Marc Andreessen: The real AI boom hasn’t even started yet
Description
Marc Andreessen is a founder, investor, and co-founder of Netscape, as well as co-founder of the venture capital firm Andreessen Horowitz (a16z). In this conversation, we dig into why we’re living through a unique and one of the most incredible times in history, and what comes next. *We discuss:* 1. Why AI is arriving at the perfect moment to counter demographic collapse and declining productivity 2. How Marc has raised his 10-year-old kid to thrive in an AI-driven world 3. What’s actually going to happen with AI and jobs (spoiler: he thinks the panic is “totally off base”) 4. The “Mexican standoff” that’s happening between product managers, designers, and engineers 5. Why you should still learn to code (even with AI) 6. How to develop an “E-shaped” career that combines multiple skills, with AI as a force multiplier 7. The career advice he keeps coming back to (“Don’t be fungible”) 8. How AI can democratize one-on-one tutoring, potentially transforming education 9. His media diet: X and old books, nothing in between *Brought to you by:* DX—The developer intelligence platform designed by leading researchers: https://getdx.com/lenny Brex—The banking solution for startups: https://www.brex.com/product/business-account?ref_code=bmk_dp_brand1H25_ln_new_fs Datadog—Now home to Eppo, the leading experimentation and feature flagging platform: https://www.datadoghq.com/lenny *Episode transcript:* https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom *Archive of all Lenny's Podcast transcripts:* https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0 *Where to find Marc Andreessen:* • X: https://x.com/pmarca • Substack: https://pmarca.substack.com • Andreessen Horowitz’s website: https://a16z.com • Andreessen Horowitz’s YouTube channel: https://www.youtube.com/@a16z *Where to find Lenny:* • Newsletter: https://www.lennysnewsletter.com • X: https://twitter.com/lennysan • LinkedIn: https://
Summary
Generated by gpt-5.6-terraAt-a-Glance
- Verdict: Watch fully
- Core thesis: Andreessen argues that AI is a historically important productivity and capability revolution whose biggest near-term effect will be to create AI-augmented, cross-functional individuals and companies—not simply eliminate jobs.
- Why it matters: The conversation offers directly reusable operating models for agent orchestration, AI-native product building, workforce design, model/app moat assessment, and developing high-agency operators.
- Best use: Use this as a strategic framing session: extract the principles for training AI-native builders, redesigning work around tasks and agent orchestration, and staying flexible rather than locking into premature AI market-structure assumptions.
Executive Summary
Andreessen’s central economic argument is that AI arrives at an unusually favorable moment: measured productivity growth has been weak for roughly 50 years while birth rates are declining across much of the developed world and China. Rather than viewing AI primarily as a labor-displacement threat, he sees it as needed capacity—software agents and robots can raise output and offset a shrinking workforce. Even an extreme productivity scenario, he argues, should create lower prices and greater purchasing power rather than generalized impoverishment.
At the individual level, his prescription is to become a “super-empowered individual”: develop genuine depth in one craft while using AI to acquire operating ability in adjacent domains. He expects the traditional boundaries among engineering, product management, and design to blur because each function can now use AI to execute parts of the others’ work. The durable unit is not the job title but the bundle of tasks; the people who can redesign their task mix and orchestrate AI systems will gain scope.
For technical work, Andreessen expects programming to shift from manually authoring every line toward directing multiple coding agents, reviewing outputs, debugging, changing specifications, and understanding system behavior. He strongly rejects the idea that AI removes the need for technical fundamentals: deep knowledge of the stack is what lets an operator recognize agent failure modes and provide useful corrective feedback. He also recommends using AI as a tutor—not merely a production tool—through assignments, quizzes, critiques, and iterative instruction.
For founders and investors, he describes three escalating levels of AI nativeness: AI may redefine the product category itself; it may radically increase employee output; and it may eventually change the definition of a company, potentially allowing very small teams to supervise large fleets of agents. He is notably noncommittal on where AI moats will settle. Models, agent harnesses, and applications all have credible arguments for and against defensibility, so his preferred strategy is broad experimentation paired with founders who hold highly specific, determined plans.
Key Takeaways
- Claim: AI should be viewed as a productivity and demographic complement, not mainly as an engine of permanent mass unemployment. | Evidence: Andreessen says U.S. productivity growth has run at roughly half the 1940-1970 rate and about one-third the 1870-1940 rate, while fertility is below replacement in many countries, including China and the U.S. He argues that without AI, depopulation would imply economic shrinkage; AI and robots can substitute for missing labor. | Implication: Ken should plan around labor scarcity plus higher output per operator: use agents to expand capacity, while treating reskilling and task redesign as core operating work rather than as optional efficiency projects. | Caveat: This is a macroeconomic thesis, not evidence that every profession, company, or geography will avoid painful short-term task displacement or transition costs.
- Claim: The winning career strategy is deep competence in one domain plus AI-enabled fluency across adjacent domains. | Evidence: Andreessen describes a three-way “Mexican standoff” among engineers, product managers, and designers: each can increasingly use AI to perform portions of the other two functions. He cites Scott Adams’s idea that being good at two things creates more than double the value, because the combination makes a person unusually non-fungible. | Implication: Favor operators who are technically or functionally excellent but can also specify products, evaluate UX, coordinate agents, and make tradeoffs across disciplines. Build hiring and development frameworks around compound capability rather than narrow role labels. | Caveat: Breadth without depth is not the recommendation; Andreessen repeatedly emphasizes a deep vertical foundation that allows the person to judge quality and intervene when AI fails.
- Claim: Engineering is shifting from code production to multi-agent orchestration, but code and systems fundamentals remain strategically essential. | Evidence: Andreessen describes top programmers as orchestrating roughly a dozen coding bots in parallel, shifting between outputs, debugging failures, and revising specifications. He analogizes this to prior abstraction shifts—from human “calculators,” to machine code, assembly, C, and scripting languages—where the task changed but the underlying technical knowledge still differentiated the best practitioners. | Implication: Agent systems should retain strong human review, observability, and architectural ownership. Train builders to inspect agent reasoning and artifacts rather than treating generation as a black box. | Caveat: He frames AI-generated code as sufficient for “mediocre” output without deep expertise; his claim about enduring technical depth is specifically for people aiming to create high-quality, consequential systems.
- Claim: AI should be used as an active tutor and feedback system, not just as a task executor. | Evidence: He recommends asking models to teach a skill, simplify explanations, generate assignments, quiz the learner, and evaluate work. He also endorses watching agent work to learn architecture and asking after a failure, “What could I have done differently to avoid this error?” His education example is the Bloom Two Sigma effect: one-on-one tutoring historically improves outcomes by two standard deviations, and AI may make an approximation economically accessible. | Implication: Create deliberate AI apprenticeship loops for Ken’s team: agents should both execute and explain, generate practice tasks, expose decision traces, and conduct postmortems on failed runs. | Caveat: The transcript does not establish that current AI tutoring reliably reproduces the Bloom Two Sigma result; it presents AI as a plausible route to scalable personalized instruction.
- Claim: AI-native founders are redesigning products, teams, and eventually the firm itself—not merely adding a chatbot feature. | Evidence: Andreessen’s three-layer framework is: determine whether AI is additive or redefines the product category; convert employees into AI-augmented operators and decide whether fewer people or more output is optimal; then ask whether a founder can supervise an agent army, approaching a one-person billion-dollar outcome. He names Bitcoin, Ethereum, Instagram, and WhatsApp as examples of outsized outcomes with very small core teams. | Implication: Evaluate AI opportunities at all three levels: product substitution/reinvention, internal operating leverage, and the minimum human control plane required to run the business reliably. | Caveat: He treats fully autonomous or one-person companies as an open frontier, acknowledging that real-world operations and support edge cases make the model harder outside pure software.
- Claim: It is premature to declare where durable AI moats will reside; adaptability and portfolio-style experimentation are more rational than confident structural forecasts. | Evidence: Andreessen notes that frontier models require immense capital, compute, talent, and regulatory capacity, suggesting oligopoly potential. Yet within roughly 1-1.5 years of ChatGPT, multiple U.S. and Chinese labs plus open-source alternatives reached comparable capability. He similarly contrasts the view that apps are disposable “wrappers” with the view that domain-specific application integration creates lasting value. He points out that Claude Code reportedly built Co-work in about a week and a half, illustrating both rapid product creation and weak apparent barriers. | Implication: Do not anchor strategy on a single assumed layer of value capture. Build replaceable model dependencies, test proprietary workflow/data/distribution advantages, and maintain options across models, agent harnesses, and vertical applications. | Caveat: His conclusion is epistemic humility, not that moats do not matter. He explicitly says outcomes could still include concentrated model markets or proprietary breakthroughs.
- Claim: “AGI” is less useful as a singular event than as a continuing movement from human-equivalent performance toward superhuman capability in valuable tasks. | Evidence: Andreessen distinguishes a “cosmic” AGI/singularity definition from a practical definition—performing a basket of economically valuable tasks at human level. He argues that human biological limits are not a meaningful ceiling for machines and expects models to exceed expert performance in coding, medicine, law, mathematics, and other verifiable domains. | Implication: Focus planning on capability gradients and task thresholds rather than waiting for an AGI label. Continuously move tasks into agent workflows when performance, verification, cost, and governance are acceptable. | Caveat: His IQ-based illustrations and predictions of broad superhuman performance are speculative forecasts, not demonstrated outcomes in the transcript.
Detailed Brief
Why adoption in the physical economy may be slower than model progress
- Claims: AI capability improvements will not automatically translate into immediate transformation of every economic sector.; The key distinction is rapid progress in “bits” versus slower progress in “atoms”: physical-world deployment is constrained by institutions as much as technology.; Regulation, licensing, unions, cartels, monopolies, political processes, and built-world inertia can slow the practical replacement or augmentation of existing systems.
- Evidence: Andreessen partly concedes Peter Thiel’s critique that the built environment has changed far less since 1970 than it did between 1870-1930 or 1930-1970.; He uses healthcare as the main example: even if AI is clinically capable, it cannot independently receive a medical license, prescribe drugs, or perform procedures, and incumbent professional and institutional structures can resist substitution.; He points to the persistence of old infrastructure—buildings, bridges, dams, and the lack of new cities or California high-speed rail—as evidence that physical transformation is structurally slow.
- Caveats: Institutional resistance can delay benefits as well as harms; capability alone should not be used to forecast deployment timelines.; His characterization of healthcare professions and institutions as cartels is his viewpoint, not a neutral institutional analysis.
- Implications: Separate model capability roadmaps from real-world adoption roadmaps.; For regulated or physical-domain AI, treat authorization, accountability, workflow integration, and political economy as first-class parts of the product architecture.
Agent oversight patterns and voice/interface signals
- Claims: Human-AI collaboration resembles managing a colleague: high-quality feedback requires a model of why the other party made its choices.; Multi-agent critique is a practical pattern: one AI can create an artifact while another evaluates, debugs, or challenges it.; Andreessen sees voice, wearables, and persistent capture/input interfaces as a major next interface wave.
- Evidence: He recommends having one AI write code and another debug it, describing the approach as getting models to argue with each other; the host calls these “LLM councils.”; He highlights Replit as evidence that vibe coding can engage even a 10-year-old in extended creation, and describes his son building Star Trek: The Next Generation LCARS-style simulators.; He names Sesame, Meta glasses, Limitless-style wearables, and Whisperflow. He says Whisperflow can interpret spoken formatting instructions such as requesting bullet points rather than transcribing them literally.
- Caveats: The product references are personal endorsements and examples, not comparative evaluations or validated market forecasts.; Model explanations and self-critiques should not automatically be treated as faithful representations of internal reasoning or as proof of correctness.
- Implications: Design agent control planes around independent review, artifact-level validation, and structured adversarial checks rather than accepting a single-agent output.; Monitor voice-first capture and wearable interfaces as likely high-frequency entry points into personal and operational agent systems.
Information strategy: immediacy, durable knowledge, and practitioner access
- Claims: Andreessen uses a “barbell” media diet: real-time information through X plus older books that have survived over time, while remaining skeptical of much current middle-horizon commentary.; He believes direct access to domain practitioners through podcasts, newsletters, and similar formats provides substantial information advantage relative to heavily mediated mass media.; He sees Silicon Valley’s willingness to share knowledge, despite leakage costs for individual firms, as a compounding ecosystem advantage that helps it repeatedly shift to new technology waves.
- Evidence: His test for current media is to reread last week’s newspaper and observe how many forecasts or narratives already appear irrelevant.; He argues that practitioners often genuinely want to explain their work, even if they are also “talking their book.”; He characterizes AI as approximately the ninth major platform wave in Silicon Valley and describes the region as a “company town” whose company is Silicon Valley itself.
- Caveats: Direct practitioner content is also self-interested; access should be paired with source triangulation and empirical checks.
- Implications: Build research intake around primary sources, builders’ technical writeups, product usage, and direct conversations—not broad media consensus.; Treat public sharing as both an intelligence source and a competitive-leakage tradeoff when deciding what Ken’s organization should publish.
Notable Concepts & Terms
- Super-empowered individual: A person with deep expertise who uses AI to become dramatically more productive and to operate competently across adjacent functions.
- Task loss vs. job loss: Andreessen’s core labor lens: jobs are bundles of tasks, and tasks are likely to change or move to AI before entire job categories disappear.
- Mexican standoff: His metaphor for engineers, PMs, and designers each gaining AI-enabled ability to perform parts of the other roles, eroding functional silos.
- Non-fungibility: Larry Summers’s career principle, as relayed by Andreessen: combine skills so that you are difficult to swap out for a generic specialist.
- Bloom Two Sigma effect: The finding that one-on-one tutoring can improve outcomes by two standard deviations; Andreessen sees AI tutoring as a possible scalable approximation.
- AI as the philosopher’s stone: Andreessen’s metaphor for converting abundant sand/silicon into scarce thought, emphasizing AI as a general-purpose cognitive lever.
- Determinate vs. indeterminate optimism: Peter Thiel’s framework: founders should be determinate optimists with a specific plan, while venture ecosystems can rationally use diversified, indeterminate optimism across many experiments.
- Bits vs. atoms: The distinction between fast-moving digital innovation and slower physical-world change constrained by institutions, construction, and regulation.
Operator Notes / Why Ken Should Care
- Create an AI capability ladder for each core operator: one deep domain, two adjacent working competencies, and explicit agent-orchestration skill milestones.
- Add a mandatory “inspect, explain, validate” loop to agent workflows: require operators to review plans and artifacts, identify failure causes, and use separate agents for critique or testing.
- Audit work by task bundle, not headcount or job title: identify tasks that can be automated, tasks that should become higher-leverage human judgment, and tasks that can be newly absorbed by existing roles.
- Architect model portability into the control plane. Avoid treating any current model, agent harness, or frontier-vendor lead as a settled moat.
- For AI-native ventures or internal products, assess opportunities on three separate dimensions: category reinvention, workforce leverage, and the human control plane needed for edge cases and accountability.
- Build a structured AI tutoring program for the team using generated exercises, quizzes, critique, and failure postmortems; measure whether it improves independent judgment rather than just output volume.
- For regulated or real-world deployments, maintain a separate adoption-risk register covering licensing, workflow ownership, liability, compliance, and incumbent resistance.
Source/Metadata
- Title: Marc Andreessen: The real AI boom hasn’t even started yet
- Transcript words: 35290
- Duration seconds: 6275
- Timestamp note: No usable timestamps or chapter markers were present in the supplied transcript; the transcript also contains substantial duplicated passages.
Transcript
If we didn't have AI, we'd be in a panic right now about what's going to happen to the economy. We've actually been in a regime for 50 years of very slow technological change in the face of declining population growth. The timing has worked out miraculously well. We're going to have AI and robots precisely when we actually need them. The remaining human workers are going to be at a premium, not at a discount. How big of a deal is the moment in time that we are living through right now? This is a very, very historic time. AI is the philosopher's stone. Now we have a technology that transfers the most common thing in the world, which is sand, converted into the most rare thing in the world, which is thought. We spend a lot of time with the most cutting-edge AI-forward founders. The most leading-edge founders are thinking, can you have entire companies where the founder does everything? There's all this concern that young people, jobs are not going to be there for them. AI is replacing them. Everybody wants to talk about job loss, but really what you want to look at is task loss. The job persists longer than the individual tasks. What's your sense of the future of three very specific roles: product manager, engineer, designer? There's a Mexican standoff happening between those three roles. Every coder now believes they can also be a product manager and a designer because they have AI. Every product manager thinks they can be a coder and a designer, and then every designer knows they can be a product manager and a coder. They're actually all correct. What happens is the additive effect of being good at two things is more than double. The additive effect of being good at three things is more than triple. You become a super relevant specialist in the combination of the domains. People aren't fully grasping how much this is changing. People who really want to improve themselves and develop their careers should be spending every spare hour, in my view, at this point talking to an AI, being like, all right, train me up. Today, my guest is Marc Andreessen, one of the most seminal figures in tech and in business. He invented the web browser, built the world's largest venture firm. He's also a multi-time founder and an investor in essentially every generational tech company, and is also one of the most clear-minded, lateral, and insightful thinkers about both the past and the future of technology. In this very special conversation, we chat about how unique and significant the moment that we are all living through right now is, what skills he's teaching his kids to thrive in the AI future, what happens to product managers, designers, and engineers in the coming years, where moats exist in AI, what the most AI-native founders are doing differently, and so much more that is just scratching the surface of this very deep and important conversation. You are going to walk away from this chat being smarter about what is going on in the world right now and where things are heading. A huge thank you to my newsletter community and folks on X for suggesting topics and questions for this conversation. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. It helps tremendously. And if you become an insider subscriber of my newsletter, you get a year free of over 20 incredible products, including a year free of Lovable, Replit, Bold, Gamma, N8N, Linear, Superhuman, Devon, PostHog, Descript, Whisperflow, Perplexity, Warp, Granola, Magic Patterns, Raycast, Chapier, Demobin, and Stripe Atlas. Head on over to LennysNewsletter.com and click Product Pass. With that, I bring you Marc Andreessen after a short word from our sponsors. Today's episode is brought to you by DX, the developer intelligence platform designed by leading researchers. To thrive in the AI era, organizations need to adapt quickly. But many organization leaders struggle to answer pressing questions like, which tools are working? How are they being used? What's actually driving value? DX provides the data and insights that leaders need to navigate this shift. With DX, companies like Dropbox, Booking.com, Adyen, and Intercom get a deep understanding of how AI is providing value to their developers and what impact AI is having on engineering productivity. To learn more, visit DX's website at getdx.com/lenny. That's getdx.com/lenny. If you're a founder, the hardest part of starting a company isn't having the idea. It's scaling the business without getting buried in back-office work. That's where Brex comes in. Brex is the intelligent finance platform for founders. With Brex, you get high-limit corporate cards, easy banking, high-yield treasury, plus a team of AI agents that handle manual finance tasks for you. They'll do all the stuff that you don't want to do, like file your expenses, scour transactions for waste, and run reports, all according to your rules. With Brex's AI agents, you can move faster while staying in full control. One in three startups in the United States already runs on Brex. You can too at brex.com. Marc Andreessen, thank you so much for being here, and welcome to the podcast. Awesome, Lenny. Thank you. It's great to be here. I want to start with just a big-picture question. I have a billion directions I want to go, but I think this is going to give us a little bit of a frame of reference. How big of a deal is the moment in time that we are living through right now? This is a very, very historic time. I think 2025 was maybe the most interesting year in my entire career and probably life, and I would expect 2026 to exceed that. Wow, that says a lot. Yeah, I've seen some stuff. It feels like two things are happening. One is the trust that a lot of people have had in what you could describe as legacy institutions around the world is, I think, in full-scale collapse right now. By the way, there's a lot of data to support that, and so I think there are a lot of structures and orders and institutions that people have just relied on for a long time that have proven not to be up for the challenge. And then, corresponding with that, the national and global conversation has become, let's say, liberated. This incredible revolution that we have in what I've described as freedom of speech, freedom of thought, and the ability for people to openly discuss things that maybe they couldn't discuss even a few years ago has just dramatically expanded, and I think that's now on a one-way train for a much broader range of discourse. And then there are also these incredibly massive geopolitical shifts that are happening, and obviously the U.S. is changing a lot. Europe is changing a lot. China is changing a lot. Latin America, by the way, is changing a lot. Very dramatic events are playing out down there right now. All over the world, I think a lot of assumptions are being pulled out into the daylight and reexamined. And then it's the fact that all these things are happening at the same time. You've got all of these countries and industries where things are in increasing upheaval, but you have AI as this new technology that's going to really affect things. And then you've got citizens being able to fully participate, being able to argue things out. So those three big mega things are all colliding at the same time. And I think we're probably just at the very beginning of all three of those. And those all feel like historical moment shifts, comparable in magnitude to maybe the fall of the Berlin Wall in 1989, maybe the end of World War II, moments like that. It certainly feels like that. Good God. What a time to be alive. Yeah. In terms of the AI piece, which is where a lot of people are trying to figure out what to do, what do you think isn't being priced in yet in terms of the impact AI is going to have on, say, the world or just people listening? The other thing, I think at this point, it's pretty clear with our technology hats on that this stuff is really working now. There was this, when there was a ChatGPT moment three years ago. By the way, only three years ago was the ChatGPT moment. And the big question was, all right, this is incredibly fun and creative. We have machines now that can compose Shakespearean sonnets and rap lyrics. This is amazing. But then there was this big question: can you harness this technology for reasoning and for problem-solving in domains that really matter, medicine and science and law and so forth? And it turns out the answer to that is yes. The other thing, I think at this point, I think it's pretty clear with our technology hats on that this stuff is really working now, right? And so there was this, when there was a chat GPT moment three years ago. By the way, only three years ago, right, was the chat GPT moment. And the big question was, all right, this is incredibly fun and creative. And we have machines now that can compose Shakespearean sonnets and rap lyrics. And this is amazing. But then there was this big question: can you harness this technology for reasoning and for problem solving in domains that really matter, medicine and science and law and so forth? And it turns out the answer to that is yes, right? And the last 12 months, and especially even just the last three months, have really proven that AI can really do, you're seeing it all now. You can actually, AI is now developing new math theorems. Over the holiday break, there's the, but it feels like the AI coding thing really hit critical mass. And the world's best programmers, right, including Linus Torvalds, for the first time over the holiday break basically said, yeah, AI is now coding better than we can. And so that's incredibly, incredibly powerful. And I think we all, I think, assume that AI now is going to get really good at reasoning in any domain in which there are verifiable answers. And so that's going to include many very important domains. So the technology feels like it's moving fast and it's going to be working really well. I think the thing that is not well understood, I think a lot of people have, I think, a lot of people in the industry have what I would describe as this one-dimensional thing, which is, okay, as a result of the technology now working, AI just sweeps the world and changes everything. And I think that's the wrong frame, or I think it's based on an incomplete understanding of the world that we live in or the world that we've been living in for the last 80 years. And I would call out two things in particular. So one is, it has, I think it's felt to us, in the U.S. and the West for the last 30 years or 50 years, it's felt like we've been in a time of great technological change. But actually, if you look for evidence of that, in statistical evidence of that, analytical evidence of that, you basically can't find it. And in particular, economists have a way of measuring the rate of technological change in the economy, that is productivity growth, which we could talk about what that means. But basically, it's the mathematical expression of the impact of technology on the economy. And productivity growth for the last 50 years has actually been very low, not very high. So we all feel like it's been very high. There's been lots of technological change. What's actually happening is it's been very low. And in fact, the pace of productivity growth in the U.S. is running at a half of what it, in my lifetime, in our lifetimes, it's been running at about half the pace that it ran in between 1940 and 1970. And it's been running at about a third the pace that it ran between about 1870 to about 1940. And so statistically, in the U.S., in the West, technology progress in the economy, technology impact in the economy, has actually slowed way down. And so the AI thing is going to hit, but it's hitting an environment in which we have actually had almost no technological progress in the actual economy for a very long time. So we could talk about that. And then there's this other incredible thing that's happening, which is the demographic collapse, right? It's a Western phenomenon and increasingly a global phenomenon, which is the rate of reproduction of the human species is in rapid decline. And there are many countries, including the U.S., where the rate of reproduction is under two, meaning that many, many countries around the world, by the way, including China, which is a really big deal, are actually going to depopulate over the next century. And so you have this precondition that says there's actually been very little technological progress happening in the world and the world is going to depopulate. Right. And so AI is going to enter a world in which those two things are true. And I think this is incredibly important because we actually need AI to work in order to get productivity growth up, which is what we need to get economic growth up. And we actually need AI to work because we're going to need machines to do all the jobs that we're not going to have people to do because we're literally going to depopulate. We're going to depopulate the planet over the next hundred years. And so I think the interplay of these factors is going to be much more interesting and frankly more complex than a lot of people have been thinking. I'm going to follow this thread about kids. I know you have a kid, and one of my favorite lenses into how people think and what they value is what they're teaching their kids, what they're steering their kids towards. Are there specific skills or careers that you're steering your kid towards? The way I think about this, anyway, yeah, we have a 10-year-old, and so we actually homeschool, and so we think a lot about this. So I think the way to think about the impact of AI on people, specifically people as individuals, I think it's actually, a lot of people just focus on this very, I would say, straightforward and overly simplistic view of literally job gains, job losses, which we can talk about. But there's two specific things at the level of an individual person or an individual kid. So I think it's pretty clear that AI is going to take people who are good at doing things and it's going to make them very good at doing things. Right. And so it's going to be a tool that's going to raise the average across the board. And look, you see that playing out already. Anybody who's in a position where they need to write something or design something or write code or whatever, if they're pretty good at it today, they use AI and all of a sudden they're very good at it. And so there's that aspect to it. And I think the way the education system at large is going to teach AI is going to be based hopefully a lot on that. But then there's this other thing that's happening, which we're also starting to see. And we're really seeing it particularly in coding right now, where the really great people are becoming spectacularly great. Right. And so you use the term, you think about the super empowered individual. Right. So the individual who is really good at coding or really good at making movies or really good at making songs or really good at designing, making art, or whatever those things are. Or podcasting or hopefully venture capital. If you're very good at it and you can really harness AI, you can become spectacularly great and super productive. Right. And I'm sure you have a lot of friends in this category as well. But the really, really good coders are experiencing this right now. My friends who are really good coders, they're like, oh, my God. All of a sudden, I'm not twice as good as I used to be. I'm 10 times as good as I used to be. And so I think at the unit of N equals one, of an individual kid, I think the question is how do you get them in a position where they're this super empowered individual such that they're going to be really deep in whatever it is they're going to do. But they're going to be deep in a way that's going to let them fully use the power of AI to be not just great, but to be spectacularly great. And I think that's the real opportunity. And at least that's what we're shooting for. And that's what I would encourage parents to shoot for. So what I heard there is essentially agency. They're oh, my God. All of a sudden, I'm not twice as good as I used to be. I'm 10 times as good as I used to be. And so I think at the unit of N equals one, of an individual kid, the question is how do you get them in a position where they're this super empowered individual such that they're going to be really deep in whatever it is they're going to do. But they're going to be deep in a way that's going to let them fully use the power of AI to be not just great, but to be spectacularly great. And I think that's going to be the real, that's the real opportunity. And at least that's what we're shooting for. And that's what I would encourage parents to shoot for. So what I heard there is essentially agency. That's where that we see on Twitter all the time is building an agency, them not waiting for someone to tell them what to do, figuring out what to do. Yeah. Yeah. So this thing with this term agency that's become very, very popular in California for the last couple of years. It's really interesting because I had a lot of trouble with this early on because I'm like, agency? What are they talking about? And what they're talking about is initiative, willingness to, you could just do things. What is it? The symbol bridge has the great term live player. You can be a primary participant in events. And at first I was like, well, yeah, that's obvious. Right. Of course. And then I'm like, oh, actually, it's not so obvious anymore because to your point. And I think so much of our society is based on there are all these rules, and everybody gets taught by default you're supposed to follow all these rules. Right. And then everybody, if you break the rules, everybody gets freaked out. It's like, oh, my God, he broke the rules. And so we have somehow worked our way kind of, I don't know, psychologically, sociologically, into a state in which I guess the natural assumption for a lot of people is the thing that, for example, you want to train kids to do is follow all the rules. And you could argue that, for example, the school system, the K through 12 school system or whatever, has gotten more and more focused on it over time. And it's like, yeah, no, you should actually. And again, especially unit N equals one, of your kid. It's like, look, there's something to be had. I just had this conversation with my 10-year-old last night. Actually, I rolled out the concept of, in order to lead, you must first learn to obey. Right. In order to issue orders, you must learn how to follow orders. And trying to keep him with some level of structure in his life and not just pure agency. But, yeah, look, some rules are important and so forth. But, yeah, no, look, there is a huge premium in life on being somebody who is able to fully take responsibility for things, fully take charge, run an organization, lead a project, create something new. And maybe, yeah, that has been a little bit diminished in our culture over the last 30 years. It's healthy that there's now a term for that that is coming back into vogue. And again, that's how I view AI for kids, is like, okay, AI should be the ultimate lever on the world for a kid with agency to be able to say, okay, I can actually be a primary contributor. Right. Whether that's I can be a primary contributor in everything from developing new areas of physics to writing code to being an artist, to writing novels. Whatever that thing is, I can fully participate in the world. I can really change things. And that feeling, the combination of that idea combined with this technology, feels very healthy to me. What is that quote about? Give me a lever and I'll move the world. And I'll move the world. Yeah, that's exactly right. Well, so it's actually funny you mentioned that. So the early scientists, including Isaac Newton, were super obsessed with this concept of alchemy. Right. They developed, Newton developed Newtonian physics and he developed calculus and all these things. But the thing he was really obsessed with was alchemy, which was the thing he could never get to work. Right. And alchemy was the transmutation of lead into gold, which meant the transmutation of something that was very common, which was lead, into something that was very rare and valuable, which was gold. And there was this, he spent decades trying to figure out this thing called the philosopher's stone, which would be basically the machine or the process that would be able to transmute the common thing into the rare thing. And he never figured it out. And it was incredibly frustrating. Nobody ever figured that out. And now we literally have AI, a technology that transfers sand into thought. Right. It just blew my mind. Right. The most common thing in the world, which is sand, converted into the most rare thing in the world, which is thought. Right. And so AI is the philosopher's stone. It actually is that. And it's just this incredibly powerful tool. And that's where I get so excited. I mean, and again, this is what we're doing with our 10-year-old, which was like, all right, the primary thing that we want to make sure to do is to make sure that he knows fully how to leverage and get benefit out of the philosopher's stone. Right. Which is to say AI. And then that's certainly central to everything we're teaching him. There's this meme going around that Silicon Valley people don't let their kids use computers. And there may be a handful of people who are like that. I don't know. I think it's more honestly the other way around, which is the more you're plugged into stuff in Silicon Valley, the more important it is to make sure that your kids actually fully understand this and know how to use it. And that's certainly the mode that we're in. And that's certainly the mode that I would encourage parents to think about. I did not know your kid was homeschooled. That is super interesting. There is almost a statement on education in today's day. Maybe, is there any thoughts there? I'm just, for folks that maybe aren't in your tax bracket that want to help their kids be successful, maybe homeschool, maybe not, what advice would you have? This is the challenge. And again, this goes to your original question, which is education. There's two completely different ways to talk about, think about education. The way that's usually thought about and talked about is at the level of a nation, right? So it's like a national level issue or maybe a state level issue in the U.S., which is basically like, how do you educate all the kids? And of course, that's incredibly important. And of course, you're going to need some level of large-scale system, like the national K through 12 school system or something like that, in order to do that. But then there's this other question, which is like, at N equals one, for an individual kid, what can you do with an individual kid? And so I'll just give you the ultimate answer to that question, which is, it's been known for centuries that the ideal way to teach a kid at the unit of N equals one, by far, the ideal way to do it is with one-on-one tutoring. So, so, it's a national-level issue, or maybe a state-level issue in the U.S., which is, how do you educate all the kids? And of course, that's incredibly important. And of course, you're going to need some level of large-scale system, like the national K through 12 school system or something like that, in order to do that. But then there's this other question, which is, at N equals one, for an individual kid, what can you do with an individual kid? And so I'll just give you the ultimate answer to that question, which is, it's been known for centuries that the ideal way to teach a kid at the unit of N equals one, by far, the ideal way to do it is with one-on-one tutoring. If you just have an individual kid and the goal is to maximize an individual kid, by far, you get the best results with one-on-one tutoring. And this is something that every royal family knew in history. It's something that every aristocratic class knew in history. There's all these amazing examples. Alexander the Great was tutored by Aristotle. He took over the world, right? Many of the great kings and queens, royal families and aristocrats and so forth, over the course of centuries, always had this approach. There's actually also statistical evidence, analytical evidence, that this is correct. There's this massive question in the field of education, which is how do you improve educational outcomes? And it turns out it's very hard to improve educational outcomes, except there's one method that always does it, which is called the Bloom Two Sigma effect, which is there's one method of education that routinely raises student outcomes. It's by two standards of deviation. And we'll take a kid from the 50th percentile to the 99th percentile, and that's one-on-one tutoring. Right? So again, if you go back to it equals one, you have a kid and a tutor, and they're in this very tight loop with each other, where the kid is able to constantly be on the leading edge of what they're capable of doing. And they can move incredibly fast, and they get correction in real time, you get these better outcomes. But, to your question, it's never been economically feasible for anybody other than the richest people in society to be able to provide one-on-one tutoring for kids. AI provides the very real prospect of being able to do that, right? Because obviously now, if you have a kid that's super interested in something, and they can talk to an LLM about it, and they can ask an infinite number of questions, and they can get instantaneous feedback. And in fact, you can even tell an LLM, teach me how to do the following. And you can say, wow, I don't quite understand what you're saying, dumb it down for me a little bit. Okay, now quiz me, do I actually understand this? People can just do this today, right? And so I think there's this massive opportunity for parents in many walks of life to be, with a little bit of time and focus, able to say, okay, my kid's probably still going to go through the traditional education system, but I'm going to augment this with AI tutoring. And of course, there's going to be tons of startups, right, and there already are, that are going to try to build all the products and services for this. Khan Academy, on the nonprofit side, has a big push to do this. And so I think the broad answer might be a hybrid approach with schools plus one-to-one tutoring through AI. There's also this great, you may have heard, there's this great school, new private school system called Alpha, in which everything I just described is the basis of their philosophy, which is, it's a combination of in-person schools and teachers, but it's also heavily based on AI and AI tutoring. And so I think there is a magic formula in here that I think is going to apply much more broadly. And really, for parents interested in this, now would be a great time to really start to think hard about that and to look at the options. It's interesting because there's all this concern that young people, jobs are not going to be there for them, AI is replacing them. On the flip side, there's what you're describing here. It feels like people coming in learning today are going to move so fast and learn so much more. And where do you sit on this divide of young people are in big trouble or they're actually going to be the ones winning in the end? Yeah. So the job substitution, job loss thing is just very reductive. It's, I think, an overly simplistic model. And again, it goes back to what I said at the very beginning, which is we've actually been in a regime for 50 years of very slow technological change in the economy. And so, again, like I said, it's at half the rate of the previous era and then a third the rate of 100 years ago. And so we're coming out of this phase where we've had almost no technological progress in the economy. We've had remarkably little job churn as a result of that relative to any historical period. And so even if AI ticks up, even if AI triples productivity growth in the economy, which would be a massively big deal, it would take us back to the same level of job churn that was happening between 1870 and 1930. And if you go back and you read accounts of 1870 and 1930, people just thought the world was awash with opportunity, right? At that rate of technological transformation, kids were able to develop new careers into new areas of the economy, building new kinds of products and services. I mean, a huge part of everything in our modern world today was invented and proliferated during that period. And so even if AI triples the pace of economic change in the economy, it's going to just translate to a much higher rate of economic growth. It's going to translate to a much higher rate of job growth. And there'll be some level of task-level and job-level substitution that will take place, but that will be swamped by the macro effects of economic growth and innovation that will happen. And corresponding to that, there will be hiring booms, quite honestly, I think, all over the place. And then again, go back to the other thing, which is this is all happening in the face of declining population growth and increasingly population shrinkage. And so human workers in many, many, many countries over the next 10, 20, 30 years are going to be at more and more of a premium, literally because you're going to have shrinking population levels. We don't really want to get into politics particularly, but it does feel like the world broadly is going to reverse course on the rates of immigration we've had for the last 50 years. It seems to be a broad-based thing happening, with the rise of nationalism, concerns about the rate of immigration, and immigration historically in countries like the U.S. has ebbed and flowed over time based on how the national mood shifts. And so if you combine, in a country like the U.S. or any country in Europe, declining population with less immigration, the remaining human workers are going to be at a premium, not at a discount. And so I think that combination of faster productivity growth, faster economic growth, and then slower population growth and less immigration actually means there's going to be much less of this dystopian no-jobs thing. I just think it's probably totally off base. That is extremely interesting. So what I'm hearing is you're not super worried about job loss. Is the key here that the timing just works out? Does population decrease, all these kind of have to line up for there not to be this massive job loss with AI? Yeah, well, look, if we didn't have AI, we'd be in a panic right now about what's going to happen to the economy. Right? Because what we would be staring at is a future of depopulation, and depopulation without new technology would just mean that the economy shrinks. And so I think that combination of faster productivity growth, faster economic growth, and then slower population growth and less immigration actually means there's going to be much less of this dystopian no-jobs thing. I just think it's probably totally off base. That is extremely interesting. So what I'm hearing is you're not super worried about job loss. Is the key here that the timing just works out? Does population decrease? Do all these have to line up for there not to be this massive job loss with AI? Yeah, well, look, if we didn't have AI, we'd be in a panic right now about what's going to happen to the economy. Right. Because what we would be staring at is a future of depopulation, and depopulation without new technology would just mean that the economy shrinks. Right. So it would mean that the economy itself shrinks over time. The opportunity diminishes. There are no new jobs. There are no new fields. There's no new source of consumer demand for spending on things. And so you would be very worried about going into a period of severe decline or stagnation. And essentially, you'd be looking at these very dystopian scenarios of an economy self-euthanizing itself over time. And it's going to be very worried about the opposite of what everybody thinks that they're worried about. The only reason we're not worried about that is because we now know that we have the technology that can substitute for the lack of population growth, and also for the lack of immigration, likely. And so I would say the timing has worked out miraculously well, in the sense that we're going to have robots precisely when we actually need them to keep the economy from actually shrinking. And I just think that's fundamentally a good-news story. To get to the mass job loss thing that people are worried about on the other side of things, you'd have to look at far, far, far higher rates of productivity growth. You'd have to look at rates of productivity growth that are 10, 20, 30, 50 percent a year, something like that, which are orders of magnitude higher than we've ever had in any economy in the history of the planet. It's possible that we get that. I mean, look, I have my utopian temptation along with everybody else. If AI radically transforms everything overnight, then maybe let's play out the utopian scenario. You get to a much higher level of productivity growth. You get to a much higher level of technological change. Corresponding to that, you'll have a massive economic boom. You'll have massive growth in the economy. And then, corresponding with that, you'll have a collapse in prices. And so the price of goods and services that are affected by or commoditized by AI, the prices of those goods and services will collapse. Right. It'll be price deflation. And then, as a consequence of price deflation, everything that people are buying today gets a lot cheaper. And that's the equivalent of a gigantic increase in wealth right across society. Take it this way. This is actually worth talking about because people, I think, get sideways on this issue. So if AI is going to transform the economy as much as the utopians or dystopians think that it will, the necessary economic calculation of what happens is massive productivity growth. The consequence of massive productivity growth, what that literally means mechanically, is more output requiring less input. Right. So you get more economic output for less input. So you're substituting in AI for human workers or whatever. And as a consequence, you get this massive boom in output with much lower input costs. The result of that is you get gluts of goods and services in all those affected sectors. The result of those gluts is you get collapsing prices. Right. The collapsing prices mean that the thing today that costs you $100 now costs you $10 and now costs you $1. That's the equivalent of giving everybody a giant raise. Right. Because now they have all this additional spending power. That additional spending power then translates to economic growth, the development of new fields. Everybody's materially much better off very quickly. And then, by the way, if you do have unemployment coming out the other side of that, it's now much cheaper to provide the social safety net to prevent people from being immiserated. Right. Because the prices of all the goods and services that a welfare program has to pay for, they're all collapsing. And so the price of health care collapses, the price of housing collapses, the price of education collapses, the price of everything else collapses because of this incredible impact that AI is having. And so in this utopian-dystopian scenario that people have, there's no scenario in which everybody's just poor. In fact, it's quite the opposite, which is everybody gets a lot richer because prices collapse. And then it's actually much easier to pay for the social safety net for the people who, for some reason, can't find a job. And so maybe we end up in that scenario. I mean, the optimistic part of me says, yeah, maybe AI is that powerful, and maybe the rest of the economy can actually change to accommodate that, and maybe that'll happen. But the result of that is going to be a much better news story than people think it's going to be. And again, everything I've just described, by the way, is just a very straightforward extrapolation of very basic economics. I'm not making any bold predictions in what I just said. This is just a straightforward mechanical process that plays itself out if you have higher rates of productivity growth, which are necessarily the result of higher rates of technological growth. And so I think we're looking at, to be clear, a world that's not radically transformed the way that maybe the utopians think that it will be or the dystopians think it will be. I think it'll be more incremental, for reasons we can discuss. But I think that incremental process is going to be a good-news process. And then even if it's much faster, it's also going to be a good-news process. It'll just be a good-news process in the other way that I just described. I love hearing optimism and good news. I will also add that I was researching you ahead of this chat, and you've been right so many times about where the world is heading. That's why I'm especially excited to talk to you. I'll give you a short list. I imagine there are many more things. Okay, so one, you were right about the web and web browsers becoming important. You were right about software eating the world. Check. In 2011, you said that in 10 years, we're going to have 5 billion people using smartphones. And I believe the actual number ended up being 6 billion. You also had this debate with Peter Thiel that I came across where you were debating whether technology has stopped progressing or if new technology will continue to emerge. And you were arguing there is progress. Progress will continue. And he was like, no, I think we're done with cool technology. You were right. I imagine there are many more things you were right about. So again, I love hearing your predictions because I feel like they're actually going to turn out to be correct. So I will just start by saying I've been wrong about tons of things, but I buried those out back behind the shed. Delete them from the Internet. No browser can discuss. Yes, I have them nuked out of the Internet archives so that they're never seen again. So I'm wrong plenty of times also. But yeah, I mean, look, I think some of those are right. By the way, I will say on the Peter one, I have come much more around to Peter's point of view. I would probably argue that one quite a bit differently today than I did, and I would give his view a lot more credit. And it actually goes to the discussion we just had, which is the real form of what Peter was arguing was we have lots of progress in bits. Right. But we have very little progress in atoms. Right. And that's the real core of what he was arguing. And I think I was a little bit missing that or glossing that over a little bit because I was so focused on making sure people understood, no, there actually is still progress happening in bits. By the way, I will say on the Peter one, I have come. I've come much more around to Peter's point of view. I would probably argue that one quite a bit differently today than I did. And I would give his view a lot more credit. And it actually goes to the discussion, the conversation we just had, which is the real form of what Peter was arguing was we have lots of progress in bits. We have lots of progress in bits. Right. But we have very little progress in atoms. Right. And that's the real core of what he was arguing. And I think I was a little bit, I don't know, missing that or glossing that over a little bit because I was so focused on making sure people understood, no, there actually is still progress happening in bits. But I think a lot of his critiques around the lack of progress in atoms is real. And again, this goes back to this thing of in the last, and he, he's talked about this for a long time. In the last 50 years, there has just been very little technological innovation in most of the economy. There's been very little technological innovation, in particular, anything involving atoms. There's been very little real-world technological change. There just hasn't been; the built world is just not that different today than it was 50 years ago. And again, if you compare and contrast 1870 and 1930, it was a dramatically different world. If you contrast 1930 and 1970, it was a dramatically different world. If you contrast 1970 and today, it's not that different. Right. And look, you just see that. You could just walk around and it's just like, oh yeah, there's a bunch of buildings that were built in 1960. Right. And there's a bridge that was built in 1930. And there's a dam that was built in 1910. And there's a city that was founded in 1880. And what have we done? Where are new cities? Where are new dams? Where's the California high-speed rail? What's going on here? And so I think he is right about a lot of that. Again, this is also why I think that AI is not going to have as rapid an impact. It's not going to be, again, this utopian or dystopian view of everything changes overnight. I think it just can't happen because of the reasons that Peter articulates, which is there's so much about how the world works that's just wrapped up in red tape, bureaucratic process, rules, restrictions, the politics, by the way, unions, cartels, oligopolies. There's all these structures in the world that are economic or political or regulatory structures that basically prevent things from changing. And so let's take a great example, AI's impact on the health care system. By rights, AI is going to have a dramatic impact on the health care system, and in very positive ways. But large parts of the medical system today are cartels. Right. And so there's the doctors' cartel, and nurses are a cartel, hospitals are a cartel. And then there's this push to nationalize all the health care systems. And then you've got a government monopoly. Right. And guess what cartels and monopolies don't like? They don't like rapid change. Right. And so you show up as a kid and you're like, wow, I've got this new technology to do AI medicine. And they're like, oh, well, does it threaten Dr. Jobs? Well, in that case, we're going to block it. And I think a lot of consumers, by the way, I see this in my life, and you'll probably see this in your life also, which is, ChatGPT is almost certainly a better doctor than your doctor today. But ChatGPT can't get a license to practice medicine. Right. So it can't substitute for a doctor. It can't prescribe medications. Right. It can't perform procedures. Right. And so there are these, anyway. Anyway, so Peter, I think, was very articulate, and has been for a long time, on, no, there are actually real structural impediments in the economy and in the political system that we have that actually prevent any rates of change that are anywhere near the rates of change that people have had in the past. And you can maybe say optimistically, maybe the presence of the new magic technology of AI, maybe it causes us to revisit a lot of these assumptions for the first time in decades, to really say, OK, is this really the world we want to live in? Don't we actually want to get to the future faster? So maybe that would be the optimistic view. It's time to build, somebody famously said. I actually have that in my calendar as my, when I start to work, it's time to build. That's my block in the morning of the day. All right. Thank you for that. OK, I love the way you go from macro to end-of-one. And I want to go to end-of-one. A lot of the listeners of this podcast are product managers. They're engineers. They're designers. There are a lot of founders, but there are also a lot of non-founders. There's a lot of people building product that aren't founders. And obviously a lot of people are worried about where their career is going. Is one of these roles going to disappear? Is one of these roles going to do really well? How do I stay up to date? You're close with a lot of teams, a lot of product teams. What's your sense of the future of these three very specific roles: product manager, engineer, designer? This, I think, is a really funny question. So these three roles in particular, obviously, are the central roles for building, for tech companies. And so the way I've been describing it is the concept of the Mexican standoff, right? Which is the movie scene where the two guys have guns pointing at each other's heads. And then there's, if you watch John Woo movies, he loves to have, he does the three-way Mexican standoff where you've got a triangle of people and, in a John Woo movie, they've got guns in both hands. So each is aiming at the other two. And you've got this standoff situation. And so the way I've been describing this is there's a Mexican standoff happening between those three roles, between product manager, designer, and coder, specifically the following, which is every coder now believes they can also be a product manager and a designer. Right? Because they have AI. Every product manager thinks they can be a coder and a designer. And then every designer knows they can be a product manager and a coder. Right? And so people in each of those roles now know or believe that with AI, they don't need the other two roles anymore. Right? They can do that because they can have AI do that. And then, of course, there's the real irony, which is all three of them are going to realize that AI can also be a better manager. Right? So they're going to be aiming the guns up the org chart, but that's probably the next phase. And what I think is so fascinating about this Mexican standoff is they're actually all kind of correct, I think. Right? Which is AI is actually a pretty good, it's actually now a really good coder. It's actually now a really good designer, and it's also a really good product manager. Right? It's actually good at doing all three of those things, or at least doing a lot of the tasks involved in those three jobs. And so again, this goes back to the super, this idea of the super-empowered individual, where if I'm a coder, step one is, I need to make sure that I really understand AI coding and what that means and how coding is going to change in the future. That I need to understand specifically how to go from being a coder who writes code entirely by hand to being a coder who orchestrates a dozen instances of coding bots. There's a change in the actual job of coding itself, which is happening right now. But the other part of it is, OK, how do I become that super-empowered individual? It's actually good at doing all three of those things, or at least doing a lot of the tasks involved in those three jobs. And so again, this goes back to this kind of idea of the super empowered individual, where if I'm a coder, step one is I need to make sure that I really understand AI coding and what that means and how coding is going to change in the future. That I need to understand specifically how to go from being a coder who writes code entirely by hand to being a coder who orchestrates a dozen instances of coding bots. There's a change in the actual job of coding itself, which is happening right now. But the other part of it is, okay, how do I become that super empowered individual? How do I become a coder that also then harnesses AI so that I can also be a great product manager and I can also be a great designer? Right? And then the same thing for the product manager, which is how do I make sure that I can now use coding tools? How do I make sure I can also do AI-based design? And the same thing for the designer, which is how do I use AI to also become a coder and also become a product manager? And then what you did is maybe those individual roles change. Maybe those are not any more stovepipe roles the way that they have been for the last 30 years or whatever. But what happens is that the talented people in any of those roles become super empowered, and they become good at doing all three of those things. And then those people become incredibly valuable because then those are people who can actually build and design new products right from scratch, which is the most valuable thing. And so I think that's the opportunity. I love this answer. So what I'm hearing is essentially, if you're amazing at any of these three roles, you will do well. Number one, if you're amazing at these roles, that's great. But also, part of being amazing at these roles is being able to fully harness the new technology, right? So if you're a master coder today and you don't ever get to the point where you figure out how to use AI to leverage your coding skills and do more, at some point you are going to hit an issue, right? Here's another way economists talk about this, which is there's the concept of the job, but the job is not actually the atomic unit of what happens in the workplace. The atomic unit of what happens in the workplace is the task. And so the way the economists think about it is a job is a bundle of tasks, and everybody wants to talk about job loss, but really what you want to look at is task loss, right? The tasks changing. The classic example of task changing was once upon a time executives never used typewriters or personal computers themselves, right? If you were a vice president of a company in 1970 or whatever, you did not have a typewriter or a computer on your desk typing things. You had a secretary who you dictated memos to, right? And then there was this change where emails started to show up. And what would happen was the job of the secretary then changed from sending out letters with stamps on them to sending or receiving emails with the other admins. And then the secretary would print out the email and bring it into the executive's office. The executive would read the email on paper, scrawl the reply, and give that message back to the secretary, who would go back and type it into the computer on his or her desk and send it as an email. Fast forward to today, none of that happens. Now executives just do all their own email. They still have secretaries or admins, but they're now doing different tasks. They're travel planning and orchestrating events and doing all these other things that great admins do. And then the task set, ironically, of the executive has expanded to do actually more of the clerical work themselves, actually sit there and type their memos, which again, 50 years ago, they never would have done. And so the executive job still exists. The secretary job still exists. But the tasks have changed. And I think that's a great example of what's going to happen in coding. The tasks are going to change. It's what's going to happen in product management. The tasks are going to change. Designer tasks are going to change. And so the job persists longer than the individual tasks. And then as the tasks change enough, that's when the jobs change. And so at the level of an individual, you want to think of, okay, I have this job. The job is a bundle of tasks. I need to be really good at making sure that I can swap the tasks out, right? I can really adapt, use the new technology, get really good at AI coding, for example. And then you want to add skills. I can also get really good at design. I can also get really good at product management because I've got this new tool. And so you want to pick up more and more scope as you do that. And then 10 years from now, is your job title coder or coder-designer-product manager, or is it just I build products? Or is it just I tell the AI how to build products? Whatever that job is called, who even knows what it's going to be. But it's going to be incredibly important because the people doing that job are going to be orchestrating the AI. And so that's the track that the best people are going to be on. And I think that that's the thing to lean hard into. I think people aren't fully grasping just specifically software engineering and how much that is changing. It's pretty clear we're going to be in a world soon where engineers are not actually writing code, which I think a year ago we would not have thought. And now it's just clearly where this is heading. It's like there's going to be this artisanal experience of sitting there writing code, which is so crazy, how much that job is going to change. Yeah. So again, here I go back, and again, I did part of maybe the history lesson, but I go back. Do you know that the original definition of the term calculator, do you know what that referred to? No. It referred to people, right? Right. So back before there were electronic calculators or computers or any of these things, the way that you would actually do computing, the way that you would do calculating, like the way that an insurance company would calculate actuarial tables or the military would calculate troop logistics formulas or whatever it was, the way that you would do it is you would actually have a room full of people. And by the way, these are big rooms, so you can have hundreds or thousands or tens of thousands of people doing this. And you would actually figure out, you have somebody at the head of the room who was responsible for whatever the mathematical equation was. And then they would parcel out the individual mathematical calculations to people sitting at desks who were doing them all by hand. Right. And that job title was, those people were calculators. Right. And so we've gone from a world in which you literally have people doing mathematical equations by hand. Then we got the first computers. The first computers, of course, didn't have programming languages, right? They only had machine code, right? So the first computers were programmed with ones and zeros. And so the task of the programmer became doing the ones and zeros. And then that became punch cards. And there are still people today whose job as a programmer was to build the punch cards. And then you got actually this big breakthrough, which was called assembly language, which was basically the way to do machine code but with some level of English added to it. Right. And that job title was those people were calculators. Right. And so we've gone from a world in which you literally have people doing mathematical equations by hand, by hand. Then we got the first computers. The first computers, of course, didn't have programming languages. Right. They only had machine code. Right. So the first computers were programmed with ones and zeros. And so the task of the programmer became do the ones and zeros. And then that became punch cards. And you can still, there's still people kicking today whose job as a programmer was to build the punch cards. And then you got actually this big breakthrough, which was called assembly language, which was the way to do machine code, but with some level of English added to it. And then the best programmers did assembly language. And then when I was coming up, it was higher-level languages like C that compiled into machine code. And that's what programmers did. And then I still remember when scripting languages, we developed JavaScript and Nescape and then Python took off and Perl and these other scripting languages. But scripting languages took off in the 2000s. There was this big fight in the technical community, which is, is scripting real programming or not. Right. Because it's cheating, right? Because real programmers write code that compiles to machine code, and real programmers do memory management themselves. And they do all this whole craft of writing C code. And these JavaScript or Pythed programmers are just doing this lightweight thing. It doesn't even really count as coding. And of course the answer is yes, it very much counted. And now most coding is done with the scripting languages, right? Which, you see my point, the scripting languages have abstracted away five layers of detail underneath that people used to do by hand, and they don't anymore. And then your point, AI coding is the next layer on that. AI coding actually abstracts away the process of actually writing the scripting code. Right. And so in one sense, this is a really big deal for all the obvious reasons. But on the other hand, it's like, okay, this is the next layer of the task redefinition under the job of programmer. Right. Now what's the job of the programmer? It's to your point, it's not necessarily to write the code by hand, but what it is now is all right. Now, if you talk to the world's best programmers today, what they'll tell you is, oh, my job is I'm sitting there and I'm orchestrating 10 code bots, right? Coding bots that are running in parallel. Right. And literally they sit there and they shift from browser to browser or terminal to terminal. And their day job now is arguing with AI bots to try to get them to write the right code. Right. And then they debug it and fix the problems and change the spec. And then they're like, oh, my God, I'm going to go back and do all these things. And so now the job of the programmer is to argue with the coding bots. But if you don't know how to write the code yourself, you don't know how to evaluate what the coding bots are giving you. Right. And so you asked about our 10-year-old is super into computers and super into programming. And what I'm telling you, he's using Claude and ChatGPT and co-pilot and all these things. And what I'm telling him is, look, and by the way, he loves vibe coding. He's on Replit all the time doing vibe coding, doing games. He's sitting there. It's hysterical, right? Because he's sitting there, it's a 10-year-old basically who spends two hours at dinner arguing with an AI for fun. Right. Right. But what I'm telling him is no, look, you need to still fully understand and learn how to write and understand code because the coding bots are giving you code. If it doesn't work, or if it's not doing what you expect, or it's not fast enough or whatever, you need to be able to understand the results of what the AI is giving you. Right. In the same way that somebody who's writing scripting language code does need to understand ultimately how the microprocessor works. And so again, it's this up-leveling of capability, where you actually want the depths to be able to go down and be able to understand what the thing is actually doing, even if you're not spending your day actually doing that by hand. And again, I look at that and I'm like, okay, now programmers are going to be 10 times or 100 times or 1000 times more productive than they used to be. Right. And that is overwhelmingly a good thing. The tasks are definitely changing. The nature of the job is changing. But are human beings going to be involved in the coding process and overseeing the AI and coding and all that? And the answer is, of course, absolutely, a hundred percent. Like no question. So you're in the camp of still learning to go still a valuable skill. Oh yeah, totally. Well, again, if you want to be one of these super, look, if you just want to put yourself on autopilot and I can't be bothered and I'm just going to have AI write the code and it's going to generate whatever it does and that's fine, and I'm going to be, if the goal is to be a mediocre coder, then just let the AI do it. It's fine. The AI is going to be perfectly good at generating infinite amounts of mediocre code. No problem. It's all good. If the goal is I want to be one of the best software people in the world, and I want to build new software products and technologies that really matter, then yeah, you 100% want, you still, but you want to go all the way down. You want your skill set to go all the way down to the assembly, to assembly and machine code. You want to understand every layer of the stack. You want to deeply understand what's happening at the level of the chip, right? And the network and so forth. By the way, you also really deeply want to understand how the AI itself works, right? Because if people understand how the AI works, they're clearly able to get more value out of it than somebody who doesn't understand how it works. Right. I mean, you're always more productive if you know how the machine works, right, when you use the machine. And so yeah, the super empowered individual on the other end of this that wants to do great things with the new technology, yes, you a hundred percent want to understand this thing all the way down the stack because you want to be able to understand what it's giving you. Right. And when something doesn't work or when something isn't right, you want to be able to really quickly understand why that is. By the way, again, this goes back to education. AI is your best friend at helping you learn all that. Right. Because it's like, oh, I need to understand, I don't know, this isn't fast enough. I need to figure out as a coder how to do a different approach in memory management or something. And you can be like, well, shit. I don't quite know how to do that. Okay, AI, let's spend 10 minutes. Teach me how to do this. Right. Teach me what this all means. Right. So all of a sudden you have this incredibly synergistic relationship with AI, where it's also helping you get better at the same time that it's doing a lot of work for you. By the way, I was going to say, I was a big Pearl programmer. I was an engineer for 10 years, and that was my language of choice. You, do you remember, I don't know when you were doing it, but do you remember that at least early on, did you ever hit this where C coders were looking down their nose at you being like, for sure. For sure. I don't quite know how to do that. Okay. AI, let's spend 10 minutes. Teach me how to do this. Right. Teach me what this all means. Right. So all of a sudden, you have this incredibly synergistic relationship with AI, where it's also helping you get better at the same time that it's doing a lot of work for you. By the way, I was going to say, I was a big Pearl, programmer. I was an engineer for 10 years, and that was my language of choice. Do you remember, I don't know when you were doing it, but do you remember, at least early on, did you ever hit this where C coders were looking down their nose at you, being like, for sure. For sure. It's like, this is so slow. It's not going to scale. What are you spending all your time on this thing? Yeah, exactly. And of course, again, it was this thing where they were sort of correct, which is at the beginning, it wasn't fast enough or whatever. By the end, they were definitely wrong. Right. Which is, it got much better, much faster. And it's, it swept the world. Most coding today happens in scripting languages. And then, by the way, the people along the way, the people who really understood the scripting languages and the people who understood all the lower-level systems, they were the ones who were able to actually make the scripting languages work really well. Right. And so that was a great example of this kind of adaptation. And then again, the result of that was a far higher number of people writing code with scripting languages than were ever writing code with lower-level languages. And I think this will just be a more dramatic version of that. I love that Perl was designed by a linguist. I don't know if you remember that part, and that's what made it so nice to code with. Well, that's funny because, of course, it was so notorious for being impossible to understand. So. How ironic. Yeah. This episode is brought to you by Datadog, now home to Epo, the leading experimentation and feature flagging platform. Product managers at the world's best companies use Datadog, the same platform their engineers rely on every day, to connect product insights to product issues like bugs, UX friction, and business impact. It starts with product analytics, where PMs can watch replays, review funnels, dive into retention, and explore their growth metrics. Where other tools stop, Datadog goes even further. 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Coming back to this kind of triad, the other element that I hear more and more of is the skill of taste and design and user experience. It feels like that's a very hard skill to learn. And to me, it tells me design is going to be much more valuable in the future. Yeah, that's right. And again, here, this is a great example. So again, the task level of design the perfect icon, right, is going to be like, all right, the AI is going to do that all day long. It's going to give you a thousand icon designs. It's going to be great. It's going to be fantastic. Whatever. And there will still, by the way, there will still be some level of human icon design or whatever. But AI is going to get really good at that. But what are we trying to do? Capital-D design of, all right, what is this thing for? And how is this going to function in the world of human beings? And what's going to, is this going to make people happy when they use it? Is this going to make people feel good about themselves? Is it going to fit into the rest of their life? Is it going to, I don't know, challenge them in the right way? All these kinds of higher-level questions that the great designers have always thought about. The job of designer, right, will involve much more of those higher-level, more important components. And then again, with AI doing a lot more of the underlying tasks. And so, one way to think about it is, I don't know, you think of the world's best designers, Jony Ive or whatever. And you could be like, wow, if I'm a designer today, if I'm a 25-year-old designer and I aspire to be Jony Ive in a decade, all of a sudden I have a new path that I can use to get there. Which is, because Jony Ive did everything he did without AI. Now a young designer can be like, wow, if I really harness AI, in a decade I'm going to be the best designer the world's ever seen. Because it's not just going to be me, it's going to be me plus being so super empowered by this technology to be able to do so much more. And then so much more of my time and attention is going to be able to be focused on these higher-level things that most designers never get to. And I think that's going to be another great example of that. So maybe what I'm hearing here is kind of this T-shaped strategy of, if you want to be successful in any three of these roles, be very, very, very good at that specific role: product management, engineering, design, and then get good enough at these other two roles. Well, I think that's great. I think that's really, really relevant. And then, Scott Adams unfortunately just passed away, which is a real tragedy. But I've referred for years to Scott Adams. He had this famous kind of career advice he would give people, which I think makes a lot of sense, which dovetails with what you're saying. He used to say, look, I could have been a pretty good cartoonist, or I could have been pretty good at business. But the fact that I was a cartoonist who understood business made me spectacularly great at making Dilbert. Right? Because even the world's best cartoonists who didn't understand business could have never written Dilbert. And then the world's best business people who didn't know how to do cartoons couldn't have done Dilbert. It took somebody who actually had both of those skills to be able to make Dilbert, which is one of the most successful cartoons in history. Right? And so, the way Scott always described it was that, from a career development standpoint, the additive effect of being good at two things is more than double. Right? The additive effect of being good at three things is more than triple. Right? Because you become a super relevant specialist in the combination of the domains. And you see this all over, I mean, you see this all over the economy. But I'll give you an example. Hollywood, just Hollywood as an example. There are a lot of writers who can't direct a movie, and they can be very successful writers. There are a lot of directors who can't write a movie. They can be very successful directors. But the superstars in the entertainment industry are the people who can write and direct. Right? And they have a term for those. They call those auteurs. Right? And those are the people who are the real creative forces that move the field. And so again, and by the way, Hollywood, it's just really funny. I've been spending a lot of time talking to Hollywood people about AI. Hollywood has the same Mexican standoff going right now that we described in tech, except in Hollywood, for example, for filmmaking, it's the director, the writer, and the actor. Hollywood, just Hollywood as an example, there are a lot of writers who can't direct a movie, and they can be very successful writers. There are a lot of directors who can't write a movie. They can be very successful directors. But the superstars in the entertainment industry are the people who can write and direct. Right? And they don't have a term for those. They call those auteurs. Right? And those are the people who are the real creative forces that move the field. And so, again, and by the way, Hollywood, it's just really funny. I've been spending a lot of time talking to Hollywood people about AI. Hollywood has the same Mexican standoff going right now that we described in tech, except in Hollywood, for example, for filmmaking, it's the director, it's the writer, and the actor. Right? Because the director is now thinking, wow, I don't need the writer anymore because the AI can write the script. And I don't need the actor anymore because I can have AI actors. The writer is saying, wow, I don't need the director because I can direct the movie, and the actor is saying, I don't need either one of these guys. I can have the AI direct the thing. I can have the AI write the thing. I'm just going to show up and do my performance. Right? And so it's the same kind of triangular configuration. And again, what's great about it is they're all correct. Right? Each person in each of those three fields is going to be able to expand laterally and pick up those additional skills. And then, as a consequence, you're going to have more people who can write and direct, or write and act, or direct and act, or do all three. And I think, to your point, your T-shaped thing, I think that's going to be true across the entire economy. And if you think about the T, if you think about the T configuration, it's like, yeah, the breadth, the breadth, the top of the T is like, how many individual domains are you familiar enough with to be able to use the AI tools to be able to do really good work? And then this part of the T is how deep can you go in at least one of those domains so that you really, really deeply know what you're doing. But if you're super deep on coding and you can use AI to do design and you can use AI to do product management, right? That's your T right there. And you're a triple threat at the top of the T, but with this level of technical grounding underneath that. And at that point, again, you're the super empowered individual. You're going to be able to just perform feats of magic. For example, in terms of designing and building your products, the people in my generation couldn't even have dreamed of. And so I think that this is a universal kind of theory that can apply across the entire economy. I'm going to invent a new framework right now. Okay. Forget the T framework. I'm picturing an F sideways or an E, where there's three, two or three, I don't know, downward parts. And so what I'm hearing is get good at least two. Yeah, that's right. I think that's right. Yeah. The combination. Yeah. My friend Larry Summers had a different version of the Scott Adams thing, which is he used to tell people, he said, the key for career planning is, he said, don't be fungible. Right. And that's, he's an economist, and so that was economics. And what that means essentially is don't be replaceable. And so don't be a cog. Right? So, and what that meant was don't just be one thing. Right. So if you're quote-unquote just a designer, just a product manager, just a coder, then in theory, you can be swapped in or out. But if you have this E or F laying on a side kind of thing, and if you have this combination of things, it's actually quite rare, then all of a sudden you're not fungible. Not only are you not fungible, you're actually massively important because you're one of the only people in the world who can actually do that combination of things. And yeah, your ability to become one of those people is titanically enhanced with AI as compared to anything we've ever seen before. This is so interesting because I've worked with people that are good at these two skills, and they were always called unicorns at the company. She can code and design. Oh my God. And what I'm hearing here is this is what you need to become. You need to become really good at at least two things there. I think you used the term smokestack or something, where it's like PM over here, engineer, design. And what I'm hearing here is you need to get good at at least two of these skills. The silos of these two roles are disappearing. That's right. That's right. And again, I can't overstress the following for anybody listening to this. It's like, this is amazing. There's never been a technology before where you can ask it, teach me how to do this thing. So I always feel like people spend too much. It's one of these things where so much focus on figuring out how to use a large language model is like, okay, what am I going to try to get it to do for me? Right. Which is, of course, very important. But the other side of it is, what can I get it to teach me how to do? Right. And it's just as good at that. Right. And so again, this level of latent superpower, people who really want to improve themselves and develop their career should be spending every spare hour, in my view, at this point, talking to an AI, being like, all right, train me up, tell me, super empower me. Tell me how to, train me, train me how to be, I'm a coder, train me how to be a product manager. It will happily do that. It knows exactly how to do that. Run me, make me problems, make me assignments, then evaluate my results. Right. And it will do that just as happily as it will do work, quote-unquote, for you. Two tricks I've heard along those lines. One is to watch the output, what the agent is doing and thinking as it's doing the work. So if you're not an engineer, just sit there and watch it think and make decisions. And it's almost become this layer on top of learning to code, is learning to see what the agent is doing and thinking, because that teaches you about architecture. And the other is a couple of podcast guests have mentioned this: when you get stuck and then you figure out how to unstick yourself, you ask it, what could I have done differently? What could I have said that would have avoided this error in the first place? Yeah, that's right. That's right. Yeah. Look, on that first one, and this again, that's what I'm doing with my 10-year-old. Yeah. Look, if you ask an AI, I don't know, write me this code, and then it comes back and it doesn't work right, if all you know is single function, I asked it and it gave me back something that's not good, well, what do you even do with that? Like, you don't understand why it gave you that result. Do you really understand it? Do you even understand what to tell it to try to get it to do something different? But to your point, if you actually watch what it's doing, and then you have the grounding, kind of that leg of your E or your F, if you have that grounding, then you can be like, oh, I see what it's doing. I see where it made the mistake. I see where it went sideways. And then you're all of a sudden able to intervene and able to say, no, no, that's not what I meant. Do this other thing. Right? And so, again, this is a big part of having the actual kind of synergistic relationship, is that you understand. And by the way, look, I mean, this is like everything I'm saying is, everything, But to your point, if you actually walk, if you actually watch what it's doing, and then you have the grounding, that leg of the E or your F. If you have that grounding, then you can be, oh, I see what it's doing. I see where it made the mistake. I see where it went sideways. And then you're all of a sudden able to intervene and able to say, no, no, that's not what I meant, to do this other thing. Right? And again, this is a big part of having the actual synergistic relationship, is that you understand. And by the way, look, this is everything I'm saying, everything that we're saying right now also is the same as if you're working with human beings, right? You and I are colleagues and I would ask you to do something, you'd come back with something completely different. I do need to understand what was happening in your head, right? In order to be able to get, you need to give you feedback, right? If I just tell you, oh, that's wrong, nothing happens. I need to actually understand, I need to have theory of mind, right? I need to understand what you were thinking in order to really give you the right feedback. And the great thing with AI is AI will happily sit there and explain all day long why it's doing what it's doing. It'll happily critique itself. You can do this, by the way. It's also a very fun thing where you can have one AI critique the other AI, right? Which is another thing, which is you have one AI write the code, you have another AI debug the code. And so you can actually use, you can play the AI off against each other and get them to argue with each other. And yeah, these are all the kinds of skills that are going to become, I think, incredibly valuable. I think people call those LLM councils. Yes. They're talking to each other. Yeah, that's right. That's right. I do feel like if I were, I have no design background. I've always wanted to design. I've always wanted to be a great designer. It feels like that's the hardest one to learn of all these three by just watching and talking, right? Because there's a lot of exposure hours, as folks have used this term, just how do you learn to be a great designer? That feels like that's going to be really hard and valuable. So my true confession is I've always wanted to be a cartoonist, but I have no art skills. But as we're talking, I'm like, it might be time. Your time has come, Mark. Yes. I want to pivot to founders, maybe your bread and butter. You spent a lot of time with the most cutting-edge AI-forward founders. I'm curious what you see them do, how you see them, some way they operate that's maybe blowing your mind about how the future of starting company looks, how the future of AI-forward companies look. Yeah. So this is a great, very topical topic that's all playing out in real time right now on the leading edge. So I think there's three layers of it, and see if this makes sense. I think there's three layers of it. I think layer one is they're thinking, all right, how does AI redefine the products themselves? Right. And this is the time-honored thing that happens with technology transitions. And this is kind of what a lot of venture capital is based on, which is, okay, there's a new technology that comes out and maybe it's the personal computer or the iPhone or the internet, or now it's AI. And it's like, all right, is this a new capability that gets added to existing products? Right. So all of a sudden you've got, I don't know, an existing software business, and now you've got your PC version of it, and now you've got your iPhone version of it, and you just keep on going and you add the new technology, kind of gets added into the mix. It's another ingredient to an existing formula. And of course, a lot of new technologies are like that, right? I don't know when flash storage came out or something, it didn't really redefine the software industry because people just went from using hard disk to using flash storage or something. But when the internet came out, basically old-school on-prem software, for the most part, not entirely, but a lot of it died and just got replaced by web software. Right. And so sometimes you get the kind of it's additive to an existing thing. Sometimes you get the actually it redefines an entire product category, redefines an industry, the actual company, in many cases, the companies themselves turn over. And so there's this question. An example, you just mentioned Nano Banana. A great example is there are these businesses like, just take Adobe. Photoshop has built a, whatever, 40-year franchise in image editing. Okay. Is AI a feature now that gets added to Photoshop to be able to do AI-based image editing? Or do you just stop editing images entirely because you're using Nano Banana and your images are just being generated and it's just easier to have AI generate a new image than it is to try to edit an old one. And so I think there are many areas of tech in which that question is being asked. And the answers, I think, will vary by domain, but obviously as a venture firm, we're betting hard on many of these categories being totally reinvented. And a lot of the best founders are trying to figure out how to do that. So that's AI changing the definition of the product. I think the next layer is actually a lot of what we've already talked about, which is AI changing the jobs. And so it's a lot of what we already talked about, but like, okay, if I'm a founder of a company and I've got, if I have room in my budget for a hundred coders, how do I get those coders to be super-empowered AI coders, not the kind of coders I used to have? And if they're super-empowered AI coders, then does that mean, do I still need the hundred? Maybe now I only need 10, or does that mean I still want a hundred, but now they're doing 10 times more? Right. And so, as you know, a lot of the best founders are working on that right now. And then I think the third shoe to drop hasn't quite dropped yet, but it's the big one, which is, all right, the basic idea of having a company, right, does that change? And again, here you've got this concept of the superpowered individual, which is like, okay, can you have entire companies where basically the founder does everything, right? Because what the founder is doing is overseeing an army of AI bots. And there's this holy grail in our industry that's been running for a long time, which is can you have the one-person billion-dollar outcome? And we've had a few of those over the years. Bitcoin is probably the most spectacular example, with Ethereum right behind it, which wasn't quite one person, but a very small team. You had Instagram and WhatsApp that had very big outcomes with very small teams. Every once in a while, you get one of these things where something hits and you just have a very small number of people associated with it. the founder does everything right because what the founder is doing is overseeing an army of AI bots. And there's this holy grail in our industry that's been running for a long time, which is, can you have the one-person billion-dollar outcome? And we've had a few of those over the years. Bitcoin is probably the most spectacular example, with Ethereum right behind it, which wasn't quite one person, but a very small team. You had Instagram and WhatsApp that had very big outcomes with very small teams. Every once in a while, you get one of these things where something hits and you just have a very small number of people associated with it. But that said, most software companies obviously end up with huge numbers of employees. And so I think the most leading founders are thinking, okay, how do I reconstitute the actual very definition or idea of having a company? And can you have a company that's literally just all AI? And if you're doing so, if you're doing anything in the real world, that's hard, but if you're doing software, that seems like it might be feasible in some cases. And then there's the ultimate example of that, which is, can you have autonomous AI economy stuff happening where you have AI bots in the blockchain or something that are basically out there functioning as a business and making money, and literally where the AI does all the work itself and just issues me dividends? And so maybe that's the final outlier result. We have a few founders who are chasing that kind of thing. So I would describe that as the ladder that the best founders are on. Super interesting. This whole idea of a one-person billion-dollar company, I think it depends on your definition of what this is. An outcome I could see. Having run my newsletter as one person with some contractors, there's so many little annoying things that I have to deal with, with support tickets and issues and bugs. It's hard for me to imagine actually a one-person billion-dollar company, even if AI is handling so much of your support, because there are just so many random edge cases that I'm just constantly filling out forms. And so I guess it depends on, do you have contractors? Does that count? What does it mean to be a one person? But I can't see that happening. Yeah. I mean, look, Bitcoin, Satoshi pulled it off. But the open source community, does that count? I don't know. Yeah. I guess it counts. Okay. Yeah, exactly. Right. So yeah, that, that, that, that, yeah. And I would say, I don't propose to have answers here, but more just the smartest people I know, or many of the smartest people I know, are thinking hard about this. Yeah. What do you think about moats? A big question constantly in AI, the fact that everything's changing. What's your guys' thesis on moats in AI? Is that even a thing? Do you care? My experience with really big technological transformations, and of course I live this directly with the internet and I saw this happen, is the really big technological transformations take a long time to play out. And there are all of these structural implications that just cascade out over time. And then there's this rush to judgment upfront where people say, oh, it's therefore obvious that XYZ. It's therefore obvious that this kind of company is going to be the company of the future, not that kind. It's obvious that this incumbent is going to be able to adapt and this other one isn't. It's obvious that there's economic opportunity in this kind of startup and not in these others. It's obvious that the moats are going to be in this area of the technology, but not in this other area. And what everybody does is they state those things with just an enormous amount of self-assurance, where they really sound like they have all the answers. And then what happens is these ideas saturate the media, right? Because the media naturally prizes definitive answers over open questions. Because when CNBC is booking guests, they want a guest who's going to come on and say, yes, this is the way it's going to be, X. Not, I think that's a really good question, and let's debate it from eight different angles. And what I found is, if you look back on those predictions a few years later, and you can do this, by the way, if you pull up coverage of the internet from 1993 through 1997, or even through 2005 or 2010, and you look at the kinds of confident statements people were making in the first 10 or 15 years, I would say almost all of them are wrong, generally quite badly wrong. And so I think the process, with massive technological change, is going to be a massive amount of technological change. It's going to be five or six layers of structural change that will play out over time. And again, we've talked about a lot of this, but the implications on what are the definitions of products, what are the definitions of companies, what are the definitions of jobs, what are the definitions of industries, how does this play out on the national level, how does this play out at the global level, how does this intersect with politics, how does this intersect with unions, how does this intersect with war, what's China going to do? And so it's just like, there are just a tremendous number of unknowns, a very, very large number of unknowns. And I think it's just really, really dangerous to prejudge these things. And so I'll just give it, I'll just run this as a thought experiment so you can see what you think on this. Do AI models, are AI models themselves defensible? Is there a moat on AI models? And on the one hand, you'd be like, wow, it certainly seems like there is, or should be, because if something takes billions of dollars to build, and you need this incredible critical mass of compute data, and there's only a certain number of engineers in the world that know how to do this, and they are getting paid like NBA stars, and then these companies have to deal with all these crazy political issues, and press issues, and reputational stuff, and regulatory and legal, all of that translates to okay, probably at the end of this, there's going to be two or three companies that are going to end up with 100%, I don't know, whatever, 50-50, or 30-30-30, or 90-10, one, or whatever its market share is, and then they're going to have whatever probability they have, and it's going to be a kind of classic oligopoly. Or maybe one company is going to win definitively. It'll be a monopoly. And by the way, those outcomes have happened in software many times before. And so maybe that will be the outcome. The other side of it is, if you had told me three years ago that in the Christmas of ChatGPT, within basically a year to year and a half, there would be five other American companies that would have basically exactly capable products, and then there would be another five companies out of China that would have exactly capable products, and then there would and then they're going to have whatever probability they have, and it's going to be a classic oligopoly, or maybe one company's going to win definitively. It'll be a monopoly in that. And by the way, those outcomes have happened in software many times before. And so maybe that will be the outcome. The other side of it is, if you had told me three years ago that in the Christmas of ChatGPT, within a year to year and a half, there would be five other American companies that would have exactly capable products, and then there would be another five companies out of China that would have exactly capable products, and then there would additionally be open source that was basically the same, I would have been like, wow, the thing that seemed like it was black magic all of a sudden has become commoditized really fast, which, by the way, is exactly what happened, right? Within a year of GPT-3 coming out, there were open source GPT-3s running on a fraction of the hardware that were available for free. And then there were five, now you've got, fully in the game, you've got Google, and you've got Anthropic, and you've got XAI, and you've got Meta, and you've got all these other companies, and then DeepSeek, and Kimmy, and all these other Chinese companies. And so even at the level of LLMs, or AI models, you can squint and make that argument either way. By the way, the same thing at the level of apps, right? One school of thought is the apps are not a thing, because the model is just going to do everything. But another way of looking at it is no, actually adapting the model into a domain involving human beings, where you need to actually have it fit for purpose to be able to function in the medical industry, or the legal industry, or whatever, or coding, no, you actually need, the application level is actually going to matter enormously. And maybe the LLMs can monetize, and maybe the value goes to the apps. And again, you can squint either way on that one. And I know very smart people who are on both sides of that argument. And so my honest answer on this is, I think we're in a process of discovery over time, which is, the way I think about this structurally is, it's a complex adaptive system. The technology itself provides one of the inputs. The legal and regulatory process is another input. Actual individual choices made by entrepreneurs matter a lot. The economics matter a lot. Availability of investor capital varies over time. That matters a lot. And this is a complex system. And so we actually don't know the outcomes on this yet. And we need to be open to surprises at the structural level of what happens. And of course, as a VC, this is very exciting, because it means we're doing this now. We should make bets along every one of these strategies and see how this plays out. And I'll just say, there may be one particularly brilliant, I don't know, edge fund manager or something who has this all figured out. But I guess I would say, if they exist, I haven't met them yet. So what I'm hearing here is don't over-obsess with moats at this point, because we have no idea what it'll end up being. And as much as it may feel like, okay, there's no way OpenAI will lose this lead, clearly we're seeing a lot of competition. GPT wrapper point is really great. It was such a derogatory term, I don't know, a year ago, just like, you're just a GPT wrapper. Now it's like the companies that are the biggest companies, fastest-growing companies in the world. Yeah, well, it's a little bit like, even just with, this has been, if three years ago was the holiday of ChatGPT, this last month or whatever has been the holiday of, particularly, Claude Code, right, for coding. But it's pretty amazing, because it's like, okay, there was Claude, which was obviously a great accomplishment. But then there's Claude Code, which is an app, right? It's a Claude wrapper, right? It's an agent harness. And then they did this amazing thing where they came out with, was it Co-worker? Co-work. Co-work. Co-work. And remember what they said of Co-work, which is that Claude Code wrote Co-work in a week? Yeah, a week and a half. Yep. A hundred percent. Right. Well, and there are two ways of looking at that, which is like, wow, that's really, I mean, obviously, that's really impressive that Claude Code was able to build Co-work in a week and a half. Yeah. That's great. That's amazing. The other way to look at it is Co-work was developed in a week and a half. How much complexity could there be? How much of a barrier to entry can there be in something that was developed in a week and a half? And then again, it's this push and this pull thing where it's like, wow, it's incredibly functional, incredibly valuable, and people are all over the world every day now like, wow, I can't believe what I can do with this. It's like the most magical product ever. But at the same time, it took a week and a half. Right. And so every other model company, I'm sure you'd have to expect, is sitting there being like, okay, obviously we need to build an agent harness. And then obviously we need to build a Co-work thing for regular people. And obviously, I don't, I'm not even saying I know anything, but obviously they're all going to do that. Right. And so how defensible is that? And in six months, and we've seen this happen before, is Claude Code going to get lapped the same way that GitHub Copilot got lapped? The history in the last three years has been everything that looks like the fundamental breakthrough gets basically replicated in a lab very quickly. Many of the smartest people I know in the field, when I really talk to them, get a couple of drinks in them, they're like, yeah, one theory is there really aren't any secrets among the big labs. The big labs all have the same information, and they all have the same knowledge, and they kind of lap each other on a regular basis. But there's not a lot of proprietary anything at this point. And then, again, evidence of that is DeepSeek came out of left field and basically was a re-implementation of a lot of the ideas of the American big labs and had some original ideas of its own. But wow, it wasn't that hard for some hedge fund in China to do it. And so how much defensibility is there? But on the other side of it, you've got, wow, these big labs are now paying individual engineers like they're rock stars. And they're incredibly bright and creative people. And maybe there's a dozen nascent ideas in any one of these labs that it's actually going to be a huge breakthrough And then, and then, again, evidence of that is, DeepSeek came out of left field and was a re-implementation of a lot of the ideas of the American big labs and had some original ideas of its own. But wow, it wasn't that hard for some hedge fund in China to do it. And so how much defensibility is there? But on the other side of it, you've got, wow, these big labs are now paying individual engineers like they're rock stars. And they're incredibly bright and creative people. And maybe there's a dozen nascent ideas in any one of these labs that it's actually going to be a huge breakthrough that's going to be hard to replicate. And so again, it's just, I think we just need to, I don't know, my views, my view of myself, I need to put a big discount on my forecasting ability on this one. For me, it's much less interesting to try to say, okay, as a consequence, industry structure in five years is going to be X, the big winner in the category is going to be company Y, the big product killer app is going to be Z. I don't think I can predict that. I think a much, much better use of my time is being very flexible and adaptable at a time like this. So with all this in mind, do you feel like there's something you're paying attention to more to help you decide, okay, this is where we want to place our bet? Or is the answer essentially the strategy you guys have, which is place a lot of bets? You guys raised the largest fund in history. Is that the way you win in this world? Yeah. So for us, we obviously have a very, very deliberate strategy. One way to think about this: use the Peter Thiel formulation. If you remember the Peter Thiel formulation, he said there's a two by two: there's optimism and pessimism, and then there's determinate and indeterminate, right? And so he always argued that Silicon Valley is characterized by too much what he calls indeterminate optimism, right? And what he always described, what he meant by that is basically, I think the way he would describe it is an indeterminate optimist who thinks the world is going to be better but can't explain why, right? Some combination of things is going to happen to make the world better, even if we don't know what those things are. And I think he, at least historically, would say that's basically, that risks at least being wishful thinking or delusional thinking. And what the world needs more is determinate optimists, which are people who are like, no, the world is going to be better because I'm going to do this specific thing, right? And he would classify, for example, Elon. He would maybe say VCs are indeterminate optimists, and then he would say Elon is the determinate optimist where it's like, no, I'm going to build the electric car, I'm going to do solar, and then I'm going to do Mars, right? And these very concrete things. And I think there's a lot to Peter's framework, but the way I would describe it is, I think maybe if you and I disagree, a part of that would be I think the indeterminate optimism is a stronger phenomenon than at least I think he's historically represented it as, and I would put myself firmly in the indeterminate optimist category. And that's the strategy that we have at A16Z. And the reason for that is, hopefully it's not so much wishful thinking. It's more, no, what the indeterminate optimism of venture capital or the indeterminate optimism of A16Z or Silicon Valley is actually very specific, which is there are these extremely bright and capable people like Elon and many others who are founders and product creators, right? And each of those individual people is a determinate optimist. Each of them individually has a very strong view of what they're going to do. David Plylari But the great virtue of the capitalist system, the great virtue of the American economy, the great virtue of Silicon Valley, is we don't just have one of those, we don't just have 10 of those, we have a hundred and a thousand and then 10,000 of those. And the way to optimize the outcome is to have as many of those as possible, be as good as possible, run as hard as possible. And the nature of the future is we just don't know all the answers, and that's okay. And the right way to deal with that is to run as many experiments as possible and have as many smart people try to do as many interesting things as possible. And so, yeah, I would put myself firmly on the side of the indeterminate optimist. David Plylari I mean, I'm wondering if the answer to the question of what you look for now more and more is this determinate, optimistic founder that has this massive ambition and is actually working on achieving it. David Plylari Yeah, no, that's right. That's right. I mean, look, the founders need to be determinate optimists. They need to have a very specific plan. David Plylari Yeah, no. And yeah, look, the critique always, the critique from the founders is, oh, you VCs have it easy, because you don't actually have to commit, right? You don't actually have to make the bed you lay in. You can place multiple bets, you can have a portfolio. You should have a lot more sympathy for us as founders because we only get to make the one bet. And there's truth to that. The counterargument on that is the founders get to run their companies. We don't. So we don't get to put our hand on the steering wheel. David Plylari And so the great virtue of being a determinate optimist is you actually get to single-mindedly execute against that goal. And look, in the long run, who does history remember? History remembers Henry Ford, right? Not whatever the seed investor was who seeded Ford Motor Company and 10 other car companies that failed, right? And so the determinate optimist is the founder and the company builder and the engineer. These are the people who actually do this thing and deserve 99.9999% of the credit. But having said that, I do think there is a role for having some indeterminate optimists in the background helping along the way and helping keep the whole cycle going. David Plylari Do you think about AGI in shifting your investment thesis? As we approach AGI and hit AGI, as an investor, how do you think about your investment thesis changing? David Plylari Yeah. So I've always had a little bit of an issue. I've always struggled with the concept of AGI because, well, there's defined terms, which is where I struggle with it. There's the prosaic definition of AGI, and then there's the cosmic definition. And the way I would describe it is, well, let's start with the cosmic one. So the cosmic one is basically it's the singularity, right? And so AGI is the moment where you enter the singularity, which is to say where the world fundamentally changes. And the rules of the old world are gone. We're now operating in a new domain. And then the full definition of singularity is it's a world in David Plylari: Yeah. So I've always had a little bit of an issue. I've always struggled with the concept of AGI because, at least, there's defined terms, which is where I struggle with it. There's the prosaic definition of AGI, and then there's the, I don't know, cosmic definition. And the way I would describe it is, well, let's start with the cosmic one. So the cosmic one is the singularity, right? And so AGI is the moment where you enter the singularity, which is to say where the world fundamentally changes. And the rules of the old world are gone. We're now operating in a new domain. And then the full definition of singularity is a world in which human judgment is no longer really relevant because you get this self-improvement loop. The AI is improving itself, and it's racing, so-called takeoff scenarios. You can see this takeoff thing where the AI is improving itself, and the machines are making decisions so much faster than people, and people are just sitting there watching the machine do its thing. And I described, I don't really think that's, I don't think we live in that world. Whether you could call that utopian or dystopian, I don't think we're lucky or unlucky enough to live in that world. We could debate that. We can talk about that more. But the prosaic definition of AGI that at least I think the industry participants have converged on, and tell me if you agree with this, is when the AI could do every economically relevant task as good as a person. The way the co-founder of Anthropic put it is a basket of the most valuable economic tasks. So 10, 15, not every single economically valuable task. Okay. Got it. Yeah. So it's maybe even a slightly reduced definition. And by the way, you are clearly getting close to that, if we're not already there. And so on that one, I feel like the cosmic one overstates what's going to happen, and then I feel like the AGI definition that you just gave understates what's going to happen. It's almost too reductionist. And the reason for that is, I don't think there's any reason to assume that human skill level is the cap on anything, right? And so the way we say that is AGI always is, the definition you gave, the definition I gave, it's always relative in comparison to a human worker, right? And it's like, I don't know, human skill level caps out at a certain point, but that's because of the inherent biological limitations of the human organism, right? I'll give you an example: human IQ, what they call fluid intelligence or the sort of G factor of fluid intelligence. IQ, I think, tops out in humans as a species right around 160, right? At 160, it's Einstein level, Einstein, Feynman. In terms of IQ. In terms of IQ. You just top out at 160. The 160 IQ people are the ones who come up with new physics. There's only a small handful of those. Generally speaking, when we run into somebody in the world who's incredibly smart, who's a best-selling author, or one of the world's best research scientists, or one of the world's best doctors, whatever, it would be probably 140. It's the IQ that you're looking for there. If you're looking for a really good lawyer, it's probably 130. If you're looking for a really good line manager in a business, it's probably 110. If you're looking for an accountant, a small business accountant who's good at doing the books for small businesses, it's probably 105, right? And so the scope of impressive human ability, the ability of the human organism to do intellectually impressive things, is sort of that 110 to 160 spectrum. And good news is there's a lot of those people running around, but there's not that many at 140, 150, 160. But that's just the limitations of what can fit in here, right? And there's no theoretical limit on where this goes if you release the limitations of human biology, right? And so can you have a, you already have people running these experiments to do human-equivalent IQ for existing AM models. And by the way, existing AM models right now are testing around the 130, 140 level, which means they're going to get to the 160 level. And they're arguably, on the math side, starting to get to the 160 level now. But I think we're going to have AM models relatively quickly that are going to be like 160, 180, 200, 250, 300. By the way, I think that's great, right? I feel as great about that as I do about the fact that we occasionally get an Einstein, right? Would the world be better off or worse off with more or fewer Einsteins? And the answer is, of course, the world would be better off with more Einsteins. And of course, the world would be better off with machines that have more IQ like Einstein or greater than Einstein. But I think the IQ of the machines is going to exceed that in humans. I think that's really good. And then the performance, again, it goes back to the AI coding thing that's happening. The performance against task is going to get better also. I think this is where Linus Storvalds in particular is like, yeah, okay, this thing is starting to generate better code than I can. Okay, so now we're going to have AI coders that are actually better coders than the best human coders. I think that's great. I think we're going to have AI doctors that are better than the best human doctors. I think we're going to have AI lawyers that are better than the best human lawyers, which actually is going to be very interesting to see, which I think is also great. And so I don't think there's a, I think we're used to living in a world where we just don't understand how good good can get because we've been capped by our own biology. And we're going to get to experience what it's like when you have the capability at your fingertips that's actually better than human in these domains. And so you see what I'm saying, which is I think this idea of human equivalent is just going to be like a footnote. It's like, oh yeah, that was just on Tuesday in 2026 when they hit that. And it didn't matter because the next question was, okay, what do we get to do in a world in which we actually have machines that are better than that, right? And so I think this is going to be much more of an exploratory process for actually exceeding human capability than it's going to be any sort of particular singularity moment or whatever that happens, that just happens to coincide with the human threshold. 200 IQ. That frame of reference is such a mind-expanding way to think about it, just how fast and how smart these things are going to get, and quickly. Well, I don't know if you have this experience. I have this experience all the time. Well, two experiences I have all the time. One is, I'm just like, I know I ought to be able to do this, but I just can't. It's going to take too long. I want to write this thing, or I want to have this theory on this thing, or I have a plan or whatever. And it's just like, fuck, I don't have the eight hours or, by the way, the eight weeks or the eight years, right? And I just don't know enough yet. And I'm just like, I can't do the math in my head, and my memory isn't perfect, and I can't remember. And I read, if you have this, you get interested in something, you read 10 books, and then you're like, Well, two experiences I have all the time. One is, I'm just, I know I ought to be able to do this, but I just can't. It's going to take too long. I want to write this thing, or I want to whatever. I want to have this theory on this thing, or I have a plan, or whatever. And it's just, fuck, I don't have the eight hours or, by the way, the eight weeks or the eight years. Right. And I just don't know enough yet. And I'm just, I can't do the math in my head, and my memory isn't perfect. And I can't remember. And I read, if you get interested in something, you read 10 books, and then you're like, shit, I forgot almost everything that I just read. I wish I could retain it all, but I can't. It's just, I live in this state of endless frustration. I was just, if I could just be smarter than I was, I'd be so much better at what I do, but I'm not. So there's that. And I don't know how often you have this, but I have this on a regular basis. It's just, because of what we do, I know a bunch of people who I know for fucking sure are smarter than I am. And I know it because when I talk to them, I just find myself, at a certain point, for the first half of the conversation, I'm just taking notes the entire time. For the second half of the conversation, I'm just like, fuck, fuck me. This person is just smarter than I am, and they're just outthinking me, and they're going to keep outthinking me, and I just can't. And I'm just like, all right, God damn it. I got to go home, and I got to have a drink, because I'm just not whatever that is. I'm not that. And so we're just so used to having those limitations that the idea of having machines that work for us that don't have those limitations, I just think that's much more exciting than people are giving you credit for. Oh man. I could talk to you for hours, Mark. I'm thinking, to close out the conversation, I want to ask about your media diet and your product diet. You just talked about books, reading 10 books. I think you famously read constantly. I saw an interview with you where you were just like, AirPods changed my life. I'm just listening to audiobooks now all the time. So in terms of media diet, what are you reading? What are you paying attention to these days in terms of, I don't know, podcasts, newsletters, blogs, things like that? And then any books in particular? Yeah. Yeah. So what I read is basically, I mean, I would say I read three categories of things. So in terms of general media, it's basically, I always describe it as I have almost a perfect barbell strategy, which is I read X and I read old books. Right. So it's basically either up to the minute, what's happening right now, or it's a book that was written 50 years ago that has stood the test of time. And then, presumably, there's something timeless in it. And then everything in the middle I'm always much more skeptical about. In particular, it's what I already said, which is I think if you go back and read old newspapers, nobody ever does this. It's actually really funny. Nobody ever does this. There's no market for it. But if you go back and read old newspapers, and by the way, you can do this. Just read last week's newspaper, right? I'd say we're taping on Friday, so read last Friday's newspaper. Right. And just go back and read it and be like, oh my God, none of this happened. Not that none of what they predicted played out the way that they said it would. None of this turned out to actually be that relevant or correct. They didn't understand. By the way, they had no view of what was going to happen this week. They couldn't know. And so they were making predictions and forecasts and so forth based on not having information. But it's just like, wow, none of this happened. I wish I had never read this. Oh my God. And then it's the same thing with magazines. Go back and read old magazines, and just the endless numbers of predictions that they make. Yeah. And the problem with newspapers, at least, is they're going day to day. The thing with magazines is it's like a week or month, a long cycle. And so by the time an article even hits publication, it's often out of date. So I just have a big problem with everything in the middle. And so it's either of the moment or timeless. But then, yeah, you mentioned newsletters. I mean, the other thing, and this is maybe obvious, but I think it's probably still underrated, is the actual practitioners in the field who are actually creating content, I think, is still dramatically underrated. And I think this is a huge part of the Substack phenomenon and the newsletter phenomenon and the podcast phenomenon, is direct exposure to the people who are actually principals in the field, who actually know what they're talking about, is probably still dramatically underrated. And I think, again, the reason for that is we're used to being in this mass media kind of culture in which basically everything is mediated, right? Everything got filtered through TV interviews or newspaper interviews or magazine interviews. And obviously now, more and more, it's just, no, you actually want smart people who are actually working on something explaining themselves. And then you have new kinds of intermediation, like podcasts, that open that up for people to make that possible. And so, yeah, domain practitioners are really great. I mean, just to state the obvious, in AI, it's obviously your stuff, but also Lex. The fact that Lex Fridman can have the world's leading, or whoever, and any of you guys, there's a small handful of you guys who have access to these people, you can have the world's leading experts in the domain actually show up. And by the way, the critique always is people talk their book. Like, if I'm running a startup or whatever, I'm just selling. And there's always a little bit of that. But my experience is people love to talk about what they do, and they fundamentally want to express what they do, and they want to explain it, and they want people to understand it. And everybody enjoys that. And they get to contribute to human knowledge by doing that, and they get ego gratification by doing that. And so I think there's just tremendous amounts of alpha in listening to the world's leading experts in the space who actually just show up and talk about what they're doing. And of course, the world is awash in that today in a way that it wasn't as recently as 10 years ago. So yeah, I do as much of that as I can too. And there's also just this culture in tech, Silicon Valley in particular, of sharing, of not trying to keep these secrets. Everyone on LinkedIn is always like, how is this free? It's just the way it works. Yeah. Somebody said Silicon Valley is a company town, but the company is Silicon Valley. Right. And again, at the level of, this goes, again, there's one of these great N equals one. At the level of N equals one is somebody, and I've run startups before, I've run companies before. At the level of N equals one of running a company, about what they're doing. And of course, the world is awash in that today in a way that it wasn't as recently as 10 years ago. So I do as much of that as I can too. And there's also just this culture in tech, Silicon Valley in particular, of sharing, of not trying to keep these secrets. Everyone on LinkedIn is always like, how is this free? It's just the way it works. Yeah. Somebody said Silicon Valley is a company town, but the company is Silicon Valley. Right. And again, at the level of this goes, there's one of these great N equals one, at the level of N equals one, is somebody, and I've run startups before, I've run companies before, at the level of N equals one of running a company, that's just a giant pain in the fucking butt. Cause your secrets are walking out the door and your employees are walking out the door and the whole thing sucks. But the other side of it is you also benefit from that. Right. Cause you get to hire people with all these skills and experiences. Right. And you're in this ecosystem that acts right and channels talent and skill and knowledge and people into the new fields. And so there's the push and pull of that at the level of just being an individual CEO. At the level of just being in the ecosystem, to your point, yeah, it's an absolutely magical phenomenon. And by the way, one of the, for all the issues in Silicon Valley, I think AI, I did the count once, I think AI is the ninth major technology platform in the history of Silicon Valley. Right. Silicon Valley is still called Silicon Valley. We haven't made silicon here in decades. Right. This is called Silicon Valley because they used to make chips, right. They used to have the actual fabs in Silicon Valley, and then they designed them and they made the chips. And that was wave one starting in the 19th, that was actually more or less wave three or whatever, but that was when the area was named, in the 1950s, but now we're on wave nine. Right. And the company town phenomenon, where the company is the industry, you get the indeterminate optimism. Nobody had to sit and plan and say, okay, in the 1990s Silicon Valley is going to do the internet, in the 2000s they're going to do smartphones, in the 2010s they're going to do the cloud, and in the 2020s they're going to do AI. It just, right, the indeterminate optimism of ecosystem, the flexibility of the ecosystem, meant that Silicon Valley could morph into all these categories. And again, maybe a testimony to indeterminate optimism. This reminds me of the meme of how we're all just wrappers over sand. Everything we're building is just wrapper, wrapper, wrapper, wrapper. The wrapper thing is hysterical. Yeah. Yeah. I'm a software company and I'm a chip wrapper, right? Yeah, I'm a business application, I'm a database wrapper. Yeah, exactly. I'm a sand wrapper. Yeah, you and I, we're all now sand wrappers. Perfect. Okay. One more question along the media diet. I asked your partner, Ben Horowitz, what to talk to you about, the Z in a16z, if people don't know him. And he said that you're really into movies these days. And so, any movies you're really into these days, any movies you've absolutely loved recently? Yeah. So the movie that blew my socks off last year, which I think is the best movie of the decade for sure, and maybe of the last 15 years, is this movie. Unfortunately, it's one of these things, not a lot of people have seen it, but I would highly encourage it. It's called Eddington. Not heard of it. Have you not heard of it? Okay. So Eddington, you're going to really enjoy it. So I won't spoil too much of it. At the surface level, the following spoils nothing. At the surface level, it's set in a small town in New Mexico called Eddington, which is a small town of about 600 people. And there's a sheriff who's played by Joaquin Phoenix, who's an old crusty, basically right winger. And then there's a mayor, played by Pedro Pascal, who's basically a young hip progressive. And then the movie starts, I think, in March of 2020. So it starts when COVID first hits. And then as it plays out over the next few months, it intersects and extends into the summer of 2020. So, the George Floyd moment, and then the protests and riots and everything. So it starts with the convergence of COVID and then all the BLM stuff. And then there's a third element to it, which is there's a company, which is basically a loosely disguised version of Meta, if you read the backstory of it, which is building an AI data center on the outskirts of town. So they pull that in as a thing that looms larger and larger over time. And then the thing it really is great at is it really shows, this is a small town in New Mexico, and so everybody in the town gets fully wrapped up in all the COVID stuff and they get fully wrapped up in all the BLM stuff and they get fully wrapped up in all the tech anxiety stuff, but they're all experiencing it basically through the internet, right? Which is what actually happened. Right. So the reason I love the movie so much is, one, it's the first movie that directly grapples with 2020, with what happened in 2020, and that fully engages and grapples with all the dynamics that were playing out in the country. But the other reason is, it's the first movie that does a really good job of showing what it was like, especially in that era, to live in a world in which there were things happening in the real world and people were experiencing events online in a way that was very central in their lives. Right. And so it does a really good job of pulling in smartphones and social media in a way that movies really, really, really struggle with. And then the whole thing comes together in an incredibly entertaining way. And so I wouldn't even say, I won't even say I completely agree with the movie or whatever. And I think the director of the movie and I would probably disagree about a lot, but he really tries hard to grapple with what it is actually like to live like a human being in the 2020s in America in a way that I think many other filmmakers, who are very talented, have just been very scared of touching. And this guy, for some reason, he's just like, yeah, I'm just going to find all the third rails and I'm just going to fucking grab them. I can see why that's your favorite movie. It's great. It's great. It's great. Everybody should see it. Oh man. Okay. Final question. I want to ask about your product diet. Are there any products you use that may be less known that you love, that you want to recommend? You can mention products you're investors in if you use them constantly. I mean, we have so many that it's really hard to, I always feel it's being in the 2020s in America in a way that I think many other filmmakers who are very talented have just been very scared of touching. And this guy, for some reason, he's just like, yeah, I'm just going to find all the third rails and I'm just going to fucking grab them. I can see why that's your favorite movie. It's great. It's great. It's great. Everybody should see it. Oh man. Okay. Final question. I want to ask about your product diet. Are there any products you use that maybe are less known that you love, that you want to recommend? You can mention products your investors in if you use them constantly. I mean, we have so many that it's really hard to, I always feel it's like, who's your favorite children? So it's really hard to specify ones. But I'll talk about a few. Or just an observation. So one is my 10-year-old. I have my 10-year-old. My 10-year-old right now is a hundred percent obsessed with Replit. And, by the way, it was not from me. Do you have kids? I do. I have one two-and-a-half-year-old. Two and a half. Okay. So you haven't run into what I'm running into now, which is whatever it is you do is not cool. Right? At two and a half, whatever daddy does is the coolest thing in the fucking world. I can tell you by the time he's 10, whatever you do is deeply uncool. Right. And I'm highly aware of that. And so if I mention, oh yeah, we work on XYZ, he's like, okay. But when he discovers something, then it's cool. Or when his friends tell him about it, it's cool. And so he, through no interference on my part, discovered Replit about three months ago and discovered vibe coding and is completely obsessed with vibe coding games and all kinds of things. He'll sit and do it for hours. And so I'm seeing that phenomenon play out, which is super fun. That's one. Two is I am just completely in love with all the AI voice stuff. I think it's just absolutely amazing, hysterical. My favorite party trick at dinner parties now is to pull out Grock with Bad Rudy, which, as you've seen, it's the foul-mouthed raccoon avatar in the Grock app. So I think that's super fun. We had this company Sesame that, they went viral last year for these just incredibly intimate, emotional voice experiences. So I think the voice stuff is fantastic. I'm also super fascinated by all the voice input stuff. And so, Limitless, the company recently sold, but all the, I think, the pendants, the wearables, all that stuff is going to be big. The Meta glasses. I think there's going to be a whole wearables revolution here. I love the voice input stuff. I have this app on my phone now called Whisperflow, which is voice transcription, which works staggeringly well. It's incredible. It's a voice transcription function, but you can actually talk to the AM model while you're doing voice transcription. So it understands when you're telling it, no, no, I want bullet points over there and I want this and that. And it understands that you're not telling it to type in the words, I want bullet points. It actually understands that you want bullet points. And so that's a great example of a super useful thing. And so I think the voice mode stuff is going to be really great. Subscribers to my newsletter get a year free of Replit and Whisperflow. So there we go. What's the most memorable thing your son built with Replit? Oh, well, so he's gotten super into Star Trek. And so, so far, he's writing Star Trek simulators. So all the, by Next Generation, they actually had— Next Generation. Okay, I was going to ask which. Well, he, we actually like them all. We watched the new Starfleet Academy last night, which actually is quite good. But we watched the original. We watched them all. But it was in Next Generation where they actually developed an actual design language for the computers. Because if you watch the original series, they just had basically knobs with lights and they didn't really, they were just fucking around on set and trying to pretend they were doing it. But by Next Generation, they actually had designed a UI design language. And so one of the fun things you can do with vibe coding is you can say, give me a Star Trek Next Generation user interface for whatever, this, that, or whatever, and it actually uses the, they call it the, sorry to nerd out, they call it LCARS design language. And it'll actually build you Star Trek Next Generation bridge consoles using that design language, but with your choice of a Star Trek game, for example. And so he's going crazy for that kind of thing. Mark Leary, that sounds extremely delightful. You guys should open source release that. Mark, like I said, I could talk to you for hours. Well, you've got things to do. Anything you want to leave listeners with before we wrap up? Anything you want to double down on or just leave listeners with? Mark Leary, yeah. So a couple of things. One is, we got super lucky last week, Paki McCormick wrote the best piece ever written about us, actually, which he released. And so it's the best explanation of what we do and how we think. And so I would definitely recommend that. And then we're putting a lot, we have a great team of folks now. We're putting a lot of effort ourselves into video and content. And so I definitely recommend our YouTube channel, which I think has a lot of great stuff and is going to be very exciting in the next year. Awesome. We'll link to that. I think it's just youtube.com slash A16Z, something like that. And you guys have great stuff. Mark, thank you so much for being here. Mark Leary, awesome. Thank you for having me. I really appreciate it. Mark Leary, bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at LennysPodcast.com. See you in the next episode. Even what you, you even understand what to tell it to try to get it to do something different. But to your point, like if you actually walk, if you actually watch what it's doing, um, and, and, and, and then, and then you, you, you have the grounding, you know, kind of that leg of the E or your F. Um, if you have that grounding, then you can be like, oh, I see what it's doing. I see where it made the mistake. I see where it went sideways. And then you're all of a sudden able to intervene and able to say, no, no, that's not what I meant to do this other thing. Right? And so, and again, this is, this, this, this is a big part of having, having the actual kind of, you know, synergistic relationship, um, is that you understand. And by the way, look, I mean, this is like, everything I'm saying is, you know, everything, everything that we're saying right now also is the same as if you're working with human beings, right? Like, you know, you and I are colleagues and I, you know, would ask you to do something, you'd come back with something completely different. Like I, I do need to understand what was happening in your head, right? In order to, in order to be able to get, you need to give you feedback, right? If I just tell you, oh, that's wrong. It doesn't like, nothing happens. I need to actually understand, I need to have theory of mind, right? I need to understand what you were thinking in order to really give you the right feedback. Um, and so, and, and, you know, and again, the great thing with AI is AI will happily sit there and explain all day long why it's doing what it's doing. It'll, you know, it'll happily critique itself. You know, you can do this, by the way, it's also a very fun thing where you can have, have one AI critique the other AI, right? Um, which is another thing, which is like, you have one AI write the code, you have another AI debug the code. Um, and so you can actually use, you can play the AI off against each other and get them to argue with each other. Um, and yeah, these are all, these are all the kinds of skills that are going to become, I think, incredibly valuable. I think people call those LLM councils. Yes. They're talking to each other. Yeah, that's right. That's right. I do feel like if I were like, I'm, I have no design background. I've always wanted to design. I would, I've always wanted to be a great designer. Uh, it feels like that's the hardest one to learn of all these three by just watching and talking, right? Cause there's a lot of exposure hours as, as folks have used this term, just like, how do you learn to be a great designer? That feels like that's going to be really hard and valuable. So my, my true confession is I've always kind of wanted to be a cartoonist, but I have no like art skills, but as we're talking, I'm like, it might be time. Your time has come, Mark. Yes. I want to pivot to founders. You're maybe your bread and butter. You spent a lot of time with the most cutting edge AI forward founders. I'm curious what you see them do, how you see them, some way they operate, that's maybe blowing your mind about how the future of starting company looks, how the future of AI forward companies look. Yeah. So this is a great, it's very, you know, topical topic. That's all playing out in real time right now on the leading edge. So I, I think there's like three layers of it and see if, see if this makes sense. I think there's like three layers of it. I think layer one is they're thinking, all right, how, how does AI redefine the products themselves? Right. And this is kind of the, this is kind of the time honored, you know, kind of thing that happens with technology transitions. And this is kind of what, you know, a lot of venture capital is based on, which is, you know, okay, there's a new technology that comes out and, you know, maybe it's the personal computer or the iPhone or the internet, or now it's AI. And it's like, all right, is this a new capability that gets added to existing products? Right. So all of a sudden you've got, I don't know, an existing, you know, software business, and now you've got your, you know, PC version of it, and now you've got your iPhone version of it, and you just kind of keep on going and, you know, you kind of add the, the new technology kind of gets kind of added into the mix. You know, it's kind of another ingredient to an existing formula. And of course, you know, a lot of new technologies are like that, right. You know, I don't know when, I don't know when flash, when flash storage came out or something, you know, it didn't really, you didn't really redefine the software industry because people just went from using, you know, hard disk using flash storage or something. But when the internet came out, like basically old school on-prem software for the most part, you know, not entirely, but like a lot of it died and just got replaced by like web software. Right. And so, so sometimes you get the kind of, it's additive to an existing thing. Sometimes you get the, actually it redefines an entire product category, redefines an industry, the actual company, you know, in many cases, the companies themselves turn over. And so, so, so, you know, so there's sort of this question and like, you know, an example, you just mentioned Nano Banana. So like a great example is there, you know, there are these businesses like, you know, just take Adobe, like, you know, Photoshop is built a, whatever, 40 year franchise in image editing. Okay. Is AI a sort of a feature now that gets added to Photoshop to be able to do AI based image editing? Or, you know, do you just like stop editing images entirely because you're using Nano Banana and you're all images are just being generated and it's just easier to just have AI generate a new image than it is to try to edit an old one. And so I think, you know, there's many areas of tech in which that question is being asked. And, you know, the answers I think will vary by domain, but, you know, obviously as a venture firm, we're betting hard on many of these categories being, being totally reinvented. And a lot of the, a lot of the best founders are trying to figure out how to do that. So that, so that's kind of AI, you know, changing the definition of the product. I think the next layer is actually a lot of what we've already talked about, which is AI changing the jobs. And so it's, you know, a lot of what we already talked about, but like, okay, if I'm a founder of a company and I've got, you know, if I have, you know, room in my budget for a hundred coders, you know, how do I get those coders to be super empowered AI coders? Not, you know, not the kind of coders I used to have. And if they're super empowered AI coders, then does that mean, you know, do I still need the hundred? Maybe now I only need 10, 10, or does that mean I still want a hundred, but now they're doing 10 times more? Right. And so, you know, as you know, like a lot of the best founders are working on that right now. And then I think the third shoe to drop hasn't quite dropped yet, but it's, it's, you know, it's kind of the big one, which is like, all right, like the, the, the, the basic idea of having a company, right. You know, does that change? And, and again, here, you've got this concept of the super powered individual, which is like, okay. You know, can you have entire companies where you have, basically the founder does everything right. Because what the founder is doing is like overseeing an army of AI bots. And there's sort of this, you know, there's kind of this holy grail in our industry that's been running for a long time, which is like, can you have the, can you have like the one person billion dollar outcome? And, you know, we've had a few of those over the years. Bitcoin is probably the most spectacular example, you know, with Ethereum right behind it, you know, which wasn't quite one person, but, you know, a very small team, you know, you had, you know, kind of Instagram and WhatsApp that had very big outcomes with very small teams. You know, every once in a while, you get one of these things where you just, you know, something hits and you just have a, you know, very small number of people associated with it. You know, but that said, you know, most, most software companies obviously end up with, you know, huge numbers of employees. And so I think, you know, so the most leading founders are thinking of like, okay, how do I reconstitute the actual very definition or idea of a, of having a company? And, and, you know, can you have a company that's, that's literally basically just all AI. And so, and, and if you're doing so, you know, if you're doing anything in the real world, that's hard, but if you're doing software like that, that, that seems like it might be feasible in some cases. And then, you know, there's like the ultimate example of that, which is like, you know, can you have like, can you have like autonomous, like AI economy stuff happening where you have like AI bots in the blockchain or something, you know, that are basically out there, like functioning as a, as a business and like making money and just, you know, literally where the AI does all the work itself and just get, you know, issues me dividends. And so maybe, you know, maybe that, that, you know, maybe that, maybe that's the final outlier result we have, we have a few founders who are chasing that kind of thing. So I would describe that as, I would describe that as kind of the, the ladder that the best founders are on. Super interesting. This whole idea of a one person billion dollar company, I think it depends on your definition of what this is, like an outcome I could see. Having run, running my newsletter as one person with some contractors, there's so many little annoying things that I have to deal with, with just support tickets and issues and bugs. And like, it's hard for me to imagine actually a one person billion dollar company, even if AI is handling so much of your support, because there's just so many random edge cases that I'm just constantly filling out forms. And so I guess depends on, do you have contractors? Does that count? You know, like, what does it, what does it mean to be a one person? But I'm just like, I can't see that happening. Yeah. I mean, look, Bitcoin, Satoshi pulled it off. But like, you know, the open source community, you know, like, does that count? I don't know. Yeah. I guess, I guess it counts. Okay. Yeah, exactly. Right. So yeah, that, that, that, that, yeah. And I would say, I would say, I don't propose to have answers here, but more just like the smartest people I know are, or many of the, many of the smartest people I know are thinking hard about this. So. Yeah. What do you think about moats? A big question constantly in AI, you know, the fact that everything's changing, just what's your guys' thesis on moats in AI? Does, is that even a thing? Do you care? My experience with like really big technological transformations, and of course I, I kind of live this directly with the internet and I saw this happen, is the really big technological transformations, they, they take a long time to play out. And there's, there's all of these structural implications that just kind of cascade out over time. And then there's kind of this, this, there's this like rush to judgment upfront where people kind of say, oh, it's therefore obvious that, you know, XYZ, it's therefore obvious that this kind of company is going to be the company of the future, not that kind. It's obvious that this incumbent's going to be able to adapt and this other one isn't. It's, it's obvious that there's economic opportunity in this kind of startup and not in these others. It's obvious that the moats are going to be in this area of the technology, but not in this other area. And, and there, and you know, what everybody does is they, they kind of state those things with like just an enormous amount of self-assurance where they, they, you know, where they really sound like they have all the answers. And then, you know, what happens is this, these, these ideas kind of saturate the media, right? Because the media naturally prizes like definitive answers over open questions. Because, you know, you, you want, you know, like when CNBC is like booking guests, they want a guest who's going to come on and say, yes, this is the way it's going to be X. Not like, you know, I think that's a really good question. And let's like debate it from like eight different angles. And what I found is if you look back on those predictions a few years later, and you, you can do this by the way, if you pull up like coverage of the internet from like 1993 through like 1997, or even through like, for that matter, even through like 2005 or 2010, and you look at like the kinds of confidence statements people were making in the first 10 or 15 years, like, I would say like, almost all of them are wrong, generally, like quite badly wrong. And so I just, I think the process, I think with massive, with, there's going to be a massive amount of technological change, it's going to be like, I don't know, five or six layers of like structural change that will play out over time. And again, we've talked about a lot of this, but like, the implications on like, what are the definition of products? What are the definitions of companies? What are the definitions of jobs? What are the definitions of industries? How does this play out on the national level? How does this play out at the global level? You know, how does this, by the way, how does this intersect with politics? How does this intersect with, you know, unions? How does this intersect with, you know, war? You know, what's China going to do? You know, and so it's just like, there's just, there's, there are just a tremendous number of unknowns, like a very, very large number of unknowns. And I think it's just like really, really dangerous to prejudge these things. And so I'll just give it, I'll just give it, and it's just, I'll just run this as a thought experiment, you know, so you can see what you think on this, but it's like, you know, like, do, do AI models, are AI models themselves, like defensible? Like, is there a move on AI models? And on the one hand, you'd be like, wow, it certainly seems like there is, or should be, because like, if something takes, you know, billions of dollars to build, and you need, you know, you need this like incredible critical mass of like compute data, and there's only a certain number of engineers in the world that know how to do this. And, you know, they are getting paid like NBA stars. And, you know, and then these companies have to deal with all these like crazy, you know, political issues, and press issues, and reputational stuff, and regulatory and legal, like all of that translates to like, you know, okay, probably at the end of this, there's going to be two or three companies that are going to end up with like, you know, 100%, you know, I don't know, whatever 5050, or 303030, or 9010, one, or whatever it is market share, and then they're going to have whatever probability they have, and it's going to be a kind of a classic oligopoly, and, or maybe, you know, or maybe one company's going to win definitively, it'll be it'll be a monopoly in that. And by the way, those outcomes have happened in software many times before. And so maybe that that will be the outcome. You know, the other side of it is, you know, if you had told me three years ago, you know, that in the, you know, kind of Christmas of ChatGPT, that like, within basically a year to year and a half, there would be, you know, five other American companies that would have basically, you know, exactly capable products. And then there would be another five companies out of China that would have exactly capable products. And then there would additionally be open source that was basically the same. I would have been like, wow, like, you know, the thing that seemed like it was black magic, all of a sudden, you know, has become like commoditized really fast, you know, which, which, by the way, is exactly what happened, right? Like, you know, within within a year of chat of GPT-3 coming out, where there were their open source GPT-3s running on a fraction of the hardware, right, that were available for free. And then there were and then, you know, there were five, you know, now, now you've got, you know, in the game, you know, fully in the game, you've got Google, and you've got Anthropic, and you've got XAI, and you've got Meta, and you've got, you know, all these other companies that are, and then DeepSeek, and, you know, Kimmy, and all these other Chinese companies. And so like, even at the level of like, LLMs, or, you know, AI models, like, you can squint and make that argument either way. By the way, the same thing at the level of apps, right? It's like, you know, one school of thought is, you know, the apps, the apps are not a thing, because like, the model is just going to do everything. But another way of looking at it is no, actually, like actually adapting the model is kind of the engine into it into a domain involving human beings, where you need to, like, actually have it fit for purpose to be able to function in the medical industry, or the legal industry, or, you know, or whatever, or coding, you know, no, you actually need, like, the application level is actually going to matter enormously. And maybe the LLMs can monetize, and maybe the value goes to the apps. And again, you can kind of squint either way on that one. And I know very smart people who are on both sides of that argument. And so I, my honest answer on this is, I think we're in a process of discovery over time, which is, you know, the way I think about this kind of structurally is, it's a complex adaptive system, the technology itself, you know, provides one of the inputs, the legal and regulatory process, you know, is another input. You know, actual individual choices made by entrepreneurs, you know, matter a lot. You know, the economics matter a lot, availability of investor capital varies over time, that matters a lot. And this is a, this is a complex system. And so we actually don't know the outcomes on this yet. And we need to basically be, we need to be open to surprises at the structural level of what happens. And of course, as a, as a VC, this is very exciting, because it means we, you know, we're doing this now, we should kind of make bets along every one of these strategies, and kind of see and see how this plays out. And I just say, like, there may be like one, I don't know, there may be like one particularly brilliant, I don't know, edge for manager or something who has this all figured out. But I guess I would say if, if they exist, I haven't met them yet. So what I'm hearing here is don't over obsess with moats at this point, because we have no idea what it'll end up being and as much as it may feel like, okay, there's no way OpenAI will lose this lead. Clearly, we're seeing a lot of competition. GPT wrapper point is really great. It was such a derogatory term. I don't know, a year ago, just like, you're just GPT wrapper. Now, it's like the companies that are the biggest companies, fastest growing companies in the world. Yeah, well, it's like a little bit like, I don't know, I mean, even just like with, you know, you know, this has been the, you know, the holiday, if, you know, three years ago was the holiday of ChadGPT, this last, you know, month or whatever has been the holiday of, particularly Claude Code, right, for coding. But it's like, you know, it's pretty amazing, because it's like, okay, there was Claude, which was, you know, obviously a great accomplishment. But then there's Claude Code, which is, which is an app, right? It's a Claude wrapper, right? It's, you know, agent harness. And then, and then they did this amazing thing where they came out with, was it a coworker? Co-work. Co-work. Co-work. And, and remember what they said of Co-work, which is a Claude Code wrote Co-work in a week? Yeah, a week and a half. Yep. A hundred percent. Right. Well, and that's, and there's two ways looking at that, which is like, wow, that's really, I mean, obviously, that's really impressive that Claude Code was able to build Co-work in a week and a half. Yeah. That's great. That's amazing. The other way to look at it is Co-work was developed in a week and a half. Like, like, how, how much complexity could there be? How much of a barrier to entry can there be in something that was developed in a week and a half? And so, and then, you know, and then again, it's, it's this, it's this push and this pull thing where it's like, it's like, wow, it's incredibly, it's incredibly functional, incredibly valuable. And people are like all over the world every day now, we're like, wow, I can't believe what I can do with this. It's like the most magical product ever. But at the same time, it took a week and a half. Right. And so, right. And so every other, every other model company, you know, I'm sure you'd have to expect to sitting there being like, okay, obviously we need to build, you know, an Asian artist. And then obviously we need to build a co-work, you know, thing for, for, for regular people. And obviously, you know, I, I don't, I, I don't even saying I know anything, but just like, obviously they're all going to do that. Right. And so, you know, how defensible is that? And, you know, in six months, you know, and we've seen this happen before, like, is, is quad code going to get lapped the same way that, you know, get up, copilot got lapped. You know, the, the history in the last three years has been everything that looks like, it's like the fundamental breakthrough gets, gets basically replicated in lab very quickly. Like many of the smartest people I know in the field, when I, when I really kind of talk to them, kind of, you know, get a couple of drinks in them, they're like, yeah, they're basically, you know, one theory is like, there really aren't any secrets among the big labs. Like the, the big labs kind of all have the same information and they kind of have all the same knowledge and they're, you know, they're kind of, they lab each other on a regular basis, but you know, there, there's not a lot of proprietary anything at this point. And then, and then, you know, again, evidence of that is, you know, DeepSeek, you know, came out of left field and basically was like a, you know, re-implementation of a lot of the ideas of the American big labs and, you know, and had some original ideas of its own. But like, you know, wow, it wasn't that hard for, you know, some, you know, basically hedge fund in China to do it. And so like how much defensibility is there? But on the other side of it, you've got, wow, these big labs are now paying, you know, individual engineers, like they're rock stars. And they're, you know, incredibly bright and creative people. And, you know, maybe there's, you know, a dozen nascent ideas in any one of these labs that it's actually going to be a huge breakthrough that's going to be hard to replicate. And so again, it's just like, I think we just need to, I don't know, my views, my view of myself, I need to put like a big discount on my forecasting ability on this one. Like it, for me, it's much less interesting to try to say, okay, as a consequence, industry structure in five years is going to be X, the big winner in the category is going to be company Y, the big, you know, product killer app is going to be Z. It's like, I, this is a, I don't think I can predict that. I think we're, I think a much, much better use of my time is being, being very flexible and adaptable at a time like this. So with all this in mind, do you feel like there's something you're paying attention to more to help you decide, okay, this is where we want to place our bet? Or is the answer essentially the strategy you guys have, which is place a lot of bets, you guys raised the, the largest fund in history. Is that, is that the way you win in this world? Yeah. So for, I mean, for us, yeah, for, for us, we have, we obviously have a very, very deliberate strategy. One way to think about this use the Peter Thiel for you remember the Peter Thiel formulation of, uh, he said there's a two by two, there's optimism and pessimism, and then there's determinant and as an indeterminate and indeterminate, uh, right. Um, and so, um, and he always argued like there's, he always argued that like Silicon Valley is characterized by too much, what he calls indeterminate optimism, right. And what he, what he, what he always described, what he meant by that is basically, um, I think the way he would describe it is an indeterminate optimist who thinks the world is going to be better, but can't explain why. Right? Like some combination of things is going to happen to make the world be better, even if we don't know what those things are. And, and, you know, I think he, he at least historically would say like that's, that's basically, you know, that, that, that, that risks at least being just like wishful thinking or delusional thinking. And what the world needs more is determinant optimists, which are people who are like, no, the world is going to be better because I'm going to do this specific thing. Right. And he would classify, for example, Elon, you know, he would sort of, sort of maybe say, you know, VCs are indeterminate optimists. Um, and then he would say, you know, Elon is the determinant, determinant, determinant optimist where it's like, no, I'm going to build the electric car, I'm going to, you know, solar, and then I'm going to, you know, Mars, you're right. And I mean, these very concrete things. And I think there's a lot, I think there's a lot to Peter's framework, but the way I would describe it is I think maybe, you know, if you and I disagree, a part of that, it would be, I think the indeterminate optimism is a stronger phenomenon than at least I think he's historically represented it as, and I would put myself firmly in the indeterminate optimist category. And that's the strategy that we, that we have at A16Z, which is, and the reason for that is it's not, hopefully it's not so much wishful thinking, it's more, no, what the indeterminate optimism of venture capital or the indeterminate optimism of A16Z or Silicon Valley is very, it's actually very specific, which is there are these extremely bright and capable people like Elon and many others who are founders, right. And product and, you know, kind of product creators, right. And, and, and each of those individual people is a determinant optimist, like each of them, each of them individually has like a very strong view of what they're going to do. David Plylari But the great virtue of the capitalist system, the great virtue of the American economy, the great virtue of Silicon Valley is we don't just have one of those, we don't just have 10 of those, we have a hundred and a thousand and then 10,000 of those. And, and the way to optimize the outcome is to have as many of those as possible, be as good as possible, run as hard as possible. And then that just the, the nature of, you know, the nature of the future is like, we just don't know all the answers and that's okay. And then, and the right way to deal with that is to run as many experiments as possible and have as many smart people try to do as many interesting things as possible. And so, yeah, I would, I would put myself firmly on the side of the indeterminate optimist. David Plylari I mean, I'm wondering if the answer to the question of what you look for now more and more is this determinant, optimistic founder that has this massive ambition and is actually working on achieving it. David Plylari Yeah, no, that's right. That's right. I mean, look, the founders need to be determined, determined optimists. Like they need to have a very specific plan. David Plylari Yeah, no. And yeah, look, the critique of the critique always, you know, the critique from the founders is, oh, you VCs have it easy. Cause like, you don't have to like, you don't actually have to commit, right. You don't actually have to like make, you don't, you don't actually have to like, you know, you have to make the bed you lay in, you can like place multiple bets, you can have a portfolio, you know, you should have a lot more sympathy for us as founders, you know, because we, you know, we only get to make the one bet. You know, and there's, there's truth to that. You know, the counter argument on that is the founders get to run their companies. We don't. So, you know, we don't, we don't, we don't get to put our hand on the steering wheel. David Plylari And so, you know, the great virtue of being a determined optimist is you actually get to get to single-mindedly execute against that goal. And, and, and, you know, look in the long run, who, who does history remember? History remembers Henry Ford, right? Not, you know, whoever was the, you know, whatever the seed investor who seeded Ford, Ford Motor Company and, you know, 10 other car companies have failed. Right. And so, you know, the determined optimist is the, you know, the founder is the founder and the company builder and the engineer. I mean, these are the people who actually use this thing and, you know, deserve 99.9999% of the credit. But, you know, having said that, I do think there is a role for having some indeterminate optimist in the, in the background, not helping along the way and helping keep the whole, the whole cycle going. David Plylari Do you think about AGI in shifting your investment thesis? Like as we approach AGI and hit AGI as an investor, how do you think about your investment thesis changing? David Plylari Yeah. So I've always kind of had a little bit of an issue. I've always kind of struggled with the concept of AGI because it at least, well, there's defined terms, which is where I kind of struggle with it, which is there's like the prosaic, there's the, there's the prosaic definition of AGI. And then there's like the, I don't know, cosmic definition. And the way I would describe it as, well, let's start with the cosmic one. So the cosmic one is basically, it's the singularity, right? And so AGI is the moment where you enter the singularity, which is to say that where the world fundamentally changes. And like the rules of the old world are gone. We're not operating in a new domain. And then, you know, the kind of the full definition of singularity is like, it's a world in which, you know, human judgment is no longer really relevant because the, you know, you get this self-improvement loop. The AI is improving itself and it's sort of racing, you know, so-called takeoff scenarios. You can see at this takeoff thing where the AI is improving itself and the machines are making decisions so much faster than people and people are just sitting there watching the machine do its thing. You know, and I kind of described, I don't really, I don't really think that's, I don't, I don't think we live in that world. Like whether you could call that utopian or dystopian, like, I don't think we're lucky or unlucky enough to live in that world. We could debate that. We can talk about that more. But the prosaic definition of AGI that at least I think the industry participants have kind of converged on and tell me if you agree with this is, it's when the AI could do every economically relevant task as good as a person. The way the co-founder of Anthropic put it is like a basket of the most valuable economic tasks. So it's 10, 15, not every single economically valuable task. Okay. Got it. Yeah. So it's maybe even a slightly reduced, a slightly reduced definition. And by the way, we're going to, you are clearly getting close to that if we're not already there. And so on that one, I kind of feel like, so I kind of feel like the cosmic one overstates what's going to happen. And then I kind of feel like the kind of AGI definition that you just gave, I think it kind of understates what's going to happen. Like it's almost too reductionist. And the reason for that is, I don't think there's any reason to assume that human skill level is the cap on anything. Right? And so, the way we say that is AGI always is, you know, the definition you gave, the definition I gave, it's kind of, it's always kind of relative in comparison to a human worker. Right? And it's like, I don't know, like human skill level caps out at a certain point, but that's because of the inherent, like biological limitations of the human organism. Right? Like we're all, you know, human, I give you example, human IQ, human IQ, you know, kind of what they call fluid intelligence or the sort of G factor of kind of, you know, fluid intelligence. IQ, I think tops out in humans as a species, it tops right around 160. Right? Where at like 160, it's like Einstein level, Einstein, Feynman. In terms of IQ. In terms of IQ. Like you just tops out at 160. The 160 IQ people are the ones who come up with new physics. There's only a small handful of those. The generally speaking, when we run into somebody in the world who's like incredibly smart, who's like a bestselling author, or like a, you know, one of the world's best, I don't know, research scientists, or one of the world's best doctors, you know, whatever, it would be probably 140. It's kind of the IQ that you're looking for there. If you're looking for like a really good lawyer, it's probably 130. If you're looking for like a really good, like line manager in a business, it's probably 110. You know, if you're looking for like an accountant, like a small business accountant, who's good at doing the books for small businesses, it's probably 105. Right? And so the kind of scope of like impressive human, you know, that the ability of the human organism to do intellectually impressive things, you know, it's sort of that 110 to 160 is kind of the spectrum. And, you know, good news is there's a lot of those people running around, but like there's not that many at 140, 150, 160. But it's like, that's just, that's like the limitations of what can fit in here. Right? And it's like, there's no theoretical limit on where this goes, if you release the limitations of human biology. Right? And so can you have a, you know, you already have people running these experiments to kind of do human equivalent, you know, kind of IQ, you know, for existing AM model. And by the way, existing AM models right now are kind of testing around the 130, 140 level, which means they're going to get to the 160 level. And they're, you know, they're arguably on the mass size starting to get to the 160 level now. But like, I think we're going to have AM models relatively quickly that are going to be like 160, 180, 200, you know, 250, 300. By the way, and I think that's great, right? Like, I feel as great about that as I do about the fact that we occasionally get an Einstein. Right? It's like, would the world be better off or worse off with more or fewer Einsteins? And the answer is, of course, the world would be better off with more Einsteins. And of course, the world would be better off with machines that have IQ, you know, more IQ like Einstein or greater than Einstein. But like, I think IQ of the machines is going to exceed that in humans. I think that's really good. And then the performance, you know, again, it goes back to like the AI coding thing is happening. The performance against task is going to get better also. Like, I think, you know, this is where Linus Storvalds in particular is like, yeah, okay. Like this thing is starting to generate better code than I can. Okay, so now we're going to have AI coders that are actually better coders than the best human coders. I think that's great. I think we're gonna have AI doctors that are better than the best human doctors. I think we're gonna have AI lawyers that are better than the best human lawyers, which actually is gonna be very interesting to see, which I think is also great. And so like, I don't think there's a, I think we're used to living in a world where we just don't understand how good good can get because we've been capped by our own biology. And we're going to get to experience what it's like when you have the capability at your fingertips, that's actually better than human in these domains. And so I, you see what I'm saying, which is like, I think this idea of like human equivalent is just going to be like a footnote. It's like, oh, yeah, that was just on Tuesday, you know, in 2026 is when they hit that. And it kind of didn't matter because the next question was like, okay, what are we gonna, what are we gonna, what do we get to do in a world in which we actually have machines that are better than that? Right? And so, so I think this is going to be much more of an exploratory process for actually exceeding human capability than it's going to be any sort of particular singular singularity moment or whatever that happens, just, that just happens to coincide with the human threshold. 200 IQ. I, just like that frame of reference is such a mind expanding way to think about it, just how fast and how smart these things are going to get and quickly. Well, I don't know if you have this experience. I have this experience all the time. Well, two experiences I have all the time. One is just like, I'm just like, like, I know I ought to be able to do this, but like, I just can't, like, it's going to take too long. You know, I want to write this thing or I want to like, whatever. I want to have this theory on this thing or I have a plan or whatever. And it's just like, fuck, like I don't have the eight hours or, or by the way, the eight weeks or the eight years. Right. And like, I just don't know enough yet. And I'm just like, I can't do the math in my head and my memory isn't perfect. And like, I can't remember. And I read, you know, if you have this, you get interested in something, you read 10 books and then you're like, shit, I forgot almost everything that I just read. Like, I, I can't, I wish I could retain it all, but I can't. It's just like, I, you just have this, I, I sort of live in this kind of state of like endless frustration. I was just like, I, like if I could just be smarter than I was, like, I'd be so much better at what I do, but I'm not. So, so, so there's that. And I don't know how often you have this, but I have this on a regular basis. It's just like, you know, I, you know, because of what we do, like, I know a bunch of people who I know for fucking sure are smarter than I am. And I know it because when I talk to them, I just find myself at a certain point, you know, it's like for the first half of the conversation, I'm just taking notes the entire time. For the second half of the conversation, I'm just like, fuck, like, fuck me. Like this person is just smarter than I am. And they're just out thinking me and they're going to keep out thinking me and I just can't. And I'm just like, all right, God damn it. Like I got to go home and I got to like have a drink. Cause I'm just not, you know, I'm just not whatever that is. I'm not that. And so we're just so used to having those limitations, um, that the idea of having machines that work for us that don't have those limitations. I, I just, I think that's much more exciting than people are giving your credit for. Oh man. I could talk to you for, for hours, Mark. I'm thinking to close out the conversation. I want to ask about your media diet and your product diet. You just talked about books, reading 10 books. I think you famously read constantly. I saw an interview with you where you're just like AirPods changed my life. I'm just listening to audio books now all the time. So in terms of media diet, what do you, what are you reading? What are you paying attention to these days in terms of, I don't know, podcasts, newsletters, blogs, things like that. And then any books in particular? Yeah. Yeah. So what I read is basically, I mean, I would say I read basically three categories of things. So like in terms of like general media, um, it's basically, I sort of, um, I always describe it as I have like a almost a perfect barbell strategy, which is I read X and I read old books. Right. So it's basically either like up to the minute, what's happening right now. Um, or it's like a book that was written 50 years ago that has stood the test of time. And then, you know, we're presumably there's something timeless in it. Um, and, and then it's sort of everything in the middle. I'm always like much more skeptical about and it's particular, it's kind of what I already said, which is, I think if you go back and you read old, nobody ever does this, it's actually really funny. Nobody ever does this. There's no market for it. But if you go back and you read old newspapers and by the way, you can, you can do this, just read last week's newspaper, right? I'd say we're taping on Friday. So read last Friday, newspaper. Right. And just go back and read it and be like, Oh my God, like none of this happened. Like not that, none of what they predicted played out the way that they said that it would. None of this turned out to actually be that like relevant or correct. Like they didn't understand, like, you know, they, by the way, they had no view of what was going to happen this week. Then they couldn't know. And so they were making predictions and forecasts and so forth based on like not having information, but it's just like, wow. Like, you know, like none of this happened. Like, I wish I had never read this, like, Oh my God. And then, you know, it's kind of the same thing with magazines that go back and read old magazines. And just like the, the, the level of the, you know, the, just the endless numbers of predictions that they make. Yeah. And kind of, you know, the problem with, you know, newspapers at least are going day to day. The thing with magazines is like every, it's like a week or month, you know, kind of a long cycle. And so it's even, you know, by the time an article even hits publication, it's, you know, it's often out of date. So I just, I just have like a big problem with kind of everything in the middle. And so it's either, it's either, it's either of the moment or timeless, but then, yeah, you mentioned like newsletters. I mean, so the other thing, and you know, this is maybe obvious, but I think it's probably still underrated, which is the actual practitioners in the field who are actually creating content, I think probably is still like dramatically under underrated. And I think this is a huge part of like the sub stack phenomenon and the newsletter phenomenon and the podcast phenomenon is like direct exposure to the people who are actually principals in the field who actually know what they're talking about is probably still dramatically underrated. And I think, again, the reason for that is like, we're, we're, we're used to being in this mass media kind of culture in which basically everything is mediated, right? Everything got filtered through like TV interviews or like newspaper interviews or magazine interviews. And, and, you know, obviously now more and more, it's just, no, you actually want like smart people who are actually working on something explaining themselves. And then you have, you know, you have new kinds of intermediation, like podcasts that, that, that, that kind of open that up for people to make that possible. And so, yeah, like domain practitioners are, you know, really great. I mean, they, just to state the obvious in AI, you know, it's obviously your, your stuff, but also like, you know, like Lex, you know, the fact that like Lex Friedman can have, you know, the world's leading or, you know, whoever the, you know, and any of you guys, you know, there's a small handful of you guys who have access to these people, you could have the world's, you know, kind of leading experts in the domain actually show up. And by the way, it's, you know, it looks, the critique always is, you know, people talk their book, like, if I'm running a startup or whatever, I'm just selling, but it's like, and there's always a little bit of that. But it's also, you know, my experience is people love to talk about what they do. And, and, you know, and they, they fundamentally like want to express what they do and, and, and they want to explain it and they want people to understand it. And everybody kind of enjoys that. And they get to contribute to kind of human knowledge by doing that. And they get ego gratification by doing that. And so I think there's just actually just tremendous amounts of alpha in listening to the world's leading experts in the space who actually just like show up and talk about what they're doing. And of course, like the world is awash in that today in a way that it wasn't as recently as 10 years ago. So I, yeah, I do as much of that as I can too. And there's also just this culture in tech, Silicon Valley in particular, of sharing, of not trying to keep these secrets. Everyone on LinkedIn is always like, how is this free? Like, it's just the way it works. Yeah. It's somebody said, Silicon Valley is a company town, but the, the, the, the company is Silicon Valley. Right. And, but, and again, at the level of this goes, again, there's one of these great N equals one at the level of N equals one is somebody, you know, and I've, I've run startups before, I've run companies before at the level of N equals one of like running a company, that's just a giant pain in the fucking butt. Like, cause you know, your secrets are walking out the door and your employees are walking out the door and the whole thing sucks. But you know, the other side of it is you also benefit from that. Right. Cause you get to hire people with all these skills and experiences. Right. And you, you're in this, you're in this ecosystem that, that it acts right and channels, talents and, and, and, and skill and knowledge and people into, into the new fields. And so, you know, so that, you know, there's kind of the push and pull of that at the level of just being an individual, individual CEO, um, at the level of, of just being in the ecosystem to your point, like, yeah, it's a, it's an absolutely magical phenomenon. And by the way, like, you know, one of the, one of the, you know, for all the, for all the issues in Silicon Valley, um, you know, I think AI, I did the count once, I think AI is the ninth major technology platform in the history of Silicon Valley. Right. That, you know, Silicon Valley, Silicon Valley is still called Silicon Valley. We haven't made Silicon here in decades. Right. Uh, we used to actually, you know, this is called Silicon Valley cause they used to make chips, right. They used to have like the actual fabs were in Silicon Valley and then they, and they designed them and they made the chips. Um, and, and so, and that was, you know, wave one starting in the 19th, you know, actually that was like, actually, no, actually more or less like wave three or whatever, but like it was, you know, that was when the, the, the, the area was named like in the 1950s, but now we're on like wave nine. Right. Um, and, and the, the company town phenomenon where the company is the industry, like the, the, the, the, you get the indeterminate optimism, the nobody had, nobody had to sit and plan and say, okay, in the 1990s, Silicon Valley is going to do the internet in the two thousands, they're going to get a smartphone and the 2010s are going to do the cloud and the 2020s are going to do AI. It, it just, the, the, the, the, right. The indeterminate optimism, optimism of ecosystem, the flexibility of the ecosystem method, the, the, the, the Silicon Valley could, could morph, um, into all these categories. And again, maybe a testimony to indeterminate optimism. This reminds me of the meme of how we're all just wrappers over sand. Everything we're building is just wrapper, wrapper, wrapper, wrapper. The wrapper thing is hysterical. Yeah. Yeah. I'm a, I'm a software company and I'm a, I'm a chip wrapper, right? Um, uh, yeah, I'm a, I'm a, I'm a business application. I'm a database wrapper. Um, yeah, exactly. I'm a Sandra. I mean, yeah, you and I are, we're all now sand wrappers. Perfect. Okay. One more question along the media diet. I asked your partner, Ben Horowitz, uh, what to talk to you about, uh, the Z and a 16 Z if people don't know him. And he said that you're really into movies these days. And so I don't know any movies, any movies you really into these days, any movies you've absolutely loved recently. Yeah. So the movie that blew my socks off, uh, last year, which I think is the best movie of the decade for sure. And maybe of the last, like 15 years is this movie. Unfortunately, it's one of these things, not a lot of people have seen it, but I would highly encourage it. It's called Eddington. Not heard of it. Have you not heard of it? Okay. So Eddie, you're going to really enjoy it. So I won't, I won't spoil too much of it. So at the service level, the following spoils nothing at the service level, it's set in a small town in New Mexico called Eddington, which is a small town of about 600 people. Um, and, um, there's a, uh, sheriff, uh, who's played by Joaquin Phoenix, who's like an old crusty, basically right winger. And then there's a, um, uh, there's a mayor, uh, played by Pedro Pascal, who's basically a young hip progressive. And, uh, and then the movie starts, I think in March of 2020. And so it starts when COVID first hits. And then it sort of, as it plays out over the next few months, it, then it intersects and it, it sort of extends into the summer of 2020. So, you know, kind of the, the George Floyd moment, and then the, you know, the, the protests and riots and kind of everything. So it started the convergence of COVID and then the, um, and then the, uh, and then, and then the, uh, the, all the, uh, all the BLM stuff. And, and, and, and then, um, it, it, and then, and then there's a third kind of element to it, which is, um, there's a company, which is basically a loosely disguised version of Meta. If you read the backstory of it, which is building an AI data center on the outskirts of town. So they kind of pull that in, uh, as sort of a thing that looms larger and larger over time. And then, um, the thing it really is great at is it really shows, um, you know, this is a small town in New Mexico. And so everybody in the town gets kind of fully wrapped up in all the COVID stuff and they get fully wrapped up in all the BLM stuff and they get fully wrapped up in all the like, you know, tech anxiety stuff, but they're all experiencing it basically through the internet, right? Which, which is, which is, you know, what, what, what actually happened. Right. And so, so it's, it's, so, so the reason I love the movie so much is one, one is it's the first movie that directly grapples with 2020 of what happened in 2020. And that just like fully, fully engages and grapples with like all the dynamics that were playing out in the country. But the other reason is, it's the first movie that does a really good job of showing what it, what it, what it was like, especially in that era to live in a world in which there were things happening in the real world and people were kind of experiencing events online, you know, like in a way that was like very central in their lives. Right. Um, and so it does like a really good job of pulling in like smartphones and social media, um, in a way that, um, uh, in a way that movies really, really, really struggle with. And then the whole thing comes together in an incredibly entertaining way. Um, and so, and I wouldn't even say, I, I won't even say I completely agree with the movie or whatever. And I think the director of the movie and I would probably disagree about a lot, but, he really tries hard to like really grapple with like, what is actually like to live like a human being in the 2020s in America in a way that I think many other filmmakers who are very talented have just been very scared of touching. And this guy, for some reason, he's just like, yeah, I'm just going to find all the third rails and I'm just going to like, fucking grab them. I can see why that's your favorite movie. It's great. It's great. It's great. Everybody should see it. Oh man. Okay. Final question. I want to ask about your, uh, your product diet. Are there any products you use that maybe are less known that you love that you want to recommend? You can, you know, mention products your investors in if, if you use them constantly. I mean, we have, you know, we have so many that it's really hard to, you know, I always feel it's like, you know, who's, here's your favorite children. So it's, it's really hard to, to, uh, to, uh, to, uh, specific ones. Um, but I'll, uh, you know, I'll, I'll talk about a few. Um, I mean, or just, I'll, just observation. So one is my, my 10 year old, um, I have my 10 year old, my 10 year old right now is a hundred percent obsessed with Replit. Um, and, and by the way, it was not from me. Do you have kids? I do. I have one, two and a half year old. Two and a half. Okay. So you haven't run into what I'm running into now, which is whatever it is you do is not cool. Right? Like it's two and a half. Whatever daddy does is like the coolest thing in the fucking world. I can tell you by the time he's 10, whatever you do is like deeply uncool. Right. And I, and I'm highly aware of that. Um, and so like, if I mentioned, oh yeah, we work on XYZ, you know, he's like, okay. Um, but when he discovers something, then, then it's cool. Or when his friends tell him about it, it's cool. And so he, he, he, through no interference on my part, uh, discovered Replit about, uh, about, uh, three months ago and discovered vibe coding and is like completely obsessed with vibe coding games and all kinds of, all kinds of things. And like Twitter will sit and do it, do it for hours. And so I, I'm, I'm seeing that phenomenon play out, uh, which is super fun. Um, uh, that's one too, is I am just, completely in love with all the AI voice stuff. Um, I think it's just absolutely amazing, hysterical. Uh, my favorite, uh, party trick at dinner parties now is to pull out, uh, Grock, uh, with, uh, Bad Rudy, which is, as you've seen, it's, it's the, uh, it's a foul-mouthed raccoon, uh, uh, avatar, uh, on the, uh, in, uh, in the, uh, in the, uh, in the, uh, Grock app. So, um, uh, I think that's super fun. We had this company Sesame that had, you know, they, they went viral last year for this, uh, you know, uh, these, uh, these just incredibly like, uh, uh, you know, intimate emotional, you know, kind of voice experiences. Um, so I think the voice stuff is fantastic. I'm also super fascinated by all the voice input stuff. Um, and so, um, you know, limitless, we, uh, limitless recently, uh, the company recently, uh, uh, sold, but, um, you know, that all the, the, I think like the pendants, the wearables, like all that stuff is going to be big. The metaglasses. Um, I, I think there's going to be a whole wearables revolution here. I love the voice input stuff. I have this app on my, there's this app on my phone now called Whisperflow, which is voice transcription, which works like staggeringly well. It's like incredible. It's like a voice transcription function, but you can actually talk to the AM model while you're doing voice transcription. So you can kind of, it kind of understands when you're telling it, no, no, you know, I want bullet points over there and I want this and that. And it understands that you're not telling it to type in the words, I want bullet points. It just actually understands that you want bullet points. And so like, that's a great example of a super useful thing. And so I think the voice mode stuff is gonna be, is gonna be, it's gonna be really great. Subscribers in my newsletter get a year free of Replit and Whisperflow. So there we go. What's the, what's the most memorable thing your son built with Replit? Oh, well, so he's gotten super into Star Trek. And so, so far it's been, he's writing like Star Trek simulators. So like all the, you know, all the, by next generation, they actually had- Next generation, okay, I was gonna ask which. Well, he like, we actually, we like them all. We watched the new Starfleet Academy last night, which actually is quite, it's actually quite good. But we watched the original, you know, we watched them all. But it was in next generation where they actually developed an actual design language for the computers. Because if you watch the original series, they just had like basically, you know, knobs with lights and they didn't really, you know, they just like were like, you know, fucking around on set and trying to pretend they were doing it. But by next generation, they actually had designed, they actually had a UI design language. And so one of the, one of the fun things you can do vibe coding is you can say, give me a Star Trek next generation, you know, user interface for, you know, whatever, this, that, or whatever, and it actually uses the, they call it the seven and nerd out. They call it L cars design language. And it'll, you know, it'll actually build you like Star Trek next generation bridge consoles using that design language. But, you know, with your choice of like a Star Trek game, for example. And so he's, he's going crazy for that kind of thing. Mark Leary, that sounds extremely delightful. You guys should open source to release that. Mark, I, like I said, I could talk to you for hours. Well, you got things to do. Anything you want to leave listeners with before we wrap up, anything you want to double down on or just leave listeners with? Mark Leary, Yeah. So a couple of things. So one is we got super lucky last week, Paki McCormick wrote the best piece ever written about us, actually, which he released. And so it's the best explanation of what we do and how we think. And so I would definitely recommend that. And then, you know, we're putting a lot, we have a great team of folks now. We're putting a lot of effort ourselves into video and, you know, in content. And so I definitely recommend our YouTube channel, which I think has a lot of great stuff and is going to be very exciting in the next year. Awesome. We'll link to that. I think it's just youtube.com slash A16Z, something like that. And you guys have great stuff. Mark, thank you so much for being here. Mark Leary, Awesome. Thank you for having me. I really, I really appreciate it. Mark Leary, Bye everyone. Thank you so much for listening. 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