Podcast appearances and mentions of Mike Krieger

Mike Krieger is the head of Anthropic Labs and co-founder of Instagram. Krieger joins Big Technology Podcast live from the Big Technology AI Summit to discuss what it's like inside Anthropic the week the government forced the company to pull its frontier models, Fable and Mythos, off the market. Tune in to hear Krieger describe how working with Fable changed the way he builds — queuing up a full night of work before bed and waking to find it finished in an hour — why he insists Anthropic's safety warnings are material rather than marketing, and how Anthropic navigates being both a platform and a product as it competes with the companies building on top of it. Wired senior correspondent Lauren Goode joins as a co-interviewer. Hit play for a rare look inside the lab from the person building Anthropic's next breakout product.--- AI Agent documentary: https://www.gravitee.io/ai-agent-documentary Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

We're back with more from our live event at the Yerba Buena Center for the Arts in San Francisco. In this episode, we sit down with Dylan Field, a founder and the chief executive of the design company Figma, for what he describes as a “roller coaster” of a conversation. We cover everything from the company's “Design Is Dead” campaign to the sudden resignation of the Anthropic executive Mike Krieger from Figma's board. Then, we close things out with a special musical performance by eight wooden robotic dolls that make up the Teenage Engineering Choir. One quick correction to note: In our interview with Field, he makes reference to the SpaceX S-1 filing and misstates what the company says their addressable market for A.I. enterprise applications is. Field says “$22.9 trillion,” but the correct number from the SpaceX filing is $22.7 trillion. The decimal point makes it look small, but it's a difference of $200 billion. We'll be back on Friday with our final installment of “Hard Fork” Live.   Guests: Dylan Field, chief executive and co-founder of Figma. Dan Powell, robot conductor, New York Times music composer and “Hard Fork” theme-song creator. Teenage Engineering Choir   Additional Reading: This Start-Up's $20 Billion Sale Died. It Came Fighting Back.   We want to hear from you. Email us at hardfork@nytimes.com. Find “Hard Fork” on YouTube and TikTok. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Mike Krieger built one of the most consequential consumer apps of the last two decades as the cofounder of Instagram. He is now at the frontier of AI-native product development as head of Anthropic Labs, the team responsible for figuring out what the most capable AI models can do in the hands of real builders.When Krieger first got access to Fable 5 months before its public release, it was exciting and disorienting. “I feel like a total newbie again,” he remembers telling his team. The way he'd been thinking about productivity, strategy, and time management was out of date. The model had outpaced his workflows.Dan Shipper talked with Krieger for AI & I about what it looks like to build with a model as capable as Fable 5, including the new rhythms, challenges, and possibilities it reveals.If you found this episode interesting, please like, subscribe, comment, and share!To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperGet started with Braintrust at https://www.braintrust.dev/ Timestamps:0:03 Introduction1:48 How Fable completely reshaped Mike's workflow4:48 When to use Sonnet versus Fable10:06 What the media tracker Mike built over a weekend reveals about agent-native architecture15:00 The cost to build has collapsed19:03 Is software engineering over?21:48 How Anthropic's engineering teams work today38:39 The mechanics of verification44:39 What people should use the model to build47:24 Dynamic workflowsLinks to resources mentioned in the episode:Mike Krieger on X: https://x.com/mikeykAnthropic Labs: https://www.anthropic.comClaude Code: https://claude.ai/codeEvery: https://every.toTimestamps:0:03 Introduction1:48 How Fable completely reshaped Mike's workflow4:48 When to use Sonnet vs. Fable10:06 What the media tracker Mike built over a weekend reveals about agent-native architecture15:00 The cost to build has collapsed19:03 Is software engineering over?21:48 How Anthropic's engineering teams work today38:39 The mechanics of verification44:39 What people should use the model to build47:24 Dynamic workflowsLinks to resources mentioned in the episode:Mike Krieger on X: https://x.com/mikeykAnthropic Labs: https://www.anthropic.comClaude Code: https://claude.ai/codeEvery: https://every.to

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee

Hey, Alex here, I'll try to catch you up, but it's one of the more intense weeks in AI in recent memory. Here's the TL;DR - OpenAI dominates across the board this week! Finally launches “spud”, called it GPT 5.5 (and 5.5 Pro), and it's SOTA on most things,nearly matching the mysterious Claude Mythos but released and we can actually use it (we tested it extensively). OpenAI also took the crown in image generate with the incredible GPT-image-v2 release, beating Nano Banana 2 and pro by a significant margin, the images are incredible, this model can generate working QR codes and 360 images it's quite bonkers. Codex was updated with Computer Use (which I told you about last week), in-app browser and a bunch of other tools that match GPT 5.5 intelligence. Meanwhile, Anthropic launched an incredible research preview of Claude Design, finally admitted that Claude was dumb and reset quotas across the board, while breaking the trust of the community with removing Claude code from the pro plan. We've also got great open source updates, Kimi K2.6 and Qwen 3.6 27B are both great performers! We were live on the stream for almost 4 hours today waiting for GPT 5.5 and finally got it and tested it live on the show + had Peter Gostev on from Arena who had early access and shared with us his insights. Let's get into it! ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.OpenAI's GPT 5.5 is here - SOTA AI intelligence you can actually use (Release Blog)OpenAI finally gave us all access to their latest intelligence boost, GPT 5.5 thinking (and GPT 5.5 Pro). These models take the crown across many benchmarks, including TerminalBench (82.7%), GPDval (84%) and more. You can see the highlited versions on the image above. Though, its not uncommon for OpenAI to do some chart crimes, so @d4m1n created a chart that also showed the full benchmarks, including the ones GPT 5.5 is not beating Opus at, as you can see below, it underperforms on Humanity's Last Exam, and scaled tool use. But, benchmarks don't tell the full story. GPT 5.5 uses significantly less tokens, compared to 5.4, about 40% less. It's also more expensive, but given the lower token usage, it nets out at about ~20% price increase, while being more intelligence and faster. Tons of folks who had early access are reporting the same things, this model excels in long running tasks, Peter Gostev from Arena, who joined our live stream, showed us an incredible demo that ran overnight for over 8h! This model can work until the task is done, no longer just pausing in the middel asking for your input. The real highlight is, paired with the recent GPT-image-2 (which I'll expand on later in this newsletter), GPT 5.5 becomes an excellent UI designer. This is a big area in which Claude still has moat and OpenAI is trying to catch up here, and the real alpha now is to use both the Image gen and 5.5 in tandem to create beautiful visuals and UIs. The main thing is, after testing it quite a few times, this only works if you generate an image outside of the session that builds the actual UI. we tried a couple of times to do it in 1 session, and the resulting UI doesn't seem to be remotely close to the generated image. Only after sending this image to a completely fresh session and asking for a “pixel perfect” implementation, did GPT 5.5 start to resemble the input image and rebuild the whole ui in pixel perfect fidelity! GPT Image v2 - SOTA thinking image model, finally beating Nano Banana (Blog, Live)Like we said, OpenAI is dominating this week, and in both instances those are great models. Though, apples to apples comparison, GPT-image-v2 is a much higher jump — from previous models — than GPT 5.5! According to Artificial Analysis, the jump in how many people prefer GPT-image-2 in blind tests compared to other model is the higest we've ever seen, over 250 points. And you can clearly see it in the generations as well. Previously this week, we did a live streaming session with Peter Gostev (from Arena) and we did a deep dive comparing this new model to GPT Image 1.5, Nano Banana and Grok Imagine, and it's a clear winner across most categories.Character consistency is immaculate, high resolution imagery, instruction following, are all so so good it's a bit hard to explain in text. Reasoning visual intelligence Like with Nano Banana, this model is likely based on a big GPT image, it's no longer just diffusion, as you can see, it reasons! And apparently the more reasoning you give it (if you choose GPT pro) the better it'll be. The examples are indeed wild, the model can generate images of code that works, generate functional QR codes and bar codes! The craziest thing people figured out it can do, is functional 360 imagery (equirectangular format), you can just ask the model to create a 360 image of “scene” and then drop this in to a 360 viewer! Peter shows us on the show how he combined GPT 5.5 and Image v2 to create a sort of “street view” from a bunch of 360 images, it blew our minds. He literally spun up an overnight GPT 5.5 task in Codex that planned out the hanging gardens of Babylon, generated hundreds of equirectangular images, stitched them into a walkable interface, and had it running 8+ hours without babysitting. A street view of a place we don't actually know what it looked like, hallucinated from latent space. What a time.Day one availability is wide: Figma, Canva, Adobe Firefly, fal.ai, and Microsoft Foundry all have it. Nano Banana dominated for what felt like an eternity in AI time (it was really only a few months

Today, we check in a year after the first Unsupervised Learning x Latent Space Crossover special to discuss everything that has changed (there is a lot) in the world of AI. This episode was recorded just after AIE Europe, but before the Cursor-xAI deal.Unsupervised Learning is a podcast that interviews the sharpest minds in AI about what's real today, what will be real in the future and what it means for businesses and the world - helping builders, researchers and founders deconstruct and understand the biggest breakthroughs.Thanks to Jacob and the UL production team for hosting and editing this!Jacob Effron* LinkedIn: https://www.linkedin.com/in/jacobeffron/* X: https://x.com/jacobeffronFull Episode on Their YouTubeWe discuss:* swyx's view from the center of the AI engineering zeitgeist: OpenClaw, harness engineering, context engineering, evals, observability, GPUs, multimodality, and why conference tracks now reveal what matters most in AI* Whether AI infrastructure has finally stabilized: why “skills” may be the minimal viable packaging format for agents, why infra companies have had to reinvent themselves every year, and why application companies have had an easier time surviving model volatility* The vertical vs. horizontal AI startup debate: why application companies can act as the outsourced AI team for enterprises, why some horizontal companies still matter, and why sandboxes may be the clearest reinvention of classic cloud infrastructure for the AI era* The “agent lab” playbook: starting with frontier models, specializing for your domain, then training your own models once you have enough data, workload, and user behavior to justify the cost and latency savings* Why domain-specific model training is real, not just marketing: how companies like Cursor and Cognition can get users to choose their in-house models, and why search, domain specialization, and distillation are becoming more important* Open models, custom chips, and alternative inference infrastructure: why swyx has turned more bullish on open source, why non-NVIDIA hardware is suddenly getting real attention, and why every 10x speedup can unlock new product experiences* What it means to sell to agents instead of humans: why agent experience may mostly just be good developer experience by another name, why APIs and docs matter more than ever, and how pretraining-data incumbents are compounding advantages in an agent-first world* Why memory and personalization may become the next big wedge: today's models mostly reward frequency of mentions, but in the future, swyx expects product choice to be shaped much more by personalized memory systems* The state of the AI coding wars: why coding has become one of the largest and fastest-growing categories in AI, how Anthropic, OpenAI, Cursor, and Cognition have all ridden the wave, and why the category may still have more room to run* Capability exploration vs. efficiency: why the industry is still in a token-maxing, experiment-heavy phase where people are rewarded for spending more rather than less* Claude Code vs. Codex and the strange stickiness of coding products: why first magical product experiences may matter more than expected, and why the bigger mystery may be why only a few names have emerged as real winners so far* What the end state of the coding market might look like: two major players, a longer tail of niche products, and possible disruption if Microsoft, Mistral, xAI, or the Chinese labs push harder into coding* Where application companies still have room against the labs: why frontier labs are trying to expand into verticals like finance and healthcare, but still leave space for focused companies that own the workflow and the last mile* Why coding may be a preview of every other AI market: the first category to truly go parabolic, the clearest example of foundation model companies colliding with application companies, and a template for how future vertical AI markets may develop* Why AI valuations now feel unbounded: from billion-dollar ARR products built in a year to trillion-dollar market caps, swyx and Jacob unpack how the AI market has broken traditional startup intuitions about scale and durability* Consumer AI vs. coding AI: why ChatGPT's consumer category may have plateaued on frequency and product design, while coding continues to feel like a daily-use category with real momentum* The next product frontier beyond coding: consumer agents, computer use, and “coding agents breaking containment,” with swyx's thesis that 2025 was the year of coding agents and 2026 may be the year they begin to do everything else* Whether foundation models are really killing startup categories: why swyx is less worried for early founders, more worried for mid-size startups and traditional SaaS, and why building something ambitious may now be the best job interview for a frontier lab* AI vs. SaaS and the internal culture war around adoption: the tension between AI-native employees who want to rip out expensive software and skeptics who think quick AI-built replacements create fragile systems* Why traditional SaaS may be under real pressure: swyx's own experience spending six figures on event and sponsor management software, the temptation to rebuild it cheaply with AI, and the broader question of whether teams will trust custom AI-native replacements* Biosafety, security, and frontier model access: why swyx raised biosafety at a dinner with Anthropic's Mike Krieger, why Krieger argued security is the bigger issue, and what restricted model releases reveal about Anthropic vs. OpenAI* The era of giant models: why 10T+ parameter systems may only be a temporary rationing phase before bigger clusters arrive, why labs may increasingly keep their most powerful models private for distillation, and why scale alone no longer feels like a complete answer* Memory as the slowest scaling factor in AI: why context windows have improved far more slowly than people hoped, why million-token context still has not changed most real workflows, and why memory may be the key bottleneck for the next generation of systems* What swyx changed his mind on in the past year: becoming more bullish on open models, more convinced that the top tier of agent startups behaves very differently from the median AI company, and more optimistic about fine-tuning and specialized model adaptation* “Dark factories” and zero-human-review coding: the next frontier after zero human-written code, where models not only write the code but ship it without human review, forcing companies to rethink testing and verification from first principles* Why RL and post-training may matter more than people assumed: even if the resulting models get thrown out every few months, the data, workflows, and domain-specific improvements persist* Synthetic rubrics, Doctor GRPO, and multi-turn RL: why reinforcement learning is becoming much more domain-specific and multi-step than many people realize, opening the door to much deeper customization* The next frontier after coding: memory, personalization, and world models, including why swyx thinks world models matter not just for robotics or gaming, but for giving AI something closer to lived understanding* Fei-Fei Li, spatial intelligence, and the Good Will Hunting analogy: the idea that today's LLMs may know everything by reading it all, but still lack the lived experience that turns knowledge into a deeper kind of intelligenceTimestamps* 00:00:00 Intro preview: AI coding wars, startup pressure, and market structure* 00:00:28 Welcome to the Latent Space × Unsupervised Learning crossover* 00:01:17 What AI builders are focused on now: OpenClaw, harnesses, and infra* 00:04:33 Why AI infra is harder than apps, and where startups can still win* 00:06:39 Should companies train their own models?* 00:09:28 Open models, custom chips, and the new inference race* 00:11:25 Designing products for agents, not just humans* 00:16:49 The state of the AI coding wars in 2026* 00:19:27 Capability exploration, token-maxing, and why coding is going parabolic* 00:21:41 What the end state of the coding market could look like* 00:23:50 Where app companies still have room against the labs* 00:27:02 Why AI valuations and market swings feel unprecedented* 00:28:56 Consumer AI vs. coding AI, and why sticky products still matter* 00:32:28 What the next breakthrough product experience might be* 00:32:53 2026 thesis: coding agents break containment and eat the world* 00:35:27 Are foundation models wiping out startup categories?* 00:37:33 AI vs. SaaS, vibe coding, and internal team tensions* 00:40:01 Biosafety, security, and the politics of restricted model releases* 00:42:19 Giant models, compute constraints, and the limits of scale* 00:44:30 Memory as the real bottleneck in AI* 00:44:57 Why swyx changed his mind on open models* 00:47:44 Dark factories and the future of zero-human-review coding* 00:49:36 Why post-training and RL may matter more than people think* 00:51:50 Memory, world models, and the next frontier of intelligence* 00:53:54 The Good Will Hunting analogy for LLMs* 00:54:21 OutroTranscript[00:00:00] swyx: Isn't that crazy? That number is just mind boggling.[00:00:03] Jacob Effron: What is the state of the AI coding wars today?[00:00:05] swyx: We're in a phase of sort of like capability exploration. The general thesis that I have been pursuing now is that the same way that 2025 was a year coding agents 2026 is coding agents breaking containments to do everything else.[00:00:16] Jacob Effron: Do you worry about the foundation models just getting into a bunch of these startup categories?[00:00:21] swyx: Mid-size startups. Yes.[00:00:23] Jacob Effron: What do you think the end state of this market is[00:00:25] swyx: for the market structure to, to significantly change? There would be[00:00:28] Jacob Effron: today on unsupervised learning. We had a, a fun episode and what's really become an annual tradition, a crossover episode with our friends at Latent space.Swix and I sat down and we talked about everything happening in the AI ecosystem today. What we thought of the various changes at the model layer, what's happening in the infra world, the coding wars, and a bunch of other things. It's a ton of fun to do this with someone I really respect and another great podcaster in the game.Without further ado, here's our episode. Well switch. This is, uh, super fun to be back with another unsupervised learning, uh, latent space crossover episode.[00:01:02] swyx: Yeah,[00:01:02] Jacob Effron: I feel like a lot of places we could start, but you know, one thing I always find fascinating, uh, about the way you spend your time is you obviously are like at the epicenter of this engineering movement and community, and you run these events and conferences and put on these.Awesome talks and, and I think just have a great pulse on the zeitgeist of what's going on.[00:01:16] swyx: Yeah.[00:01:17] Jacob Effron: Maybe to, to start just what are the biggest topics people are thinking about right now?[00:01:21] swyx: Yeah, so I just came back from London, uh, where we did a IE Europe and we're doing roughly one per quarter now, which Yeah, you've[00:01:27] Jacob Effron: really up[00:01:27] swyx: the, hopefully[00:01:28] Jacob Effron: up the, up the pace.[00:01:29] swyx: It's trying. We're trying to match AI speed, youknow?[00:01:30] Jacob Effron: Yeah, exactly. The tops would be completely different, I imagine. Uh,[00:01:33] swyx: yeah. You know, I definitely curate the tracks, like you can see what I think. When you see the track list and the, the speakers that I invite, obviously Open Claw is like the story of the last four or five months, and then be, be just below that.I would consider harness engineering, context engineering to be two related topics in agents and rag. And then there's a long tail of Evergreen stuff like evals, observability, GPUs, uh, and uh, LM infra and just general, just in general. We also have other updates on like multimodality and, uh, generative media, let's call it.Um, but I definitely, the, the first three that I mentioned are top of mind people. Yeah.[00:02:13] Jacob Effron: I think harness is particular like, so interesting. Um, you know, there was this tweet from Harrison Chase, the, the lane chain, CEO, that, that caught my eye recently where he said, you know, it finally feels like we have stability, uh, around the infrastructure for, uh, you know, around ai.And I think what. He basically was implying his like, look over the past two, three years as a company at the epicenter of AI infrastructure, it was a bit like playing whack-a-mole, right? You were constantly moving around with, however, the building patterns were evolving[00:02:36] swyx: for Harrison for sure. Right? Like he's basically had to reinvent the company every year since he started Lang Chain.Right? It was Lang chain, Ang graph and LP agents and like, uh, I think he's like one of the most nimble, adept sharp people about this. Yeah. Yeah.[00:02:49] Jacob Effron: Saying now, now is finally the time stability[00:02:51] swyx: this. Yeah.[00:02:52] Jacob Effron: Yeah. Um, do you buy that or what have you kind of make of that take?[00:02:56] swyx: I think that. It, it's very expensive to say this Time is different sometimes, but when you're just writing code, like it's actually okay to just like try to make a call and I think it may not even matter if this call is right or not.Like I just don't even care that much because you can be right on a thesis, but if you don't, you don't figure out how to monetize the thesis, then who cares if you said something first that said, um, it does feel like, for example. Uh, we went through a lot of different ways of passion packaging integrations up with, uh, with agents.And it feels like we've landed at skills, which is like the minimal viable format. Yeah. Which is just a markdown file, uh, with some scripts attached to it, and I don't see how it can be more simple than that. And so there is some justification for. The stability around harnesses. I feel like there may be more adaptation with regards to maybe like the real time elements or subagents or memory or any of those like agent disciplines, let's call it in, in agent engineering.Uh, but if, if the thesis is that, okay, you just want agents are LMS with tools in the loop with a file system, what they can do. Retrieval with, with skills and all these like standard tooling that now seems to be relatively consensus then probably. That makes sense. Um, I just think like there's no point trying to stake your reputation on this thesis that we're there because if it changes again, just change with it.It's fine.[00:04:33] Jacob Effron: Yeah. It's always, you know, I've always been struck by how that is. Much more challenging for infrastructure companies and application companies. Like obviously I think, yeah. You know, on the application side you've seen, you know, Brett Taylor from Sierra Max, from Lara. Like, they're like, look, we build, you know, what's ahead of the models and we're willing to throw everything out every three months, you know, as the models get better and better.Exactly. Yeah. But the thing you at least have there is you have. Uh, you have an end customer, right? That's like decently sticky. Um, you know, they will mostly stick, you know, they'll, they'll give you a shot at least of, of building these things. What I've always found more challenging, uh, at, at the kind of like, you know, reinvent yourself every three months of the infrastructure layer, it's like, you know, developers are definitely a, a pickier audience maybe than an accounting firm or, uh, you know, a bank.Yeah. And so it's definitely a, a, a more challenging position to be in to, to have to constantly reinvent yourself.[00:05:17] swyx: Yeah. Yeah. Yeah. And, and like when they turn, it's like. Very complete. Like, they'll leave to like the, the hot new thing, uh, because there's like no defensibility, I guess. Like e even, even if you are a database, like, uh, people can migrate workloads off databases.Like it's, it's a, it's a known thing. Uh, so I think like basically what we're talking about is the vertical versus horizontal, uh, debate in, in AI startups. And uh, the way I think about it also is just that like when you are. Um, Lara, when you are a bridge, like you are the outsource AI team, right? You, you are, your job is to apply whatever state ofthe art AI methods.[00:05:55] Jacob Effron: Yeah. Like this translation layer between model capabilities and your[00:05:57] swyx: own customers. Yeah. To, to the end customers and like, well, if they didn't have you, they would've to hire in house and they're not gonna hire in house so they have you. And like, I think that's like a reasonable, like very robust to any whatever trends and, and discoveries that people make in, in the engineering layer.I do think like there is, um. It like sort of useful horizontal companies being built, but they're all. Very much like, sort of like the reinventions of classic cloud in the AI era and the, the primary one being sandboxes. Yeah. Um, which like, it's another form of compute guys, like, let's not get too excited about it.But I mean, like the, the workloads are enormous.[00:06:38] Jacob Effron: Right.[00:06:38] swyx: Yeah.[00:06:39] Jacob Effron: It's interesting, and I feel like as, as part of this, you know, the questions that folks are asking around infrastructure, there's a lot around, you know, the extent to which companies should have their own AI teams and what they should be doing in-house.And, you know, uh, I think there's questions around should people be training their own models? Should people be doing, you know, rl, uh, in-house based on the data they have? I feel like, you know, one has to evolve their takes on this every, every three months with paces. But where, where are you at on this today?[00:07:00] swyx: I think, well, I mean actually all models have gone up. Um, and obviously I'm involved in cognition and also cursors doing, doing, uh, a lot of own model training. And I think that that is some part of the, what I've been calling the agent lab playbook, where you start off with the state of the art models from, uh, from the big labs and you, uh, specialize for your domain.But once you have enough workload and enough high quality data from your users, then you can obviously train your own models and like save a lot on cost and latency and all that, all that good stuff. Um, you also get like a marketing bonus of like calling it some fancy name and putting out some research[00:07:38] Jacob Effron: from my seat.I can't tell how much of it is like actual, you know, value that's provided to the end user. And how much of it is that marketing bonus? Right. It seems some combination of the[00:07:45] swyx: I think it's both.[00:07:46] Jacob Effron: Yeah.[00:07:46] swyx: Um, no, no. There, there actually is real value. Um, and you, you know that for a number of reasons. Like one, even when it's not subsidized, people do choose it as like one of the top four or five.This is both composer two and, uh, suite 1.6 I one of the top five models. Like in a, in a fair market? In a free market, yeah. In a, in a, in a model switch. Or people do choose it and like, it's not subsidized. Like, so that's as good as it gets. Uh, but beyond that, like domain specific models, for example. For search with, with both, which both companies have absolutely makes, makes a ton of sense.Everyone says like, yeah, we should always, always do this. And honestly like, I think the infrastructure for that is becoming easier with, um, like thinking machines tinker thing as well as primary like, uh, lab stuff. Yeah, I mean like, this is one of those like reversal of the, the bitter lesson where you first bootstrap on the large models and the general purpose models to get big.And as you get very well-defined workloads that are just high quantity but not high variance, um, then you just distill down to a smaller model and run that on your own. Right. Which like totally makes sense.[00:08:50] Jacob Effron: What I'm less clear on is the kind of DIY RL use case, which I think is really mostly around, you know, improved, uh, quality for, for different things.Obviously there's probably like more efficient ways to, you know, get a smaller model that's that's faster and cheaper. And it'll be interesting to see whether. You know, obviously you had, you know, uh, two, three years ago this whole case of companies that were, you know, pre-training and claiming better outcomes in, in their domains than getting kind of cooked as each model iteration improved.You know, I wonder whether that's a, a similar story plays out in the, uh, in, in the, our all space. Yeah, for the focus on, on on pure outcomes and quality, not the cost side, which clearly your own models for cost at scale makes a ton of sense.[00:09:28] swyx: I think there are this, there are two sides of the same coin.Like you basically always want to hold, uh, quality constant or trade off a little bit of quality for a drastic decreasing cost. And that's true for everyone. Uh, one element I wanted to bring out, which is very much in favor of open models, is custom chips. So this would be cereus, but also talu. And then there's a huge range of stuff in between.This has been a huge story this past year on just like everything non Nvidia is getting bid up, including like freaking MatX is working for, which is very, which is very rewarding for me, but I think one of those things where like, oh, like the suddenly, because the number of alternative. Hard, uh, hardware is increasing and the inference that you can get is insanely high.Like, um, we're talking thousands of tokens per second instead of less than a hundred. So the trade off for qua quality doesn't hold as much anymore because the speed is so high.[00:10:24] Jacob Effron: Have you seen a lot of companies go all in on the alternative chip?[00:10:26] swyx: So cognition has Yeah. On Cerebras, uh, and, and so has OpenAIUm, uh, and so no, I don't think so beyond that, uh, and that, do you think that's like a, that's mostly, that's foreshadowing of, that's, yeah. I used to be kind of a skeptic in terms of like, okay, so what if I get my inference at a hundred to a hundred tokens per second sped up to 200 tokens per second. It's only two X faster.It's not that big a deal. Um, but when you, uh, I think every 10 x does unlock a different usage pattern. Um, and you, we have proof in Talas and, and some of the others. That you can actually, um, drastically imp improve inference speed and what happens from there? I don't even really know, like it's, it's so hard to predict when entire applications just appear at once.Yeah. Uh, and it also isn't that expensive, right? So like, um, this is one of those things where like, I, I think the, the investment cycle is gonna be multi-year. Um, and I. Would caution people to not dismiss it too, too quickly.[00:11:25] Jacob Effron: Yeah. I mean, one other like infra question I was curious to get your thoughts on is obviously it seems increasingly a lot of the cutting edge infra companies are building for agents as the buyers of their product or users of their product, right?[00:11:35] swyx: Ooh,[00:11:36] Jacob Effron: and[00:11:37] swyx: another huge theme. Yeah. Yeah.[00:11:38] Jacob Effron: And I'm trying to figure out like what. What, what do you have to do differently about selling into agents? Um, are they just the ultimate rational developers? Uh, or is there, you know,[00:11:46] swyx: no, absolutely not. Um, I think they are easily prompt, injected and, uh, very tuned towards like, basically com compounding existing winners.[00:11:57] Jacob Effron: Yeah,[00:11:57] swyx: so like if, like, congrats if you won the lottery for getting into the training data right before 2023, because now you're like installed in there for the foreseeable future. But yeah. Uh, you know, one stat that Versal, uh, CTO Malta dropped at my conference was that there are now, uh, 60% of traffic to Elle's, um, like app arch, like admin app architecture for like configuring versal applications, uh, is bought.It's not, it's not human. Uh, so like your primary customer is agents now. Um, and it's mostly co like mostly coding agents, mostly people using CLI on CP or whatever. But yeah, I mean, I think. More. I, I think step one, if it doesn't exist as an API that agents can use, it doesn't exist. Right, right. Which I think is like, uh, it's a good hygiene thing anyway, to, to make everything API available, but not as like an extra, um.Push on like products, people to not only work on the ui, um, you should probably work on the on SCLI stuff. Beyond that, I think honestly there is like, so I, I come from the sensibility of, I think everything that you are trying to do for agents experience now, which is the term that Matt Bowman and Nullify is trying to coin, is the same thing that you should have been doing for developer experience.That you should have had good docs, you should have had a consistent API, uh, that is. Mostly stateless. Um, you should have, I guess, discoverable or progressive disclosure or like search or like whatever. And so now that people have energy in like finding these customers to do that, that's great. Um, do I believe in.Extending beyond that into something like a EO, um, for gaming The chatbots? Not necessarily, but obviously there's gonna be huge advantages when people who figure out the short term wins. Yeah. And short term wins can compound.[00:13:43] Jacob Effron: Do you think these compounding advantages to like the, the pre-training data cutoff companies, like, you know, obviously over some period of time, I imagine that doesn't persist.And so as you think about like. I dunno, three, four years from now what the, you know, selection criteria end up being. Do you think it still mirrors exactly what you were saying before? Like it's exactly what you should have been doing all along to sell a good product to developers?[00:14:01] swyx: It could be, except that I think in three, four years we'll probably have much better memory and personalization.So then general a EO or GEO doesn't really matter as much. So I think whatever memory or personalization system we end up with will probably d determine what you end up choosing much more. Than, than what is currently the case, which is just frequency of mentions, let's call it. Yeah,[00:14:26] Jacob Effron: yeah.[00:14:26] swyx: Uh, so you just spa quantity and I think that's, I mean, that's something I'm looking forward to.I do think, like, like, you know, I, I think that the fundamental exercise to work through for yourself is if you start a new, um, sort of. Uh, disruptor company. Now there's a, there's a big incumbent that everyone knows, like, like superb base. Super base is like, kind of like the Postgres, like database, uh, incumbent.If you wanna start like new superb base, how would you compete with them? And I don't necessarily have the answer, but I, I, I do think like people, like resend like relatively new. I think they would start like 20, 23 and still there was, there was a recent survey where like, people. Checked what Claude recommends by default.If you just don't prompt it with anything, just say, gimme an email provider and says, resent as in like 70, 70% of each cases. Like the fact that you can get in there with like such a relatively short existence, I think is, is encouraging.[00:15:14] Jacob Effron: Yeah.[00:15:14] swyx: I do think like. Um, you do want to do whatever it is to, to like to, to get in that Very short mentions this because, um, it's not gonna be 20 of them, it's gonna be like three.[00:15:26] Jacob Effron: No, definitely. It feels like, uh, you know, probably more, more consolidation than ever. Uh, or, or kind of like, you know, uh, a winner take most market than maybe the, the, the physics of go-to market in the past. Yeah. Might have, uh, enabled.[00:15:38] swyx: The other thing also is like, semantic association is gonna be very important, uh, in the sense that like, you want to do like the combo articles where you're like, use my thing with for sale, with blah, blah.And like that all gets picked up in a, in a corpus. And so that's. Probably one thing that you, you wanna do? Well, I don't know what else. Uh, it's, it's, it's, it's one of those things where like, I think I feel, I feel I'm behind, uh, I don't know how you feel about this, but like,[00:16:04] Jacob Effron: I think AI is just everyone constantly feeling like they're behind some, uh,[00:16:08] swyx: yeah.With,[00:16:09] Jacob Effron: I wanna meet the person that doesn't feel behind,[00:16:11] swyx: but like with, with ax, right? Like, so, so like, my, my stance was that exactly what I said before, like everything that you, that you should do for agents is something that you should have done for humans anyway. Yeah. And so. To the extent that you're just getting it more energy to, to do things for agents, great.But like, uh, it's hard to articulate what new thing apart from just like more spam, um, that you should be doing. Anyway, that would be my take right now. Um, I I, I do think like there, there will be more turns at this. I think the personalization turn that is coming, um, will be big. And I don't know what that looks like because like basically we're kind of, we feel kind of tapped out on the memory side of things.[00:16:49] Jacob Effron: Yeah. I, I guess since we last chatted, you know, you, you took this role over at cognition, um, and you've obviously have a, have a front row seat to the AI coding space today. You know, I feel like coding in many ways. You know, people view it as this, like, I mean, besides being like the, the mother of all markets and this massive opportunity, I think it's kinda a preview of like, what's to come for many other spaces.Both. Yeah. You know, I feel like agents are most advanced in coding. I also feel like the, you know, competition between foundation models and application companies, you know, and, uh, mirrors what we may see in other spaces. And so maybe for our listeners, can you just lay out like what is the state of the AI coding wars today?[00:17:25] swyx: Um, it is massive, right? Like, uh, and I don't think necessarily, last time we talked about this, we appreciated the size of what[00:17:32] Jacob Effron: No, I wish we did.[00:17:33] swyx: I state of AI coding wars today, um, both opening eye philanthropic have made it their p serials to competing coding. Um, and. Tropic is like 2.5 billion in a RR just from Cloud Code.The way they recognize a RR is. Opt for debate, uh, open ai. I don't think the, a public number is known, but let's call it 2 billion as well. And then cursor is like, rumored to be 2 billion, you know? And, and those, those are like the public numbers that are known? Yeah. Um, so like huge markets that have just been created in the past one year.Like, like anthropic, just like Claude Code just recently celebrated their one year anniversary, which is, yeah, pretty nice. Um, so, and then I think, like the other thing that I see is there's, there's some other people who are like, oh, here's like the, the sort of relative penetration of, uh, Claude use cases, right?Like, and it's like coding 50% and then legal, whatever. Health, uh, it's like the, the remaining ones. And there was a very popular tweet that was like, okay, I'll look at the, the empty space and all these other use cases. If you are a new founder today, you should be betting on the other stuff because on, on a sort of catch up Yeah.Theory and my. Consider my, my pushback is the same pushback that, uh, I had on app over Google, which is like, well, well why is this time different? Like, why, if it went from let's say 10 to 50% in the past year, why can't I keep going? Uh, and like getting that wrong is actually a very painful one because you could have just did, did the momentum bet.Instead of the mean reversion bed. So I, I, I think that that is the, the state of things now that people are very, very much into psychosis. Um, they're are getting rewarded for spending more rather than spending less. And I think we're not in that phase of efficiency. We're in a phase of sort of like capability exploration.So I think people who are more crazy, who are more. Uh, creative, um, get rewarded comparatively. Yeah.[00:19:27] Jacob Effron: Well, it's interesting. I mean, it feels like behind these like token maxing, leaderboards and whatnot is this, it's like the first phase of this transition from a workforce perspective is you just gotta show your employer like, Hey, I, I use these tools.[00:19:37] swyx: Here's my nu number of tokens I cost, and that's it. They don't care about the quality. Right. It is, uh, maybe distasteful to someone who cares about the craft and, and all that. Um, but directionally everyone just wants you to go up regardless. And so, um, there it is not very discerning. It's, and it's probably very sloppy, but I think it's net fine because we're still probably underusing ai just in generally.Yeah. Um, and so I think that's like very interesting. Like we had on the podcast, uh, Ryan La Poplar from OBI, who spends a billion tokens a day. Yeah. Um, and that's for those county home, it's like something like 10,000 worth, $10,000 worth a day of API tokens. If they, they did market rates, um, and like most of us can't afford that.Yeah. But like. And, and, and probably a lot of what he does is slop.[00:20:25] Jacob Effron: Right.[00:20:25] swyx: But like, he's going to dis, he's like, if there were a new capability, he would discover it first before you because he was, he was trying and you were not trying. Right. And like, you only do things that work like, well, good for you.But like the, the people who are going to discover the next hot thing are living at the edge.[00:20:42] Jacob Effron: Right and increase in living at the edge of just having the compute budget to like run these experiments. I mean, kind of similar to what living at the edge on the research side has always been. You know, it was constrained in many ways by the amount of compute you had to run these experiments.It feels similarly on the, almost on the builder or like actualizing these tools now.[00:20:56] swyx: Yeah. The other thing that's, I mean, very obvious is philanthropic is kind of like the high price premium player. Um, that where, you know. Restricting limits or restricting model releases even is like the name of the game.Whereas Codex is like, come on in guys, use our SDK, use our login and we don't care. We're gonna reset limits. Whatever you do want to try to exploit the subsidies where you can get it. And definitely Codex is super subsidized right now. Gemini also very subsidized. Um, and. Comparatively, like, I think you should make, Hey, I guess while, while that's going on, it's not that bad to be a capabilities explorer on just the $200 a month plan from Cloud Code or from OpenAI.Um, and, uh, I I, I, my sense is that people aren't even there yet.[00:21:41] Jacob Effron: How do you think this, like, market ultimately plays? I mean, it's obviously such a big market that, you know, any slice of that market is interesting for, for anyone going after it. But I think what, what makes people so interesting in the coding market particularly is it feels like it's kind of this.Foreshadowing of what will happen in other, you know, any other kind of application market that the foundation models eventually turn to and are all their models against and gather data around. And so how do you think, you know, like does there end up being room for lots of different kinds of players or like, what do you think the end state of this market is and is that, do you think that's applicable to other markets?[00:22:10] swyx: I feel like there will be, I mean. Status quo is probably the most likely outcome, which is there are two big players and there's a small range of longer tail people that, um, fit other use cases that the, the two big players don't. That feels right to me. I think that, um, for it to, for the market structure to, to significantly change there would be, there needs to be significant change in like the economics or like the, the brand building or like the, the, the, the value propositions of the, of the companies involved and I.Haven't seen any in the last six months that, that have really changed the stories materially. So I feel like they would just keep going until something, something else happens. Something else happens, meaning like Microsoft wakes up and like goes like. Guys, we have GitHub, we have, uh, you know, we, we, we'll, we'll do something much bigger here than other, other than just copilot.Um, and, uh, that would be a big change. Um, MSL has put out a model now, and I was in a breakfast with, uh, Alex Wang, where they were like, yeah, like, we, we really, really want to go after the coding use case. We haven't done anything yet, but like, don't underestimate them. Right. Um, and, and similarly for the Chinese labs.Um, I think they're trying to go after it. Like ZAI is doing stuff. GLM uh, ZI and GLM is same thing. Um, uh, and, and so it's, so like everyone's trying to get a piece of that pie. I, I feel like the, the status quo has been pretty stable for the past, like almost a year I'll say.[00:23:39] Jacob Effron: Yeah. And is the room for the, not like, you know, for, for the application companies more on like the enterprise side or like where do the, where do the, like what surface area do the model companies leave for application companies?[00:23:50] swyx: Yeah, that's a good one. Um. It's very much evolving. Um, it, I, I, I will say because opening I did not have this, the, this level of attention on coding. Yeah. Uh, a year ago. We just don't have that much history. Right. Um, and it seems like, for example, so the big push at Open I now is the Super app. Um, is that a consumer thing?Is that like a products like. Portfolio rationalization thing, how much is that gonna take away attention from coding at the time when they actually do want to put more coding? I think it's, it's very unclear. So I do think like there's, there's all these, like in both big labs, there's. Uh, sorry. Both of the, and, and drop and, and deep minus and XAI are are separate cases.Um, they are trying to see the other time expansion areas. So cloud code for finance. Yeah. Um, uh, cloud cowork, all those, all those things. Whereas I think cursor and cognition are like comparatively just focused on coding and so I, I do think they leave space and I do think for the other verticals that also means the same thing.Right. That, uh, that they're not gonna be that. Um, intensely focused on, on, on that domain. Except for, I, I think I would mark out finance and healthcare as like the next ones, um, that they're clearly going after. Uh, I, I would say comparatively, healthcare seems more thorny. There, there, there've been some announcements about it, but like, I would respect the, the finance work a lot more just because like the, the path to money is a lot clearer.[00:25:12] Jacob Effron: Yeah, no, I mean, obviously like, I, I think, you know, maybe similar to, to the space that's being left in these other domains, you know, there's obviously. Uh, a lot that's required to actually implement these tools in enterprises, uh, versus, you know, maybe just giving them, uh, giving model access to, to folks outta the box.[00:25:27] swyx: Yeah, yeah. Yeah. So the, the agent lab thing is like, we'll do the last mile for you. Whereas I think the model labs tend to just trust the model and, and be minimalist about it. Both of them work.[00:25:38] Jacob Effron: Yeah.[00:25:38] swyx: I, I don't, I don't necessarily think one, uh, beats the other, uh, for every, for every use case. Um, all I, all I do know is that it does seem like.Uh, the large enterprises do want a dedicated partner that isn't just the model labs, which is kind of interesting.[00:25:55] Jacob Effron: We, we've been in this phase of, of pure capability exploration. And so I think nothing has been, you know, better for the large labs, right? I mean, they're always gonna be, uh, uh, the frontier of, of capability exploration.And so I think have a very good relationship with a lot of these enterprises. But ultimately over time, like. The, uh, the incentive structure of these labs is always gonna be maximal, you know, token consumption for, uh, for the end customers they work with. And there's just, I think, so few companies that have actually gotten to massive scale.Maybe coding again is the most interesting. So it's the first space that really is just completely gone, you know? Yeah. You must love it every day. Like absolutely insane. And. I think it[00:26:32] swyx: gets even. Okay. I mean, like, I think we, we say good things about crystal cognition, but the sheer liftoff of like both end UPIC and open ai.‘cause they, they, they have independent valuations. I mean, let's throw an XEI in there because it's now I ping at 1.2 trillion. That number is just mind boggling. Like I, I feel like in normal investing or normal startups, there's kind of like a ceiling market cap or valuation. Totally. That, that like you, you reach and you go like, all right, let's, it's gonna be chiller from now on.And these guys are not slow down. No.[00:27:02] Jacob Effron: Well, I also think the dynamic is fascinating about some of these later stage companies is, is, you know, in the past, I feel like in, in venture world, if you got to a certain level of scale, the question around you was really more a valuation question. And this is like why there was different phase, like, you know, types of venture people did and like the late stage growth people were just incredible at like, you know, a little bit of what's the ultimate market opportunity of this company, but also what's the right way to, to value it.Like we know it's, it's in some bands of an outcome that is like. Sure there's some variance to it, but it's like relatively understood what that bands is and then maybe you get over time surprised to the upside. Whereas any kind of like later, even the labs themselves, any later stage company, the bands of which that company might be worth right now, even in a year or two years are so massive because of how fast the ecosystem changes that it's like.Even for later stage companies, every three months could be an existential level event to the upside to the downside. Yeah. Um, and I think that, like, you are obviously seeing it in the, in the positive with code, which, you know, if you think about a company like philanthropic, you know, that. For a while, it was like unclear if they were going to have access to enough capital, um, to really stay in the, in the race, right?And then coding hit at the exact right time. They had the perfect model for it. They executed brilliantly. Um, and you know, now are, are, you know, uh, you know, one of the most valuable companies in the world.[00:28:13] swyx: Uh, at the same time, I, I don't find, I, I have zero sympathy for opening eye because they're crushing it and they're all rich.You know, this is like a high class champagne problem to have to, uh, to be number two at coding or whatever. Like, who cares? Like, you're, you're doing great.[00:28:27] Jacob Effron: Yeah. It's funny though. I can't even, I mean, you would be closer to this, uh, you know, even that you're in the AI coding space, but it's like a lot of people I talk to think Codex is just as good, if not better than Claude Code.Right. I think one thing that I've been really surprised by, and maybe, maybe Cloud Code is a better product in some ways, I'm curious your thoughts is just in consumer AI with chat GBT. You saw this big first mover advantage, right? Where admittedly today, like, I don't know, Claude Gemini. Great products.Not sure, not abundantly clear chat GBTs any better, but like. People stick with chat, GBT, it's the first thing to introduce them.[00:28:56] swyx: They stay, but they're not growing anymore. I don't know if you've seen[00:28:59] Jacob Effron: Right. But that to me is more of like a, a, a product problem than it is. They're not like, it's not like they've like lost share to someone else.My understanding is the overall problem with consumer AI today is much more of a how do you take this tool and, you know, for, for folks like us, like knowledge workers, it's like this incredible magic tool, but it's not necessarily a daily active use tool for a lot of people around the world today. And what are the like products?It's, it's kind of a category wide problem. Like in coding, for example, like. The entire space has gone parabolic. There may be some relative growth in, uh, in other consumer AI players, but it's not like consumer AI as a category is like going parabolic and they're not capturing most of that thing. I think it's actually the larger problem is much more, hey, the category has kind of hit a bit of a plateau of people haven't figured out how to bring, you know, tons more users on board.Yeah, yeah. Or increase the frequency of those users. And so it seems more of a category wide problem than it is, you know, a massive market share of change. I was gonna draw the comparison to, to the coding space where Claude Co is the first product, obviously, to introduce people to this magical experience.You know, by all accounts, codex is, is pretty damn close to as good, if not better. Um, but like still that first product, you, you would've thought that would not be a super sticky, uh, you know, product surface area. And it actually has, it turns out, I, it feels like the first lab to introduce you and experience really does, uh, keep a lot of, uh, a lot of the focus.[00:30:12] swyx: I, I think. M maybe it's like still, still early days. You know, Chad, BT is like three plus years old and Yeah. Cloud code is only one. Just turned a year. Yeah. So give it time, you know? Yeah. Like, yeah. I mean, definitely sometimes a lot of people have switched from to Codex. Maybe that will keep going. I, it's like really hard to tell.Uh, yeah. I, I, I do, I do think that. Because we are in this like, high volatility, high temperature phase. Um, the loyalty and stickiness to first movers and category creators, I don't think is as high as it might be in some other, uh, areas in our careers that we've looked at.[00:30:47] Jacob Effron: Yeah. Though, I mean, I've been surprised by the cloud code thing.I, I would've thought that, like, in many ways I always worried about the[00:30:52] swyx: enterprise. You think you would've been gone by now?[00:30:53] Jacob Effron: Not gone. But I would've, I I always worried that the, that the consumer business of these companies would be quite sticky. And then the enterprise API business. Uh, was actually like, you know, in some ways like your least loyal buyers, like they would, they would move to,[00:31:05] swyx: right, right.But, but they worked out that it wasn't the enterprise API it was enterprise product.[00:31:09] Jacob Effron: Totally. And maybe that was the, that was the secret that like, but the amount of lock-in or just default behavior that has happened in that space, uh, is, is more than I might've imagined with two products that by all accounts are pretty damn similar.Yeah.[00:31:22] swyx: No fight there. Uh, I will say I do think that Codex is still in like a catch up. Like in terms of personal experience. Um, the only thing I like out of, out of Codex is the, is like Spark and like yeah. Uh, the, I, I feel like the skills integration is a little bit better. I feel like, uh, the, the speed is a bit better.Maybe ‘cause it's in, is written in rust or whatever. Um, very minor things that you like. Almost like telling yourself rather than like objectively assessing between two, two of them. I, I, I do think, like vibes wise, I think that's going on. Um, the, the, you know, I, I feel like the, the missing questions, uh, in, in this whole debate is like, why is this so concentrated in only two names, right?Yeah. Like, um, how, where, like, where is the Gemini? You know, presence, where's the Xai presence? Um, and like they are trying, it's just they haven't made that much progress yet.[00:32:12] Jacob Effron: But what the, what the Claude Co moment does show, and it actually in some ways makes you a little more bullish on the potential for someone else to catch up because it does feel like if you're the first person to introduce some magical net new product experience, that that actually might be stickier than one might have imagined.[00:32:27] swyx: Right, right, right. Okay. Yeah.[00:32:28] Jacob Effron: And so it's, everyone can believe they have shot[00:32:29] swyx: that. What do you think that new product experience might be like? I, I, it's, it's like, and this is a failure of imagination on my part. Like, I always wonder, like, people always say this like, well, the, the thing that will save us is like being first to the next new thing.Like what is it?[00:32:41] Jacob Effron: Yeah.[00:32:42] swyx: It's like,[00:32:45] Jacob Effron: I dunno, something around like, uh, consumer agent, computer use, like hybrid. I think, obviously, I think we're like scratching the surface on the consumer side.[00:32:53] swyx: So my, my current theory is like the. Open claw is like a vision of things to come.[00:32:58] Jacob Effron: Totally.[00:32:58] swyx: Um, and uh, it's good that O open I has like the association with open claw, but by no means do they have the rights to win it.The general thesis that I have been pursuing now is that the year the same way that 2025 was the year of coding agents, 2026 is coding agents breaking containment to do everything else. Um, and so coding agents continue to still win, but because they generate software and software eats the world, so like, it's kind of like the trans.Associated property of like software, eat the world, coding agents, eat software, therefore coding agents eat the world. Um, which is like an interesting,[00:33:30] Jacob Effron: yeah, and breaking containment always an easier phase phrase in the consumer context than the enterprise one. You've seen people run these really cool, uh, experiments in their own personal lives.I think like,[00:33:37] swyx: yes.[00:33:38] Jacob Effron: Figuring out, you know, how you, obviously everyone's focused, you know, on the enterprise side now around how you create these experiences. I feel like the vibes, you know, people love to have these narratives of like, everything is completely shifted. It's like I actually, you know, open AI.Organizationally, uh, you know, volatility aside is, you know, great products, great team, great models like everyone else in the world is incentivized for there to be. Two, three more. Everyone would love more like great model companies. And so I feel like the, the natural forces of the world revolt when any one company, you know, is too much the star of the show, right?There's so many people in the ecosystem that are incentivized for that not to happen. And so I think I'd be shocked if we don't have. Uh, uh, reversion of vibes, not maybe completely the other way, but at least a little bit more equal at some point over the next six, 12 months.[00:34:24] swyx: I, I think there's just a kind of different stages when, when you talk about the world, one wanting more model companies, I talked think about like the neo labs.[00:34:30] Jacob Effron: Yeah.[00:34:31] swyx: And I mean, I don't know, is it fair to say none of them have really broken through in the past year?[00:34:35] Jacob Effron: I think that's totally fair,[00:34:37] swyx: which is rough. Um, and well, how are we gonna, how are we gonna grow that diversity in, in, in choice, like. Um, that's, this is it.[00:34:46] Jacob Effron: Yeah. It'll be really interesting to see what, what, what ends up happening with that.And you've seen, you know, folks like Nvidia, you know, very incentivized to make sure there's, there's a broader platform of, of other model providers.[00:34:57] swyx: I think, uh, I don't know people say this, but I, I, I don't think they try it hard. Nvidia tries harder to build neo clouds[00:35:05] Jacob Effron: Yeah.[00:35:06] swyx: Than neo labs.[00:35:07] Jacob Effron: Well, they try pretty damn hard to build neo Cloud, so[00:35:09] swyx: that's,[00:35:09] Jacob Effron: yeah.[00:35:10] swyx: But like, you know, let's call it like the, the core weaves of the world, much happier place in the, you know, than any neo lab built on top of them.[00:35:18] Jacob Effron: Yeah. That one might argue it's, it's easier to, to enable a neo cloud to be successful than it is. Uh, you can't will a neo lab into existence the same way you, soNvidia[00:35:25] swyx: has more direct control over it.Uh, for sure.[00:35:27] Jacob Effron: What else is kind of catching your eye today on the startup side? I mean, you worry, there's obviously this whole narrative of like, you know, the foundation models, you know, they announced a product and every stock goes down 15%. Like[00:35:36] swyx: Yeah.[00:35:37] Jacob Effron: Do you, do you worry about the foundation models just kind of eating into to a bunch of these startup categories?[00:35:43] swyx: Not really. I, I think actually like. As, uh, there's, there's, okay, there's, there's, there's the, there's the point of view of like being an investor in startups, and there's a point of view of like, do you wanna start something? And I think honestly, like the, the downside for all these is so. Minimal in, in a sense of like, the worst you do is you just get hired into one of these labs anyway.So I, I think the, the market for people who just do things and try things and try to execute in like a competent way, even if like it doesn't work out commercially, even if it just wasn't that great anyway. Like, but like that's your job interview to go into, into one of these things anyway, so, um, I don't feel that.From a, from a very, very small startup perspective, mid-size startups. Yes. Uh, I will say there's been a lot of dead, um, LM Infra, a lot of LM infra consolidation like the, the, uh, lang fuses of the world getting absorbed into, into click house. And I, I think. Like people have maybe worked out the domain specific playbook, uh, and like, I think that's okay.Um, and, and yeah, I'm not that, not that worried about, uh, okay. So, um, I, I would say I'd be more worried about traditional SaaS, like low NPSS. This is the whole AI versus SaaS debate that has, that's been going on. Uh, and, and like literally I'm going through that exact thing in my company where, so I like kind of.Thinking through this on a very visceral, visceral level, right? On one hand you have the people who say you vibe coders don't appreciate the amount of work that goes into A-A-C-R-M and like, yeah, you think you can rip out Salesforce? So did the 30 entrepreneurs before you, right? Like, like, you know, you classically underestimate the things that you don't.Deeply, no. And, and, and target audience is not you. Uh, at the same time, like we have never been able to build software so easily and customize software so easily and like Yeah, you're not gonna use 90% of the things in Salesforce. So like, yeah. What's the typical, so what have you, what[00:37:33] Jacob Effron: have you done internally?[00:37:34] swyx: So we have there the main SaaS that we do for event management and sponsor management. That's, and we paid 200 KA year for that. Not, not huge, but like chunky for, for, for my, my scale. Um, and like, yeah, I could probably spend 2000 and, and build like a custom version of that. Um, the, the, the trick has been dealing with my, the rest of my team and getting them on board.Yeah. ‘cause I'm the most ethical person on my team, but like, I can't make that decision myself. And I think in the same way I've been telling with other CEOs team leaders as well, it's like, well you can be super cloud pilled. You can be super LM psychosis and that you think that's okay, but you like you have to bring your team with you.And I think like there, the sort of widening disparity in LM psychosis in companies is causing real s real riffs because. And on one hand, on one hand, the people who are less AI native are not getting with the picture. They're not, they're actually like behind, they're actually not waking up to the fact that like you, everything you think is necessary is not actually that necessary.And in fact, exactly would be better of you if you just like held your nose and went in and when came out the other side. Yeah, only talking to agents in natural language and like your life would actually be better and you just, you're just like close-minded. There's that perspective. The other perspective is, oh, you vibe coder.You, you did this in a weekend and you got the 80% solution and now the rest of your employees. Have to pick up the rest of your s**t, right, that you, that you thought you were, you were such hot, amazing, uh, uh, at, but like, actually you didn't figure it out. And like, actually LMS are still useless at this and blah, blah, blah.So like, I think there's this huge debate going on in every company right now. Um, and like, um, you know, I have a small microcosm of it, but like, yeah, it, it's making me hesitate to, to pull the trigger. But like I will at some point, it's like maybe I've put it off for one year, but not like five. Yeah, but like, so, so like SaaS is definitely getting squeezed.Um, it does make me wonder, like, I, I do think that there's an opportunity for a more AI native, um, system of record thing that is not just Postgres. Um, or not just MongoDB, although both are very good. Maybe it's like a convex or like people Yeah. Bring up convex a lot. I don't know, like, like, I, I just feel like the sort of quote unquote firebase of, of AI apps isn't really a thing yet.Um, beyond what we have. Uh, which, which is fine. It's, it's, it's just. We could probably start in a more sort of rapid iteration cycle first before scaling up to like a Postgres or MongoDB, which are more sort of old tech. I was at a dinner with, uh, Mike Krieger, the CPO of en philanthropic, and, and he, we were just kind of going around the room going like, what are people most worried about?Yeah. And, uh, for me, uh, I, instead of security, I brought up biosafety. Yeah,[00:40:21] Jacob Effron: classic.[00:40:22] swyx: Um, actually, like I said, it was. Cliche and classic, and the rest of the table were, were like, what do you mean? Someone sitting at home can manufacture a virus that wipes out half of humanity,[00:40:32] Jacob Effron: almost like the OG Jeffrey Hinton.Like, this is why you should be scared.[00:40:35] swyx: I'm like, yeah, like the read the, you know, risk reports. Like this is like the thing. Um, I think, and Mike was just sitting there knowing he was sitting on Mythos and going like, actually it's security. Um, and I think like, um, I think the, there's, there's, part of it is.A very good marketing. Like too good. Yeah, like I would actually advise and topic to tune down the marketing because also it's, it is just a very good model and you don't have to make so many marketing claims around it. At the same time, it is not really a private model. If you give it to 40 companies.Each of whom have like 10,000 employees or whatever. Right. It's not, it's not private, it's, it's like there's bad actors in there.[00:41:18] Jacob Effron: Yeah. Hopefully, hopefully not as, uh, as bad as releasing it widely, but, uh, no, I mean, it's an interesting. You know, it's an interesting case study for how all, I mean, many model releases might, I mean, you know, this might be the first model release that looks like the rest of ‘em from from now on, right?[00:41:31] swyx: It, it, so it's, it's the, there's an overall product strategy, uh, for anthropic of like bundle, uh, you know, restrict access bundle, uh, product with model maybe.Whereas, uh, OpenAI has definitely been a lot more sort of. Philosophically aligned on like, we will just enable access everywhere and we don't know what you, what will come out of it. Right.[00:41:51] Jacob Effron: Right. Though, I mean, this current moment, uh, obviously the cynical take is also just ties to the amount of compute that both companies[00:41:56] swyx: Yeah.Right, right, right. Yeah, I think, I think that's true. I I do think like the, the, this is the, the, the scale, the dawn of like larger than 10 trillion parameter models is very interesting. I don't think it, I think it's a temporary phenomenon because we have much larger compute clusters coming online for everyone over the next like three, five years.It's, and this is like already written in, in the cards.[00:42:18] Jacob Effron: Yeah.[00:42:19] swyx: So to the extent that like, you know, will we have rationing of models, uh, above 10 trillion, uh, in like two years? I don't think so. I think everyone will have no, we'll just[00:42:29] Jacob Effron: have rationing of the next phase.[00:42:30] swyx: Right. Right. But like, that's as it should be almost like, um.My, my classic example, which I, this is just me theorizing, not anything confirmed by Google. When Google announced Gemini, they actually announced three sizes, which was Flash Pro Ultra. They never released Ultra. They only have Pro and Flash. Um, so my theory is they have ultra sitting in a basement and they just could distilling from it for, for flashing pro.Um, which like, yeah, I mean, I, I actually think that's. As it should be for any lab that they, that they do that.[00:43:02] Jacob Effron: Yeah. Just because those are the models that people actually wanna end up using. And it's just like cost prohibit.[00:43:06] swyx: It is more, yeah, it's cost. Yeah. It's, it's not the want, it's just, just, just the cost.Um, I do think, like, uh, it is interesting that, uh, for a while I was, I was considering the theory that models capped out at two, 2 trillion, and I think that's proving to be wrong. And well then if I'm wrong, how wrong? How wrong am I? Do we do 200 trillion? Do we do two quarter trillion, whatever? Um, and I don't think we have the straight answer to that, but like, uh, it's interesting that we are continuing to scale number of pers when everyone kind of assu like can see that we're not going to get like the next thousand or 1 million x from this paradigm.So like the others, like the alias of the world are working on other. Um, model architecture improvements. We need a different scaling law, I guess, because like, we're, I, I feel like people already already feel like we're tapped out on this. Like the, the end, the end state of this is we turn most of the world into data centers and like, I don't know.I don't know if we want that.[00:44:08] Jacob Effron: Yeah, I mean, uh, if the, if, if, if the return of intelligence are there, maybe, uh, maybe not so bad.[00:44:13] swyx: I, I, I think there, there's just a sheer amount of like, like un scalability that like is wrangling people's sensibilities right now. Um, especially in terms of like context lengths.Um, my classic quote is that context length is like the slowest scaling factor in, in lms.[00:44:30] Jacob Effron: Yeah.[00:44:30] swyx: Um, we, like, we took maybe. Three years to go from like 4,000 context length to a million and that's about it. Yeah. Like Gemini has had a million token context length for two years now. Um, and no one's using it.Like, so like yeah, it's memory. Memory is probably gonna be the, the biggest limiting constraint on all these things.[00:44:50] Jacob Effron: Yeah. Certainly seems that way. I guess I'm curious over the last year since you recorded last, like what's one thing you've changed your mind on?[00:44:57] swyx: I feel like I was kind of bearish on open models like last year.Um, in a sense of, like, I, I had just done the podcast with an Al[00:45:07] Jacob Effron: Yeah.[00:45:08] swyx: Of Braintrust where he, and he, I mean, you know, he has a good cross section of all the top AI companies and he says market share of open source is 5% and going down. Um, I think that's changed. I think it's going up. Um, and even if,[00:45:22] Jacob Effron: even though the capability gap does seem to be increasing.Spending on the[00:45:26] swyx: time. It's hard to tell. Yeah, it's, it's really hard to tell. ‘cause like, okay, for, for listeners, capability gap increasing is like on public benchmarks. And let's say you're comparing mythos versus like, I don't know, G-T-O-S-S or like GLM 5.1. And, um, it's, it is really hard to tell. ‘cause even if they were closing, you will also not believe that they were closing that much because it's very easy to gain the benchmarks.Yeah. So you just don't really, really know. Um, all you know is like. Uh, there's somewhat objective open router stats on like what people choose in a free market. And people do choose some of these open models in significant volume, except that a lot of them are heavily discounted. So you need to kind of like price adjust, uh, these things.So even if, even if that were true, which I, I'm not sure, like I, I, I feel like the numbers just up now instead of down. Uh, I think the. Separation between what the top tier agent labs

Amol Avasare is Head of Growth at Anthropic, which is going through the most unprecedented growth trajectory in history—scaling from $1 billion to over $19 billion in ARR in just 14 months. Previously, Amol worked on the growth teams at Mercury and MasterClass. Before that he was a founder, and he cold emailed his way into the Anthropic role when no job listing existed. Most remarkably, he overcame a traumatic brain injury from a Muay Thai match that meant he couldn't work for nearly a year.In our in-depth discussion, Amol shares:1. How Amol landed his role by cold emailing Anthropic's CPO Mike Krieger2. How Anthropic is automating growth experiments with Claude (their internal tool called “CASH”)3. Why the ratio of PMs to engineers might need to flip (more PMs than engineers) as AI makes engineers exponentially more productive4. Why activation is the single highest-leverage growth problem in AI5. Why Anthropic indexes 70/30 toward big bets (the opposite of most growth teams)6. How he uses Cowork to detect team misalignment in Slack7. How the company's focus on AI coding created a research flywheel that accelerated their models—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsVanta—Automate compliance, manage risk, and accelerate trust with AI—Episode transcript: https://www.lennysnewsletter.com/p/anthropics-1b-to-19b-growth-run—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Amol Avasare:• X: https://x.com/TheAmolAvasare• LinkedIn: https://www.linkedin.com/in/amolavasare—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 Amol and Anthropic's growth(03:15) The story of cold emailing Mike Krieger to get the job(08:28) What it's like leading growth at the fastest-growing company ever(10:46) What the growth team actually does at Anthropic(12:16) The concept of “success disasters”(13:55) Why activation is the biggest challenge in AI products(18:05) Improving Mercury's onboarding experience(20:57) The importance of adding the right kind of friction(25:10) Anthropic's org structure(27:06) Why Anthropic focuses on big bets over micro-optimizations(33:34) Automating growth experiments with Claude (CASH)(38:20) How AI is starting to identify what experiments to run(41:07) The future of PM, engineering, and design roles(47:19) Why you might need more PMs as engineers get more productive(51:13) How Amol uses AI to prototype ideas and skip PRDs(58:10) Amol's morning routine: AI analyzes 20 to 25 charts automatically(1:03:31) Getting coaching from an AI version of your manager(1:06:27) How Anthropic's focus on coding and B2B drove their success(1:12:10) Balancing growth with AI safety as a core mission(1:18:09) Advice for thriving in an AI-first future(1:22:53) Anthropic's culture and the “notebook channels” on Slack(1:35:12) Failure corner: Shutting down his startup after raising money(1:38:25) The traumatic brain injury that changed everything(1:46:49) Lightning round—References: https://www.lennysnewsletter.com/p/anthropics-1b-to-19b-growth-run—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

Mike Krieger built one of the most consequential consumer apps of the last two decades as cofounder of Instagram. He is now at the frontier of determining what makes a breakout AI-native product as co-lead of Anthropic Labs.Dan Shipper talked with Krieger for Every's AI & I about how his experience creating Instagram shapes how he thinks about building with AI, including what can be sped up and what remains stubbornly time-intensive. If you found this episode interesting, please like, subscribe, comment, and share! To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Download Grammarly for FREE at grammarly.comTimestamps Introduction: 00:01:39What's gotten easier—and what hasn't—about building products in the age of AI: 00:02:33Why vibe coding creates "indoor trees": 00:05:00How rewrites have become a normal part of the development process: 00:09:00What "agent native" product design means: 00:11:39How Mike's labs team is structured and the cofounder model: 00:24:27The best signal for a product bet is someone with "break through walls" conviction: 00:29:33Navigating enterprise customers while keeping pace with rapid AI change: 00:38:51OpenClaw, personal agents, and the product question defining 2026: 00:40:54Links to resources mentioned in the episode:Mike Krieger: https://x.com/mikeyk Agent-native architecture: https://every.to/guides/agent-native

Jenny Wen leads design for Claude at Anthropic. Prior to this, she was Director of Design at Figma, where she led the teams behind FigJam and Slides. Before that, she was a designer at Dropbox, Square, and Shopify.—We discuss:1. Why the classic discovery → mock → iterate design process is becoming obsolete2. What a day in the life of a designer at Anthropic looks like, including her AI tool stack3. Whether AI will eventually surpass humans in taste and judgment4. Why Jenny left a director role at Figma to return to IC work at Anthropic5. The three archetypes Jenny is hiring for now6. Why chatbot interfaces may be more durable than most people expect—Brought to you by:Mercury—Radically different banking: https://mercury.com/?utm_source=lennys&utm_medium=sponsored_newsletter&utm_campaign=26q1_brand_campaignOrkes—The enterprise platform for reliable applications and agentic workflows: https://www.orkes.io/Omni—AI analytics your customers can trust: https://omni.co/lenny—Episode transcript: https://www.lennysnewsletter.com/p/the-design-process-is-dead—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Jenny Wen:• X: https://x.com/jenny_wen• LinkedIn: https://www.linkedin.com/in/jennywen• Substack: https://jennywen.substack.com• Website: https://jennywen.ca—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 Jenny Wen(04:23) Why the traditional design process is dead(06:33) The two new types of design work(10:00) How widespread this shift will be(13:00) Day-to-day life as a designer at Anthropic(18:45) Jenny's AI stack(20:03) Why Figma still matters for exploration(22:25) Advice for working with engineers(24:19) How to maintain craft, quality, and trust in the AI era(27:35) Will AI ever have “taste”?(31:38) The future of chatbot interfaces(35:33) Moving from director back to IC(41:00) The 10-day build of Claude Cowork(46:06) Hiring: the three archetypes(50:44) Advice for new and senior designers(54:42) The value of “low leverage” tasks for managers(57:52) Why the best teams roast each other(01:01:45) The legibility framework(01:07:22) Lightning round and final thoughts—Referenced:• Figma: https://www.figma.com• Anthropic: https://www.anthropic.com• v0: https://v0.app• Navigating a Design Career with Jenny Wen | Figma at Waterloo: https://www.youtube.com/watch?v=OHcBPMh2ivk• Claude Cowork: https://claude.com/product/cowork• Use Claude Code in VS Code: https://code.claude.com/docs/en/vs-code• Claude Code in Slack: https://code.claude.com/docs/en/slack• Lex Fridman's website: https://lexfridman.com• Head of Claude Code: What happens after coding is solved | Boris Cherny: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens• OpenClaw: https://openclaw.ai• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Marc Andreessen: The real AI boom hasn't even started yet: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom• Socratica: https://www.socratica.info• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• Radical Candor: From theory to practice with author Kim Scott: https://www.lennysnewsletter.com/p/radical-candor-from-theory-to-practice• Evan Tana's ‘legibility matrix' on X: https://x.com/evantana/status/1927404374252269667• How to spot a top 1% startup early: https://www.lennysnewsletter.com/p/how-to-spot-a-top-1-startup-early• Palantir: https://www.palantir.com• Stripe: https://stripe.com• Linear: https://linear.app• Notion: https://www.notion.com• Julie Zhuo's website: https://www.juliezhuo.com• Sentimental Value: https://www.imdb.com/title/tt27714581• The Pitt on Prime Video: https://www.amazon.com/The-Pitt-Season-1/dp/B0DNRR8QWD• Noah Wyle: https://en.wikipedia.org/wiki/Noah_Wyle• ER on Prime Video: https://www.amazon.com/gp/video/detail/B0FWZSDYRP• Retro: https://retro.app• Granola: https://www.granola.ai—Recommended books:• Radical Candor: Be a Kick-Ass Boss Without Losing Your Humanity: https://www.amazon.com/Radical-Candor-Kick-Ass-Without-Humanity/dp/1250103509• The Power Broker: Robert Moses and the Fall of New York: https://www.amazon.com/Power-Broker-Robert-Moses-Fall/dp/0394480767• Insomniac City: New York, Oliver Sacks, and Me: https://www.amazon.com/Insomniac-City-New-York-Oliver/dp/162040494X—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

Anthropic CPO and Instagram co-founder Mike Krieger joins the AI Daily Brief to talk about the rise of vibe coding, why coding agents quietly became the breakout AI use case of 2025, and how enterprises are beginning to move from chatbots to real workload-taking agents. The conversation explores how tools like Claude Code escaped the developer box, what it takes to design products for capabilities that don't fully exist yet, and why 2026 may be the year AI starts reliably taking work off people's plates inside large organizations at Anthropic. Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kpmg.us/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy.com - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to build enterprise software in days, not months Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Interested in sponsoring the show? sponsors@aidailybrief.ai

Edwin Chen is the founder and CEO of Surge AI, the company that teaches AI what's good vs. what's bad, powering frontier labs with elite data, environments, and evaluations. Surge surpassed $1 billion in revenue with under 100 employees last year, completely bootstrapped—the fastest company in history to reach this milestone. Before founding Surge, Edwin was a research scientist at Google, Facebook, and Twitter and studied mathematics, computer science, and linguistics at MIT.We discuss:1. How Surge reached over $1 billion in revenue with fewer than 100 people by obsessing over quality2. The story behind how Claude Code got so good at coding and writing3. The problems with AI benchmarks and why they're pushing AI in the wrong direction4. How RL environments are the next frontier in AI training5. Why Edwin believes we're still a decade away from AGI6. Why taste and human judgment shape which AI models become industry leaders7. His contrarian approach to company building that rejects Silicon Valley's “pivot and blitzscale” playbook8. How AI models will become increasingly differentiated based on the values of the companies building them—Brought to you by:Vanta—Automate compliance. Simplify security.WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsCoda—The all-in-one collaborative workspace—Transcript: https://www.lennysnewsletter.com/p/surge-ai-edwin-chen—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/180055059/my-biggest-takeaways-from-this-conversation—Where to find Edwin Chen:• X: https://x.com/echen• LinkedIn: https://www.linkedin.com/in/edwinzchen• Surge's blog: https://surgehq.ai/blog—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 Edwin Chen(04:48) AI's role in business efficiency(07:08) Building a contrarian company(08:55) An explanation of what Surge AI does(09:36) The importance of high-quality data(13:31) How Claude Code has stayed ahead(17:37) Edwin's skepticism toward benchmarks(21:54) AGI timelines and industry trends(28:33) The Silicon Valley machine(33:07) Reinforcement learning and future AI training(39:37) Understanding model trajectories(41:11) How models have advanced and will continue to advance(42:55) Adapting to industry needs(44:39) Surge's research approach(48:07) Predictions for the next few years in AI(50:43) What's underhyped and overhyped in AI(52:55) The story of founding Surge AI(01:02:18) Lightning round and final thoughts—Referenced:• Surge: https://surgehq.ai• Surge's product page: https://surgehq.ai/products• Claude Code: https://www.claude.com/product/claude-code• Gemini 3: https://aistudio.google.com/models/gemini-3• Sora: https://openai.com/sora• Terrence Rohan on LinkedIn: https://www.linkedin.com/in/terrencerohan• Richard Sutton—Father of RL thinks LLMs are a dead end: https://www.dwarkesh.com/p/richard-sutton• The Bitter Lesson: http://www.incompleteideas.net/IncIdeas/BitterLesson.html• Reinforcement learning: https://en.wikipedia.org/wiki/Reinforcement_learning• Grok: https://grok.com• Warren Buffett on X: https://x.com/WarrenBuffett• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• Brian Armstrong on LinkedIn: https://www.linkedin.com/in/barmstrong• Interstellar on Prime Video: https://www.amazon.com/Interstellar-Matthew-McConaughey/dp/B00TU9UFTS• Arrival on Prime Video: https://www.amazon.com/Arrival-Amy-Adams/dp/B01M2C4NP8• Travelers on Netflix: https://www.netflix.com/title/80105699• Waymo: https://waymo.com• Soda versus pop: https://flowingdata.com/2012/07/09/soda-versus-pop-on-twitter—Recommended books:• Stories of Your Life and Others: https://www.amazon.com/Stories-Your-Life-Others-Chiang/dp/1101972122• The Myth of Sisyphus: https://www.amazon.com/Myth-Sisyphus-Vintage-International/dp/0525564454• Le Ton Beau de Marot: In Praise of the Music of Language: https://www.amazon.com/dp/0465086454• Gödel, Escher, Bach: An Eternal Golden Braid: https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567—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

Jen Abel is GM of Enterprise at State Affairs and co-founded Jellyfish, a consultancy that helps founders learn zero-to-one enterprise sales. She's one of the smartest people I've ever met on learning enterprise sales, and in this follow-up to our first chat two years ago (covering the zero to $1 million ARR founder-led sales phase), we focus on the skills founders need to learn to go from $1M to $10M ARR.We discuss:1. Why the “mid-market” doesn't exist2. Why tier-one logos like Stripe and Tesla counterintuitively make the best early customers3. The dangers of pricing your product at $10K-$20K4. Why you need to vision-cast instead of problem-solve to win enterprise deals5. Why services are the fastest way to get your foot in the door with enterprises6. How to find and work with design partners7. When to hire your first salesperson and what profile to look for—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsLovable—Build apps by simply chatting with AICoda—The all-in-one collaborative workspace—Where to find Jen Abel:• X: https://x.com/jjen_abel• LinkedIn: https://www.linkedin.com/in/earlystagesales• Website: https://www.jjellyfish.com—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) Welcome back, Jen!(04:38) The myth of the mid-market(08:08) Targeting tier-one logos(10:50) Vision-casting vs. problem-selling(15:35) The importance of high ACVs(20:45)  Don't play the small business game with an enterprise company(25:09) Design partners: the double-edged sword(28:11) Finding the right company(36:55) Enterprise sales: the art of the deal(43:21) The problem with channel partnerships(44:41) Quick summary(50:24) Hiring the right enterprise salespeople(56:49) Structuring sales compensation(01:01:01) Building relationships in enterprise sales(01:02:07) The art of cold outreach(01:07:31) Outbound tooling and AI(01:14:08) Lightning round and final thoughts—Referenced:• The ultimate guide to founder-led sales | Jen Abel (co-founder of JJELLYFISH): https://www.lennysnewsletter.com/p/master-founder-led-sales-jen-abel• Mario meme: https://www.linkedin.com/pulse/missing-meme-led-me-woman-johann-van-tonder-im6df• Kathy Sierra: https://en.wikipedia.org/wiki/Kathy_Sierra• 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• Justin Lawson on X: https://x.com/jjustin_lawson• Stripe: https://stripe.com• Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead): https://www.lennysnewsletter.com/p/building-product-at-stripe-jeff-weinstein• He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor on the future of careers, coding, agents, and more: https://www.lennysnewsletter.com/p/he-saved-openai-bret-taylor• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• Linear: https://linear.app• Linear's secret to building beloved B2B products | Nan Yu (Head of Product): https://www.lennysnewsletter.com/p/linears-secret-to-building-beloved-b2b-products-nan-yu• Gemini: https://gemini.google.com• Microsoft Copilot: https://copilot.microsoft.com• How Palantir built the ultimate founder factory | Nabeel S. Qureshi (founder, writer, ex-Palantir): https://www.lennysnewsletter.com/p/inside-palantir-nabeel-qureshi• McKinsey & Company: https://www.mckinsey.com• Deloitte: https://www.deloitte.com• Accenture: https://www.accenture.com• Building a world-class sales org | Jason Lemkin (SaaStr): https://www.lennysnewsletter.com/p/building-a-world-class-sales-org• Peter Dedene on X: https://x.com/peterdedene• Hang Huang on X: https://x.com/HH_HangHuang• Hugo Alves on X: https://x.com/Ugo_alves• A step-by-step guide to crafting a sales pitch that wins | April Dunford (author of Obviously Awesome and Sales Pitch): https://www.lennysnewsletter.com/p/a-step-by-step-guide-to-crafting• Clay: https://www.clay.com• Apollo: https://www.apollo.io• Jason Lemkin on X: https://x.com/jasonlk• Gavin Baker on X: https://x.com/GavinSBaker• Jason Cohen on X: https://x.com/asmartbear• Baywatch on Prime Video: https://www.primevideo.com/detail/Baywatch/0NU9YS8WWRNQO1NZD5DOQ3I8W6• Playground: https://www.tryplayground.com• ClassDojo: https://www.classdojo.com• Jason Lemkin's post about Replit: https://x.com/jasonlk/status/1946069562723897802—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

Chip Huyen is a core developer on Nvidia's Nemo platform, a former AI researcher at Netflix, and taught machine learning at Stanford. She's a two-time founder and the author of two widely read books on AI, including AI Engineering, which has been the most-read book on the O'Reilly platform since its launch. Unlike many AI commentators, Chip has built multiple successful AI products and platforms and works directly with enterprises on their AI strategies, giving her unique visibility into what's actually happening inside companies building AI products.We discuss:1. What people think makes AI apps better vs. what actually makes AI apps better2. What pre-training vs. post-training is, and why fine-tuning should be your last resort3. How RLHF (reinforcement learning from human feedback) actually works4. Why data quality matters more than which vector database you choose5. Why high performers are seeing the most gains from AI coding tools6. Why most AI problems are actually UX issues—Brought to you by:Dscout—The UX platform to capture insights at every stage: from ideation to production: https://www.dscout.com/Justworks—The all-in-one HR solution for managing your small business with confidence: https://ad.doubleclick.net/ddm/trackclk/N9515.5688857LENNYSPODCAST/B33689522.423713855;dc_trk_aid=616485030;dc_trk_cid=237010502;dc_lat=;dc_rdid=;tag_for_child_directed_treatment=;tfua=;gdpr=$Persona—A global leader in digital identity verification: https://withpersona.com/lenny—Where to find Chip Huyen:• X: https://x.com/chipro• LinkedIn: https://www.linkedin.com/in/chiphuyen/• Website: https://huyenchip.com/—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 Chip Huyen(04:28) Chip's viral LinkedIn post(07:05) Understanding AI training: pre-training vs. post-training(08:50) Language modeling explained(13:55) The importance of post-training(15:20) Reinforcement learning and human feedback(22:23) The importance of evals in AI development(31:55) Retrieval augmented generation (RAG) explained(38:50) Challenges in AI tool adoption(43:19) Challenges in measuring productivity(45:20) The three-bucket test(49:10) The future of engineering roles(55:31) ML Engineers vs. AI engineers(57:12) Looking forward: the impact of AI(01:05:48) Model capabilities vs. perceived performance(01:08:23) Lightning round and final thoughts—Referenced:• Chip's LinkedIn post on what actually improves AI apps: https://www.linkedin.com/posts/chiphuyen_aiapplications-aiengineering-activity-7358971409227792384-y0mf/• Prediction and Entropy of Printed English: https://www.princeton.edu/~wbialek/rome/refs/shannon_51.pdf• Why experts writing AI evals is creating the fastest-growing companies in history | Brendan Foody (CEO of Mercor): https://www.lennysnewsletter.com/p/experts-writing-ai-evals-brendan-foody•Inside the expert network training every frontier AI model | Garrett Lord (Handshake CEO): https://www.lennysnewsletter.com/p/inside-handshake-garrett-lord• First interview with Scale AI's CEO: $14B Meta deal, what's working in enterprise AI, and what frontier labs are building next | Jason Droege: https://www.lennysnewsletter.com/p/first-interview-with-scale-ais-ceo-jason-droege• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• Why AI evals are the hottest new skill for product builders | Hamel Husain & Shreya Shankar (creators of the #1 eval course): https://www.lennysnewsletter.com/p/why-ai-evals-are-the-hottest-new-skill• 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• Stanford webinar—How AI Is Changing Coding and Education, Andrew Ng & Mehran Sahami: https://www.youtube.com/watch?v=J91_npj0Nfw• He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor on the future of careers, coding, agents, and more: https://www.lennysnewsletter.com/p/he-saved-openai-bret-taylor• Anthropic co-founder on quitting OpenAI, AGI predictions, $100M talent wars, 20% unemployment, and the nightmare scenarios keeping him up at night | Ben Mann: https://www.lennysnewsletter.com/p/anthropic-co-founder-benjamin-mann• Lenny's vibe-coded app made on Lovable: https://gdoc-images-grab.lovable.app/• Story of Yanxi Palace: https://www.imdb.com/title/tt8865016/• Steve Jobs's quote: https://www.goodreads.com/quotes/427317-remembering-that-i-ll-be-dead-soon-is-the-most-important—Recommended books:• The Complete Sherlock Holmes: https://www.amazon.com/Complete-Sherlock-Holmes-Volumes/dp/0553328255• AI Engineering: Building Applications with Foundation Models: https://www.amazon.com/AI-Engineering-Building-Applications-Foundation/dp/1098166302• The Selfish Gene: https://www.amazon.com/Selfish-Gene-Anniversary-Introduction/dp/0199291152• From Third World to First: The Singapore Story: 1965-2000: https://www.amazon.com/Third-World-First-Singapore-1965-2000/dp/0060197765—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

Mike Krieger is the chief product officer at Anthropic and co-founder of Instagram. Krieger joins Big Technology Podcast to discuss Anthropic's Sonnet 4.5 launch and how the company's been able to speed up AI model development. Tune in to hear how Anthropic is using internal tools to move fast, where the next generations of model improvements will look like, and whether model orchestration will be the core differentiator between labs. We also cover how AI development compares to social media, whether AI content will ever take off, and enterprise AI's path ahead. --- Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Questions? Feedback? Write to: bigtechnologypodcast@gmail.com

Julie Zhuo is the former VP and Head of Design at Facebook (now Meta), author of the bestselling book The Making of a Manager, and co-founder of Sundial, an AI-powered data analysis company. Also, my first-ever podcast guest over 3 years ago!In our conversation, we discuss:1. The three core manager skills that translate directly to managing AI agents2. How her team uses AI to learn new skills 10x faster3. The “diagnose with data, treat with design” framework for balancing gut and data4. Why hypergrowth AI companies have terrible data infrastructure (and why it doesn't matter)5. How to give feedback that actually lands—including Julie's exact script for difficult conversations6. What Julie's teaching her kids about an AI future (hint: it's not coding or STEM)—Brought to you by:Mercury — The art of simplified financesDX — The developer intelligence platform designed by leading researchersPostHog—How developers build successful products—Transcript: https://www.lennysnewsletter.com/p/from-managing-people-to-managing-ai-julie-zhuo—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/172723725/my-biggest-takeaways-from-this-conversation—Where to find Julie Zhuo:• X: https://x.com/joulee• LinkedIn: https://www.linkedin.com/in/julie-zhuo/• Website: https://www.juliezhuo.com/• Newsletter: https://lg.substack.com/• Sundial: https://sundial.so/—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) Welcome back, Julie!(05:18) The success of The Making of a Manager(08:41) Why AI will make everyone a manager(11:38) The future of management roles(14:00) Empowering teams with AI(21:30) Specific roles being accelerated by AI(26:53) Data analysis in AI companies(32:02) The role of data in design(37:21) The evolving role of managers in the AI era(40:22) Embracing change and uncertainty(42:14) Timeless lessons for managers(49:03) Balancing strengths and weaknesses(57:49) Building a feedback culture(01:05:33) Creating win-win situations(01:09:27) Being aware of your own energy and conviction(01:12:12) Navigating disagreements with higher-ups(01:15:57) AI corner(01:20:08) Contrarian corner(01:23:14) Lightning round and final thoughts—Referenced:• Julie Zhuo on accelerating your career, impostor syndrome, writing, building product sense, using intuition vs. data, hiring designers, and moving into management: https://www.lennysnewsletter.com/p/episode-2-julie-zhuo• Waymo: https://waymo.com/• How we restructured Airtable's entire org for AI | Howie Liu (co-founder and CEO): https://www.lennysnewsletter.com/p/how-we-restructured-airtables-entire-org-for-ai• 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• Inside ChatGPT: The fastest growing product in history | Nick Turley (Head of ChatGPT at OpenAI): https://www.lennysnewsletter.com/p/inside-chatgpt-nick-turley• Behind the founder: Marc Benioff: https://www.lennysnewsletter.com/p/behind-the-founder-marc-benioff• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• The Magic Loop: https://www.lennysnewsletter.com/p/the-magic-loop• Dunning-Kruger effect: https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect• Eric Antonow on LinkedIn: https://www.linkedin.com/in/antonow/• Methaphone: https://methaphone.com/• Replit: https://replit.com/• “Baby” by Justin Bieber on Spotify: https://open.spotify.com/track/6epn3r7S14KUqlReYr77hA• Kingdom Rush: https://www.kingdomrush.com/• Dr. Becky on TikTok: https://www.tiktok.com/@drbeckyatgoodinside• Emily Oster on TikTok: https://www.tiktok.com/@profemilyoster• La La Land on Netflix: https://www.netflix.com/title/80095365• Granola: https://www.granola.ai/• Matic robots: https://maticrobots.com/• Limitless pendant: https://www.limitless.ai/• How I AI: https://www.youtube.com/@howiaipodcast—Recommended books:• The Making of a Manager: What to Do when Everyone Looks to You: https://www.amazon.com/Making-Manager-What-Everyone-Looks/dp/0525540423• High Output Management: https://www.amazon.com/High-Output-Management-Andrew-Grove/dp/0679762884/• Zen and the Art of Motorcycle Maintenance: An Inquiry into Values: https://www.amazon.com/Zen-Art-Motorcycle-Maintenance-Inquiry/dp/0061673730• Conscious Business: How to Build Value Through Values: https://www.amazon.com/Conscious-Business-Build-through-Values/dp/1622032020• Good Inside: A Practical Guide to Resilient Parenting Prioritizing Connection Over Correction: https://www.amazon.com/Good-Inside-Guide-Becoming-Parent/dp/0063159481/—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

Welcome to this classic episode. Classics are my favorite episodes from the past 10 years, published once a month. These are N of 1 conversations with N of 1 people. Sean Feeney makes you want to be a better person, friend, and leader. Sean is the co-founder of Grove House Hospitality Group and the owner of Lilia and Misi, two of New York City's most sought-after restaurants. He left his job in Trading to chase down a dream with Michelin star chef Missy Robbins. Sean leveraged his finance background to write his own rulebook for the restaurant industry, crafting several establishments that now boast several thousand people on the waitlist any given night. His story is as entertaining as it is inspiring. As we go step by step through his business endeavors, he points out all of the times he was told “it's just always been done this way” and how that revealed to him where he could innovate. Sean's restaurants are the perfect example of building a business into the fabric of a community, collaborating with other brands in authentic ways, and staying true to yourself along the way. Please enjoy this awesome conversation with Sean Feeney.  For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by ⁠⁠⁠⁠⁠WorkOS⁠⁠⁠⁠⁠. WorkOS is a developer platform that enables SaaS companies to quickly add enterprise features to their applications. With a single API, developers can implement essential enterprise capabilities that typically require months of engineering work. By handling the complex infrastructure of enterprise features, WorkOS allows developers to focus on their core product while meeting the security and compliance requirements of Fortune 500 companies. Visit ⁠⁠⁠⁠⁠WorkOS.com⁠⁠⁠⁠⁠ to Transform your application into an enterprise-ready solution in minutes, not months. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 (00:03:58) A Chance Encounter with a Michelin-Starred Chef (00:08:52) The Birth of a Culinary Partnership (00:12:49 Embracing the Genius Within (00:16:41) Innovative Approaches to the Restaurant Business (00:24:53) Creating Demand: The Art of Exclusivity (00:28:49) Learning from the Best: Insights from Kith's Success (00:34:21) Defining Exceptional Hospitality (00:44:20) The Power of Customer Relationships in Hospitality (00:52:31) Unlocking Team Potential  (00:53:33) The Philosophy of the Perfect Turn (00:54:05) Balancing Art, Commerce, and Satisfaction (00:56:13) The Impact of Authentic Experiences and Brands (01:03:24) The Evolution of a Hospitality Brand (01:06:38) Community Engagement and the Power of Simplicity (01:24:40) Creative Responses and Business Lessons Learned (01:36:05) Lessons From Working In The Restaurant Industry  (01:43:01) The Kindest Thing Anyone Has Ever Done For Sean

Howie Liu is the co-founder and CEO of Airtable, the no-code platform valued at around $12 billion. After a viral tweet declared “Airtable is dead” based on incorrect data, Howie led a radical transformation: reorganizing the entire company around AI, becoming an “IC CEO” who codes daily, and achieving over $100 million in free cash flow.What you'll learn:1. The “fast thinking” vs. “slow thinking” team structure that lets Airtable ship AI features weekly (inspired by Daniel Kahneman)2. Why Howie uses AI hourly (not daily) and is Airtable's #1 inference-cost user globally3. Why CEOs must become ICs again in the AI era (and how to restructure your calendar to make it possible)4. Why “playing” with AI tools should be mandatory—Howie tells employees to cancel all meetings for a week to experiment5. The specific skills product managers, engineers, and designers need to develop to succeed in the AI era6. Why evals can kill innovation (and when to use “vibes” instead)—Brought to you by:LucidLink—Real-time cloud storage for teamsDX—The developer intelligence platform designed by leading researchersClaude.ai—The AI for problem solvers and enterprise—Where to find Howie Liu• X: https://x.com/howietl• LinkedIn: https://www.linkedin.com/in/howieliu/• Email: howie@airtable.com—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 Howie Liu and Airtable(04:05) The “Airtable is dead” viral tweet controversy(08:07) The rise of IC CEOs(10:57) AI's paradigm shift in product development(16:27) Specific changes Airtable has made(21:38) Fast- and slow-thinking teams(32:57) The emergence of new form factors in AI models(34:48) Airtable's vision and philosophy(40:20) Empowering teams with AI tools(46:50) Encouraging experimentation and play(50:55) Cross-functional skills in product teams(01:03:35) The importance of evals and open-ended testing(01:08:06) Key strategies for AI-driven success(01:12:43) Counterintuitive startup wisdom(01:22:21) Don't step away from the details that you love(01:25:50) Advice for aspiring engineers and designers(01:30:00) Lightning round and final thoughts—Referenced:• Airtable: https://www.airtable.com/• All In podcast: https://allin.com/• Nikita Bier on X: https://x.com/nikitabier• Figma: https://www.figma.com/• The AI-native startup: 5 products, 7-figure revenue, 100% AI-written code | Dan Shipper (co-founder and CEO of Every): https://www.lennysnewsletter.com/p/inside-every-dan-shipper• Every: https://every.to/• 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• Windsurf: https://windsurf.com/• Building a magical AI code editor used by over 1 million developers in four months: The untold story of Windsurf | Varun Mohan (co-founder and CEO): https://www.lennysnewsletter.com/p/the-untold-story-of-windsurf-varun-mohan• Rippling: https://www.rippling.com/• Omni: https://www.airtable.com/lp/ai-psu-plp• How ChatGPT accidentally became the fastest-growing product in history | Nick Turley (Head of ChatGPT at OpenAI): https://www.lennysnewsletter.com/p/inside-chatgpt-nick-turley• Palantir: https://www.palantir.com/• Harvey: https://www.harvey.ai/• v0: https://v0.dev/• 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• Lovable: https://lovable.dev/• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (CEO and co-founder): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Runway Game Worlds: https://play.runwayml.com/login• Sesame: https://www.sesame.com• NotebookLM: https://notebooklm.google• Salesforce: https://www.salesforce.com• Andrew Ofstad on LinkedIn: https://www.linkedin.com/in/aofstad/• Stripe: https://stripe.com/• Eames chair: https://en.wikipedia.org/wiki/Eames_Lounge_Chair• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• IDEO design thinking: https://designthinking.ideo.com/• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• The Studio on AppleTV+: https://tv.apple.com/us/show/the-studio/umc.cmc.7518algxc4lsoobtsx30dqb52• Silicon Valley on HBOMax: https://www.hbomax.com/shows/silicon-valley/b4583939-e39f-4b5c-822d-5b6cc186172d• Self Edge: https://www.selfedge.com/• Studio D'Artisan: https://www.selfedge.com/studio-dartisan• Whitesville T-shirt: https://store.toyo-enterprise.co.jp/shopbrand/ct48/• Guest Series | Dr. Paul Conti: How to Understand & Assess Your Mental Health: https://www.hubermanlab.com/episode/guest-series-dr-paul-conti-how-to-understand-and-assess-your-mental-health—Recommended books:• Thinking, Fast and Slow: https://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555• The Three-Body Problem: https://www.amazon.com/Three-Body-Problem-Cixin-Liu/dp/0765382032• Trauma: The Invisible Epidemic: How Trauma Works and How We Can Heal From It: https://us.amazon.com/Trauma-Invisible-Epidemic-Works-Heal/dp/1683647351/—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

Brian Balfour is the founder of Reforge, the former VP of Growth at HubSpot, and a student (and teacher) of product growth. Brian has studied every major platform shift—from Facebook to Apple to Google—and he's spotted a pattern that's about to repeat with ChatGPT.In this conversation, you'll learn:1. The 4-step cycle every platform follows (and why ChatGPT just entered step 2)2. Why ChatGPT's platform launch could be bigger than Facebook's early platform3. The exact signals that ChatGPT will launch a third-party platform within six months4. Why you have six months (not years) to make your platform bet5. Why companies that don't integrate with ChatGPT will lose to competitors that do6. How Zynga grew to $1B by betting on Facebook's platform early (before it was obvious)7. Why so few companies are actually doing what they need to be doing right now—Brought to you by:DX—The developer intelligence platform designed by leading researchers: http://getdx.com/lennyBasecamp—The famously straightforward project management system from 37signals: https://www.basecamp.com/lennyMiro—A collaborative visual platform where your best work comes to life: https://miro.com/lenny—Transcript: https://www.lennysnewsletter.com/p/why-chatgpt-will-be-the-next-big-growth-channel-brian-balfour—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/170294620/my-biggest-takeaways-from-this-conversation—Where to find Brian Balfour:• X: https://twitter.com/bbalfour• LinkedIn: https://www.linkedin.com/in/bbalfour/• Website: https://brianbalfour.com/• Substack: https://blog.brianbalfour.com/• Podcast: https://www.reforge.com/podcast/unsolicited-feedback—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) Welcome back, Brian!(04:13) The changing landscape of product growth(05:09) The importance of distribution(08:14) The role of new distribution platforms(09:45) The four-step cycle of distribution platforms(17:38) Examples of platform cycles(30:01) The rise of ChatGPT(44:47) The future of AI agents(46:01) Preferred partners and platform credibility(47:18) Monetization mechanisms and free tiers(48:14) Betting strategies for startups(01:04:34) Adopting AI tools: challenges and strategies(01:08:41) The importance of hard constraints(01:14:23) Effective AI adoption in companies(01:19:05) Lightning round and final thoughts—Referenced:• The Next Great Distribution Shift: https://blog.brianbalfour.com/p/the-next-great-distribution-shift• Brian Balfour: 10 lessons on career, growth, and life: https://www.lennysnewsletter.com/p/brian-balfour-10-lessons-on-career• This Week #9: Breaking into growth, leading with influence, and (not) stepping on toes: https://www.lennysnewsletter.com/p/this-week-9-breaking-into-growth• Distribution vs. Innovation: https://a16z.com/distribution-vs-innovation/• On Platform Shifts and AI: https://caseyaccidental.com/on-platform-shifts-and-ai/• How to sell your ideas and rise within your company | Casey Winters, Eventbrite: https://www.lennysnewsletter.com/p/how-to-sell-your-ideas-and-rise-within• Thinking beyond frameworks | Casey Winters (Pinterest, Eventbrite, Airbnb, Tinder, Canva, Reddit, Grubhub): https://www.lennysnewsletter.com/p/thinking-beyond-frameworks-casey• ChatGPT: https://chatgpt.com/• Claude: https://claude.ai/• Gemini: https://gemini.google.com/• Vine: https://en.wikipedia.org/wiki/Vine_(service)• Periscope: https://en.wikipedia.org/wiki/Periscope_(service)• Myspace: https://en.wikipedia.org/wiki/Myspace• Friendster: https://en.wikipedia.org/wiki/Friendster• AltaVista: https://en.wikipedia.org/wiki/AltaVista• Lycos: https://www.lycos.com/• HubSpot: https://www.hubspot.com/• Zynga: https://www.zynga.com/• TBPN: https://www.tbpn.com/• Deedy Das on LinkedIn: https://www.linkedin.com/in/debarghyadas/• ChatGPT's product retention curves are a product manager's wet dream: https://www.linkedin.com/posts/debarghyadas_chatgpts-product-retention-curves-are-a-activity-7338384752393035776-ice1/• Windsurf: https://windsurf.com/• Building a magical AI code editor used by over 1 million developers in four months: The untold story of Windsurf | Varun Mohan (co-founder and CEO): https://www.lennysnewsletter.com/p/the-untold-story-of-windsurf-varun-mohan• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• Udemy: https://www.udemy.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• Notion: https://www.notion.com/• Airtable: https://www.airtable.com/• Monday: monday.com• Sierra: http://sierra.ai• He saved OpenAI, invented the “Like” button, and built Google Maps: Bret Taylor on the future of careers, coding, agents, and more: https://www.lennysnewsletter.com/p/he-saved-openai-bret-taylor• Introducing ChatGPT agent: bridging research and action: https://openai.com/index/introducing-chatgpt-agent/• Zigging vs. zagging: How HubSpot built a $30B company | Dharmesh Shah (co-founder/CTO): https://www.lennysnewsletter.com/p/lessons-from-30-years-of-building• Marc Andreessen on Why Optimism Is the Safest Bet: https://nymag.com/marc-andressen-2014-10-20/• Reforge: https://www.reforge.com• Reforge Insights: https://www.reforge.com/insights• Shopify: https://www.shopify.com/• 25 proven tactics to accelerate AI adoption at your company: https://www.lennysnewsletter.com/p/25-proven-tactics-to-accelerate-ai• Clouded Judgement: https://cloudedjudgement.substack.com/• NFX: https://www.nfx.com/news• James Currier: https://www.nfx.com/team/james-currier• Hallway Chat: https://www.hallwaychat.co/• Bryan Johnson on LinkedIn: https://www.linkedin.com/in/bryanrjohnson/• Silicon Valley on HBO: https://www.hbomax.com/shows/silicon-valley/b4583939-e39f-4b5c-822d-5b6cc186172d• Stick: https://tv.apple.com/us/show/stick/umc.cmc.52w04zy67tiv11p8xvbc57wmc• Ergonofis standing desks: https://ergonofis.com/en-us/collections/standing-desks• Coping with the loss of a child and protecting your time | Brian Balfour (father of 2, CEO and founder Reforge, venture partner): https://www.startupdadpod.com/coping-with-the-loss-of-a-child-and-protecting-your-time-brian-balfour-father-of-2-ceo-and-found/—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

Benjamin Mann is a co-founder of Anthropic, an AI startup dedicated to building aligned, safety-first AI systems. Prior to Anthropic, Ben was one of the architects of GPT-3 at OpenAI. He left OpenAI driven by the mission to ensure that AI benefits humanity. In this episode, Ben opens up about the accelerating progress in AI and the urgent need to steer it responsibly.In this conversation, we discuss:1. The inside story of leaving OpenAI with the entire safety team to start Anthropic2. How Meta's $100M offers reveal the true market price of top AI talent3. Why AI progress is still accelerating (not plateauing), and how most people misjudge the exponential4. Ben's “economic Turing test” for knowing when we've achieved AGI—and why it's likely coming by 2027-20285. Why he believes 20% unemployment is inevitable6. The AI nightmare scenarios that concern him most—and how he believes we can still avoid them7. How focusing on AI safety created Claude's beloved personality8. What three skills he's teaching his kids instead of traditional academics—Brought to you by:Sauce—Turn customer pain into product revenue: https://sauce.app/lennyLucidLink—Real-time cloud storage for teams: https://www.lucidlink.com/lennyFin—The #1 AI agent for customer service: https://fin.ai/lenny—Transcript: https://www.lennysnewsletter.com/p/anthropic-co-founder-benjamin-mann—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/168107911/my-biggest-takeaways-from-this-conversation—Where to find Ben Mann:• X: https://x.com/8enmann• LinkedIn: https://www.linkedin.com/in/benjamin-mann/• Website: https://benjmann.net/—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 Benjamin(04:43) The AI talent war(06:28) AI progress and scaling laws(10:50) Defining AGI and the economic Turing test(12:26) The impact of AI on jobs(17:45) Preparing for an AI future(24:05) Founding Anthropic(27:06) Balancing AI safety and progress(29:10) Constitutional AI and model alignment(34:21) The importance of AI safety(43:40) The risks of autonomous agents(45:40) Forecasting superintelligence(48:36) How hard is it to align AI?(53:19) Reinforcement learning from AI feedback (RLAIF)(57:03) AI's biggest bottlenecks(01:00:11) Personal reflections on responsibilities(01:02:36) Anthropic's growth and innovations(01:07:48) Lightning round and final thoughts—Referenced:• Dario Amodei on LinkedIn: https://www.linkedin.com/in/dario-amodei-3934934/• Anthropic CEO: AI Could Wipe Out 50% of Entry-Level White Collar Jobs: https://www.marketingaiinstitute.com/blog/dario-amodei-ai-entry-level-jobs• Alexa+: https://www.amazon.com/dp/B0DCCNHWV5• Azure: https://azure.microsoft.com/• Sam Altman on X: https://x.com/sama• Opus 3: https://www.anthropic.com/news/claude-3-family• Claude's Constitution: https://www.anthropic.com/news/claudes-constitution• Greg Brockman on X: https://x.com/gdb• Anthropic's Responsible Scaling Policy: https://www.anthropic.com/news/anthropics-responsible-scaling-policy• Agentic Misalignment: How LLMs could be insider threats: https://www.anthropic.com/research/agentic-misalignment• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• AI prompt engineering in 2025: What works and what doesn't | Sander Schulhoff (Learn Prompting, HackAPrompt): https://www.lennysnewsletter.com/p/ai-prompt-engineering-in-2025-sander-schulhoff• Unitree: https://www.unitree.com/• Arthur C. Clarke: https://en.wikipedia.org/wiki/Arthur_C._Clarke• How Reinforcement Learning from AI Feedback Works: https://www.assemblyai.com/blog/how-reinforcement-learning-from-ai-feedback-works• RLHF: https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback• Jared Kaplan on LinkedIn: https://www.linkedin.com/in/jared-kaplan-645843213/• Moore's law: https://en.wikipedia.org/wiki/Moore%27s_law• Machine Intelligence Research Institute: https://intelligence.org/• Raph Lee on LinkedIn: https://www.linkedin.com/in/raphaeltlee/• “The Last Question”: https://en.wikipedia.org/wiki/The_Last_Question• Beth Barnes on LinkedIn: https://www.linkedin.com/in/elizabethmbarnes/• “The Last Question”: https://en.wikipedia.org/wiki/The_Last_Question• Good Strategy, Bad Strategy | Richard Rumelt: https://www.lennysnewsletter.com/p/good-strategy-bad-strategy-richard• Pantheon on Netflix: https://www.netflix.com/title/81937398• Ted Lasso on AppleTV+: https://tv.apple.com/us/show/ted-lasso/umc.cmc.vtoh0mn0xn7t3c643xqonfzy• Kurzgesagt—In a Nutshell: https://www.youtube.com/channel/UCsXVk37bltHxD1rDPwtNM8Q• 5 tips to poop like a champion: https://8enmann.medium.com/5-tips-to-poop-like-a-champion-3292481a9651—Recommended books:• Superintelligence: Paths, Dangers, Strategies: https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834• The Hacker and the State: Cyber Attacks and the New Normal of Geopolitics: https://www.amazon.com/Hacker-State-Attacks-Normal-Geopolitics/dp/0674987551• Replacing Guilt: Minding Our Way: https://www.amazon.com/Replacing-Guilt-Minding-Our-Way/dp/B086FTSB3Q• Good Strategy/Bad Strategy: The Difference and Why It Matters: https://www.amazon.com/Good-Strategy-Bad-Difference-Matters/dp/0307886239• The Alignment Problem: Machine Learning and Human Values: https://www.amazon.com/Alignment-Problem-Machine-Learning-Values/dp/0393635821—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

Andrew Wilkinson is the co‑founder of Tiny, a holding company that quietly owns more than three dozen profitable internet and consumer brands, including Dribbble and the AeroPress coffee maker. Starting as a teenage barista and web designer, he's created a portfolio approaching $300 million in yearly sales (and he was personally worth over $1 billion at one point)—all without ever raising venture capital.In this conversation, you'll learn:1. The “fish where the fish are” framework for spotting high‑margin niches no one else notices2. The exact agent stack (Lindy, Replit, Limitless, and more) that supercharges Andrew's day-to-day productivity (and has replaced his assistant)3. How Andrew evaluates companies in less than 15 minutes using Buffett‑style moats and “lazy leadership”4. Telltale signs you should shut down (or never start) that startup idea5. His journey from crippling anxiety to clarity through SSRIs and ADHD medication6. His prediction that most knowledge work will be automated—and the skills to teach your kids now—Brought to you by:Sauce—Turn customer pain into product revenueEnterpret—Transform customer feedback into product growthMiro—A collaborative visual platform where your best work comes to life—Where to find Andrew Wilkinson:• X: https://x.com/awilkinson• LinkedIn: https://www.linkedin.com/in/awilkinson/—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 Andrew Wilkinson(04:07) Finding the right business idea(07:18) Avoiding common business pitfalls(11:58) Finding your unfair advantage(17:08) Fish where the fish are(20:08) Why boring is good(25:30) Bootstrapping vs. venture capital(31:20) Lessons from acquiring and managing businesses(36:47) Avoiding people problems(42:39) Leveraging AI in business and life(49:30) The Limitless device(53:13) Job displacement and AI's future impact(58:20) Advice for new grads(01:02:50) Parenting in the age of AI(01:05:26) The pursuit of happiness beyond wealth(01:10:10) Mental health and medication(01:16:45) Lightning round and final thoughts—Referenced:• Andrew's post on X with the Charlie Munger quote: https://x.com/awilkinson/status/1265653805443506182• Metalab: https://www.metalab.com/• Letterboxd: https://letterboxd.com/• AeroPress: https://aeropress.com/• Brian Armstrong on X: https://x.com/brian_armstrong• Warren Buffett's quote: https://quotefancy.com/quote/931119/Warren-Buffett-I-am-a-better-investor-because-I-am-a-businessman-and-a-better-businessman• Flow: https://www.getflow.com/• Instacart: https://www.instacart.com/• Things: https://culturedcode.com/things/• Dustin Moskovitz on LinkedIn: https://www.linkedin.com/in/dmoskov/• Salesforce: https://www.salesforce.com/• Serato: https://serato.com/• Chris Sparling on X: https://x.com/_sparling_• Lindy: https://www.lindy.ai/• 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• David Ogilvy: https://en.wikipedia.org/wiki/David_Ogilvy_(businessman)• Malcolm Gladwell's website: https://www.gladwellbooks.com/• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder and CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (CEO and co-founder): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Limitless: https://www.limitless.ai/• Perplexity: https://www.perplexity.ai/• Claude: https://claude.ai/• ChatGPT: https://chatgpt.com/• Gemini: https://gemini.google.com/app• William Gibson's quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly• Palm Treo: https://en.wikipedia.org/wiki/Palm_Treo• Sam Altman on X: https://x.com/sama• Dario Amodei on X: https://x.com/darioamodei• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• Challengers on AppleTV+: https://tv.apple.com/us/movie/challengers/umc.cmc.53cuz33n4e74ixj8whccj87oc• Matic vacuum: https://maticrobots.com/• Jerzy Gregorek's quote: https://www.goodreads.com/quotes/8652595-hard-choices-easy-life-easy-choices-hard-life• Tiny: https://www.tiny.com/• Dribbble: https://dribbble.com/—Recommended books:• The Laws of Human Nature: https://www.amazon.com/Laws-Human-Nature-Robert-Greene/dp/0525428143• How to Get Rich: One of the World's Greatest Entrepreneurs Shares His Secrets: https://www.amazon.com/How-Get-Rich-Greatest-Entrepreneurs/dp/1591842719—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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe

Peter Deng has led product teams at OpenAI, Instagram, Uber, Facebook, Airtable, and Oculus and helped build products used by billions—including Facebook's News Feed, the standalone Messenger app, Instagram filters, Uber Reserve, ChatGPT, and more. Currently he's investing in early-stage founders at Felicis. In this episode, Peter dives into his most valuable lessons from building and scaling some of tech's most iconic products and companies.What you'll learn:1. Peter's one‑sentence test for hiring superstars2. Why your product (probably) doesn't matter3. Why you don't need a tech breakthrough to build a huge business4. The five PM archetypes, and how to build a team of Avengers5. Counterintuitive lessons on growing products from 0 to 1, and 1 to 1006. The importance of data flywheels and workflows—Brought to you by:Paragon—Ship every SaaS integration your customers wantPragmatic Institute—Industry‑recognized product, marketing, and AI training and certificationsContentsquare—Create better digital experiences—Where to find Peter Deng:• X: https://x.com/pxd• LinkedIn: https://www.linkedin.com/in/peterxdeng/—In this episode, we cover:(00:00) Introduction to Peter Deng(05:41) AI and AGI insights(11:35) The future of education with AI(16:53) The power of language in leadership(21:01) Building iconic products(36:44) Scaling from zero to 100(41:56) Balancing short- and long-term goals(47:12) Creating a healthy tension in teams(50:02) The five archetypes of product managers(55:39) Primary and secondary archetypes(58:47) Hiring for growth mindset and autonomy(01:15:52) Effective management and communication strategies(01:19:23) Presentation advice and self-advocacy(01:25:50) Balancing craft and practicality in product management(01:30:40) The importance of empathy in design thinking(01:35:45) Career decisions and learning opportunities(01:42:05) Lessons from product failures(01:45:42) Lightning round and final thoughts—Referenced:• OpenAI: https://openai.com/• Artificial general intelligence (AGI): https://en.wikipedia.org/wiki/Artificial_general_intelligence• Head of ChatGPT answers philosophical questions about AI at SXSW 2024 with SignalFire's Josh Constine: https://www.youtube.com/watch?v=mgbgI0R6XCw• Professors Are Using A.I., Too. Now What?: https://www.npr.org/2025/05/21/1252663599/kashmir-hill-ai#:~:text=Now%20What• Herbert H. Clark: https://web.stanford.edu/~clark/• Russian speakers get the blues: https://www.newscientist.com/article/dn11759-russian-speakers-get-the-blues/• Ilya Sutskever (OpenAI Chief Scientist)—Building AGI, Alignment, Future Models, Spies, Microsoft, Taiwan, & Enlightenment: https://www.dwarkesh.com/p/ilya-sutskever• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• Kevin Systrom on LinkedIn: https://www.linkedin.com/in/kevinsystrom/• Building a magical AI code editor used by over 1 million developers in four months: The untold story of Windsurf | Varun Mohan (co-founder and CEO): https://www.lennysnewsletter.com/p/the-untold-story-of-windsurf-varun-mohan• Microsoft CPO: If you aren't prototyping with AI, you're doing it wrong | Aparna Chennapragada: https://www.lennysnewsletter.com/p/microsoft-cpo-on-ai• 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• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (CEO and co-founder): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Granola: https://www.granola.ai/• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder and CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Fidji Simo on LinkedIn: https://www.linkedin.com/in/fidjisimo/• Airtable: https://www.airtable.com/• George Lee on LinkedIn: https://www.linkedin.com/in/geolee/• Andrew Chen on LinkedIn: https://www.linkedin.com/in/andrewchen/• Lauryn Motamedi on LinkedIn: https://www.linkedin.com/in/laurynmotamedi/• Twilio: https://www.twilio.com/• Nick Turley on LinkedIn: https://www.linkedin.com/in/nicholasturley/• Ian Silber on LinkedIn: https://www.linkedin.com/in/iansilber/• Thomas Dimson on LinkedIn: https://www.linkedin.com/in/thomasdimson/• Joey Flynn on LinkedIn: https://www.linkedin.com/in/joey-flynn-8291586b/• Ryan O'Rourke's website: https://www.rourkery.com/• Joanne Jang on LinkedIn: https://www.linkedin.com/in/jangjoanne/• Behind the founder: Marc Benioff: https://www.lennysnewsletter.com/p/behind-the-founder-marc-benioff• Jill Hazelbaker on LinkedIn: https://www.linkedin.com/in/jill-hazelbaker-3aa32422/• Guy Kawasaki's website: https://guykawasaki.com/• Eric Antonow on LinkedIn: https://www.linkedin.com/in/antonow/• Sachin Kansal on LinkedIn: https://www.linkedin.com/in/sachinkansal/• IDEO design thinking: https://designthinking.ideo.com/• The 7 Steps of the Design Thinking Process: https://www.ideou.com/blogs/inspiration/design-thinking-process• Linear's secret to building beloved B2B products | Nan Yu (Head of Product): https://www.lennysnewsletter.com/p/linears-secret-to-building-beloved-b2b-products-nan-yu• Jeff Bezos's quote: https://news.ycombinator.com/item?id=27778175• Friendster: https://en.wikipedia.org/wiki/Friendster• Myspace: https://en.wikipedia.org/wiki/Myspace• How LinkedIn became interesting: The inside story | Tomer Cohen (CPO at LinkedIn): https://www.lennysnewsletter.com/p/how-linkedin-became-interesting-tomer-cohen• “Smile” by Jay-Z: https://www.youtube.com/watch?v=SSumXG5_rs8&list=RDSSumXG5_rs8&start_radio=1• The Wire on HBO: https://www.hbo.com/the-wire• Felicis: https://www.felicis.com/—Recommended books:• Sapiens: A Brief History of Humankind: https://www.amazon.com/Sapiens-Humankind-Yuval-Noah-Harari/dp/0062316095• The Design of Everyday Things: https://www.amazon.com/Design-Everyday-Things-Revised-Expanded/dp/0465050654• The Silk Roads: A New History of the World: https://www.amazon.com/Silk-Roads-New-History-World/dp/1101912375—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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe

Sander Schulhoff is the OG prompt engineer. He created the very first prompt engineering guide on the internet (two months before ChatGPT's release) and recently wrote the most comprehensive study of prompt engineering ever conducted (co-authored with OpenAI, Microsoft, Google, Princeton, and Stanford), analyzing over 1,500 academic papers and covering more than 200 prompting techniques. He also partners with OpenAI to run what was the first and is the largest AI red teaming competition, HackAPrompt, which helps discover the most state-of-the-art prompt injection techniques (i.e. ways to get LLMS to do things it shouldn't). Sander teaches AI red teaming on Maven, advises AI companies on security, and has educated millions of people on the most state-of-the-art prompt engineering techniques.In this episode, you'll learn:1. The 5 most effective prompt engineering techniques2. Why “role prompting” and threatening the AI no longer works—and what to do instead3. The two types of prompt engineering: conversational and product/system prompts4. A primer on prompt injection and AI red teaming—including real jailbreak tactics that are still fooling top models5. Why AI agents and robots will be the next major security threat6. How to get started in AI red teaming and prompt engineering7. Practical defense to put in place for your AI products—Brought to you by:Eppo—Run reliable, impactful experimentsStripe—Helping companies of all sizes grow revenueVanta—Automate compliance. Simplify security—Where to find Sander Schulhoff:• X: https://x.com/sanderschulhoff• LinkedIn: https://www.linkedin.com/in/sander-schulhoff/• Website: https://sanderschulhoff.com/• AI Red Teaming and AI Security Masterclass on Maven: https://bit.ly/44lLSbC• Free Lightning Lesson “How to Secure Your AI System” on 6/24: https://bit.ly/4ld9vZL—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 Sander Schulhoff(04:29) The importance of prompt engineering(06:30) Real-world applications and examples(10:54) Basic prompt engineering techniques(23:46) Advanced prompt engineering techniques(29:00) The role of context and additional information(39:24) Ensembling techniques and thought generation(49:48) Conversational techniques for better results(50:46) Introduction to prompt injection(52:27) AI red teaming and competitions(54:23) The growing importance of AI security(01:02:45) Techniques to bypass AI safeguards(01:05:21) Challenges in AI security and future outlook(01:18:33) Misalignment and AI's potential risks(01:25:03) Final thoughts and lightning round—Referenced:• Reid Hoffman's tweet about using AI agents: https://x.com/reidhoffman/status/1930416063616884822• AI Engineer World's Fair: https://www.ai.engineer/• What Is Artificial Social Intelligence?: https://learnprompting.org/blog/asi• Devin: https://devin.ai/• Cursor: https://www.cursor.com/• Inside Devin: The world's first autonomous AI engineer that's set to write 50% of its company's code by end of year | Scott Wu (CEO and co-founder of Cognition): https://www.lennysnewsletter.com/p/inside-devin-scott-wu• 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• Granola: https://www.granola.ai/• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (CEO and co-founder): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder & CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• 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• Technique #3: Examples in Prompts: From Zero-Shot to Few-Shot: https://learnprompting.org/docs/basics/few_shot?srsltid=AfmBOor2owyGXtzJZ8n0fJVCctM7UPZgZmH-mBuxRW4t9-kkaMd3LJVv• The Prompt Report: Insights from the Most Comprehensive Study of Prompting Ever Done: https://learnprompting.org/blog/the_prompt_report?srsltid=AfmBOoo7CRNNCtavzhyLbCMxc0LDmkSUakJ4P8XBaITbE6GXL1i2SvA0• State-of-the-Art Prompting for AI Agents | Y Combinator: https://www.youtube.com/watch?v=DL82mGde6wo• Use XML tags to structure your prompts: https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/use-xml-tags• Role Prompting: https://learnprompting.org/docs/basics/roles?srsltid=AfmBOor2jcxJQvWBZyFa030Qt0fIIov3hSiWvI9VFyjO-Qp478EPJIU7• Is Role Prompting Effective?: https://learnprompting.org/blog/role_prompting?srsltid=AfmBOooiiyLD-0CsCYZ4m3SDhYOmtTyaTzeDo0FvK_i1x1gLM8MJS-Sn• Introduction to Decomposition Prompting Techniques: https://learnprompting.org/docs/advanced/decomposition/introduction?srsltid=AfmBOoojJmTQgBlmSlGYQ8kl-JPpVUlLKkL4YcFGS5u54JyeumUwlcBI• LLM Self-Evaluation: https://learnprompting.org/docs/reliability/lm_self_eval• Philip Resnik on X: https://x.com/psresnik• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• Introduction to Ensembling Prompting: https://learnprompting.org/docs/advanced/ensembling/introduction?srsltid=AfmBOooGSyqsrjnEbXSYoKpG0ZlpT278NHQA6Fd8gMvNTJlWu7-qEYzh• Random forest: https://en.wikipedia.org/wiki/Random_forest• Chain-of-Thought Prompting: https://learnprompting.org/docs/intermediate/chain_of_thought?srsltid=AfmBOoqwE7SXlluy2sx_QY_VOKduyBplWtIWKEJaD6FkJW3TqeKPSJfx• Prompt Injecting: https://learnprompting.org/docs/prompt_hacking/injection?srsltid=AfmBOoqGgqbfXStrD6vlw5jy8HhEaESgGo2e57jyWL8lkZKktt_P6Zvn• Announcing HackAPrompt 2.0: The World's Largest AI Red-Teaming Hackathon: https://learnprompting.org/blog/announce-hackaprompt-2?srsltid=AfmBOopXKsHxy4aUtsvPCUtEu7x74NCAEnlTIdNzo7nfMDVwZ9ilTlkp• Infant with rare, incurable disease is first to successfully receive personalized gene therapy treatment: https://www.nih.gov/news-events/news-releases/infant-rare-incurable-disease-first-successfully-receive-personalized-gene-therapy-treatment• Building a magical AI code editor used by over 1 million developers in four months: The untold story of Windsurf | Varun Mohan (co-founder and CEO): https://www.lennysnewsletter.com/p/the-untold-story-of-windsurf-varun-mohan• Copilot: https://copilot.microsoft.com/chats/rcxhzvKgZvz8ajUrKdBtX• GitHub Copilot: https://github.com/features/copilot• Defensive Measures: https://learnprompting.org/docs/prompt_hacking/defensive_measures/introduction• Sam Altman on X: https://x.com/sama• Three Laws of Robotics: https://en.wikipedia.org/wiki/Three_Laws_of_Robotics• Anthropic's new AI model turns to blackmail when engineers try to take it offline: https://techcrunch.com/2025/05/22/anthropics-new-ai-model-turns-to-blackmail-when-engineers-try-to-take-it-offline/• Palisade Research: https://palisaderesearch.org/• When AI Thinks It Will Lose, It Sometimes Cheats, Study Finds: https://time.com/7259395/ai-chess-cheating-palisade-research/• A.I. Chatbots Defeated Doctors at Diagnosing Illness: https://www.nytimes.com/2024/11/17/health/chatgpt-ai-doctors-diagnosis.html• 1883 on Paramount+: https://www.paramountplus.com/shows/1883/• Black Mirror on Netflix: https://www.netflix.com/title/70264888• Daylight Computer: https://daylightcomputer.com/• Theodore Roosevelt's quote: https://www.goodreads.com/quotes/622252-i-wish-to-preach-not-the-doctrine-of-ignoble-ease• HackAPrompt 2.0: https://www.hackaprompt.com/—Recommended books:• Ender's Game: https://www.amazon.com/Enders-Ender-Quintet-Orson-Scott/dp/0812550706• The River of Doubt: Theodore Roosevelt's Darkest Journey: https://www.amazon.com/River-Doubt-Theodore-Roosevelts-Darkest/dp/0767913736—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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe

Mike Krieger is the chief product officer of Anthropic and the co-founder of Instagram. After leaving Meta, he co-founded Artifact, an AI-powered news app that I absolutely loved, and joined Anthropic to lead product in 2024.In this episode, you'll learn:• How Anthropic uses AI to write 90-95% of code for some products and the surprising new bottlenecks this creates• Why embedding product managers with AI researchers yields 10x the impact of traditional product development• The three areas where product teams can still add massive value as AI gets smarter• How Anthropic plans to compete with OpenAI long-term• How to use Claude as your product strategy partner (with specific prompting techniques)• Why Mike shut down Artifact despite loving the product, and what founders can learn from it• Where AI startups should build to avoid getting killed by OpenAI, Anthropic, and Google• Why MCP (Model Context Protocol) might reshape how all software works• The counterintuitive product metrics that matter for AI• How to evaluate whether your company is maximizing AI's potential or just scratching the surface—Brought to you by:Productboard—Make products that matterStripe—Helping companies of all sizes grow revenueOneSchema—Import CSV data 10x faster—Where to find Mike Krieger:• X: https://x.com/mikeyk• LinkedIn: https://www.linkedin.com/in/mikekrieger/—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 Mike Krieger(04:20) What Mike has changed his mind about regarding AI capabilities(07:38) How to avoid scary AI scenarios(08:55) Skills kids will need in an AI world(11:53) How product development changes when 90% of code is written by AI(17:07) Claude helping with product strategy(21:16) A new way of working(23:55) The future value of product teams in an AI world(27:18) Prompting tricks to get more out of Claude(29:52) The Rick Rubin collaboration on “vibe coding”(32:42) How Mike was recruited to Anthropic(35:55) Why Mike shut down Artifact(42:41) Anthropic vs. OpenAI(47:11) Where AI founders should play to avoid getting squashed(51:58) How companies can best leverage Anthropic's models and APIs(54:29) The role of MCPs (Model Context Protocols)(58:25) Claude's questions for Mike(01:03:15) Claude's heartfelt message to Mike—Referenced:• Anthropic: https://www.anthropic.com/• Claude Opus 4: https://www.anthropic.com/claude/opus• Dario Amodei on X: https://x.com/darioamodei• AI 2027: https://ai-2027.com/• Tobi Lütke's leadership playbook: Playing infinite games, operating from first principles, and maximizing human potential (founder and CEO of Shopify): https://www.lennysnewsletter.com/p/tobi-lutkes-leadership-playbook• Claude Shannon: https://en.wikipedia.org/wiki/Claude_Shannon• Information theory: https://en.wikipedia.org/wiki/Information_theory• TypeScript: https://www.typescriptlang.org/• Python: https://www.python.org/• Rust: https://www.rust-lang.org/• Bending the universe in your favor | Claire Vo (LaunchDarkly, Color, Optimizely, ChatPRD): https://www.lennysnewsletter.com/p/bending-the-universe-in-your-favor• Announcing a brand-new podcast: “How I AI” with Claire Vo: https://www.lennysnewsletter.com/p/announcing-a-brand-new-podcast-how• A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo: https://www.youtube.com/watch?v=IxkvVZua28k• Jack Clark on LinkedIn: https://www.linkedin.com/in/jack-clark-5a320317/• Artifact: https://en.wikipedia.org/wiki/Artifact_(app)• Joel Lewenstein on LinkedIn: https://www.linkedin.com/in/joel-lewenstein/• Daniela Amodei on LinkedIn: https://www.linkedin.com/in/daniela-amodei-790bb22a/• Boris Cherny on LinkedIn: https://www.linkedin.com/in/bcherny/• Gunnar Gray on LinkedIn: https://www.linkedin.com/in/gunnargray/• The Model Context Protocol: https://www.anthropic.com/news/model-context-protocol• 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• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (CEO and co-founder): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder and CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• Jimmy Kimmel Live: https://www.youtube.com/user/JimmyKimmelLive• ChatGPT: https://chatgpt.com/• Gemini: https://gemini.google.com/app• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Windsurf: https://windsurf.com/• Menlo Ventures: https://menlovc.com/• Harvey: https://www.harvey.ai/• Manus: https://manus.im/• Bench: https://www.bench-ai.com/• Strategy Letter V: https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/• Kevin Scott on LinkedIn: https://www.linkedin.com/in/jkevinscott/—Recommended books:• The Goal: A Process of Ongoing Improvement: https://www.amazon.com/Goal-Process-Ongoing-Improvement/dp/0884271951• The Way of the Code: The Timeless Art of Vibe Coding: https://www.thewayofcode.com/• The Hard Thing About Hard Things: Building a Business when There Are No Easy Answers―Straight Talk on the Challenges of Entrepreneurship: https://www.amazon.com/Hard-Thing-About-Things-Building/dp/0062273205—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. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe

This week, we dive into Kevin's recent column about how A.I. is affecting the job market for new graduates, and debate whether the job apocalypse is already here for entry-level work. Then Mike Krieger joins us to discuss the new Claude 4 model, the future of work and the online chatter over whether an A.I. system could blackmail you. And finally, it's time to open up the case files for another round of Hard Fork Crimes Division.Guest:Mike Krieger, chief product officer at AnthropicAdditional Reading:For Some Recent Graduates, the A.I. Job Apocalypse May Already Be HereAnother Suspect Is Charged in Bitcoin Kidnapping and Torture CaseElizabeth Holmes's Partner Has a New Blood-Testing Start-UpWe want to hear from you. Email us at hardfork@nytimes.com. Find “Hard Fork” on YouTube and TikTok. Unlock full access to New York Times podcasts and explore everything from politics to pop culture. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify.

This week, we are revisiting a conversation between Lightspeed partner Michael Mignano and Anthropic's head of product, Mike Krieger. Mike is known for co-founding Instagram, one of the most beloved pieces of consumer technology, and now he has taken his talents to Anthropic. They discuss the challenges AI product builders face and the evolution of product innovation and draw parallels between two transformative eras: the social media revolution that gave birth to Instagram and today's AI renaissance. Episode Chapters: (00:00) Introduction(00:54) Mike Krieger's Journey to Anthropic(03:17) Building Product Strategy at Anthropic(07:43) Rapid Iteration and Safety(10:58) Differentiating AI Models and User Experience(17:57) Impact of AI on Consumer Products and Business Models(24:39) Enterprise vs. Consumer Product Strategy(29:19) AI in Personal Life Management(30:15) Open Source and Claude Integrations(33:09) AI-Assisted Product Development(37:13) Scaling Teams and Processes at Anthropic(42:17) Reflections on AI and Future ProspectsStay in touch:www.lsvp.comX: https://twitter.com/lightspeedvpLinkedIn: https://www.linkedin.com/company/lightspeed-venture-partners/Instagram: https://www.instagram.com/lightspeedventurepartners/Subscribe on your favorite podcast app: generativenow.coEmail: generativenow@lsvp.comThe content here does not constitute tax, legal, business or investment advice or an offer to provide such advice, should not be construed as advocating the purchase or sale of any security or investment or a recommendation of any company, and is not an offer, or solicitation of an offer, for the purchase or sale of any security or investment product. For more details please see lsvp.com/legal.

Welcome to this classic episode. Classics are my favorite episodes from the past 10 years, published once a month. These are N of 1 conversations with N of 1 people. There aren't many people like Cyan Banister. Her life story is remarkable. She was homeless at a young age, dropped out of high school, and five years ago she suffered an extremely rare stroke. Yet, in spite of everything, she is one of the most optimistic and curious people you can hope to meet. Cyan is also one of the great angel investors of this era, having invested early in SpaceX, Uber, Postmates, and Deepmind to name a few winners. She became the first female investing partner at Peter Thiel's Founders Fund and now invests at Long Journey Ventures. Our conversation is as much about investing as it is about the essence of life and how connecting with that will help us in our professional pursuits. It is also full of awesome stories about people and companies like SpaceX and Bill Murray. Please enjoy this great conversation with Cyan Banister. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 [00:04:06] Contrarian Thinking in Investing [00:05:30] Joining Founders Fund and Learning from Peter Thiel [00:11:15] Investing in Companies that Change Lives [00:14:00] The Importance of Overcoming Adversity for Founders [00:16:02] Personal Journey and Choosing Hope [00:20:56] Embracing Curiosity and Wonder in the Face of Adversity [00:21:20] Reconnecting with Our Inner Child [00:24:46] The Interruption and Resumption of the Conversation [00:27:20] The Power of Intuition in Business Decisions [00:32:28] The Story Behind the Investment in Uber [00:38:46] Conclusion: Following the White Rabbit of Curiosity [00:39:08] Investing in Uber: The Beginning [00:41:50] The Impact of Success: Personal Wealth and Privacy [00:50:22] The Intersection of Spirituality and Investing [00:59:34] The Bill Murray Experience: A Lesson in Presence [01:09:54] The Violin Kid: A Tale of Curiosity and Generosity [01:12:43] The Evolution of Investing: A Personal Journey [01:16:34] The Philosophy of Giving: The Universe's Return [01:17:36] The Spirit of a Venture Firm: Founders Fund [01:23:09] The Power of Integral Family Systems [01:28:41] The Trillion Dollar Question: Disrupting Hollywood [01:36:05] The Future of Artistry: AI and Creativity [01:41:39] The Power of Kindness

Kevin Weil is the chief product officer at OpenAI, where he oversees the development of ChatGPT, enterprise products, and the OpenAI API. Prior to OpenAI, Kevin was head of product at Twitter, Instagram, and Planet, and was instrumental in the development of the Libra (later Novi) cryptocurrency project at Facebook.In this episode, you'll learn:1. How OpenAI structures its product teams and maintains agility while developing cutting-edge AI2. The power of model ensembles—using multiple specialized models together like a company of humans with different skills3. Why writing effective evals (AI evaluation tests) is becoming a critical skill for product managers4. The surprisingly enduring value of chat as an interface for AI, despite predictions of its obsolescence5. How “vibe coding” is changing how companies operate6. What OpenAI looks for when hiring product managers (hint: high agency and comfort with ambiguity)7. “Model maximalism” and why today's AI is the worst you'll ever use again8. Practical prompting techniques that improve AI interactions, including example-based prompting—Brought to you by:• Eppo—Run reliable, impactful experiments• Persona—A global leader in digital identity verification• OneSchema—Import CSV data 10x faster—Where to find Kevin Weil:• X: https://x.com/kevinweil• LinkedIn: https://www.linkedin.com/in/kevinweil/—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) Kevin's background(04:06) OpenAI's new image model(06:52) The role of chief product officer at OpenAI(10:18) His recruitment story and joining OpenAI(17:20) The importance of evals in AI(24:59) Shipping quickly and consistently(28:34) Product reviews and iterative deployment(39:35) Chat as an interface for AI(43:59) Collaboration between researchers and product teams(46:41) Hiring product managers at OpenAI(48:45) Embracing ambiguity in product management(51:41) The role of AI in product teams(53:21) Vibe coding and AI prototyping(55:55) The future of product teams and fine-tuned models(01:04:36) AI in education(01:06:42) Optimism and concerns about AI's future(01:16:37) Reflections on the Libra project(01:20:37) Lightning round and final thoughts—Referenced:• OpenAI: https://openai.com/• The AI-Generated Studio Ghibli Trend, Explained: https://www.forbes.com/sites/danidiplacido/2025/03/27/the-ai-generated-studio-ghibli-trend-explained/• Introducing 4o Image Generation: https://openai.com/index/introducing-4o-image-generation/• Waymo: https://waymo.com/• X: https://x.com• Facebook: https://www.facebook.com/• Instagram: https://www.instagram.com/• Planet: https://www.planet.com/• Sam Altman on X: https://x.com/sama• A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo: https://www.youtube.com/watch?v=IxkvVZua28k• OpenAI evals: https://github.com/openai/evals• Deep Research: https://openai.com/index/introducing-deep-research/• Ev Williams on X: https://x.com/ev• OpenAI API: https://platform.openai.com/docs/overview• Dwight Eisenhower quote: https://www.brainyquote.com/quotes/dwight_d_eisenhower_164720• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder & CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• StackBlitz: https://stackblitz.com/• Claude 3.5 Sonnet: https://www.anthropic.com/news/claude-3-5-sonnet• Anthropic: https://www.anthropic.com/• Four-minute mile: https://en.wikipedia.org/wiki/Four-minute_mile• Chad: https://chatgpt.com/g/g-3F100ZiIe-chad-open-a-i• Dario Amodei on LinkedIn: https://www.linkedin.com/in/dario-amodei-3934934/• Figma: https://www.figma.com/• Julia Villagra on LinkedIn: https://www.linkedin.com/in/juliavillagra/• Andrej Karpathy on X: https://x.com/karpathy• Silicon Valley CEO says ‘vibe coding' lets 10 engineers do the work of 100—here's how to use it: https://fortune.com/2025/03/26/silicon-valley-ceo-says-vibe-coding-lets-10-engineers-do-the-work-of-100-heres-how-to-use-it/• Cursor: https://www.cursor.com/• Windsurf: https://codeium.com/windsurf• GitHub Copilot: https://github.com/features/copilot• Patrick Srail on X: https://x.com/patricksrail• Khan Academy: https://www.khanacademy.org/• CK-12 Education: https://www.ck12.org/• Sora: https://openai.com/sora/• Sam Altman's post on X about creative writing: https://x.com/sama/status/1899535387435086115• Diem (formerly known as Libra): https://en.wikipedia.org/wiki/Diem_(digital_currency)• Novi: https://about.fb.com/news/2020/05/welcome-to-novi/• David Marcus on LinkedIn: https://www.linkedin.com/in/dmarcus/• Peter Zeihan on X: https://x.com/PeterZeihan• The Wheel of Time on Prime Video: https://www.amazon.com/Wheel-Time-Season-1/dp/B09F59CZ7R• Top Gun: Maverick on Prime Video: https://www.amazon.com/Top-Gun-Maverick-Joseph-Kosinski/dp/B0DM2LYL8G• Thinking like a gardener not a builder, organizing teams like slime mold, the adjacent possible, and other unconventional product advice | Alex Komoroske (Stripe, Google): https://www.lennysnewsletter.com/p/unconventional-product-advice-alex-komoroske• MySQL: https://www.mysql.com/—Recommended books:• Co-Intelligence: Living and Working with AI: https://www.amazon.com/Co-Intelligence-Living-Working-Ethan-Mollick/dp/059371671X• The Accidental Superpower: Ten Years On: https://www.amazon.com/Accidental-Superpower-Ten-Years/dp/1538767341• Cable Cowboy: https://www.amazon.com/Cable-Cowboy-Malone-Modern-Business/dp/047170637X—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. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Mike Krieger is the Co-Founder of Instagram and now CPO @ Anthropic.  In Today's Episode with Mike Krieger We Discuss: 03:07 Where Will Value Be Created and Sustained in a World of AI? 04:59 Are Foundation Models Commoditised Today? 08:36 Should Founders Build for the Models of Today or Build for Models of the Future 12:19: Why Will Models Become More Different Than More Similar 16:38: Will Human or Synthetic Data Be More Prominent in the Future  19:28 Model Quality vs. Product UX 23:36 The Competitive Landscape of AI 32:27 Do We Underestimate China's AI Capabilities 33:59 What Did Anthropic Learn from Deepseek 34:07 Is Deepseek a Sustaining and Credible Threat? 37:04 Transitioning from Model Provider to Application Provider 38:26 Where Has Anthropic Chronically Under-Invested 39:08 Why Has Anthropic Been Slow On Consumer Product Development 43:50 What is the Role of a Software Developer in the Future 48:29 Balancing API and Consumer Products 51:09 Is Europe Stronger or Weaker in a World of AI 52:40 Quickfire Round: Insights and Reflections  

Invest Like the Best: Read the notes at at podcastnotes.org. Don't forget to subscribe for free to our newsletter, the top 10 ideas of the week, every Monday --------- Welcome to this classic episode. Classics are my favorite episodes from the past 10 years published once a month. These are N of one conversations with N of one people. There's nobody I've met quite like Doug Leone. Doug led one of the world's most successful venture firms, Sequoia, for over 25 years after he was given responsibility for the firm by its founder, Don Valentine, in 1996. Alongside Mike Moritz, the pair managed its expansion from a single $150m early-stage fund into an $85 billion global powerhouse. It was a privilege to sit down with Doug and learn from him. We talk about his tough start at Sequoia, get into the technicalities of great go-to-market motions, and survey his advice for other investors in the industry. A key theme that will stick with me from this conversation is Doug's insistence on keeping things simple and clear. I listen to this at least once a year. I hope you enjoy it. Subscribe to Colossus Review. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Passthrough. Passthrough streamlines subscription documents, KYC, and AML compliance, so you can focus on running your fund, not managing paperwork. New SEC Update 31 CFR hits investment firms in under a year, and managers are getting ready for it now. If you think basic OFAC screening is enough, think again. You'll need continuous monitoring of your investors and all their beneficial owners across multiple watchlists, plus a comprehensive anti money laundering program. Passthrough has already processed 50,000 LPs and built the complete solution. Don't risk SEC deficiency letters, fines, or regulatory enforcement. Visit passthrough.com to get compliant now. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus Show Notes [00:00:00] Welcome to Invest Like the Best [00:05:21] What Don Valentine's heart was like [00:08:30] The most productive and unproductive parts of Don's toughness  [00:12:55] Why it's so important to understand someone's core motivations [00:18:44] The most formative experiences he had prior to becoming an investor that impacted his investing the most  [00:22:37] What venture looks like to him today relative to his prior career [00:28:37] Whether or not he'd go into venture today if he was in his late 20s  [00:34:10] Helping companies circumnavigate mediocre positioning  [00:39:15] How interacting with companies early on has changed over the ears [00:43:12] Whether or not new entrants into venture should build firms with enterprise value  [00:48:14] Sussing out the killer gene in somebody  [00:51:04] How successful people can instill the lessons learned from hardship into their children  [00:54:30] Whether or not competitive advantage can be architected ahead of time when building a company   [00:57:21] The early 2000s clawback at Sequoia and what navigating that period was like  [01:01:06] What he's learned about picking the right LPs and partnering with them [01:04:18] Making sure that performance is on everyone's minds all the time  [01:09:59] The kindest thing anyone has ever done for him

Welcome to this classic episode. Classics are my favorite episodes from the past 10 years published once a month. These are N of one conversations with N of one people. There's nobody I've met quite like Doug Leone. Doug led one of the world's most successful venture firms, Sequoia, for over 25 years after he was given responsibility for the firm by its founder, Don Valentine, in 1996. Alongside Mike Moritz, the pair managed its expansion from a single $150m early-stage fund into an $85 billion global powerhouse. It was a privilege to sit down with Doug and learn from him. We talk about his tough start at Sequoia, get into the technicalities of great go-to-market motions, and survey his advice for other investors in the industry. A key theme that will stick with me from this conversation is Doug's insistence on keeping things simple and clear. I listen to this at least once a year. I hope you enjoy it. Subscribe to Colossus Review. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Passthrough. Passthrough streamlines subscription documents, KYC, and AML compliance, so you can focus on running your fund, not managing paperwork. New SEC Update 31 CFR hits investment firms in under a year, and managers are getting ready for it now. If you think basic OFAC screening is enough, think again. You'll need continuous monitoring of your investors and all their beneficial owners across multiple watchlists, plus a comprehensive anti money laundering program. Passthrough has already processed 50,000 LPs and built the complete solution. Don't risk SEC deficiency letters, fines, or regulatory enforcement. Visit passthrough.com to get compliant now. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus Show Notes [00:00:00] Welcome to Invest Like the Best [00:05:21] What Don Valentine's heart was like [00:08:30] The most productive and unproductive parts of Don's toughness  [00:12:55] Why it's so important to understand someone's core motivations [00:18:44] The most formative experiences he had prior to becoming an investor that impacted his investing the most  [00:22:37] What venture looks like to him today relative to his prior career [00:28:37] Whether or not he'd go into venture today if he was in his late 20s  [00:34:10] Helping companies circumnavigate mediocre positioning  [00:39:15] How interacting with companies early on has changed over the ears [00:43:12] Whether or not new entrants into venture should build firms with enterprise value  [00:48:14] Sussing out the killer gene in somebody  [00:51:04] How successful people can instill the lessons learned from hardship into their children  [00:54:30] Whether or not competitive advantage can be architected ahead of time when building a company   [00:57:21] The early 2000s clawback at Sequoia and what navigating that period was like  [01:01:06] What he's learned about picking the right LPs and partnering with them [01:04:18] Making sure that performance is on everyone's minds all the time  [01:09:59] The kindest thing anyone has ever done for him

“Focus on doing the right things instead of a bunch of things.” – Mike Krieger. Today, we are shifting gears and not discussing business and careers.  Instead, my guest and I will discuss what you need to know to navigate the college admissions process successfully.    As a parent who has been through the entire college process and experience, I wish I had known my guest a decade ago. I hope that my parents, who are constantly fighting with their kids to get the college admission process done and done correctly, I hope that you get some answers on how to make it easier on you and your kids!   YouTube: https://youtu.be/UepqouMINoc   About Karen Marks:  Karen is the President and Founder of North Star Admissions Consulting. Since 2012, she has helped clients get into their dream colleges and graduate schools, with more than 70 million in scholarships. Before founding North Star, Karen served on Dartmouth's undergraduate admissions committee and was the Dartmouth Associate Director of Admissions at the Tuck School of Business. She is an attorney and a graduate of Cornell University.   How to Get In Touch With Karen Marks:  Website:  http://www.northstaradmissions.com/ Email:  Karen@northstaradmissions.com Free Gift: https://northstaradmissions.com/college-admissions-mistakes/    Stalk me online! LinkTree: https://linktr.ee/conniewhitman Subscribe to the Enlightenment of Change podcast on your favorite podcast streaming service or YouTube.  New episodes are posted every week. Listen to Connie dive into new sales and business topics or problems you may have.  

Karina Nguyen leads research at OpenAI, where she's been pivotal in developing groundbreaking products like Canvas, Tasks, and the o1 language model. Before OpenAI, Karina was at Anthropic, where she led post-training and evaluation work for Claude 3 models, created a document upload feature with 100,000 context windows, and contributed to numerous other innovations. With experience as an engineer at the New York Times and as a designer at Dropbox and Square, Karina has a rare firsthand perspective on the cutting edge of AI and large language models. In our conversation, we discuss:• How OpenAI builds product• What people misunderstand about AI model training• Differences between how OpenAI and Anthropic operate• The role of synthetic data in model development• How to build trust between users and AI models• Why she moved from engineering to research• Much more—Brought to you by:• Enterpret—Transform customer feedback into product growth• Vanta—Automate compliance. Simplify security• Loom—The easiest screen recorder you'll ever use—Find the transcript at: https://www.lennysnewsletter.com/p/why-soft-skills-are-the-future-of-work-karina-nguyen—Where to find Karina Nguyen:• X: https://x.com/karinanguyen_• LinkedIn: https://www.linkedin.com/in/karinanguyen28• Website: https://karinanguyen.com/—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 Karina Nguyen(04:42) Challenges in model training(08:21) Synthetic data and its importance(12:38) Creating Canvas(18:33) Day-to-day operations at OpenAI(20:28) Writing evaluations(23:22) Prototyping and product development(26:57) Building Canvas and Tasks(33:34) Understanding the job of a researcher(35:36) The future of AI and its impact on work and education(42:15) Soft skills in the age of AI(47:50) AI's role in creativity and strategy development(53:34) Comparing Anthropic and OpenAI(57:11) Innovations and future visions(01:07:13) The potential of AI agents(01:11:36) Final thoughts and career advice—Referenced:• What's in your stack: The state of tech tools in 2025: https://www.lennysnewsletter.com/p/whats-in-your-stack-the-state-of• Anthropic: https://www.anthropic.com/• OpenAI: https://openai.com/• What is synthetic data—and how can it help you competitively?: https://mitsloan.mit.edu/ideas-made-to-matter/what-synthetic-data-and-how-can-it-help-you-competitively• GPQA: https://datatunnel.io/glossary/gpqa/• Canvas: https://openai.com/index/introducing-canvas/• Barret Zoph on LinkedIn: https://www.linkedin.com/in/barret-zoph-65990543/• Mira Murati on LinkedIn: https://www.linkedin.com/in/mira-murati-4b39a066/• JSON Schema: https://json-schema.org/• Anthropic—100K Context Windows: https://www.anthropic.com/news/100k-context-windows• Claude 3 Haiku: https://www.anthropic.com/news/claude-3-haiku• A.I. Chatbots Defeated Doctors at Diagnosing Illness: https://www.nytimes.com/2024/11/17/health/chatgpt-ai-doctors-diagnosis.html• Cursor: https://www.cursor.com/• How AI will impact product management: https://www.lennysnewsletter.com/p/how-ai-will-impact-product-management• Lee Byron on LinkedIn: https://www.linkedin.com/in/lee-byron/• GraphQL: https://graphql.org/• Claude in Slack: https://www.anthropic.com/claude-in-slack• Sam Altman on X: https://x.com/sama• Jakub Pachocki on LinkedIn: https://www.linkedin.com/in/jakub-pachocki/• Lennybot: https://www.lennybot.com/• ElevenLabs: https://elevenlabs.io/• Westworld on Prime Video: https://www.amazon.com/Westworld-Season-1/dp/B01N05UD06• A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo: https://www.youtube.com/watch?v=IxkvVZua28k• Tuple: https://tuple.app/• How Shopify builds a high-intensity culture | Farhan Thawar (VP and Head of Eng): https://www.lennysnewsletter.com/p/how-shopify-builds-a-high-intensity-culture-farhan-thawar—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. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Shaun Clowes is the chief product officer at Confluent and former CPO at Salesforce's MuleSoft and at Metromile. He was also the first head of growth at Atlassian, where he led product for Jira Agile and built the first-ever B2B growth team. In our conversation, we discuss:• Why most PMs are bad, and how to fix this• Why great AI products are all about the data• Why he changed his mind about being data-driven• How to build your B2B growth team• How to choose your next career stop• Much more—Brought to you by:• Enterpret—Transform customer feedback into product growth• BuildBetter—AI for product teams• Wix Studio—The web creation platform built for agencies—Find the transcript at: https://www.lennysnewsletter.com/p/why-great-ai-products-are-all-about-the-data-shaun-clowes—Where to find Shaun Clowes:• X: https://x.com/ShaunMClowes• LinkedIn: https://www.linkedin.com/in/shaun-clowes-80795014/• Website: https://shaunclowes.com/about-shaun• Reforge: https://www.reforge.com/profiles/shaun-clowes—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) Shaun's background(05:08) The state of product management(09:33) Becoming a 10x product manager(13:23) Specific ways to leverage AI in product management(17:15) Feedback rivers(19:20) AI's impact on data management(24:35) The future of enterprise businesses with AI(35:41) Data-driven decision-making(45:50) Building effective growth teams(50:18) The evolution of product-led growth(56:16) Career insights and decision-making(01:07:45) Failure corner(01:12:32) Final thoughts and lightning round—Referenced:• Steve Blank's website: https://steveblank.com/• Getting Out of the Building. 2 Minutes to See Why: https://www.youtube.com/watch?v=TbMgWr1YVfs• OpenAI: https://openai.com/• Claude: https://claude.ai/• Sachin Rekhi on LinkedIn: https://www.linkedin.com/in/sachinrekhi/• Video: Building Your Product Intuition with Feedback Rivers: https://www.sachinrekhi.com/video-building-your-product-intuition-with-feedback-rivers• Confluent: https://www.confluent.io• Workday: https://www.workday.com/• Lenny and Friends Summit: https://lennyssummit.com/• A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo: https://www.youtube.com/watch?v=IxkvVZua28k• Anthropic: https://www.anthropic.com/• Salesforce: https://www.salesforce.com/• Atlassian: https://www.atlassian.com/• Jira: https://www.atlassian.com/software/jira• Ashby: https://www.ashbyhq.com/• Occam's razor: https://en.wikipedia.org/wiki/Occam%27s_razor• Breaking the rules of growth: Why Shopify bans KPIs, optimizes for churn, prioritizes intuition, and builds toward a 100-year vision | Archie Abrams (VP Product, Head of Growth at Shopify): https://www.lennysnewsletter.com/p/shopifys-growth-archie-abrams• Charlie Munger quote: https://www.goodreads.com/quotes/11903426-show-me-the-incentive-and-i-ll-show-you-the-outcome• Elena Verna on how B2B growth is changing, product-led growth, product-led sales, why you should go freemium not trial, what features to make free, and much more: https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company• The ultimate guide to product-led sales | Elena Verna: https://www.lennysnewsletter.com/p/the-ultimate-guide-to-product-led• Metromile: https://www.metromile.com/• Tom Kennedy on LinkedIn: https://www.linkedin.com/in/tom-kennedy-37356b2b/• Building Wiz: the fastest-growing startup in history | Raaz Herzberg (CMO and VP Product Strategy): https://www.lennysnewsletter.com/p/building-wiz-raaz-herzberg• Wiz: https://www.wiz.io• Colin Powell's 40-70 rule: https://www.42courses.com/blog/home/2019/12/10/colin-powells-40-70-rule• Detroiters on Netflix: https://www.netflix.com/title/80165019• Glean: https://www.glean.com/• Radical Candor: Be a Kick-Ass Boss Without Losing Your Humanity: https://www.amazon.com/Radical-Candor-Kick-Ass-Without-Humanity/dp/1250103509• Listen: Five Simple Tools to Meet Your Everyday Parenting Challenges: https://www.amazon.com/Listen-Simple-Everyday-Parenting-Challenges/dp/0997459301• Empress Falls Canyon and abseiling: https://bmac.com.au/blue-mountains-canyoning/empress-falls-canyon-and-abseiling—Recommended books:• The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses: https://www.amazon.com/Lean-Startup-Entrepreneurs-Continuous-Innovation/dp/0307887898• Inspired: How to Create Products Customers Love: https://www.amazon.com/Inspired-Create-Products-Customers-Love/dp/0981690408—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. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

My guest today is Ric Elias. Ric is the CEO and co-founder of Red Ventures, which has a portfolio of fast-growing digital businesses like Lonely Planet, The Points Guy, Bankrate, and large investments in a variety of other businesses across industries. He began the business in 2000 and has grown it to now a global company with thousands of employees. Ric walks us through the early struggles that have led to what is now a flourishing investing platform, but mostly this episode is a masterclass on cultural values and philosophies that transcend mere financial gain. We discuss the difference between living good and well, the power of forgiveness, and compounding more than just your capital. Ric's story is one of resilience, humility, and grace. His story about being in the front row of the plane that Captain Sully landed in the Hudson is singular and very moving. Please enjoy my conversation with Ric Elias. Subscribe to Glue Guys! For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- 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. I think this platform will become the standard for investment managers, and if you run an investing firm, I highly recommend you find time to speak with them. 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. Imagine completing your research five to ten times faster with search that delivers the most relevant results, helping you make high-conviction decisions with confidence. AlphaSense provides access to over 300 million premium documents, including company filings, earnings reports, press releases, and more from public and private companies. Invest Like the Best listeners can get a free trial now at Alpha-Sense.com/Invest and experience firsthand how AlphaSense and Tegas help you make smarter decisions faster. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 (00:04:29) Understanding Red Ventures: Origin and Evolution (00:06:44) Early Challenges and Turning Points (00:10:15) Operational Success and Company Culture (00:16:10) Insights on Motivation and Growth (00:25:35) Reflections on Money and Personal Well-being (00:32:39) The Hudson River Plane Crash Experience (00:42:25) Reconnecting with Puerto Rico and New Ventures (00:47:10) Underdogs to Champions (00:49:56) Building Trust and Team Dynamics (00:52:19) Balancing Speed and Sustainability (00:56:05) The Role of Confidence and Courage (01:00:49) The Pursuit of Purpose Over Profit (01:06:39) Recruitment and Company Culture (01:11:20) Future of Business and AI (01:23:20) The Kindest Thing Anyone Has Ever Done For Ric

This week's episode is special because it marks the launch of a new Colossus show: Glue Guys. My guests this week are the hosts of the new show, Shane Battier, Alex Smith, and Ravi Gupta. Shane, Ravi, and Alex's stories are remarkable. Each has been at the very top of their profession. Shane as an NCAA and NBA champion. Alex as the first pick in the NFL draft, 3 time-pro bowler, and 16-year NFL veteran. Ravi as a leader at KKR, and then as a critical leader turning around & building Instacart, and now as a partner at Sequoia. But those parts of their stories pale in comparison to the stories you'll hear on Glue Guys.  Glue Guys is an unfolding manual for HOW to be a professional, a teammate, a leader, and even a friend and family member. In this episode of ILtB, we explore the general concept of glue guys—the people who are obsessed with helping the team win, whether they are the leader or newest member of the team. Please enjoy this conversation with Shane, Alex, and Ravi. Subscribe to Glue Guys! For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- 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. I think this platform will become the standard for investment managers, and if you run an investing firm, I highly recommend you find time to speak with them. Head to ridgelineapps.com to learn more about the platform. — This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 (00:05:06) Talent vs. Teamwork: A Business Perspective (00:06:56) Talent vs. Teamwork: A Sports Perspective (00:09:40) Personal Stories of Overcoming Adversity (00:12:50) The Importance of Trust and Mission Focus (00:21:09) Facing Rock Bottom and Rising Again (00:34:59) The Hardest Moments and Lessons Learned (00:42:39) Unsung Heroes of Special Teams (00:45:01) Mentorship and Team Dynamics (00:46:49) Lessons from Duke Basketball (00:49:25) Instacart and the All-In Mentality (00:51:52) Building Successful Teams (00:55:42) Family Legacy and Personal Sacrifice (01:05:02) Strength and Vulnerability in Leadership (01:10:36) The Urge to Dominate and Winning (01:14:41) The Kindest Thing Anyone Has Ever Done For Alex

Today, I'm talking with Mike Krieger, the new chief product officer at Anthropic, one of the hottest AI companies in the industry. Anthropic's main product right now is Claude, the name of both its industry-leading AI model and a chatbot that competes with ChatGPT.  Mike has a fascinating resume: he was the cofounder of Instagram, and then started AI-powered newsreader Artifact. I was a fan of Artifact, so I wanted to know more about the decision to shut it down as well as the decision to sell it to Yahoo. And then I wanted to know why Mike decided to join Anthropic and work in AI — an industry with a lot of investment, but very few consumer products to justify it. What's this all for?  Links:  Instagram co-founder Mike Krieger is Anthropic's new chief product officer | The Verge Instagram's co-founders are shutting down their Artifact news app | The Verge Yahoo resurrects Artifact inside a new AI-powered News app | The Verge Authors sue Anthropic for training AI using pirated books | The Verge The text file that runs the internet | The Verge Anthropic's crawler is ignoring websites' anti-AI scraping policies | The Verge Golden Gate Claude | Anthropic Inside the white-hot center of AI doomerism | New York Times Dario Amodei, CEO of Anthropic, on the paradoxes of AI safety | Hard Fork No one's ready for this | The Verge OpenAI announces SearchGPT, its AI-powered search engine | The Verge Amazon-backed Anthropic rolls out Claude AI for big business | CNBC Transcript: https://www.theverge.com/e/24001603 Credits: Decoder is a production of The Verge and part of the Vox Media Podcast Network. Our producers are Kate Cox and Nick Statt. Our editor is Callie Wright. Our supervising producer is Liam James. The Decoder music is by Breakmaster Cylinder. Learn more about your ad choices. Visit podcastchoices.com/adchoices

My guest today is Bret Taylor. His resume is absurd. He built google maps--famously rewriting the whole thing in a weekend. He was the CTO of Facebook in critical years. He founded Quip. He was the chair of the board at Twitter. He was the co-CEO of Salesforce...the incredible list goes on. Now, Bret is the co-founder of Sierra, a conversational AI platform for businesses, and he is the chairman of the board at OpenAI. We discuss the past, present, and future of AI agents: new programs that will begin doing incredible amounts of work for us humans in astonishing ways that are a thrill to talk about. Bret believes agents will become a meaningful part of the future and transform the ways in which we interact with technology. We discuss a strategic approach to AI integration, the different categories of agents and their scopes, and the essentials of craftsmanship. Please enjoy this discussion with Bret Taylor. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- 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. I think this platform will become the standard for investment managers, and if you run an investing firm, I highly recommend you find time to speak with them. Head to ridgelineapps.com to learn more about the platform. — This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 (00:04:27) The Dynamics of Small Teams in Software Development (00:05:46) Challenges of Large Teams and Bureaucracy (00:06:27) The Google Maps Legendary Rewrite Story (00:13:59) Introduction to AI Agents (00:16:48) Types of AI Agents and Their Applications (00:22:15) Building Robust AI Agents for Customer Experience (00:33:28) The Future of AI Agents and Customer Interaction (00:45:12) Impact of AI on Productivity and Inequality (00:51:05) Technological Evolution and Societal Changes (00:56:25) The Role of Multimodal Models in AI (00:57:19) The Future of Human-Computer Interaction (01:00:15) Building Companies in the AI Era (01:05:36) OpenAI's Unique Structure and Mission (01:11:22) Insights on Sales and Customer Success (01:20:06) Balancing Ambition and Personal Life (01:21:35) Preparing for the Agent Era (01:26:20) The Kindest Thing Anyone Has Ever Done for Bret

My guest this week is Gavin Baker. Gavin is the managing partner and CIO of Atreides Management, and he has been on the show many times before. He is one of my favorite investors to talk to and this may be my favorite conversation with him. Gavin first started covering Nvidia as an investor at the turn of the millennium, making him the perfect guest to discuss all things AI and investing. There is so much detail in this discussion and I'm incredibly grateful to Gavin for sharing his wisdom with us again. Please enjoy this fantastic conversation with Gavin Baker. 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. Ramp is the fastest-growing FinTech company in history and it's backed by more of my favorite past guests (at least 16 of them!) than probably any other company I'm aware of. It's also notable that many best-in-class businesses use Ramp—companies like Airbnb, Anduril, and Shopify, as well as investors like Sequoia Capital and Vista Equity. They use Ramp to manage their spending, automate tedious financial processes, and reinvest saved dollars and hours into growth. At Colossus and Positive Sum, we use Ramp for exactly the same reason. Go to Ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 (00:04:42) The Magnificent Seven and Tech Competition (00:06:29) Generative AI and Scaling Laws (00:08:36) Challenges in AI Infrastructure (00:15:02) The Future of AI and Data Centers (00:17:51) Efficiency in AI Models (00:35:14) Synthetic Data and AI Training (00:42:37) Inference and the Role of Smartphones (00:48:35) Investment Implications in AI (00:49:09) Opportunities for New Companies (00:51:20) Challenges at the Application Layer (00:52:25) AI's Impact on Advertising (00:53:40) AI ROI Debate (00:54:39) SaaS Metrics and AI Disruption (00:55:59) AI-First Application Companies (01:00:50) The Future of Robotics (01:14:01) Leadership in Tech Giants (01:24:05) The Evolution of Investing

My guest today is Vlad Tenev. Vlad is the CEO and co-founder of Robinhood. It was such a treat to sit down with him and discuss the behind-the-scenes of a revolutionary business we all know well. He details Robinhood's journey to zero-cost trading and what it means to build a consumer-centric financial product. Vlad believes in finding the harmonies across mathematics and art and applies this lens to everything he builds. We discuss Robinhood's new credit card and more products on the horizon, the company's toughest moments, including the Gamestop episode, and the compelling future of AI in financial services. Please enjoy this conversation with Vlad Tenev. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- 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. I think this platform will become the standard for investment managers, and if you run an investing firm, I highly recommend you find time to speak with them. Head to ridgelineapps.com to learn more about the platform. This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 (00:03:56) The Next Frontier in AI: Reasoning and Logical Deductions (00:06:19) Challenges and Approaches in AI Development (00:09:08) Formal Mathematics and AI Integration (00:11:23) Practical Applications of Mathematical Superintelligence (00:17:30) Robinhood's Journey to Zero-Cost Trading (00:24:38) Building a Consumer-Friendly Trading Platform (00:28:52) Robinhood Gold and the Future of Financial Services (00:35:51) Understanding Robinhood's Business Model (00:42:34) Navigating the GameStop Crisis (00:49:17) Improving Customer Satisfaction (00:52:43) Reputation Repair (00:54:52) The Future of Financial Services (00:59:06) Crypto and AI in Finance (01:08:09) Building a High-Performance Culture (01:11:42) The Kindest Thing Anyone Has Ever Done for Vlad

My guest today is Sarah Guo. Sarah is the founder and CEO of Conviction, an early-stage venture capital firm built to serve AI companies. She started Conviction in 2022 after 9 years at Greylock because she believes AI is the most important technological advancement of our lifetime. In our conversation, Sarah discusses the challenges and rewards of leaving an established investing firm to start her own venture. She shares her unique perspective on the AI landscape and reveals her predictions for what we should expect on the AI frontier. Please enjoy this great conversation with the very impressive Sarah Guo.  For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- 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. I think this platform will become the standard for investment managers, and if you run an investing firm, I highly recommend you find time to speak with them. Head to ridgelineapps.com to learn more about the platform. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes: (00:00:00) Our Partners: Ridgeline and Tegus (00:03:30) Welcome to Invest Like the Best (00:04:30) Introduction & Recruiting in Venture Capital (00:05:06) Key Traits for Early-Stage Venture Capitalists (00:06:57) Lessons from Early Investments (00:07:47) The Journey of Building a Company (00:09:01) The Decision to Start Conviction (00:11:36) Launching Conviction and Initial Steps (00:13:43) First Investment at Conviction (00:14:00) Evaluating AI Application Companies (00:16:29) Challenges and Opportunities in AI Applications (00:23:57) Minimum Viable Quality in AI Products (00:33:19) Future of AI and Frontier Models (00:38:56) The Unpredictable Future of AI (00:40:16) The Importance of Efficiency in AI Models (00:44:28) The Business of AI: Costs and Margins (00:45:47) Infrastructure and Hardware Challenges (00:48:54) The Competitive Landscape of AI Chips (00:54:24) The Future of AI and Society (00:56:34) Opportunities and Innovations in AI (01:02:09) Concerns and Ethical Considerations (01:03:36) Debates and Research in AI (01:09:01) Personal Reflections and Closing Thoughts

My guest today is Hemant Taneja. Hemant is the CEO and Managing Director of General Catalyst, the global venture capital firm you'll hear us refer to as GC. GC has set out to build resiliency across critical industries worldwide. The firm leverages technology to retool sectors such as healthcare, energy, defense, and manufacturing and explores innovative capital structures to support founders and businesses. Hemant discusses how the firm is positioned to respond to the aftermath of crises, including the pandemic, wars, energy issues, and beyond. We also discuss the building of a category-defining healthcare company, Livongo and much more. Please enjoy this conversation with Hemant Taneja.  Listen to Founders Podcast 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. Ramp is the fastest growing FinTech company in history and it's backed by more of my favorite past guests (at least 16 of them!) than probably any other company I'm aware of. It's also notable that many best-in-class businesses use Ramp—companies like Airbnb, Anduril, and Shopify, as well as investors like Sequoia Capital and Vista Equity. They use Ramp to manage their spending, automate tedious financial processes, and reinvest saved dollars and hours into growth. At Colossus and Positive Sum, we use Ramp for exactly the same reason. Go to Ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes: (00:00:00) Our Partners: Ramp and Tegus (00:03:00) Welcome to Invest Like the Best (00:03:57) Introducing Hemant Taneja and General Catalyst (00:04:17) Global Resilience and Innovation Post-Pandemic  (00:05:56) Re-Globalization and Manufacturing  (00:07:03) Building Livongo: A 20-Year Overnight Success  (00:13:23) Aligning Incentives in Healthcare  (00:15:40) Re-imagining the Investment Business  (00:20:54) Evolution of General Catalyst (00:27:04) Succession and Trust in Asset Management  (00:35:00) Founder-Centric Capital Goals  (00:36:32) Balancing Growth and Liquidity  (00:41:39) AI and Onshoring Productivity  (00:47:10) Defense Investments and Ethics  (00:50:11) Geopolitics and Regulation  (00:53:16) Reflections on Leadership and Strategy  (01:01:14) Hemant's Future Plans  (01:02:55) The Kindest Thing Anyone Has Ever Done for Him

Today, we are replaying what we call a forever episode, which are the few episodes of our show that we think will be as popular a decade from now as they are today. Every time I re-listen to this episode with David Senra, I leave wildly energized and wanting to share that feeling. So we are re-releasing it today for anyone who missed it the first time or hadn't yet discovered Invest Like the Best. David Senra has studied history's great founders and entrepreneurs in more depth than anyone I've ever met, and I'd wager more than anyone else alive. In this conversation, we cover many of the most common themes he's discovered studying hundreds of entrepreneurs like Estée Lauder, John Rockefeller, Enzo Ferrari, and Edwin Land. Please enjoy this great conversation with David Senra. Listen to Founders Podcast  For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 [00:03:01] First question - When he first fell in love with reading [00:07:01] What's rooted in his own history that's made him obsessive about studying history's great entrepreneurs and founders - Founders Podcast [00:10:34] The first time he connected with someone as a positive role model that he was reading about  [00:13:45] How often obsession is apparent in the founders he's studied across hundreds of biographies  [00:18:08] What is often behind obsession and how people listening can apply the lessons to their own lives [00:22:45] The dynamic and relationship between inspiration and perspiration  [00:27:11] Commonalities between the layers of leadership and support underneath founders [00:31:52] Where else he's seen ego rear its head in good and bad ways  [00:38:34] How often do great founders break the law or enter gray areas of it  [00:41:22] The role constant learning and listening plays in success [00:45:12] Talking about how anything worth doing is worth doing to excess  [00:52:18] Describing the soul of founders and businesses [00:58:39] What he's learned about all of these founders as it relates to marketing  [01:04:38] A common story that process is often art  [01:08:10] Who David's idols are in podcasting [01:14:55] Major aspects of people he's studied that haven't been discussed yet [01:19:55] The kindest thing anyone has ever done for David

My guest today is Martin Casado. Martin is a partner at Andreessen Horowitz and first joined me on Invest Like the Best in 2022. So much has changed since then, and it was awesome to have Martin back to discuss all of the different implications of this AI revolution. Before joining a16z, Martin pioneered software-defined networking and co-founded Nicira, which was bought by VMware for $1.3 billion in 2012. He has studied, built, and invested in digital infrastructure his whole career which has primed him to go in-depth in this interview on the immense opportunities and challenges AI presents among creativity, policy-making, agentic systems, real-world data structures, and beyond. Please enjoy this conversation with Martin Casado.  Listen to Founders Podcast For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 (00:01:48) The Future of AI and Creativity (00:03:11) Economic Implications of AI (00:04:33) AI's Impact on Content Creation (00:08:21) Challenges in AI and Robotics (00:12:16) Human Data and AI Training (00:20:30) Investing in AI and Robotics (00:26:00) Defensibility and Competition in AI (00:33:22) Regulatory Considerations (00:35:26) Internet Era Parallels and Security Concerns (00:40:25) Open Source vs. Closed Source in Tech (00:43:45) Market Annealing and Category Creation (00:46:13) Data and Hardware Innovations in AI (00:55:55) Agents and the Future of AI

My guest today is Modest Proposal, joining me for our third conversation and the first in a few years. Modest is anonymous online, but one of the more thoughtful investors I know, overseeing a large pool of capital in public and private markets. He offers insight into many different corners of today's landscape, covering AI's frontier models versus open-source models, overcapacity issues in transportation in our post-COVID world, the potential economic impact of GLP-1 drugs, and more. Please enjoy my conversation with Modest Proposal. Listen to Founders Podcast For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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) Comparison to Mid-2000s Commodity Markets (00:07:18) The Role of AI and Power Consumption (00:09:29) NVIDIA and the Future of AI Investment (00:13:10) Commercialization of AI and Market Dynamics (00:23:14) Public vs. Private Market Performance (00:28:03) Post-COVID Capital Cycles (00:30:32) Capital Expenditures and Post-COVID Market Distortions (00:31:47) Amazon's Capacity Expansion and Market Inflections (00:33:45) Challenges in Displacing Market Leaders (00:37:50) Behavioral Barriers in GLP-1 Adherence (00:39:58) Public vs. Private Market Allocations (00:45:08) International Equities and Japanese Market Potential (00:47:35) Market Structure and Trading Dynamics (00:53:22) AI Models and Future Market Implications

My guest today is Robert Greene, author of many books but perhaps most famous for his books "48 Laws of Power" and "Mastery." He has spent his life studying why people behave like they do and why some go on to build great things. I love his idea of finding your life's purpose, which we explore in detail. Please enjoy my conversation with Robert Greene. Listen to Founders Podcast For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 (00:03:17) First Question - Exploring Reality and Human Behavior (00:07:41) The Concept of Masks and Social Roles (00:10:47) The Sublime and Social Conventions (00:13:48) Writing 'The 48 Laws of Power' (00:16:38) Defining and Understanding Power (00:18:01) Historical Figures and Adaptation (00:23:59) Modern Applications of Power Laws (00:31:57) The Boldness of Deception (00:32:54) Exploring Good and Evil (00:35:56) The Art of Seduction and AI (00:38:31) Defining Mastery (00:42:44) Discovering Your Life's Task (00:51:53) The Power of Observation (00:59:56) The Kindest Thing Anyone Has Ever Done for Robert

My guest today is Pat Grady, a longtime growth investor at Sequoia and one of the firms senior leaders. Pat has been a part of a long list of legendary investments, ranging from Snowflake, Zoom, ServiceNow, Qualtrics, Okta, Hubspot, Notion, and OpenAI, among many others. There aren't many investors who reference as well at Pat, both inside and outside of his firm. We talk about investing, building an investing firm, and building enduring companies. Please enjoy this great conversation with Pat Grady. Listen to Founders Podcast For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 (00:05:48) Doug Leone's Leadership and Changes (00:06:54) Creating Internal Pressure and Structure (00:10:46) Sequoia's Team Values and Family Influence (00:13:40) Assessing Founders and Investments (00:20:28) Winning Competitive Investments (00:24:45) Pat's Early Career at Sequoia (00:29:38) Memo Writing and Investment Criteria (00:35:20) Evaluating Companies Through Three Business Criteria (00:40:15) Building Sustainable Competitive Advantage (00:47:48) Turning Bad Numbers into Good Investments (00:51:20) The AI Frontier: Market and People (01:01:13) Harvey: The AI Legal Assistant (01:05:33) Sequoia's Platform Strategy (01:17:16) The Importance of Teamwork and Performance (01:26:07) Legendary Potential: Relentless Application of Force (01:28:37) The Kindest Thing Anyone Has Ever Done for Pat

My guest today is Frank Blake. Frank is the former chairman and CEO of Home Depot. I recently interviewed Home Depot co-founder Ken Langone and became fascinated by the business's impressive lineup of leaders through the decades. Frank led the company from 2007 to 2014 and shares how he carried on the legacy of Ken and the others, upholding their culture of an inverted hierarchy and producing seven consecutive years of growth for the largest home improvement retailer in America. We discuss his hyper focus on solving their customer's problems before their own, investing time into the employee experience, and his intentionality with how he is perceived as a leader. Please enjoy this discussion with Frank Blake. Listen to Founders Podcast For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 (00:04:37) The Inverted Pyramid Leadership Model (00:08:38) Communication and Listening in Leadership (00:15:19) Lessons from Legacies of Great Home Depot Leaders (00:27:02) Frank's Personal Leadership Journey (00:33:32) Reagan's Leadership Style and Influence (00:37:26) Key Responsibilities of a CEO (00:40:27) Delta's Leadership During COVID-19 (00:46:45) Financial Strategies in Asset-Intensive Industries (00:47:27) Home Depot's Strategic Shift (00:53:33) Competitive Dynamics with Lowe's (00:55:36) Building an Effective Board (00:58:16) The Impact of Home Depot on Employees' Lives (01:01:52) The Kindest Thing Anyone Has Ever Done for Frank

My guest today is Adam Sandow. Adam is the chairman and CEO of SANDOW Companies and the executive chairman and founder of Material Bank. He has built an entire ecosystem of businesses and brands that have brought him into the game of media, materials, and beyond. From creating the beauty product subscription model to getting magazines in the hands of billionaires to transforming the design industry with overnight access to samples, when Adam starts a business he writes his own rulebook. We discuss the founding stories of his most interesting companies, his obsession with targeting pain points, and his philosophies for when to go all in and betting on himself. Please enjoy this great discussion with Adam Sandow. Listen to Founders Podcast For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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  (00:04:12) Building a Media Empire (00:06:01) The Birth of the Beauty Subscription Model (00:09:56) Revolutionizing Magazine Circulation (00:14:46) The Contrarian Approach to Media (00:16:08) The Origin of MediaJet (00:18:35) The Future of Print and Digital Media (00:27:25) The Genesis of Material Bank (00:35:23) Building a Compelling Model for Manufacturers (00:37:26) Innovative Logistics and Partnership with FedEx (00:40:32) The Importance of High-Quality Content (00:43:49) Building and Buying Media Properties (00:46:01) Creating Unique Value Propositions (00:54:22) The Role of Print in the Digital Age (00:58:41) Nurturing an Ecosystem of Businesses (01:03:37) The Kindest Thing Anyone Has Ever Done for Adam

My guest today is Howie Liu. Howie is the co-founder and CEO of Airtable, a no-code app platform that allows teams to build on top of their shared data and create productive workflows. The business began in 2013 and now has use cases built out for over 300,000 organizations. As Airtable begins to integrate AI and the latest LLMs into its product, Howie has maintained a focus on an intuitive building experience, allowing anyone to build out their workflow within minutes or hours. We discuss the future of the platform in the era of AI, his perspective on horizontal versus vertical software solutions, and his crucial moments as a leader in building a critical component to the advancement of productivity. Please enjoy this discussion with Howie Liu.  Listen to Founders Podcast For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 (00:06:49) Exploring Horizontal vs. Vertical Software in the AI Era (00:11:00) The Future of Customized Applications (00:15:28) Perspectives on AI's Future and Enterprise Adoption (00:18:13) The Evolution of LLMs and Their Impact on Software Development (00:23:33) Harnessing AI for Business Transformation and Innovation (00:27:28) Reflecting on Airtable's Founding and Evolution (00:33:23) Airtable's Approach to Customer Engagement and Innovation (00:39:59) The Impact of AI on Platform Versatility and Market Penetration (00:46:00) Achieving Product-Market Fit and Initial Monetization (00:50:23) Scaling Up and Securing the First Unicorn Round (00:51:52) Rapid Growth and Organizational Scaling Challenges (00:55:00) Reflecting on Tough Decisions in the Business (01:02:55) The Role of Capital Allocation in Expanding Airtable (01:06:55) The Kindest Thing Anyone Has Ever Done For Howie

My guest today is Mark Groden. Mark is the Founder and CEO of Skyryse, a company on a mission to make general aviation as safe as commercial aviation and change the future of flying. As you may know, helicopter accidents are far more likely than airplane accidents, and Skyryse is revolutionizing helicopter flight through a safer and simpler universal flying system. Mark is the quintessential example of somebody doing their life's work and I have no doubt you will come to that conclusion for yourself after listening to his story. He's determined, through Skyryse, to drive aviation deaths down to zero, and we discuss all of the details, big and small, that have laid the groundwork for realizing this dream. Please enjoy this conversation with Mark Groden. Listen to Founders Podcast For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus 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 (00:03:53) From Childhood Fascination to Professional Pursuit (00:05:47) Understanding General Aviation vs. Commercial Aviation (00:07:05) The Safety Gap in General Aviation (00:10:27) The Evolution of Aircraft Technology and Safety (00:16:20) The Mechanic of Flying a Helicopter (00:21:40) Justifying the Existing Dangers of Helicopter Flight (00:24:45) The Future of Flying Cars and Urban Air Mobility (00:27:23) Economies of Scale in Aviation and the Path Forward (00:35:26) The Evolution of Autonomous Flight (00:37:58) The Promise of SkyOS: Revolutionizing Flight with AI (00:42:04) Piloting the Future: How Automation Empowers Pilots (00:45:43) Exploring the Business of Flight and Future Innovations (00:51:08) What Is Holding Back The Future of Flying (00:57:08) Mission-Driven Innovation: A Personal Journey (01:00:46) The Kindest Thing Anyone Has Ever Done For Mark