shopify, you're like principal, engineer, infra guy. Um, you also head of kernel labs, uh, inside of Shopify, right?And then you consulted for read wise and that it kind of gave you that, that idea. I just wanted you to tell that story. Um, maybe I, you've told it before, but, uh, just introduce the, the. People to like the, the new workload, the sort of aha moment for turbo PufferSimon Hørup Eskildsen: For sure. So yeah, I spent almost a decade at Shopify.I was on the infrastructure team, um, from the fairly, fairly early days around 2013. Um, at the time it felt like it was growing so quickly and everything, all the metrics were, you know, doubling year on year compared to the, what companies are contending with today. It's very cute in growth. I feel like lot some companies are seeing that month over month.Um, of course. Shopify compound has been compounding for a very long time now, but I spent a decade doing that and the majority of that was just make sure the site is up today and make sure it's up a year from now. And a lot of that was really just the, um, you know, uh, the Kardashians would drive very, very large amounts of, of data to, to uh, to Shopify as they were rotating through all the merch and building out their businesses.And we just needed to make sure we could handle that. Right. And sometimes these were events, a million requests per second. And so, you know, we, we had our own data centers back in the day and we were moving to the cloud and there was so much sharding work and all of that that we were doing. So I spent a decade just scaling databases ‘cause that's fundamentally what's the most difficult thing to scale about these sites.The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was elastic search. It was very, very difficult to deal with. And I saw a lot of projects that were just being held back in their ambition by using it.swyx: And I mean, self-hosted.Self-hosted. ‘causeSimon Hørup Eskildsen: it's, yeah, and it commercial, this is like 2015, right? So it's like a very particular vintage. Right. It's probably better at a lot of these things now. Um, it was difficult to contend with and I'm just like, I just think about it. It's an inverted index. It should be good at these kinds of queries and do all of this.And it was, we, we often couldn't get it to do exactly what we needed to do or basically get lucine to do, like expose lucine raw to, to, to what we needed to do. Um, so that was like. Just something that we did on the side and just panic scaled when we needed to, but not a particular focus of mine. So I left, and when I left, I, um, wasn't sure exactly what I wanted to do.I mean, it spent like a decade inside of the same company. I'd like grown up there. I started working there when I was 18.swyx: You only do Rails?Simon Hørup Eskildsen: Yeah. I mean, yeah. Rails. And he's a Rails guy. Uh, love Rails. So good. Um,Alessio: we all wish we could still work in Rails.swyx: I know know. I know, but some, I tried learning Ruby.It's just too much, like too many options to do the same thing. It's, that's my, I I know there's a, there's a way to do it.Simon Hørup Eskildsen: I love it. I don't know that I would use it now, like given cloud code and, and, and cursor and everything, but, um, um, but still it, like if I'm just sitting down and writing a teal code, that's how I think.But anyway, I left and I wasn't, I talked to a couple companies and I was like, I don't. I need to see a little bit more of the world here to know what I'm gonna like focus on next. Um, and so what I decided is like I was gonna, I called it like angel engineering, where I just hopped around in my friend's companies in three months increments and just helped them out with something.Right. And, and just vested a bit of equity and solved some interesting infrastructure problem. So I worked with a bunch of companies at the time, um, read Wise was one of them. Replicate was one of them. Um, causal, I dunno if you've tried this, it's like a, it's a spreadsheet engine Yeah. Where you can do distribution.They sold recently. Yeah. Um, we've been, we used that in fp and a at, um, at Turbo Puffer. Um, so a bunch of companies like this and it was super fun. And so we're the Chachi bt moment happened, I was with. With read Wise for a stint, we were preparing for the reader launch, right? Which is where you, you cue articles and read them later.And I was just getting their Postgres up to snuff, like, which basically boils down to tuning, auto vacuum. So I was doing that and then this happened and we were like, oh, maybe we should build a little recommendation engine and some features to try to hook in the lms. They were not that good yet, but it was clear there was something there.And so I built a small recommendation engine just, okay, let's take the articles that you've recently read, right? Like embed all the articles and then do recommendations. It was good enough that when I ran it on one of the co-founders of Rey's, like I found out that I got articles about, about having a child.I'm like, oh my God, I didn't, I, I didn't know that, that they were having a child. I wasn't sure what to do with that information, but the recommendation engine was good enough that it was suggesting articles, um, about that. And so there was, there was recommendations and uh, it actually worked really well.But this was a company that was spending maybe five grand a month in total on all their infrastructure and. When I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it's gonna be like 30 grand a month. That just wasn't tenable. Right?Like Read Wise is a proudly bootstrapped company and it's paying 30 grand for infrastructure for one feature versus five. It just wasn't tenable. So sort of in the bucket of this is useful, it's pretty good, but let us, let's return to it when the costs come down.swyx: Did you say it grows by feature? So for five to 30 is by the number of, like, what's the, what's the Scaling factor scale?It scales by the number of articles that you embed.Simon Hørup Eskildsen: It does, but what I meant by that is like five grand for like all of the other, like the Heroku, dinos, Postgres, like all the other, and this then storage is 30. Yeah. And then like 30 grand for one feature. Right. Which is like, what other articles are related to this one.Um, so it was just too much right to, to power everything. Their budget would've been maybe a few thousand dollars, which still would've been a lot. And so we put it in a bucket of, okay, we're gonna do that later. We'll wait, we will wait for the cost to come down. And that haunted me. I couldn't stop thinking about it.I was like, okay, there's clearly some latent demand here. If the cost had been a 10th, we would've shipped it and. This was really the only data point that I had. Right. I didn't, I, I didn't, I didn't go out and talk to anyone else. It was just so I started reading Right. I couldn't, I couldn't help myself.Like I didn't know what like a vector index is. I, I generally barely do about how to generate the vectors. There was a lot of hype about, this is a early 2023. There was a lot of hype about vector databases. There were raising a lot of money and it's like, I really didn't know anything about it. It's like, you know, trying these little models, fine tuning them.Like I was just trying to get sort of a lay of the land. So I just sat down. I have this. A GitHub repository called Napkin Math. And on napkin math, there's just, um, rows of like, oh, this is how much bandwidth. Like this is how many, you know, you can do 25 gigabytes per second on average to dram. You can do, you know, five gigabytes per second of rights to an SSD, blah blah.All of these numbers, right? And S3, how many you could do per, how much bandwidth can you drive per connection? I was just sitting down, I was like, why hasn't anyone build a database where you just put everything on O storage and then you puff it into NVME when you use the data and you puff it into dram if you're, if you're querying it alive, it's just like, this seems fairly obvious and you, the only real downside to that is that if you go all in on o storage, every right will take a couple hundred milliseconds of latency, but from there it's really all upside, right?You do the first go, it takes half a second. And it sort of occurred to me as like, well. The architecture is really good for that. It's really good for AB storage, it's really good for nvm ESSD. It's, well, you just couldn't have done that 10 years ago. Back to what we were talking about before. You really have to build a database where you have as few round trips as possible, right?This is how CPUs work today. It's how NVM E SSDs work. It's how as, um, as three works that you want to have a very large amount of outstanding requests, right? Like basically go to S3, do like that thousand requests to ask for data in one round trip. Wait for that. Get that, like, make a new decision. Do it again, and try to do that maybe a maximum of three times.But no databases were designed that way within NVME as is ds. You can drive like within, you know, within a very low multiple of DRAM bandwidth if you use it that way. And same with S3, right? You can fully max out the network card, which generally is not maxed out. You get very, like, very, very good bandwidth.And, but no one had built a database like that. So I was like, okay, well can't you just, you know, take all the vectors right? And plot them in the proverbial coordinate system. Get the clusters, put a file on S3 called clusters, do json, and then put another file for every cluster, you know, cluster one, do js O cluster two, do js ON you know that like it's two round trips, right?So you get the clusters, you find the closest clusters, and then you download the cluster files like the, the closest end. And you could do this in two round trips.swyx: You were nearest neighbors locally.Simon Hørup Eskildsen: Yes. Yes. And then, and you would build this, this file, right? It's just like ultra simplistic, but it's not a far shot from what the first version of Turbo Buffer was.Why hasn't anyone done thatAlessio: in that moment? From a workload perspective, you're thinking this is gonna be like a read heavy thing because they're doing recommend. Like is the fact that like writes are so expensive now? Oh, with ai you're actually not writing that much.Simon Hørup Eskildsen: At that point I hadn't really thought too much about, well no actually it was always clear to me that there was gonna be a lot of rights because at Shopify, the search clusters were doing, you know, I don't know, tens or hundreds of crew QPS, right?‘cause you just have to have a human sit and type in. But we did, you know, I don't know how many updates there were per second. I'm sure it was in the millions, right into the cluster. So I always knew there was like a 10 to 100 ratio on the read write. In the read wise use case. It's, um, even, even in the read wise use case, there'd probably be a lot fewer reads than writes, right?There's just a lot of churn on the amount of stuff that was going through versus the amount of queries. Um, I wasn't thinking too much about that. I was mostly just thinking about what's the fundamentally cheapest way to build a database in the cloud today using the primitives that you have available.And this is it, right? You just, now you have one machine and you know, let's say you have a terabyte of data in S3, you paid the $200 a month for that, and then maybe five to 10% of that data and needs to be an NV ME SSDs and less than that in dram. Well. You're paying very, very little to inflate the data.swyx: By the way, when you say no one else has done that, uh, would you consider Neon, uh, to be on a similar path in terms of being sort of S3 first and, uh, separating the compute and storage?Simon Hørup Eskildsen: Yeah, I think what I meant with that is, uh, just build a completely new database. I don't know if we were the first, like it was very much, it was, I mean, I, I hadn't, I just looked at the napkin math and was like, this seems really obvious.So I'm sure like a hundred people came up with it at the same time. Like the light bulb and every invention ever. Right. It was just in the air. I think Neon Neon was, was first to it. And they're trying, they're retrofitted onto Postgres, right? And then they built this whole architecture where you have, you have it in memory and then you sort of.You know, m map back to S3. And I think that was very novel at the time to do it for, for all LTP, but I hadn't seen a database that was truly all in, right. Not retrofitting it. The database felt built purely for this no consensus layer. Even using compare and swap on optic storage to do consensus. I hadn't seen anyone go that all in.And I, I mean, there, there, I'm sure there was someone that did that before us. I don't know. I was just looking at the napkin mathswyx: and, and when you say consensus layer, uh, are you strongly relying on S3 Strong consistency? You are. Okay.SoSimon Hørup Eskildsen: that is your consensus layer. It, it is the consistency layer. And I think also, like, this is something that most people don't realize, but S3 only became consistent in December of 2020.swyx: I remember this coming out during COVID and like people were like, oh, like, it was like, uh, it was just like a free upgrade.Simon Hørup Eskildsen: Yeah.swyx: They were just, they just announced it. We saw consistency guys and like, okay, cool.Simon Hørup Eskildsen: And I'm sure that they just, they probably had it in prod for a while and they're just like, it's done right.And people were like, okay, cool. But. That's a big moment, right? Like nv, ME SSDs, were also not in the cloud until around 2017, right? So you just sort of had like 2017 nv, ME SSDs, and people were like, okay, cool. There's like one skew that does this, whatever, right? Takes a few years. And then the second thing is like S3 becomes consistent in 2020.So now it means you don't have to have this like big foundation DB or like zookeeper or whatever sitting there contending with the keys, which is how. You know, that's what Snowflake and others have do so muchswyx: for goneSimon Hørup Eskildsen: Exactly. Just gone. Right? And so just push to the, you know, whatever, how many hundreds of people they have working on S3 solved and then compare and swap was not in S3 at this point in time,swyx: by the way.Uh, I don't know what that is, so maybe you wanna explain. Yes. Yeah.Simon Hørup Eskildsen: Yes. So, um, what Compare and swap is, is basically, you can imagine that if you have a database, it might be really nice to have a file called metadata json. And metadata JSON could say things like, Hey, these keys are here and this file means that, and there's lots of metadata that you have to operate in the database, right?But that's the simplest way to do it. So now you have might, you might have a lot of servers that wanna change the metadata. They might have written a file and want the metadata to contain that file. But you have a hundred nodes that are trying to contend with this metadata that JSON well, what compare and Swap allows you to do is basically just you download the file, you make the modifications, and then you write it only if it hasn't changed.While you did the modification and if not you retry. Right? Should just have this retry loops. Now you can imagine if you have a hundred nodes doing that, it's gonna be really slow, but it will converge over time. That primitive was not available in S3. It wasn't available in S3 until late 2024, but it was available in GCP.The real story of this is certainly not that I sat down and like bake brained it. I was like, okay, we're gonna start on GCS S3 is gonna get it later. Like it was really not that we started, we got really lucky, like we started on GCP and we started on GCP because tur um, Shopify ran on GCP. And so that was the platform I was most available with.Right. Um, and I knew the Canadian team there ‘cause I'd worked with them at Shopify and so it was natural for us to start there. And so when we started building the database, we're like, oh yeah, we have to build a, we really thought we had to build a consensus layer, like have a zookeeper or something to do this.But then we discovered the compare and swap. It's like, oh, we can kick the can. Like we'll just do metadata r json and just, it's fine. It's probably fine. Um, and we just kept kicking the can until we had very, very strong conviction in the idea. Um, and then we kind of just hinged the company on the fact that S3 probably was gonna get this, it started getting really painful in like mid 2024.‘cause we were closing deals with, um, um, notion actually that was running in AWS and we're like, trust us. You, you really want us to run this in GCP? And they're like, no, I don't know about that. Like, we're running everything in AWS and the latency across the cloud were so big and we had so much conviction that we bought like, you know, dark fiber between the AWS regions in, in Oregon, like in the InterExchange and GCP is like, we've never seen a startup like do like, what's going on here?And we're just like, no, we don't wanna do this. We were tuning like TCP windows, like everything to get the latency down ‘cause we had so high conviction in not doing like a, a metadata layer on S3. So those were the three conditions, right? Compare and swap. To do metadata, which wasn't in S3 until late 2024 S3 being consistent, which didn't happen until December, 2020.Uh, 2020. And then NVMe ssd, which didn't end in the cloud until 2017.swyx: I mean, in some ways, like a very big like cloud success story that like you were able to like, uh, put this all together, but also doing things like doing, uh, bind our favor. That that actually is something I've never heard.Simon Hørup Eskildsen: I mean, it's very common when you're a big company, right?You're like connecting your own like data center or whatever. But it's like, it was uniquely just a pain with notion because the, um, the org, like most of the, like if you're buying in Ashburn, Virginia, right? Like US East, the Google, like the GCP and, and AWS data centers are like within a millisecond on, on each other, on the public exchanges.But in Oregon uniquely, the GCP data center sits like a couple hundred kilometers, like east of Portland and the AWS region sits in Portland, but the network exchange they go through is through Seattle. So it's like a full, like 14 milliseconds or something like that. And so anyway, yeah. It's, it's, so we were like, okay, we can't, we have to go through an exchange in Portland.Yeah. Andswyx: you'd rather do this than like run your zookeeper and likeSimon Hørup Eskildsen: Yes. Way rather. It doesn't have state, I don't want state and two systems. Um, and I think all that is just informed by Justine, my co-founder and I had just been on call for so long. And the worst outages are the ones where you have state in multiple places that's not syncing up.So it really came from, from a a, like just a, a very pure source of pain, of just imagining what we would be Okay. Being woken up at 3:00 AM about and having something in zookeeper was not one of them.swyx: You, you're talking to like a notion or something. Do they care or do they just, theySimon Hørup Eskildsen: just, they care about latency.swyx: They latency cost. That's it.Simon Hørup Eskildsen: They just cared about latency. Right. And we just absorbed the cost. We're just like, we have high conviction in this. At some point we can move them to AWS. Right. And so we just, we, we'll buy the fiber, it doesn't matter. Right. Um, and it's like $5,000. Usually when you buy fiber, you buy like multiple lines.And we're like, we can only afford one, but we will just test it that when it goes over the public internet, it's like super smooth. And so we did a lot of, anyway, it's, yeah, it was, that's cool.Alessio: You can imagine talking to the GCP rep and it's like, no, we're gonna buy, because we know we're gonna turn, we're gonna turn from you guys and go to AWS in like six months.But in the meantime we'll do this. It'sSimon Hørup Eskildsen: a, I mean, like they, you know, this workload still runs on GCP for what it's worth. Right? ‘cause it's so, it was just, it was so reliable. So it was never about moving off GCP, it was just about honesty. It was just about giving notion the latency that they deserved.Right. Um, and we didn't want ‘em to have to care about any of this. We also, they were like, oh, egress is gonna be bad. It was like, okay, screw it. Like we're just gonna like vvc, VPC peer with you and AWS we'll eat the cost. Yeah. Whatever needs to be done.Alessio: And what were the actual workloads? Because I think when you think about ai, it's like 14 milliseconds.It's like really doesn't really matter in the scheme of like a model generation.Simon Hørup Eskildsen: Yeah. We were told the latency, right. That we had to beat. Oh, right. So, so we're just looking at the traces. Right. And then sort of like hand draw, like, you know, kind of like looking at the trace and then thinking what are the other extensions of the trace?Right. And there's a lot more to it because it's also when you have, if you have 14 versus seven milliseconds, right. You can fit in another round trip. So we had to tune TCP to try to send as much data in every round trip, prewarm all the connections. And there was, there's a lot of things that compound from having these kinds of round trips, but in the grand scheme it was just like, well, we have to beat the latency of whatever we're up against.swyx: Which is like they, I mean, notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like Yeah, just like talk through that kind of.Simon Hørup Eskildsen: Last time I was in San Francisco, I was talking to one of the engineers actually, who, who was one of our champions, um, at, AT Notion.And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed. You know, Uhhuh like, it's like the way I think about, it's like I have to earn a return on whatever the clouds charge me and then my customers have to earn a return on that. And it's like very simple, right?And so there has to be gross margin all the way up and that's how you build the product. And so then our customers have to make the right set of trade off the turbo Puffer makes, and if they're happy with that, that's great.swyx: Do you feel like you're competing with build internally versus buy or buy versus buy?Simon Hørup Eskildsen: Yeah, so, sorry, this was all to build up to your question. So one of the notion engineers told me that they'd sat and probably on a napkin, like drawn out like, why hasn't anyone built this? And then they saw terrible. It was like, well, it literally that. So, and I think AI has also changed the buy versus build equation in terms of, it's not really about can we build it, it's about do we have time to build it?I think they like, I think they felt like, okay, if this is a team that can do that and they, they feel enough like an extension of our team, well then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the, through the test, right? Like we had some very, very long nights to to, to do that POC.And they were really our biggest, our second big customer off the cursor, which also was a lot of late nights. Right.swyx: Yeah. That, I mean, should we go into that story? The, the, the sort of Chris's story, like a lot, um, they credit you a lot for. Working very closely with them. So I just wanna hear, I've heard this, uh, story from Sole's point of view, but like, I'm curious what, what it looks like from your side.Simon Hørup Eskildsen: I actually haven't heard it from Sole's point of view, so maybe you can now cross reference it. The way that I remember it was that, um, the day after we launched, which was just, you know, I'd worked the whole summer on, on the first version. Justine wasn't part of it yet. ‘cause I just, I didn't tell anyone that summer that I was working on this.I was just locked in on building it because it's very easy otherwise to confuse talking about something to actually doing it. And so I was just like, I'm not gonna do that. I'm just gonna do the thing. I launched it and at this point turbo puffer is like a rust binary running on a single eight core machine in a T Marks instance.And me deploying it was like looking at the request log and then like command seeing it or like control seeing it to just like, okay, there's no request. Let's upgrade the binary. Like it was like literally the, the, the, the scrappiest thing. You could imagine it was on purpose because just like at Shopify, we did that all the time.Like, we like move, like we ran things in tux all the time to begin with. Before something had like, at least the inkling of PMF, it was like, okay, is anyone gonna hear about this? Um, and one of the cursor co-founders Arvid reached out and he just, you know, the, the cursor team are like all I-O-I-I-M-O like, um, contenders, right?So they just speak in bullet points and, and facts. It was like this amazing email exchange just of, this is how many QPS we have, this is what we're paying, this is where we're going, blah, blah, blah. And so we're just conversing in bullet points. And I tried to get a call with them a few times, but they were, so, they were like really writing the PMF bowl here, just like late 2023.And one time Swally emails me at like five. What was it like 4:00 AM Pacific time saying like, Hey, are you open for a call now? And I'm on the East coast and I, it was like 7:00 AM I was like, yeah, great, sure, whatever. Um, and we just started talking and something. Then I didn't know anything about sales.It was something that just comp compelled me. I have to go see this team. Like, there's something here. So I, I went to San Francisco and I went to their office and the way that I remember it is that Postgres was down when I showed up at the office. Did SW tell you this? No. Okay. So Postgres was down and so it's like they were distracting with that.And I was trying my best to see if I could, if I could help in any way. Like I knew a little bit about databases back to tuning, auto vacuum. It was like, I think you have to tune out a vacuum. Um, and so we, we talked about that and then, um, that evening just talked about like what would it look like, what would it look like to work with us?And I just said. Look like we're all in, like we will just do what we'll do whatever, whatever you tell us, right? They migrated everything over the next like week or two, and we reduced their cost by 95%, which I think like kind of fixed their per user economics. Um, and it solved a lot of other things. And we were just, Justine, this is also when I asked Justine to come on as my co-founder, she was the best engineer, um, that I ever worked with at Shopify.She lived two blocks away and we were just, okay, we're just gonna get this done. Um, and we did, and so we helped them migrate and we just worked like hell over the next like month or two to make sure that we were never an issue. And that was, that was the cursor story. Yeah.swyx: And, and is code a different workload than normal text?I, I don't know. Is is it just text? Is it the same thing?Simon Hørup Eskildsen: Yeah, so cursor's workload is basically, they, um, they will embed the entire code base, right? So they, they will like chunk it up in whatever they would, they do. They have their own embedding model, um, which they've been public about. Um, and they find that on, on, on their evals.It. There's one of their evals where it's like a 25% improvement on a very particular workload. They have a bunch of blog posts about it. Um, I think it works best on larger code basis, but they've trained their own embedding model to do this. Um, and so you'll see it if you use the cursor agent, it will do searches.And they've also been public around, um, how they've, I think they post trained their model to be very good at semantic search as well. Um, and that's, that's how they use it. And so it's very good at, like, can you find me on the code that's similar to this, or code that does this? And just in, in this queries, they also use GR to supplement it.swyx: Yeah.Simon Hørup Eskildsen: Um, of courseswyx: it's been a big topic of discussion like, is rag dead because gr you know,Simon Hørup Eskildsen: and I mean like, I just, we, we see lots of demand from the coding company to ethicsswyx: search in every part. Yes.Simon Hørup Eskildsen: Uh, we, we, we see demand. And so, I mean, I'm. I like case studies. I don't like, like just doing like thought pieces on this is where it's going.And like trying to be all macroeconomic about ai, that's has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they're doing and I hope some of the other coding labs that use Turbo Puffer will do the same.Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths.They like you. It's very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer's bucket. Um, so it's, it's, it's really, really well designed.swyx: And so this is like extra stuff they did to work with you because you are not part of Cursor.Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql.I dunno. Um, but like, it's silly to like be all in on like one particularly query pattern.Simon Hørup Eskildsen: I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I'm gonna butcher it here. Um, and you know, I'm a, I'm a database scalability person. I'm not a, I, I dunno anything about training models other than, um, what the internet tells me and what.The way he describes is that this is just like cash compute, right? It's like you have a point in time where you're looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that.And, um, how the value of that will change over time. I'm, I'm not sure, but there seems to be a lot of value in that.Alessio: Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it's searching it again later.Yeah. What are maybe some of the new types of workloads or like changes you've had to make to your architecture for it?Simon Hørup Eskildsen: I think you're right. When I think of rag, I think of, Hey, there's an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing.Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we're just the tool call, right? And that's increasingly what we see our customers doing. Um, what we're seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can't.And I'm also now, when I use the cursor agent, I also see them doing more concurrency than I've ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That's also what the agents are doing. So that's new. It means just an enormous amount of queries all at once to the dataset while it's warm in as few turns as possible.swyx: Can I clarify one thing on that?Simon Hørup Eskildsen: Yes.swyx: Is it, are they batching multiple users or one user is driving multiple,Simon Hørup Eskildsen: one user driving multiple, one agent driving.swyx: It's parallel searching a bunch of things.Simon Hørup Eskildsen: Exactly.swyx: Yeah. Yeah, exactly. So yeah, the clinician also did, did this for the fast context thing, like eight parallel at once.Simon Hørup Eskildsen: Yes.swyx: And, and like an interesting problem is, well, how do you make sure you have enough diversity so you're not making the the same request eight times?Simon Hørup Eskildsen: And I think like that's probably also where the hybrid comes in, where. That's another way to diversify. It's a completely different way to, to do the search.That's a big change, right? So before it was really just like one call and then, you know, the LLM took however many seconds to return, but now we just see an enormous amount of queries. So the, um, we just see more queries. So we've like tried to reduce query, we've reduced query pricing. Um, this is probably the first time actually I'm saying that, but the query pricing is being reduced, like five x.Um, and we'll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts of queries. Um, that's one thing that's changed. I think the right, the right ratio is still very high, right? Like there's still a, an enormous amount of rights per read, but we're starting probably to see that change if people really lean into this pattern.Alessio: Can we talk a little bit about the pricing? I'm curious, uh, because traditionally a database would charge on storage, but now you have the token generation that is so expensive, where like the actual. Value of like a good search query is like much higher because they're like saving inference time down the line.How do you structure that as like, what are people receptive to on the other side too?Simon Hørup Eskildsen: Yeah. I, the, the turbo puffer pricing in the beginning was just very simple. The pricing on these on for search engines before Turbo Puffer was very server full, right? It was like, here's the vm, here's the per hour cost, right?Great. And I just sat down with like a piece of paper and said like, if Turbo Puffer was like really good, this is probably what it would cost with a little bit of margin. And that was the first pricing of Turbo Puffer. And I just like sat down and I was like, okay, like this is like probably the storage amp, but whenever on a piece of paper I, it was vibe pricing.It was very vibe price, and I got it wrong. Oh. Um, well I didn't get it wrong, but like Turbo Puffer wasn't at the first principle pricing, right? So when Cursor came on Turbo Puffer, it was like. Like, I didn't know any VCs. I didn't know, like I was just like, I don't know, I didn't know anything about raising money or anything like that.I just saw that my GCP bill was, was high, was a lot higher than the cursor bill. So Justine and I was just like, well, we have to optimize it. Um, and I mean, to the chagrin now of, of it, of, of the VCs, it now means that we're profitable because we've had so much pricing pressure in the beginning. Because it was running on my credit card and Justine and I had spent like, like tens of thousands of dollars on like compute bills and like spinning off the company and like very like, like bad Canadian lawyers and like things like to like get all of this done because we just like, we didn't know.Right. If you're like steeped in San Francisco, you're just like, you just know. Okay. Like you go out, raise a pre-seed round. I, I never heard a word pre-seed at this point in time.swyx: When you had Cursor, you had Notion you, you had no funding.Simon Hørup Eskildsen: Um, with Cursor we had no funding. Yeah. Um, by the time we had Notion Locke was, Locke was here.Yeah. So it was really just, we vibe priced it 100% from first Principles, but it wasn't, it, it was not performing at first principles, so we just did everything we could to optimize it in the beginning for that, so that at least we could have like a 5% margin or something. So I wasn't freaking out because Cursor's bill was also going like this as they were growing.And so my liability and my credit limit was like actively like calling my bank. It was like, I need a bigger credit. Like it was, yeah. Anyway, that was the beginning. Yeah. But the pricing was, yeah, like storage rights and query. Right. And the, the pricing we have today is basically just that pricing with duct tape and spit to try to approach like, you know, like a, as a margin on the physical underlying hardware.And we're doing this year, you're gonna see more and more pricing changes from us. Yeah.swyx: And like is how much does stuff like VVC peering matter because you're working in AWS land where egress is charged and all that, you know.Simon Hørup Eskildsen: We probably don't like, we have like an enterprise plan that just has like a base fee because we haven't had time to figure out SKU pricing for all of this.Um, but I mean, yeah, you can run turbo puffer either in SaaS, right? That's what Cursor does. You can run it in a single tenant cluster. So it's just you. That's what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer's VPC, that's what an for example, philanthropic does.swyx: What I'm hearing is that this is probably the best CRO job for somebody who can come in and,Simon Hørup Eskildsen: I mean,swyx: help you with this.Simon Hørup Eskildsen: Um, like Turbo Puffer hired, like, I don't know what, what number this was, but we had a full-time CFO as like the 12th hire or something at Turbo Puffer, um, I think I hear are a lot of comp.I don't know how they do it. Like they have a hundred employees and not a CFO. It's like having a CFO is like a runningswyx: business man. Like, you know,Simon Hørup Eskildsen: it's so good. Yeah, like money Mike, like he just, you know, just handles the money and a lot of the business stuff and so he came in and just hopped with a lot of the operational side of the business.So like C-O-O-C-F-O, like somewhere in between.swyx: Just as quick mention of Lucky, just ‘cause I'm curious, I've met Lock and like, he's obviously a very good investor and now on physical intelligence, um, I call it generalist super angel, right? He invests in everything. Um, and I always wonder like, you know, is there something appealing about focusing on developer tooling, focusing on databases, going like, I've invested for 10 years in databases versus being like a lock where he can maybe like connect you to all the customers that you need.Simon Hørup Eskildsen: This is an excellent question. No, no one's asked me this. Um, why lockey? Because. There was a couple of people that we were talking to at the time and when we were raising, we were almost a little, we were like a bit distressed because one of our, one of our peers had just launched something that was very similar to Turbo Puffer.And someone just gave me the advice at the time of just choose the person where you just feel like you can just pick up the phone and not prepare anything. And just be completely honest, and I don't think I've said this publicly before, but I just called Lockey and was like local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you.But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working. So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people and we're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards and.Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before. As I said, I didn't even know what a seed or pre-seed round was like before, probably even at this time. So I was just like very honest with him. And I asked him like, Lockie, have you ever have, have you ever invested in database company?He was just like, no. And at the time I was like, am I dumb? Like, but I think there was something that just like really drew me to Lockie. He is so authentic, so honest, like, and there was something just like, I just felt like I could just play like, just say everything openly. And that was, that was, I think that that was like a perfect match at the time, and, and, and honestly still is.He was just like, okay, that's great. This is like the most honest, ridiculous thing I've ever heard anyone say to me. But like that, like that, whyswyx: is this ridiculous? Say competitor launch, this may not work out. It wasSimon Hørup Eskildsen: more just like. If this doesn't work out, I'm gonna close up shop by the end of the mo the year, right?Like it was, I don't know, maybe it's common. I, I don't know. He told me it was uncommon. I don't know. Um, that's why we chose him and he'd been phenomenal. The other people were talking at the, at the time were database experts. Like they, you know, knew a lot about databases and Locke didn't, this turned out to be a phenomenal asset.Right. I like Justine and I know a lot about databases. The people that we hire know a lot about databases. What we needed was just someone who didn't know a lot about databases, didn't pretend to know a lot about databases, and just wanted to help us with candidates and customers. And he did. Yeah. And I have a list, right, of the investors that I have a relationship with, and Lockey has just performed excellent in the number of sub bullets of what we can attribute back to him.Just absolutely incredible. And when people talk about like no ego and just the best thing for the founder, I like, I don't think that anyone, like even my lawyer is like, yeah, Lockey is like the most friendly person you will find.swyx: Okay. This is my most glow recommendation I've ever heard.Alessio: He deserves it.He's very special.swyx: Yeah. Yeah. Yeah. Okay. Amazing.Alessio: Since you mentioned candidates, maybe we can talk about team building, you know, like, especially in sf, it feels like it's just easier to start a company than to join a company. Uh, I'm curious your experience, especially not being n SF full-time and doing something that is maybe, you know, a very low level of detail and technical detail.Simon Hørup Eskildsen: Yeah. So joining versus starting, I never thought that I would be a founder. I would start with it, like Turbo Puffer started as a blog post, and then it became a project and then sort of almost accidentally became a company. And now it feels like it's, it's like becoming a bigger company. That was never the intention.The intentions were very pure. It's just like, why hasn't anyone done this? And it's like, I wanna be the, like, I wanna be the first person to do it. I think some founders have this, like, I could never work for anyone else. I, I really don't feel that way. Like, it's just like, I wanna see this happen. And I wanna see it happen with some people that I really enjoy working with and I wanna have fun doing it and this, this, this has all felt very natural on that, on that sense.So it was never a like join versus versus versus found. It was just dis found me at the right moment.Alessio: Well I think there's an argument for, you should have joined Cursor, right? So I'm curious like how you evaluate it. Okay, I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy.It's like an interesting technical problem. I should just build it within Cursor and then they don't have to encrypt all this stuff. They don't have to obfuscate things. Like was that on your mind at all orSimon Hørup Eskildsen: before taking the, the small check from Lockie, I did have like a hard like look at myself in the mirror of like, okay, do I really want to do this?And because if I take the money, I really have to do it right. And so the way I almost think about it's like you kind of need to ha like you kind of need to be like fucked up enough to want to go all the way. And that was the conversation where I was like, okay, this is gonna be part of my life's journey to build this company and do it in the best way that I possibly can't.Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don't, I think some people, it doesn't occur to me that everyone takes it that seriously. And maybe I take it too seriously, I don't know. But that was like a very intentional moment.And so then it was very clear like, okay, I'm gonna do this and I'm gonna give it everything.Alessio: A lot of people don't take it this seriously. But,swyx: uh, let's talk about, you have this concept of the P 99 engineer. Uh, people are 10 x saying, everyone's saying, you know, uh, maybe engineers are out of a job. I don't know.But you definitely see a P 99 engineer, and I just want you to talk about it.Simon Hørup Eskildsen: Yeah, so the P 99 engineer was just a term that we started using internally to talk about candidates and talk about how we wanted to build the company. And you know, like everyone else is, like we want a talent dense company.And I think that's almost become trite at this point. What I credit the cursor founders a lot with is that they just arrived there from first principles of like, we just need a talent dense, um, talent dense team. And I think I've seen some teams that weren't talent dense and like seemed a counterfactual run, which if you've run in been in a large company, you will just see that like it's just logically will happen at a large company.Um, and so that was super important to me and Justine and it's very difficult to maintain. And so we just needed, we needed wording for it. And so I have a document called Traits of the P 99 Engineer, and it's a bullet point list. And I look at that list after every single interview that I do, and in every single recap that we do and every recap we end with.End with, um, some version of I'm gonna reject this candidate completely regardless of what the discourse was, because I wanna see people fight for this person because the default should not be, we're gonna hire this person. The default should be, we're definitely not hiring this person. And you know, if everyone was like, ah, maybe throw a punch, then this is not the right.swyx: Do, do you operate, like if there's one cha there must have at least one champion who's like, yes, I will put my career on, on, on the line for this. You know,Simon Hørup Eskildsen: I think career on the line,swyx: maybe a chair, butSimon Hørup Eskildsen: yeah. You know, like, um, I would say so someone needs to like, have both fists up and be like, I'd fight.Right? Yeah. Yeah. And if one person said, then, okay, let's do it. Right?swyx: Yeah.Simon Hørup Eskildsen: Um. It doesn't have to be absolutely everyone. Right? And like the interviews are always the sign that you're checking for different attributes. And if someone is like knocking it outta the park in every single attribute, that's, that's fairly rare.Um, but that's really important. And so the traits of the P 99 engineer, there's lots of them. There's also the traits of the p like triple nine engineer and the quadruple nine engineer. This is like, it's a long list.swyx: Okay.Simon Hørup Eskildsen: Um, I'll give you some samples, right. Of what we, what we look for. I think that the P 99 engineer has some history of having bent, like their trajectory or something to their will.Right? Some moment where it was just, they just, you know, made the computer do what it needed to do. There's something like that, and it will, it will occur to have them at some point in their career. And, uh. Hopefully multiple times. Right.swyx: Gimme an example of one of your engineers that like,Simon Hørup Eskildsen: I'll give an eng.Uh, so we, we, we launched this thing called A and NV three. Um, we could, we're also, we're working on V four and V five right now, but a and NV three can search a hundred billion vectors with a P 50 of around 40 milliseconds and a p 99 of 200 milliseconds. Um, maybe other people have done this, I'm sure Google and others have done this, but, uh, we haven't seen anyone, um, at least not in like a public consumable SaaS that can do this.And that was an engineer, the chief architect of Turbo Puffer, Nathan, um, who more or less just bent this, the software was not capable of this and he just made it capable for a very particular workload in like a, you know, six to eight week period with the help of a lot of the team. Right. It's been, been, there's numerous of examples of that, like at, at turbo puff, but that's like really bending the software and X 86 to your will.It was incredible to watch. Um. You wanna see some moments like that?swyx: Isn't that triple nine?Simon Hørup Eskildsen: Um, I think Nathan, what's calledAlessio: group nine, that was only nine. I feel like this is too high forSimon Hørup Eskildsen: Nathan. Nathan is, uh, Nathan is like, yeah, there's a lot of nines. Okay. After that p So I think that's one trait. I think another trait is that, uh, the P 99 spends a lot of time looking at maps.Generally it's their preferred ux. They just love looking at maps. You ever seen someone who just like, sits on their phone and just like, scrolls around on a map? Or did you not look at maps A lot? You guys don't look atswyx: maps? I guess I'm not feeling there. I don't know, butSimon Hørup Eskildsen: you just dis What about trains?Do you like trains?swyx: Uh, I mean they, not enough. Okay. This is just like weapon nice. Autism is what I call it. Like, like,Simon Hørup Eskildsen: um, I love looking at maps, like, it's like my preferred UX and just like I, you know, I likeswyx: lotsAlessio: of, of like random places, soswyx: like,youswyx: know.Alessio: Yes. Okay. There you go. So instead of like random places, like how do you explore the maps?Simon Hørup Eskildsen: No, it's, it's just a joke.swyx: It's autism laugh. It's like you are just obsessed by something and you like studying a thing.Simon Hørup Eskildsen: The origin of this was that at some point I read an interview with some IOI gold medalistswyx: Uhhuh,Simon Hørup Eskildsen: and it's like, what do you do in your spare time? I was just like, I like looking at maps.I was like, I feel so seen. Like, I just like love, like swirling out. I was like, oh, Canada is so big. Where's Baffin Island? I don't know. I love it. Yeah. Um, anyway, so the traits of P 99, P 99 is obsessive, right? Like, there's just like, you'll, you'll find traits of that we do an interview at, at, at, at turbo puffer or like multiple interviews that just try to screen for some of these things.Um, so. There's lots of others, but these are the kinds of traits that we look for.swyx: I'll tell you, uh, some people listen for like some of my dere stuff. Uh, I do think about derel as maps. Um, you draw a map for people, uh, maps show you the, uh, what is commonly agreed to be the geographical features of what a boundary is.And it shows also shows you what is not doing. And I, I think a lot of like developer tools, companies try to tell you they can do everything, but like, let's, let's be real. Like you, your, your three landmarks are here, everyone comes here, then here, then here, and you draw a map and, and then you draw a journey through the map.And like that. To me, that's what developer relations looks like. So I do think about things that way.Simon Hørup Eskildsen: I think the P 99 thinks in offs, right? The P 99 is very clear about, you know, hey, turbo puffer, you can't run a high transaction workload on turbo puffer, right? It's like the right latency is a hundred milliseconds.That's a clear trade off. I think the P 99 is very good at articulating the trade offs in every decision. Um. Which is exactly what the map is in your case, right?swyx: Uh, yeah, yeah. My, my, my world. My world.Alessio: How, how do you reconcile some of these things when you're saying you bend the will the computer versus like the trade

Companies Complying with or Directly Impacted by Transparency Laws Major generative AI developers are broadly subject to AB 2013, which requires them to publicly disclose high-level summaries of the datasets used to train their models.OpenAI, Anthropic, and Google were among the first companies to voluntarily comply with the law, publishing the required training data documentation on their websites when the law took effect on January 1, 2026.Meta is also heavily impacted by these laws and is frequently cited for its extensive efforts to harvest public and copyrighted data across the internet to train its foundation models.Companies Actively Challenging the LawxAI (founded by Elon Musk) is the primary company fighting the legislation. In late December 2025, xAI filed a federal lawsuit against California Attorney General Rob Bonta to block the enforcement of AB 2013. xAI argues that forcing it to disclose its training data constitutes an unconstitutional taking of its trade secrets and violates its First Amendment rights. In March 2026, a federal judge denied xAI's request for a preliminary injunction to halt the law.Separately, xAI is under investigation by the California Attorney General and received a cease-and-desist letter over its AI chatbot, Grok. The tool's "spicy mode" has allegedly been used to generate nonconsensual sexually explicit deepfakes and child sexual abuse material.Companies Sued Over AI Training Data and Copyright The push for transparency laws like AB 2013 and AB 412 stems largely from a massive wave of lawsuits filed by authors, artists, and media companies who allege that AI developers misappropriated their intellectual property to train models. Companies currently defending against these copyright lawsuits include:OpenAI and Microsoft (sued by The New York Times, The Daily News, the Authors Guild, Raw Story Media, and others).Anthropic (sued by Concord Music Group and various authors).Google and YouTube (sued by Mike Huckabee, David Milette, and others).Perplexity AI (sued by Dow Jones, The New York Times, and the Chicago Tribune).Stability AI, Midjourney, Runway AI, and Deviant Art (sued by visual artists and Getty Images).Meta, Nvidia, Databricks, and Mosaic ML.AI audio, music, and voice generation companies like Suno, Udio, Lovo, and ElevenLabs.Ross Intelligence (sued by Thomson Reuters for allegedly using copyrighted Westlaw data to train its own legal search tool).Other AI Companies Facing State ScrutinyCharacter.AI: Sued by the Kentucky Attorney General in January 2026 for consumer protection violations, alleging the company's companion chatbots preyed on children and contributed to psychological manipulation and self-harm. Google was also sued in related private litigation due to its substantial investment in Character.AI.Clearview AI: Cited by privacy advocates as a notorious example of unethical data sourcing, having scraped billions of images from social media to build a massive facial recognition database.

What does it really take to sell an AI-native product into the Fortune 500? In this episode of Founded & Funded, Madrona Managing Director Matt McIlwain sits down with two founders deep in the trenches of enterprise AI adoption, Esha Joshi (Co-founder, Yoodli⁩) and Anup Chamrajnagar (Co-founder, Gradial.) Their companies are selling into some of the world's most complex organizations, like Google, SAP, Snowflake, Databricks, and more. And they break down what founders often underestimate about enterprise AI sales. They dive into: Why most AI pilots fail and how to prevent it The "three-legged stool" of enterprise sales How AI review boards are reshaping buying cycles Securing long-term contracts Pricing AI: seats vs. usage vs. outcomes Navigating non-deterministic AI failures with customers Building champions who accelerate their careers with AI If you're building an AI-native company and selling into enterprises, this is for you. Full Transcript: https://www.madrona.com/this-is-how-fortune-500-companies-are-buying-ai-today Chapters: (00:00) – Introduction (03:37) – Early AI Pilots: What Worked (and What Didn't) (05:01) – Sell Pain, Not Features (06:25) – Why Enterprise Expectations Are Higher Now (07:48) – Moving From "Wow" Factor to Durable Outcomes (09:17) – How to Structure a Pilot That Converts (10:35) – Expanding Beyond the Initial Wedge (13:41) – Turning Pilots Into 12-Month Contracts (14:47) – Navigating Procurement & AI Governance Boards (16:02) – What's Changed (and What Hasn't) in Enterprise Sales (16:45) – How to Increase Deal Velocity (19:39) – Using AI to Improve Your Own Sales Ops (20:20) – Are You Replacing Jobs with AI? (23:14) – Building Career-Accelerating Champions (23:46) – When AI Outputs Go Wrong (Real Stories) (25:23) – Why the Pilot Never Stops (29:04) – Pricing AI: Seats vs. Usage vs. Outcomes (34:48) – Go-To-Market Partnerships That Unlock Enterprise (37:25) – The Role of Forward-Deployed Engineers (38:44) – Final Advice for AI Founders Selling to Enterprise

We chart how AI leapt from chat to code, why product is now the leverage point, and how startups can market to algorithms without losing trust. David Yakobovitch shares hard-won views on moats, data, defense tech, and the immigrant energy powering American dynamism.• leaders and market share across Google, OpenAI, Anthropic• vibe coding benefits, code quality risks, review loops• prompt libraries, agent swarms, PRD automation• weekly shipping pace and the SaaS squeeze• marketing to algorithms, buyer agents, bot traffic control• pilot to production gap, rise of forward-deployed engineers• moats beyond models via domain, workflow, and proprietary data• China's progress, open source, and on-device AI bets• defense tech, swarms, and physical AI opportunities• endurance mindset, yoga discipline, and founder stamina• personal workflows across Gemini, Claude, and OpenAI• investing across seed and growth with outcome focusThe model wars aren't theoretical anymore—they're shaping how software gets built, shipped, and sold. We sit down with David Yakobovitch, GP at Data Power Capital and former global product lead at Google, to map where AI is actually working in 2026: vibe coding that shrinks teams, agent swarms that harden quality, and product-led moats that outlast model churn. David pulls back the curtain on how Claude, OpenAI, and Google now compete neck and neck on code and content, why prompt engineering as a job vanished while prompts became more valuable, and how forward-deployed engineers bridge the stubborn pilot-to-production gap that has haunted data projects for a decade.We explore go-to-market in a world where buyer agents screen your pitch before a human blinks. That means structuring materials for machines, tuning sites for humans and crawlers, and building demos that agents can evaluate safely. We also go into what happens as models commoditize: the moat shifts to domain depth, proprietary offline data, secure connectors, and measurable workflow outcomes. From small language models running on CPUs in air‑gapped containers to Apple's on-device bet, the edge is back—especially for Europe's sovereignty demands and public sector buyers.Then we widen the lens. Defense and “physical AI” blend hardware and autonomy: swarms, hypersonics, and resilient edge compute that must perform in the real world. David shares why he's backing both the silicon and the software, and how American dynamism—powered by immigrants and impatient builders—remains a durable advantage. Along the way, we trade notes on multi-model workflows, open source momentum, China's narrowed gap, and the endurance mindset that carries teams through the disappointment dip after the first shiny demo.David Yakoboitch: https://www.linkedin.com/in/davidyakobovitch/David Yakobovitch is a General Partner and Managing Director of DataPower Capital, a New York City-based venture capital firm investing across Applied AI, Inference Infrastructure, and DeepTech.  With a portfolio of over 36 companies, David is an investor in the most defining frontier technology firms of our era, including OpenAI, Anthropic, xAI, Neuralink, DataBricks, Groq, Cruesoe, Anduril and SpaceX. David is a leading voice as the host of HumAIn, a podcast focused on Applied and Responsible AI.  Previously, David served as a Global Product Lead aWebsite: https://www.position2.com/podcast/Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/Sandeep Parikh: https://www.instagram.com/sandeepparikh/Email us with any feedback for the show: sparkofages.podcast@position2.com

In this talk, Juan, Analytics Engineer and author of Fundamentals of Analytics Engineering share his professional journey from studying psychological research in Colombia to becoming one of the first analytics engineers in the Netherlands. We explore the evolution of the role, the shift toward engineering rigor in data modeling, and how the landscape of tools like dbt and Databricks is changing the way teams work.You'll learn about:- The fundamental differences between traditional BI engineering and modern analytics engineering.- How to bridge the gap between business stakeholders and technical data infrastructure.- The technical "glue" that connects Python and SQL for robust data pipelines.- The importance of automated testing (generic vs. singular tests) to prevent "silent" data failures.- Strategies for modeling messy, fragmented source data into a unified "business reality."- The current state of the "Lakehouse" paradigm and how it impacts storage and compute costs.- Expert advice on navigating the dbt ecosystem and its emerging competitors.Links:- DE Course: https://github.com/DataTalksClub/data-engineering-zoomcamp- Luma: https://luma.com/0uf7mmupTIMECODES:0:00 Juan's psychological research and transition to data4:36 Riding the wave: The early days of analytics engineering7:56 Breaking down the gap between analysts and engineers11:03 The art of turning business reality into clean data16:25 Why data engineering is about safety, not just speed20:53 Reimagining data modeling in the modern era26:53 To split or not to split: Finding the right team roles30:35 Python, SQL, and the technical toolkit for success38:41 How to stop manually testing your data dashboards46:34 Bringing software engineering rigor to data workflows49:50 Must-read books and resources for mastering the craft55:42 The future of dbt and the shifting tool landscape1:00:29 Deciphering the lakehouse: Warehousing in the cloud1:11:16 Pro-tips for starting your data engineering journey1:14:40 The big debate: Databricks vs. Snowflake1:18:28 Why every data professional needs a local communityThis talk is designed for data analysts looking to level up their engineering skills, data engineers interested in the business-logic layer, and data leaders trying to structure their teams more effectively. It is particularly valuable for those preparing for the Data Engineering Zoomcamp or anyone looking to transition into an Analytics Engineering role.Connect with Juan- Linkedin - https://www.linkedin.com/in/jmperafan/ - Website - https://juanalytics.com/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

Today we sit down with Doug May, SVP of Productivity at Harness, to discuss one of the most critical yet overlooked aspects of a healthy organization: Sales Productivity. Doug has had an illustrious career at elite organizations including Datadog and Databricks, and he brings that expertise to Harness, where he has cut ramp time in half and increased per-rep contribution by 43%. We explore the "F1 engineering team" analogy of GTM support, why productivity metrics are the ultimate indicator of a company's health, and the specific questions every candidate should ask to de-risk their next career move.

Dean Quiambao anticipates a “very, very strong year” for IPOs, stretching into 2027. He expects a lot of exciting names in the back half of the year, especially from AI-native companies. He thinks they'll make a big splash in the markets, comparing it to the Olympics. Anticipated IPOs include Anthropic, OpenAI, SpaceX, and Databricks, and other names with massive market caps. Dean also speaks to why companies are staying private longer, and what valuation risks could be hanging over the IPO space.======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – / schwabnetwork Follow us on Facebook – / schwabnetwork Follow us on LinkedIn - / schwab-network About Schwab Network - https://schwabnetwork.com/about

March 3rd, Computer History Museum CODING AGENTS CONFERENCE, come join us while there are still tickets left.https://luma.com/codingagentsChris Fregly is currently focused on building and scaling high-performance AI systems, writing and teaching about AI infrastructure, helping organizations adopt generative AI and performance engineering principles on AWS, and fostering large developer communities around these topics.Performance Optimization and Software/Hardware Co-design across PyTorch, CUDA, and NVIDIA GPUs // MLOps Podcast #363 with Chris Fregly, Founder, AI Performance Engineer, and InvestorJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractIn today's era of massive generative models, it's important to understand the full scope of AI systems' performance engineering. This talk discusses the new O'Reilly book, AI Systems Performance Engineering, and the accompanying GitHub repo (https://github.com/cfregly/ai-performance-engineering). This talk provides engineers, researchers, and developers with a set of actionable optimization strategies. You'll learn techniques to co-design and co-optimize hardware, software, and algorithms to build resilient, scalable, and cost-effective AI systems for both training and inference. // BioChris Fregly is an AI performance engineer and startup founder with experience at AWS, Databricks, and Netflix. He's the author of three (3) O'Reilly books, including Data Science on AWS (2021), Generative AI on AWS (2023), and AI Systems Performance Engineering (2025). He also runs the global AI Performance Engineering meetup and speaks at many AI-related conferences, including Nvidia GTC, ODSC, Big Data London, and more.// Related LinksAI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch 1st Edition by Chris Fregly: https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/Coding Agents Conference: https://luma.com/codingagents~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Chris on LinkedIn: /cfreglyTimestamps:[00:00] SageMaker HyperPod Resilience[00:27] Book Creation and Software Engineering[04:57] Software Engineers and Maintenance[11:49] AI Systems Performance Engineering[22:03] Cognitive Biases and Optimization / "Mechanical Sympathy"[29:36] GPU Rack-Scale Architecture[33:58] Data Center Reliability Issues[43:52] AI Compute Platforms[49:05] Hardware vs Ecosystem Choice[1:00:05] Claude vs Codex vs Gemini[1:14:53] Kernel Budget Allocation[1:18:49] Steerable Reasoning Challenges[1:24:18] Data Chain Value Awareness

"They are changing venture capital from a 30% tax to 0% tax. If Robinhood succeeds, it makes Sequoia and Andreessen's business model untenable." — Keith TeareThe Silicon Gods must have their blood. And they've finally come for the funders of disruption, the venture capitalists, who are now being disrupted by something called Public Venture Capital (PVC). That, at least, is the view of That Was The Week publisher Keith Teare, who leads his newsletter this week with Robinhood's new venture fund. This new stock-trading app for millennials is going after Sequoia and Andreessen Horowitz—not by competing on deal flow, but by charging 0% carry instead of 20-30%. Robinhood promises it blows the doors off traditional venture capital.But Keith urges caution over PVCs. Robinhood is packaging late-stage private assets—companies like Databricks that would have IPO'd years ago but are staying private longer. By the time retail investors get access, employees are already cashing out through tender offers because they think the peak is near. The poster child: Figma, which did secondaries at $12 billion after Adobe's $20 billion acquisition failed. A lot of (dumb) people bought at the top and are now slightly less stupid.Fortunately, this week's tech roundup isn't just about get-rich-quick investment schemes. We also discuss Yasha Mounk's sobering experiment: he asked AI to write a political philosophy paper and found it "depressingly good"—publishable in an academic journal. Keith reframes this supposed "death of the humanities" as automation, not democratization. The humans aren't being leveled up; they're masquerading as producers while AI does the work. But craft still matters. When technology relieves humans of the mundane, he hopes, it elevates the special.Lastly but not least, we get to the abundance debate. Peter Diamandis and Singularity University have promised something called "exponential abundance" by 2035. Keith is sympathetic. I am not. The only thing I'm willing to guarantee is that we'll still be talking abundantly about abundance in 2035. And that the Silicon Valley Gods will have their blood. Five Takeaways●      Robinhood Is Charging 0% Carry: Sequoia and Andreessen take 20-30% of profits. Robinhood takes nothing. If they scale, the traditional VC model becomes untenable.●      But You're Buying at the Top: These are late-stage assets. Employees are selling through tender offers because they think peak valuation is near. Ask the people who bought Figma at $12 billion.●      AI Is Automating the Humanities: Yasha Mounk found AI could write "depressingly good" political philosophy. This isn't democratization—it's humans masquerading as producers.●      Craft Still Retains Its Power: Technology relieves humans of the mundane—and elevates the special. Creativity that breaks through will always command attention.●      The Abundance Debate Continues: Diamandis says abundance by 2035. Keith agrees land is already abundant. Andrew calls this "such a stupid thing to say." About the GuestKeith Teare is the publisher of That Was The Week and Executive Chairman of SignalRank. He is a serial entrepreneur and longtime observer of Silicon Valley. Keith joins Keen On America every Saturday for The Week That Was.ReferencesCompanies mentioned:●      Robinhood is launching a publicly listed venture fund, raising up to $1 billion at $25/share with 0% carry. They already have $340 million in assets including Databricks.●      Figma is cited as a cautionary tale: after Adobe's failed $20 billion acquisition, it did secondaries at $12 billion—many bought at the top.●      Polymarket is a prediction market platform that Robinhood has responded to by adding prediction markets to its offerings.People mentioned:●      Yasha Mounk wrote about AI writing "depressingly good" political philosophy papers that could be published in academic journals.●      Peter Diamandis and Dr. Alexander Wisner-Gross of Singularity University argue that exponential abundance is coming by 2035.●      Packy McCormick wrote about power in the age of intelligence.About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States—hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:(00:00) - Introduction: If it's Saturday, it must be revolution (02:11) - Robinhood's venture fund announcement (03:17) - What is Robinhood's day job? (07:43) - Secondary markets and tender offers (10:33) - Democratization or late-stage risk? (14:09) - Is Robinhood just gambling? (16:08) - Private vs. public market returns (19:02) - Is finance merging with betting? (24:23) - Blowing the doors off Sequoia and Andreessen (26:27) - Yasha Mounk: AI automating the humanities (28:47) - Where does power go in the age of AI? (30:42) - Craft retains its power (31:33) - The abundance debate (34:00) - Is land abundant? Andrew loses patience (00:00) - Chapter 15 (00:00) - Chapter 16 (00:00) - Introduction: If it's Saturday, it must be revolution (02:11) - Robinhood's venture fund announcement (03:17) - What is Robinhood's day job? (07:43) - Secondary markets and tender offers (10:33) - Democratization or late-stage risk? (14:09) - Is Robinhood just gambling? (16:08) - Private vs. public market returns (19:02) - Is finance merging with betting? (24:23) - Blowing the doors off Sequoia and Andreessen (26:27) - Yasha Mounk: AI automating the humanities

Recorded 10/29/25Vincent's Slava Rubin and Sacra's Jan-Erik Asplund discussed Databricks, Groq, Anduril, Anthropic, and Canva, five of the hottest pre-IPO companies in the asset class - and how investors can get access to them.Presented by the Fundrise Innovation Fund.https://fundrise.com/Vincent

We're back! In this episode of Alter Everything, Josh Burkhow sits down with Ari Kaplan, Head of Evangelism at Databricks and a pioneer of AI in sports. From building operating systems as a kid and studying at Caltech to transforming baseball analytics and now shaping enterprise AI strategy, Ari shares how physics-inspired thinking, relentless curiosity, and better data have driven his career. They explore the evolution from databases to generative AI, common mistakes organizations make with GenAI, why data engineering matters more than prompt engineering, and how true evangelism is about planting seeds and not pushing hype.PanelistsAri Kaplan, Head of Evangelism @ Databricks – LinkedInJoshua Burkhow, Chief Evangelist @ Alteryx – @JoshuaB, LinkedInTopicsDatabricksMajor League Baseball analytics & the Moneyball eraAI in sports, healthcare, and enterpriseGenerative AI & data engineering foundationsData + AI governanceRaoul Wallenberg humanitarian investigationAlter Everything podcast

Collate is building a semantic intelligence platform that unifies fragmented metadata tooling across the modern data stack. With 12,000+ community members, 3,000+ open source deployments, and 400+ code contributors, the company has proven that open source can be a systematic GTM engine, not just a distribution tactic. In this episode of BUILDERS, I sat down with Suresh Srinivas, Co-Founder & CEO of Collate, to explore his journey from the Hadoop core team at Yahoo, through founding Hortonworks, to architecting data systems processing 4 trillion events daily at Uber—and why that experience led him to rebuild metadata infrastructure from scratch. Topics Discussed: Why platform builders at Yahoo and Hortonworks struggled to drive business value despite powerful technology The metadata fragmentation problem: how siloed tools lack unified vocabularies and end-to-end context Collate's contrarian decision to build Open Metadata from zero rather than spinning out Uber's internal tooling Engineering an open core GTM model that generates nearly 100% inbound sales from technical practitioners Scaling community contribution: moving from feedback loops to 400+ code contributors Hiring a CMO to translate technical value into business-leader messaging without losing practitioner trust The convergence thesis: structured data, knowledge graphs, and semantic layers as the foundation for reliable AI GTM Lessons For B2B Founders: Architect your open source for GTM leverage, not just distribution: Suresh built Open Metadata as a unified platform consolidating data discovery, observability, and governance—previously fragmented across multiple tools. This architectural decision created natural upgrade paths to Collate's managed offering. The lesson: open source architecture should solve a complete job-to-be-done that reveals commercial value through usage, not just demonstrate technical capability. 100+ daily practitioner conversations beats any user research: Collate maintains ongoing dialogue with their community across Snowflake, Databricks, and other integrations. Suresh called this "a product manager's dream"—immediate feedback on what breaks, what's missing, and what workflow improvements matter. For infrastructure startups, this beat rate of validated learning is nearly impossible to replicate through traditional customer development. High-velocity releases build credibility faster than pedigree: Starting from scratch without Yahoo or Uber's brand meant proving commitment through shipping cadence. Collate's strategy: demonstrate you'll be around and responsive before asking for production deployments. This matters more in open source than closed-source where sales cycles force commitment conversations earlier. Separate technical-buyer and business-buyer GTM motions explicitly: Collate's founding team spoke fluently to data engineers and architects who lived the metadata problem daily. Their CMO hire (after establishing product-market fit) brought expertise in articulating business impact—ROI on data initiatives, compliance risk reduction, AI readiness—without the founders faking business-speak. The timing matters: hire for the motion you're entering, not the one you're in. Play the long game with builder-culture companies: At Uber, internal tools were 2-3 years ahead of vendor solutions but became technical debt as teams moved to new problems. Suresh's advice: "Keep in touch with these larger companies. Your technology will improve and you will have better conversation with larger technical companies." The wedge is timing—catch them when maintenance burden outweighs building pride, typically 24-36 months post-launch. Design for all company scales from day one: Unlike Uber's internal metadata platform built for massive scale with corresponding complexity, Open Metadata works for small teams through enterprises. This wasn't just good design—it was GTM expansion strategy. Building only for scale locks you into enterprise-only sales. Building only for simplicity caps your ACV. The middle path requires architectural discipline upfront. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Databricks announcing a new $5B funding round at a $134B valuation. Making the company the fourth largest private company in the U.S. We speak to CEO Ali Ghodsi about the company's future and how AI is disrupting the software ecosystem. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to another episode of Christopher Lochhead: Follow Your Different, featuring the legendary Ray Wang. In this memorable conversation, Christopher and Ray dive deep into the latest developments shaping the world of technology, business, and careers. From dissecting recent tech earnings from giants like Apple, Meta, Tesla and Microsoft to sharing insights from Davos and contemplating the implications of AI for the future of work and entrepreneurship. This episode delivers high-caliber analysis and practical takeaways for anyone navigating today’s rapidly evolving landscape. You're listening to Christopher Lochhead: Follow Your Different. We are the real dialogue podcast for people with a different mind. So get your mind in a different place, and hey ho, let's go. Lessons from Davos and the New Economic Realities Returning from a bustling Davos, Ray Wang shares his observations on how global leaders and executives are tackling an era defined by uncertainty, rapid technology adoption and a relentless pursuit of efficiency. One of Ray's core takeaways is the prevailing theme of “margin compression,” where even the world's largest corporations are working harder than ever just to achieve modest growth. Companies are now measured by their ability to scale exponentially, as illustrated by India's ISRO launching rockets at a fraction of NASA's cost, fundamentally altering competitive dynamics across industries. Ray explains that the rise of AI turbocharges this transformation by opening up “infinite possibilities.” Companies no longer just compete on physical or financial assets, but on their ability to harness vast data resources, quickly innovate and make sharp strategic choices about what problems to solve—and, crucially, what not to do. Privacy challenges, especially for companies like Apple, arise in this new era, making it difficult to deliver world-class AI solutions while maintaining rigorous data protection standards. Both Christopher and Ray emphasize that managing growth, inflation and investment are more complex than ever, with the U.S. outpacing much of the world in GDP growth, yet operating in a global environment rife with policy and market uncertainties. AI, Tech Earnings, and the Rise of the New IPO Era The conversation pivots to the massive investment and exuberance surrounding generative AI and tech infrastructure. Ray points out that while there are fears about overbuilding capacity or creating a circular funding loop among AI companies, there is still significant real opportunity. The current phase has seen enormous capital pour into building data centers and scalable AI platforms. Landmark IPOs from OpenAI, Databricks and others are expected to reshape the tech landscape. Despite market fluctuations and some outsized reactions to earnings, the fundamentals for big tech remain robust. Companies like Apple have solidified their status as luxury brands, even as others like Tesla and Meta retool and pivot to sustain long-term relevance and unlock new revenue streams such as robotics and energy. At the structural level, venture capital itself is in flux. Many VC firms have become indistinguishable from private equity, constrained both by too much and too little available capital relative to the demands of today's tech startups. The gap between small angel, family office, or solo GP funds and the mega funds has widened so much that the “middle” has all but disappeared. It is now entirely possible for one-person companies, through the leverage of AI and autonomous agents, to achieve scale and revenues previously thought impossible. Ray predicts it is likely we will see a single founder build a billion-dollar annual revenue company within the next five years, echoing the democratization and disruption that generative AI promises. Building Legendary Companies and Careers in the Age of AI Christopher and Ray close their discussion by exploring what all these rapid changes mean for leaders and individuals. For CEOs and entrepreneurs, the formula for thriving is clear but audacious. Leaders must design their companies to be fully autonomous and authentic, constantly reinventing their business as if they were attempting to disrupt themselves. Boards need to be stacked with people who grasp the new fundamentals: margin compression, exponential scale, and infinite possibilities brought by AI. Combining domain expertise with technical agility is more critical than ever, as the fusion of seasoned judgment and lightning-fast, innovative execution is where breakthroughs occur. On a personal level, Ray stresses that knowledge and execution are becoming commodities, rapidly automated by advances in AI. To stay relevant, individuals must become “macro analysts,” adept at synthesizing big ideas and patterns, deeply immersed in experimenting with new technologies and surrounded by others who are passionate about their own crafts. The traditional playbooks for career building, education, and even family strategies are being rewritten in real-time. The U.S. faces global competition for talent and innovation, and entrepreneurial energy is no longer confined to Silicon Valley or New York. The nature of immigration, investment and even educational choices must be reconsidered for new generations. In a world where the location and structure of opportunity are shifting, only those who embrace change, foster diverse collaborations and pursue purpose will continue to define the next era of legendary achievement. As both Christopher and Ray reflect, living and leading like Rob Burgess—embracing boldness, curiosity and authenticity—remains the path to being truly legendary in this rapidly changing world. To hear more from Ray Wang and his updates on the world of Tech and AI, download and listen to this episode. Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and authors a business strategy and technology blog that has received millions of page views per month.  Wang also serves as a non-resident Senior Fellow at The Atlantic Council's GeoTech Center. Since 2003, Ray has delivered thousands of live and virtual keynotes around the world that are inspiring and legendary. Wang has spoken at almost every major tech conference. His ground-breaking bestselling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015.  Ray's new book about Digital Giants and the future of business titled, Everybody Wants to Rule the World will be released July 2021 by Harper Collins Leadership. Wang is well quoted and frequently interviewed in media outlets such as the Wall Street Journal, Fox Business News, CNBC, Yahoo Finance, Cheddar, CGTN America, Bloomberg, Tech Crunch, ZDNet, Forbes, and Fortune.  He is one of the top technology analysts in the world. Links Follow Ray Wang! Website | Twitter | LinkedIn | Constellation Research | DisrupTV We hope you enjoyed this episode of Christopher Lochhead: Follow Your Different™! Christopher loves hearing from his listeners. Feel free to email him, connect on Facebook, X (formerly Twitter), Instagram, and subscribe on Apple Podcast / Spotify!

Anna Anisin is a seasoned entrepreneur, ecosystem builder, and business owner with deep roots in the tech world and a passion for creativity.Starting her entrepreneurial journey at 16, Anna has since achieved multiple successful exits and built a career around scaling brands, building communities, and pioneering new paths in marketing innovation.Today, Anna leads DataScience.Salon, one of the most trusted communities in AI and machine learning, and runs FormulatedBy, a boutique B2B marketing firm specializing in demand generation, experiential strategy, and AI-driven marketing. Under her leadership, FormulatedBy has served over 100 brands including AWS, IBM, Databricks, Oracle, and many of the most influential startups in AI/ML and deep tech.Most recently, Anna launched the

In this episode of the Crazy Wisdom podcast, host Stewart Alsop welcomes Roni Burd, a data and AI executive with extensive experience at Amazon and Microsoft, for a deep dive into the evolving landscape of data management and artificial intelligence in enterprise environments. Their conversation explores the longstanding challenges organizations face with knowledge management and data architecture, from the traditional bronze-silver-gold data processing pipeline to how AI agents are revolutionizing how people interact with organizational data without needing SQL or Python expertise. Burd shares insights on the economics of AI implementation at scale, the debate between one-size-fits-all models versus specialized fine-tuned solutions, and the technical constraints that prevent companies like Apple from upgrading services like Siri to modern LLM capabilities, while discussing the future of inference optimization and the hundreds-of-millions-of-dollars cost barrier that makes architectural experimentation in AI uniquely expensive compared to other industries.Timestamps00:00 Introduction to Data and AI Challenges03:08 The Evolution of Data Management05:54 Understanding Data Quality and Metadata08:57 The Role of AI in Data Cleaning11:50 Knowledge Management in Large Organizations14:55 The Future of AI and LLMs17:59 Economics of AI Implementation29:14 The Importance of LLMs for Major Tech Companies32:00 Open Source: Opportunities and Challenges35:19 The Future of AI Inference and Hardware43:24 Optimizing Inference: The Next Frontier49:23 The Commercial Viability of AI ModelsKey Insights1. Data Architecture Evolution: The industry has evolved through bronze-silver-gold data layers, where bronze is raw data, silver is cleaned/processed data, and gold is business-ready datasets. However, this creates bottlenecks as stakeholders lose access to original data during the cleaning process, making metadata and data cataloging increasingly critical for organizations.2. AI Democratizing Data Access: LLMs are breaking down technical barriers by allowing business users to query data in plain English without needing SQL, Python, or dashboarding skills. This represents a fundamental shift from requiring intermediaries to direct stakeholder access, though the full implications remain speculative.3. Economics Drive AI Architecture Decisions: Token costs and latency requirements are major factors determining AI implementation. Companies like Meta likely need their own models because paying per-token for billions of social media interactions would be economically unfeasible, driving the need for self-hosted solutions.4. One Model Won't Rule Them All: Despite initial hopes for universal models, the reality points toward specialized models for different use cases. This is driven by economics (smaller models for simple tasks), performance requirements (millisecond response times), and industry-specific needs (medical, military terminology).5. Inference is the Commercial Battleground: The majority of commercial AI value lies in inference rather than training. Current GPUs, while specialized for graphics and matrix operations, may still be too general for optimal inference performance, creating opportunities for even more specialized hardware.6. Open Source vs Open Weights Distinction: True open source in AI means access to architecture for debugging and modification, while "open weights" enables fine-tuning and customization. This distinction is crucial for enterprise adoption, as open weights provide the flexibility companies need without starting from scratch.7. Architecture Innovation Faces Expensive Testing Loops: Unlike database optimization where query plans can be easily modified, testing new AI architectures requires expensive retraining cycles costing hundreds of millions of dollars. This creates a potential innovation bottleneck, similar to aerospace industries where testing new designs is prohibitively expensive.

Google about to snatch the crown… and a lot of y'all still stuck worshipping Nvidia like it's the only AI play that matter. I'm telling you right now: the market switches leaders — and when that leadership flips, it leaves people behind who don't see the shift coming.In this episode I break down why I believe Alphabet (Google) can become the #1 most valuable company, how AI chips + Gemini + YouTube + Cloud partnerships are stacking the deck, and why Nvidia still can run… but the competition is finally heavy. We also get into Apple picking Gemini, big tech power moves, Meta spending like a maniac on nuclear energy, and the 2026 IPO watchlist (SpaceX, OpenAI, Anthropic, Databricks, Stripe, Revolut, Canva — and my sleeper pick will surprise you).High-intent SEO keywords we touch naturally: Google stock, Alphabet stock, Gemini AI, Nvidia competition, AI chips, Big Tech leadership rotation, Apple Gemini deal, Google Cloud, YouTube revenue, AI investing, market leadership switching, Meta nuclear energy deal, 2026 IPOs, SpaceX IPO, OpenAI IPO, Anthropic IPO, Databricks IPO, Stripe IPO, Canva IPO, AI infrastructure stocks.Apple Picked Google Gemini. Bad News for Nvidia?Join our Exclusive Patreon!!! Creating Financial Empowerment for those who've never had it.

a16z cofounders Marc Andreessen and Ben Horowitz join a16z general partner Erik Torenberg and Not Boring founder Packy McCormick for a conversation on how the media and information ecosystem has changed over the past decade. The discussion breaks down the shift toward a more open and decentralized speech environment, the rise of writer- and creator-led platforms like Substack, and the erosion of centralized media gatekeepers. Marc and Ben also tie these dynamics to their investing worldview, outlining how supply-driven markets, major technological step changes, and reputation-driven venture platforms shape outcomes in the AI era.Timecodes: 00:00  Introduction00:46  How the media ecosystem is changing4:20  Why a16z invested in Substack6:28  Supply-driven markets and new content creation8:07  Why writers felt trapped by media companies10:09  Databricks and the 10x cloud multiplier13:58  Long-form podcasting proves demand15:40  What the new fund signals about the future16:24  AI as a universal problem solver18:49  Why market sizing is broken20:45  Go-to-market, policy, and platform power22:37  Turning inventors into confident CEOs25:58  Borrowing power to scale faster27:29  Building dreamers, not killing dreams30:46  Reputation as a core competitive advantage35:57  Taking arrows in public38:56  Avoiding big company failure modes40:39  Autonomous teams inside a16z41:54  Venture capital as the last job46:01  Why intangibles matter more than ever48:17  Original thinkers with charisma50:06  Why Zoomers are differentResources: https://www.notboring.co/p/a16z-the-power-brokershttps://www.a16z.news/p/firm-fundFollow Marc Andreessen on X: https://twitter.com/pmarcaFollow Ben Horowitz on X: https://twitter.com/bhorowitzFollow Erik Torenberg on X: https://twitter.com/eriktorenbergFollow Packy McCormick on X: https://twitter.com/packyM Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://twitter.com/eriktorenberg](https://x.com/eriktorenbergPlease 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.  Stay Updated:Find 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.

What does real AI transformation look like when leaders stop chasing prototypes and start demanding outcomes they can actually measure? That question sat at the center of my conversation with Alex Cross, Chief Technology Officer for EMEA at CI&T, alongside Melissa Smith, as we unpacked why so many organizations feel stuck between AI ambition and business reality. There is no shortage of excitement around AI, but there is growing skepticism too, especially from leadership teams who have seen pilots come and go without clear return. This episode focuses on how CI&T is addressing that gap head on. Alex shared how CI&T frames its work as AI-enabled transformation rather than simply layering AI tools onto existing processes. The distinction matters.  Instead of using AI to speed up broken workflows, CI&T reshapes how work gets done so AI becomes part of value creation itself. We explored a standout example from ITAU, the largest bank in Latin America, where deep modernization work helped deliver gains that most executives only ever see in strategy decks.  Productivity rose sharply, digital launch cycles collapsed from years to months, customer satisfaction jumped, and the commercial impact reached hundreds of millions in uplift. These are the kinds of results that change boardroom conversations. A big part of how CI&T gets there is its proprietary Flow platform. Alex explained how Flow gives clients a day-one AI environment, removing the heavy upfront cost and complexity that often slows momentum. Instead of spending months building platforms before any value appears, teams can move from proof of concept to production in as little as six to eight weeks. Flow also plays a second role that many AI programs miss, acting as a measurement layer so performance, efficiency, and ROI are visible rather than assumed. We also talked about why partnerships matter when execution is the goal. CI&T works closely with hyperscalers like AWS and Databricks, combining native tools with its own codified expertise. That combination has helped the company achieve an unusually high success rate in bringing AI initiatives to production, a challenge many organizations still struggle with. For Alex, the difference comes down to a relentless focus on production readiness and collaboration between business and technology teams from day one. Looking ahead, the conversation turned to CI&T's expansion across EMEA and what the company's 30th year represents. Rather than chasing every new trend, the focus is on productizing services around real client problems, whether that is legacy modernization, efficiency, or growth. The goal is to bridge strategy and execution in a way that feels practical, fast, and accountable. If you are leading AI initiatives and wondering why progress feels slower than the hype suggests, this episode offers a grounded perspective from the front lines. So, as organizations head into another year of bold AI plans, the real question becomes this. Are you building faster caterpillars, or are you ready to do the harder work required to turn ambition into something that can truly scale? Useful Links Connect with Alex Cross Connect With Melissa Smith Learn more about CI&T Follow CI&T on LinkedIn and YouTube Thanks to our sponsors, Alcor, for supporting the show.

Aishwarya Naresh Reganti and Kiriti Badam have helped build and launch more than 50 enterprise AI products across companies like OpenAI, Google, Amazon, and Databricks. Based on these experiences, they've developed a small set of best practices for building and scaling successful AI products. The goal of this conversation is to save you and your team a lot of pain and suffering.We discuss:1. Two key ways AI products differ from traditional software, and why that fundamentally changes how they should be built2. Common patterns and anti-patterns in companies that build strong AI products versus those that struggle3. A framework they developed from real-world experience to iteratively build AI products that create a flywheel of improvement4. Why obsessing about customer trust and reliability is an underrated driver of successful AI products5. Why evals aren't a cure-all, and the most common misconceptions people have about them6. The skills that matter most for builders in the AI era—Brought to you by:Merge—The fastest way to ship 220+ integrations: https://merge.dev/lennyStrella—The AI-powered customer research platform: https://strella.io/lennyBrex—The banking solution for startups: https://www.brex.com/product/business-account?ref_code=bmk_dp_brand1H25_ln_new_fs—Transcript: https://www.lennysnewsletter.com/p/what-openai-and-google-engineers-learned—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/183007822/referenced—Get 15% off Aishwarya and Kiriti's Maven course, Building Agentic AI Applications with a Problem-First Approach, using this link: https://bit.ly/3V5XJFp—Where to find Aishwarya Naresh Reganti:• LinkedIn: https://www.linkedin.com/in/areganti• GitHub: https://github.com/aishwaryanr/awesome-generative-ai-guide• X: https://x.com/aish_reganti—Where to find Kiriti Badam:• LinkedIn: https://www.linkedin.com/in/sai-kiriti-badam• X: https://x.com/kiritibadam—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Aishwarya and Kiriti(05:03) Challenges in AI product development(07:36) Key differences between AI and traditional software(13:19) Building AI products: start small and scale(15:23) The importance of human control in AI systems(22:38) Avoiding prompt injection and jailbreaking(25:18) Patterns for successful AI product development(33:20) The debate on evals and production monitoring(41:27) Codex team's approach to evals and customer feedback(45:41) Continuous calibration, continuous development (CC/CD) framework(58:07) Emerging patterns and calibration(01:01:24) Overhyped and under-hyped AI concepts(01:05:17) The future of AI(01:08:41) Skills and best practices for building AI products(01:14:04) Lightning round and final thoughts—Referenced:• LevelUp Labs: https://levelup-labs.ai/• Why your AI product needs a different development lifecycle: https://www.lennysnewsletter.com/p/why-your-ai-product-needs-a-different• Booking.com: https://www.booking.com• Research paper on agents in production (by Matei Zaharia's lab): https://arxiv.org/pdf/2512.04123• Matei Zaharia's research on Google Scholar: https://scholar.google.com/citations?user=I1EvjZsAAAAJ&hl=en• The coming AI security crisis (and what to do about it) | Sander Schulhoff: https://www.lennysnewsletter.com/p/the-coming-ai-security-crisis• Gajen Kandiah on LinkedIn: https://www.linkedin.com/in/gajenkandiah• Rackspace: https://www.rackspace.com• The AI-native startup: 5 products, 7-figure revenue, 100% AI-written code | Dan Shipper (co-founder/CEO of Every): https://www.lennysnewsletter.com/p/inside-every-dan-shipper• Semantic Diffusion: https://martinfowler.com/bliki/SemanticDiffusion.html• LMArena: https://lmarena.ai• Artificial Analysis: https://artificialanalysis.ai/leaderboards/providers• Why humans are AI's biggest bottleneck (and what's coming in 2026) | Alexander Embiricos (OpenAI Codex Product Lead): https://www.lennysnewsletter.com/p/why-humans-are-ais-biggest-bottleneck• Airline held liable for its chatbot giving passenger bad advice—what this means for travellers: https://www.bbc.com/travel/article/20240222-air-canada-chatbot-misinformation-what-travellers-should-know• Demis Hassabis on LinkedIn: https://www.linkedin.com/in/demishassabis• We replaced our sales team with 20 AI agents—here's what happened | Jason Lemkin (SaaStr): https://www.lennysnewsletter.com/p/we-replaced-our-sales-team-with-20-ai-agents• Socrates's quote: https://en.wikipedia.org/wiki/The_unexamined_life_is_not_worth_living• Noah Smith's newsletter: https://www.noahpinion.blog• Silicon Valley on HBO Max: https://www.hbomax.com/shows/silicon-valley/b4583939-e39f-4b5c-822d-5b6cc186172d• Clair Obscur: Expedition 33: https://store.steampowered.com/app/1903340/Clair_Obscur_Expedition_33/• Wisprflow: https://wisprflow.ai• Raycast: https://www.raycast.com• Steve Jobs's quote: https://www.goodreads.com/quotes/463176-you-can-t-connect-the-dots-looking-forward-you-can-only—Recommended books:•  When Breath Becomes Air: https://www.amazon.com/When-Breath-Becomes-Paul-Kalanithi/dp/081298840X• The Three-Body Problem: https://www.amazon.com/Three-Body-Problem-Cixin-Liu/dp/0765382032• A Fire Upon the Deep: https://www.amazon.com/Fire-Upon-Deep-Zones-Thought/dp/0812515285—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Today we're breaking down Databricks, a $130B private company that helps companies collect, store, and process very large amounts of data, and then use that data to run analytics and train machine learning models. Databricks sits in the middle of modern data systems, connecting raw data pipelines to the tools teams use to analyze information and build AI. If you've worked on large-scale data or AI projects, there's a good chance Databricks was part of the stack, often operating behind the scenes. My guest is Alan Tu, portfolio manager and analyst at WCM Investment Management, which invested in Databricks in late 2024. Alan explains what Databricks actually does for customers, why it remains one of the least understood large private software companies, and how its academic origins and founding team shaped its evolution from an early data-engineering product into a broad commercial platform. We also discuss common misconceptions about the business, how Databricks fits into the modern AI stack, what has changed since the last time we covered the company, and how its scale, product strategy, and capital position differentiate it from competitors. Note: This conversation was recorded on December 10, 2025, so all numbers are reflective of what was publicly available on that date. Please enjoy this breakdown of Databricks. For the full show notes, transcript, and links to the best content to learn more, check out the episode page⁠⁠⁠⁠⁠⁠⁠ here.⁠⁠⁠⁠⁠⁠⁠ —- This episode is brought to you by⁠⁠⁠⁠ ⁠Portrait Analytics⁠⁠⁠⁠⁠ - your centralized resource for AI-powered idea generation, thesis monitoring, and personalized report building. Built by buy-side investors, for investment professionals. We work in the background, helping surface stock ideas and thesis signposts to help you monetize every insight. In short, we help you understand the story behind the stock chart, and get to "go, or no-go" 10x faster than before. Sign-up for a free trial today at⁠⁠⁠⁠ ⁠portraitresearch.com⁠⁠⁠⁠⁠ — Business Breakdowns is a property of Colossus, LLC. For more episodes of Business Breakdowns, visit⁠⁠⁠⁠⁠⁠⁠ joincolossus.com/episodes⁠⁠⁠⁠⁠⁠⁠. Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠). Timestamps  (00:00:00) Welcome to Business Breakdowns (00:02:34) Introducing Databricks and Guest Alan Tu (00:03:22) Understanding Databricks' Core Functionality (00:09:15) The Founding Story of Databricks (00:23:54) Databricks' Evolution and Product Expansion (00:30:06) Databricks vs. Snowflake: Market Competition (00:35:36) Databricks' Strategic Vision and Market Impact (00:38:14) The Rise of Big Data and Databricks' Core Value (00:39:27) Understanding Databricks Through a Credit Card Fraud Use Case (00:44:35) Databricks' Role in AI and Machine Learning (00:51:12) The Competitive Landscape and Cloud Partnerships (00:54:54) Financial Dynamics and Pricing Strategies (01:09:37) The Future of Databricks: Risks and Long-Term Vision (01:12:54) Conclusion and Final Thoughts

Jason Lemkin is the founder of SaaStr, the world's largest community for software founders, and a veteran SaaS investor who has deployed over $200 million into B2B startups. After his last salesperson quit, Jason made a radical decision: replace his entire go-to-market team with AI agents. What started as an experiment has transformed into a new operating model, where 20 AI agents managed by just 1.2 humans now do the work previously handled by a team of 10 SDRs and AEs. In this conversation, Jason shares his hands-on experience implementing AI to run his sales org, including what works, what doesn't, and how the GTM landscape is quickly being transformed.We discuss:1. How AI is fundamentally changing the sales function2. Why most SDRs and BDRs will be “extinct” within a year3. What Jason is observing across his portfolio about AI adoption in GTM4. How to become “hyper-employable” in the age of AI5. The specific AI tools and tactics he's using that have been working best6. Practical frameworks for integrating AI into your sales motion without losing what works7. Jason's 2026 predictions on where SaaS and GTM are heading next—Brought to you by:DX—The developer intelligence platform designed by leading researchersVercel—Your collaborative AI assistant to design, iterate, and scale full-stack applications for the webDatadog—Now home to Eppo, the leading experimentation and feature flagging platform—Transcript: https://www.lennysnewsletter.com/p/we-replaced-our-sales-team-with-20-ai-agents—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/182902716/my-biggest-takeaways-from-this-conversation—Where to find Jason Lemkin:• X: https://x.com/jasonlk• LinkedIn: https://www.linkedin.com/in/jasonmlemkin• Website: https://www.saastr.com• Substack: https://substack.com/@cloud—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Jason Lemkin(04:36) What SaaStr does(07:13) AI's impact on sales teams(10:11) How SaaStr's AI agents work and their performance(14:18) How go-to-market is changing in the AI era(19:19) The future of SDRs, BDRs, and AEs in sales(22:03) Why leadership roles are safe(23:43) How to be in the 20% who thrive in the AI sales future(28:40) Why you shouldn't build your own AI tools(30:10) Specific AI agents and their applications(36:40) Challenges and learnings in AI deployment(42:11) Making AI-generated emails good (not just acceptable)(47:31) When humans still beat AI in sales(52:39) An overview of SaaStr's org(53:50) The role of human oversight in AI operations(58:37) Advice for salespeople and founders in the AI era(01:05:40) Forward-deployed engineers(01:08:08) What's changing and what's staying the same in sales(01:16:21) Why AI is creating more work, not less(01:19:32) Why Jason says these are magical times(01:25:25) The "incognito mode test" for finding AI opportunities(01:27:19) The impact of AI on jobs(01:30:18) Lightning round and final thoughts—Referenced:• Building a world-class sales org | Jason Lemkin (SaaStr): https://www.lennysnewsletter.com/p/building-a-world-class-sales-org• SaaStr Annual: https://www.saastrannual.com• Delphi: https://www.delphi.ai/saastr/talk• Amelia Lerutte on LinkedIn: https://www.linkedin.com/in/amelialerutte/• Vercel: https://vercel.com• What world-class GTM looks like in 2026 | Jeanne DeWitt Grosser (Vercel, Stripe, Google): https://www.lennysnewsletter.com/p/what-the-best-gtm-teams-do-differently• Everyone's an engineer now: Inside v0's mission to create a hundred million builders | Guillermo Rauch (founder and CEO of Vercel, creators of v0 and Next.js): https://www.lennysnewsletter.com/p/everyones-an-engineer-now-guillermo-rauch• Replit: https://replit.com• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• ElevenLabs: https://elevenlabs.io• The exact AI playbook (using MCPs, custom GPTs, Granola) that saved ElevenLabs $100k+ and helps them ship daily | Luke Harries (Head of Growth): https://www.lennysnewsletter.com/p/the-ai-marketing-stack• Bolt: https://bolt.new• Lovable: https://lovable.dev• Harvey: https://www.harvey.ai• Samsara: https://www.samsara.com/products/platform/ai-samsara-intelligence• UiPath: https://www.uipath.com• Denise Dresser on LinkedIn: https://www.linkedin.com/in/denisedresser• Agentforce: https://www.salesforce.com/form/agentforce• SaaStr's AI Agent Playbook: https://saastr.ai/agents• Brian Halligan on LinkedIn: https://www.linkedin.com/in/brianhalligan• Brian Halligan's AI: https://www.delphi.ai/minds/bhalligan• Sierra: https://sierra.ai• Fin: https://fin.ai• Deccan: https://www.deccan.ai• Artisan: https://www.artisan.co• Qualified: https://www.qualified.com• Claude: https://claude.ai• HubSpot: https://www.hubspot.com• Gamma: https://gamma.app• Sam Blond on LinkedIn: https://www.linkedin.com/in/sam-blond-791026b• Brex: https://www.brex.com• Outreach: https://www.outreach.io• Gong: https://www.gong.io• Salesloft: https://www.salesloft.com• Mixmax: https://www.mixmax.com• “Sell the alpha, not the feature”: The enterprise sales playbook for $1M to $10M ARR | Jen Abel: https://www.lennysnewsletter.com/p/the-enterprise-sales-playbook-1m-to-10m-arr• Clay: https://www.clay.com• Owner: https://www.owner.com• Momentum: https://www.momentum.io• Attention: https://www.attention.com• Granola: https://www.granola.ai• Behind the founder: Marc Benioff: https://www.lennysnewsletter.com/p/behind-the-founder-marc-benioff• Palantir: https://www.palantir.com• Databricks: https://www.databricks.com• Garry Tan on LinkedIn: https://www.linkedin.com/in/garrytan• Rippling: https://www.rippling.com• Cursor: https://cursor.com• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• The new AI growth playbook for 2026: How Lovable hit $200M ARR in one year | Elena Verna (Head of Growth): https://www.lennysnewsletter.com/p/the-new-ai-growth-playbook-for-2026-elena-verna• Pluribus on AppleTV+: https://tv.apple.com/us/show/pluribus/umc.cmc.37axgovs2yozlyh3c2cmwzlza• Sora: https://openai.com/sora• Reve: https://app.reve.com• Everything That Breaks on the Way to $1B ARR, with Mailchimp Co-Founder Ben Chestnut: https://www.saastr.com/everything-that-breaks-on-the-way-to-1b-arr-with-mailchimp-co-founder-ben-chestnut/• The Revenue Playbook: Rippling's Top 3 Growth Tactics at Scale, with Rippling CRO Matt Plank: https://www.youtube.com/watch?v=h3eYtzBpjRw• 10 contrarian leadership truths every leader needs to hear | Matt MacInnis (Rippling): https://www.lennysnewsletter.com/p/10-contrarian-leadership-truths—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Plus: Invictus Growth Partners to acquire Informed.IQ, an AI-based fraud detection company. And PayPal applies to establish its own bank. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

My guest today is David George. David is a General Partner at Andreessen Horowitz, where he leads the firm's growth investing business. His team has backed many of the defining companies of this era – including Databricks, Figma, Stripe, SpaceX, Anduril, and OpenAI – and is now investing behind a new generation of AI startups like Cursor, Harvey, and Abridge. This conversation is a detailed look at how David built and runs the a16z growth practice. He shares how he recruits and builds his team a “Yankees-level” culture, how his team makes investment decisions without traditional committees, and how they work with founders years before investing to win the most competitive deals. Much of our conversation centers on AI and how his team is investing across the stack, from foundational models to applications. David draws parallels to past platform shifts – from SaaS to mobile – and explains why he believes this period will produce some of the largest companies ever built. David also outlines the models that guide his approach – why markets often misprice consistent growth, what makes “pull” businesses so powerful, and why most great tech markets end up winner-take-all. David reflects on what he's learned from studying exceptional founders and why he's drawn to a particular type, the “technical terminator.” Please enjoy my conversation with David George. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠ ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ramp⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ridgeline⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Head to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about the platform. ----- This episode is brought to you by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AlphaSense⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Invest Like the Best listeners can get a free trial now at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Alpha-Sense.com/Invest⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Show Notes: (00:00:00) Welcome to Invest Like The Best (00:04:00) Meet David George (00:03:04) Understanding the Impact of AI on Consumers and Enterprises (00:05:56) Monetizing AI: What is AI's Business Model (00:11:04) Investing in Robotics and American Dynamism (00:13:31) Lessons from Investing in Waymo (00:15:55) Investment Philosophy and Strategy (00:17:15) Investing in Technical Terminators (00:20:18) Market Leaders Capture All of the Value Creation (00:24:56) The Maturation of VC and Competitive Landscape (00:28:18) What a16z Does to Win Deals (00:33:06) David's Daily Routine: Meetings Structure and Blocking Time to Think (00:36:34) Why David Invests: Curiosity and Competition (00:40:12) The Unique Culture at Andreessen Horowitz (00:42:46) The Perfect Conditions for Growth Investing (00:47:04) Push v. Pull Businesses (00:49:19) The Three Metrics a16z Uses to Evaluate AI Companies (00:52:15) Unique Products and Unique Distribution (00:54:55) Tradeoffs of the a16z Firm Structure (00:59:04) a16z's Semi-Algorithmic Approach to Selling (01:00:54) Three Ways Startups can Beat Incumbents in AI (01:03:44) The Kindest Thing