Marc Andreessen: The real AI boom hasn’t even started yet

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://www.linkedin.com/in/lennyrachitsky/ *In this episode, we cover:* (00:00) Introduction to Marc Andreessen (04:27) The historic moment we’re living in (06:52) The impact of AI on society (11:14) AI’s role in education and parenting (22:15) The future of jobs in an AI-driven world (30:15) Marc's past predictions (35:35) The Mexican standoff of tech roles (39:28) Adapting to changing job tasks (42:15) The shift to scripting languages (44:50) The importance of understanding code (51:37) The value of design in the AI era (53:30) The T-shaped skill strategy (01:02:05) AI’s impact on founders and companies (01:05:58) The concept of one-person billion-dollar companies (01:08:33) Debating AI moats and market dynamics (01:14:39) The rapid evolution of AI models (01:18:05) Indeterminate optimism in venture capital (01:22:17) The concept of AGI and its implications (01:30:00) Marc's media diet (01:36:18) Favorite movies and AI voice technology (01:39:24) Marc's product diet (01:43:16) Closing thoughts and recommendations *Referenced:* • Linus Torvalds on LinkedIn: https://www.linkedin.com/in/linustorvalds • The philosopher’s stone: https://en.wikipedia.org/wiki/Philosopher%27s_stone • Alexander the Great: https://en.wikipedia.org/wiki/Alexander_the_Great • Aristotle: https://en.wikipedia.org/wiki/Aristotle • Bloom’s 2 sigma problem: https://en.wikipedia.org/wiki/Bloom%27s_2_sigma_problem • Alpha School: https://alpha.school • In Tech We Trust? A Debate with Peter Thiel and Marc Andreessen: https://a16z.com/in-tech-we-trust-a-debate-with-peter-thiel-and-marc-andreessen • John Woo: https://en.wikipedia.org/wiki/John_Woo • Assembly: https://en.wikipedia.org/wiki/Assembly_language • C programming language: https://en.wikipedia.org/wiki/C_(programming_language) • Python: https://www.python.org • Netscape: https://en.wikipedia.org/wiki/Netscape • Perl: https://www.perl.org • Scott Adams: https://en.wikipedia.org/wiki/Scott_Adams • Larry Summers’s website: https://larrysummers.com • Nano Banana: https://gemini.google/overview/image-generation • Bitcoin: https://bitcoin.org • Ethereum: https://ethereum.org • Satoshi Nakamoto: https://en.wikipedia.org/wiki/Satoshi_Nakamoto • Inside ChatGPT: The fastest-growing product in history | Nick Turley (Head of ChatGPT at OpenAI): https://www.lennysnewsletter.com/p/inside-chatgpt-nick-turley ... References continued at: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email [email protected]._ Lenny may be an investor in the companies discussed.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  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.

• 0:16Lenny Rachitsky?

  How big of a deal is the moment in time that we're living through right now?

• 0:21Speaker 00

  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.

• 0:30Lenny Rachitsky?

  You spend a lot of time with the most cutting-edge AI-forward founders.

• 0:33Speaker 00

  The most leading-edge founders are thinking of, can you have entire companies where the founder does everything?

• 0:38Lenny Rachitsky?

  There's all this concern that young people, jobs are not going to be there for them, AI is replacing them.

• 0:43Speaker 00

  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.

• 0:49Lenny Rachitsky?

  What's your sense of just the future of three very specific roles? Product manager, engineer, designer?

• 0:54Speaker 00

  There's like 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 kind of 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.

• 1:17Lenny Rachitsky?

  People aren't fully grasping how much this changing.

• 1:20Speaker 00

  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.

• 1:29Lenny Rachitsky?

  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

• 2:14Lenny Rachitsky?

  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, replant, bold, gamma, NAD, linear, superhuman, Devon, post hog, D script, whisper flow, perplexity, warp, granola, magic patterns, raycast, champion, R D mob and and stripe atlas. Head on over to lennysnewsletter.com and click product pass. With that, I bring you

• 2:57Lenny Rachitsky?

  Mark and Drayson 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.

• 3:34Lenny Rachitsky?

  To learn more, visit DX's website at getdx.com/leni. That's getdx.com/leni. 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.

• 4:19Lenny Rachitsky?

  One in three startups in the United States already runs on Brex. You can too at brex.com.

• 4:31Lenny Rachitsky?

  Marc Andreessen, thank you so much for being here and welcome to the podcast.

• 4:36Speaker 00

  Awesome, Lenny. Thank you. It's great to be here.

• 4:38Lenny Rachitsky?

  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?

• 4:50Speaker 00

  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 think I would expect 2026 to exceed that.

• 5:00Lenny Rachitsky?

  Wow, that says a lot.

• 5:00Speaker 00

  Yeah, I've seen some stuff. So it feels like two things are happening. One is the the trust that a lot of people have had in kind of what you described as kind of legacy institutions around the world is, I think, in kind of full-scale collapse right now. By the way, there's a lot of data to support that. And so I think there's just, there's like a lot of structures and orders and institutions that people have just relied on for a long time that have just proven to not be up for the, up for the challenge. And then kind of corresponding with that is the national and global conversation have become like, let's say liberated. And so, you know, this sort of incredible revolution that we have in kind of, you know, what I would describe as freedom of speech, freedom of thought, ability for people to openly discuss things that maybe they couldn't discuss even a few years ago, you know, has just dramatically

• 5:45Speaker 00

  expanded. And I think that's, that's now on a one-way train for just a much broader range of discourse. And then, you know, there's also just these like incredibly massive geopolitical shifts that are happening. And obviously, the US is changing a lot. Europe is changing a lot. China's changing a lot. Latin America, by the way, is changing a lot. Very dramatic, you know, events playing out down there right now, you know, kind of all over the world. Like, I think a lot of assumptions are being pulled out into the daylight and reexamined. And then it's kind of the fact that all these things are happening at the same time, right? And so you've got all of these countries and industries, you know, where things are kind of increasingly upheaval, but you have AI as this kind of new technology that's going to really affect things. And then you've got, you know, people, you know, citizens being able to fully participate, being able to argue things out. And so it's kind of like those three kind of big mega things are kind

• 6:30Speaker 00

  of all colliding at the same time. And I think we're probably just the very beginning of all three of those. And those all feel like kind of, you know, historical, you know, moment shifts, you know, comparable in magnitude to maybe the fall of the Berlin Wall in 1989, you know, maybe, maybe the end of World War II, you know, kind of moments like that. It certainly feels like that.

• 6:48Lenny Rachitsky?

  Good God. What a time to be alive.

• 6:52Speaker 00

  Yeah.

• 6:52Lenny Rachitsky?

  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?

• 7:03Speaker 00

  The AI thing, I think at this point, I think it's pretty clear with our technology hats on that like this stuff is really working now, right? And so there was this, you know, kind of, you know, when there was a chat GPT moment, you know, three years ago, it was only, by the way, only three years ago, right? Was the chat GPT moment. And the big question was, all right, this is like incredibly fun and creative and like, we have machines now that can compose Shakespearean sonnets and rap lyrics and like, you know, this is amazing. But then there was, you know, there's this big question like, can you harness this technology for reasoning and for, you know, problem solving in domains that like really matter, you know, medicine and science and law and so forth. And, you know, it turns out the answer to that is yes, right? And, you know, the last 12 months and especially the last, even just the last three months have really proven that like AI can really do like, you know, you're seeing it all now,

• 7:48Speaker 00

  you know, it can actually, you know, AI is now developing new math theorems. You know, they're, you know, over the holiday break, you know, there's sort of the, but it feels like the AI coding thing, you know, really hit critical mass. And the world's best, the world's best programmers, right? Including like Linus Turvold, you know, for the first time over the holiday break, basically said, yeah, AI is now coding better than we can. And so that, you know, that's incredibly, incredibly powerful. And I think we all, you know, kind of, 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, you know, that's going to include like many very important domains. So, um, so like 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 a, I think, you

• 8:32Speaker 00

  know, a lot of people in the industry have kind of what I would describe as this one dimensional thing, which is, okay, as a result of the technology now working, AI just kind of sweeps, sweeps the world and changes everything. And I think that's, that's kind of the wrong, that's kind of the wrong framework. 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, you know, 80 years.

• 8:51Speaker 00

  And I recall two things in particular. So one is it has, I think it's felt to us like in the US and the West for the last, you know, whatever 30 years or 50 years, it's felt like we've been at a time of great technological change. But actually, if you look for actually evidence of that, like in statistical evidence of that, analytical evidence of that, like 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 sort of 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.

• 9:33Speaker 00

  And in fact, the pace of productivity growth, like in the US, is running at like a half of what it, in my lifetime, in our lifetimes, it's been running at about a 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 US, in the West, technology progress in the economy, technology impact on the economy has actually slowed way down. And so, you know, 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, like, just incredible thing that's happening, which is the, you know, the demographic collapse,

• 10:16Speaker 00

  right? It's sort of a Western phenomenon, an increasingly global phenomenon, which is, you know, the rate of reproduction of the human species is in rapid decline. And, you know, there are many countries, you know, including the US, where, you know, the rate of reproduction is, you know, under two, you know, meaning that, you know, 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 kind of precondition that says there's actually been very little technological progress happening in the world, and the world is going to depopulate. And so AI is going to enter the world in which those two things are true. And I think it's 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, you know, we're going to need

• 11:01Speaker 00

  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, I think the interplay of these factors is going to be much more interesting and frankly more, more complex than a lot of people have been thinking.

• 11:15Lenny Rachitsky?

  I'm going to follow this thread about kids. And now you have a kid. And one of my most, 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.

• 11:25Speaker 00

  Yeah.

• 11:26Lenny Rachitsky?

  Are there specific skills or I don't even careers that you're steering your kid towards?

• 11:31Speaker 00

  The way I think about this, anyway, yeah, we have a 10 year old and so, you know, we actually homeschool and so we, we think a lot about this. So I think the way to think about the impact of AI on people, on specifically people as individuals, I think it's, it's actually, you know, a lot of people just focus on kind of this, you know, this kind of very, I would say straightforward and overly simplistic view of just literally job gains, you know, job losses, which we could talk about. But there's two specific things at the level of like 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 sort of raise the average kind of across the board. And, you know, look, you see that playing out already. You know, anybody who's in a position where they need to, you know, write something or

• 12:16Speaker 00

  design something or write code or whatever, if they're, if they're pretty good at it today, they use, they use AI and all of a sudden they're very good at it. And so they're, they're sort of that aspect to it. And I think the, the, the way the education system at large is going to teach, is going to kind of teach AIs is going to be based, you know, 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 like spectacularly great, right? And so you just you kind of use it, use the term, you think about like the super empowered individual, right? So the individual who is like really good at coding or really good at making movies or really good at making songs or really good at designing, you know, making art or whatever,

• 13:00Speaker 00

  whatever those things are, or, you know, or podcasting, or, you know, hopefully venture capital. You know, if you're very good at it and you can really harness AI, you can become spectacularly great and like super productive, right? And, you know, I'm sure you have a lot of friends in this, in this category as well, but like, you know, the really, really good coders are experiencing this right now. My friends are really good coders, like, oh, my God, all of a sudden, I'm not twice as good as I used to be. I'm like 10 times as good as I used to be. And so I think at the, at the unit of like N equals one of like an individual kid, I think the question is kind of how do you get them in a position where they're kind of this kind of super empowered individual such that they're going to be really kind of deep in whatever it is they're going to do, but they're going to, 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

• 13:43Speaker 00

  great, but to be like spectacularly great. And I think that, that, that's, that's going to be the real, you know, that, that, that's the real opportunity and that, you know, at least that's what we're shooting for and that's what I would encourage parents to shoot for.

• 13:53Lenny Rachitsky?

  So what I heard there is essentially agency, this word 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.

• 14:01Speaker 00

  Yeah, yeah. So this, this, this, this thing with this, this term agency, this become very, very, you know, very popular, certainly in California for the last couple of years. It's really interesting because it's, it's, I had a lot of trouble with this early on because I'm like, agency, okay, what are they talking about? And what, what they're kind of talking about is like, you know, initiative, you know, you know, willingness to, you know, you could just do things, you know, what is it? The, the Timo Bridge has the great term live player, you know, you, you can be like a premier participant in events. And at first I was like, well, yeah, like, that's kind of obvious, right? Like, of course. And then I'm like, oh, actually, it's not so obvious anymore because kind of your, your point, I think so much of our society is based on like, there are all these rules

• 14:43Speaker 00

  and everybody gets taught kind of by default, you're supposed to follow all these rules. Right? And then everybody, if you like, break the rules, like, everybody gets freaked out. It's like, oh, my God, he broke the rules. And so like, we, we, we have somehow worked our, our way, our way, kind of, you know, I don't know, psychologically, sociologically, you know, kind of into a state in which I guess the natural assumption for a lot of people is, you know, the thing that you, for example, the thing you want to train kids to do is like, follow all the rules. Um, and you know, you could argue that kind of, you know, for example, the, you know, the school system, K through 12 school system, whatever, has gotten kind of more and more focused on it over time. And it's like, yeah, it's like, no, you, you should actually, and again, especially unit, unit N equals one, like of your kid. It's like, and look, there's, there's something to be had. We, I just had this conversation with my 10 year old last night, actually, I, I, I rolled out

• 15:26Speaker 00

  the concept of, uh, you know, in order to lead, you must first learn to obey. Right? In order to, you know, issue orders, you must learn how to follow orders and, you know, you know, kind of trying to keep, keep him with some level of structure in his life. And that's, and that's just pure agency. But yeah, I mean, and so, look, you know, some rules are important and so forth. But yeah, no, look, there, there is like a huge, there's just a huge premium in life on being somebody who is able to like, fully take responsibility for things. Fully take charge, run an organization, lead a project, create something new. And, you know, maybe, yeah, that, that has been maybe a little bit diminished in our culture over the last 30 years. You know, it's, it's healthy, you know, that, that, you know, that there's now a term for that, that, that is coming back, back into vogue. And then, and then, and again, that's how I view AI for kids is like, okay.

• 16:11Speaker 00

  AI should be the ultimate letter in 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, you know, developing new areas of physics to writing code, to being an artist. Uh, you know, to writing, you know, to writing novels, like, you know, whatever that thing is, I, I can fully participate in the world. I can really change things. And I, and I, that, that feel, the combination of that idea combined with this technology feels very healthy to me.

• 16:35Lenny Rachitsky?

  What is that quote about? Give me a lever and I'll move the world?

• 16:38Speaker 00

  And I'll move the world. Yeah, that's exactly right. Well, so it's actually funny. You mentioned that. So the, um, the, uh, the, the early kind of scientists, including like Isaac Newton, were super obsessed with, with, you know, this concept of alchemy. Right? It's like, you know, they, you know, they, you know, they developed like, you know, Newton, he's like, developed Newtonian physics and he developed like 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, 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 you know, they, there was this, he spent, you know, decades trying to figure out this thing called the philosopher's stone, which would be basically the, the machine or the process that would, that would be able to transmute the rare, you know, the common thing into the rare thing, lead into gold. And he never figured it out. And, you know, it's incredibly frustrating. Nobody ever figured that out.

• 17:23Speaker 00

  And now we literally with AI have a technology that transfers sand into thought.

• 17:30Lenny Rachitsky?

  It just blew my mind.

• 17:31Speaker 00

  Right. The, the, the most common thing in the world, which is sand, converted into the most rare thing in the world, which is thought. Right. And, and so AI is, it is, it is the, it is the philosopher's stone. Like it, it is that. It actually is that. And it's just this incredibly powerful tool. Um, and, and that's where I, that's where I get so excited. I mean, and again, this is what we're doing with our 10 year old, which is like, all right, it's primary thing that we want to make sure to, to do is to make sure that he knows fully how to leverage and, and get, and get benefit out of the philosopher's stone, right? Which is, uh, you know, which is to say AI and that, that, and then, you know, that's certainly central to everything we're teaching him. You know, there's, there's this meme going around that, um, you know, Silicon Valley people don't let their kids use computers. And I, I just, I, I, there may be a handful of people who are like that. I, I don't, you know, I don't know. Um, I, I think it's more honestly the other way

• 18:14Speaker 00

  around, which is, uh, the, you know, the more you're kind of plugged into stuff in Silicon Valley, the, 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, that's certainly the mode that I would encourage parents to think about.

• 18:26Lenny Rachitsky?

  I did not know your kid was homeschooled. That is super interesting. There, it's almost a, a statement on, you know, 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, what advice would you have?

• 18:42Speaker 00

  This is the challenge. And, and again, this, this kind of goes to how you're, you know, kind of 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 kind of at the level of like a nation, right? So, you know, it's like a national level issue or maybe a state level issue in the US, 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 like some level of large scale system, like the, you know, the national K through 12 school system or something like that, you know, in order, in order to do that. But then there's this other question, which is like, N equals one, for an individual kid, like, what can you do with, with an individual kid? And so I'll just give you kind of the ultimate, you know, kind of the ultimate answer to

• 19:24Speaker 00

  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. Like, 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, and this is something that like 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? Like, you know, many of the great kings and queens, you know, royal families and aristocrats and so forth, you know, over the course of centuries, you know, kind of always had, always had this approach. There's actually also statistical evidence, analytical evidence that this is correct.

• 20:09Speaker 00

  There, there's this, you know, massive question in the field of education, which is how do you improve educational outcomes? And basically, it turns out, it's just, it's very hard to improve educational outcomes, except there's one method that always does it, which is called the, it's called the Bloom two sigma effect, which is there's one method of education that routinely raises student outcomes 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 like, at N equals one, you have like a kid and a tutor, and they're in this like, you know, very tight loop with each other, you know, where the kid is able to constantly kind of be on the leading edge of what they're capable of doing, and they can, they, you know, they, they can move incredibly fast, and they get kind of correction in real time. You get these better outcomes. But, you know, to your question, like, 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.

• 20:53Speaker 00

  AI provides the very real prospect of being able to do that, right? Because obviously now, right? If you have a kid that's like super interested in something and they can talk to, you know, an L L M 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 L L M, it's like, you know, teach me how to do the following. And you can say, you know, wow, that's like, I don't quite understand what you're saying. Like dumb it down for me a little bit. Okay, now quiz me, you know, do I actually understand this? Like, people can just do this today, right? And so I, I think there's this like massive opportunity for, for parents, you know, in, in many walks of life to be, you know, with, with, with a little bit of time and focus to be able to say, okay, you know, my, my kid's probably still going to go through a traditional education system, but I'm going to augment this with AI tutoring. And of course, there, you know, and of course, there's going to be tons of startups, right?

• 21:38Speaker 00

  And there already are that are, that are going to try to build on all the, all the products and services for this. Khan Academy, you know, on the nonprofit side, has a big push to do this. And so, you know, I think the, 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 kind of the basis of their philosophy, which is, you know, it's a combination of in-person schools and teachers, but it's also, you know, heavily based on AI and AI tutoring. And so I, I think there's like a, there, there is a magic formula in here that I think is going to apply much more broadly. And I, and it really, for parents interested in this, I, now would be a great time to really start to think hard about that and, and to look at the options.

• 22:17Lenny Rachitsky?

  It's interesting because there's all this concern that young people, uh, 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 be moved so fast and learn so much more. And, and where do you sit on this divide of like, young people are in big trouble or they're actually going to be the ones winning in the end?

• 22:37Speaker 00

  Yeah, so the job, the job substitution, job loss thing is just, it's very reductive. It's, it's, I think it's 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, you know, and again, like I said, it's like at, at half the rate of the, of the previous era and then a third the rate of like a hundred years ago. And so we're, we're, we're coming out of this kind of phase where we've had like 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 like ticks up, even if AI triples productivity growth in the economy, which would like 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 to 1930, people just thought the world was awash with opportunity.

• 23:22Speaker 00

  Right, at that rate of technological transformation, kids were able to like develop new careers into new areas of the economy, building new kinds of products and services. I mean, you know, a huge part of our, of everything in our modern world today was kind of invented and proliferated kind of during that period. And so even if AI like triples the pace of economic change in the economy, it's going to just translate to like a much higher rate of economic growth is going to transfer, translate to a much higher rate, higher rate of job growth. And you know, there, there'll be some level of like 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 that then corresponding to that, there will be, you know, there will, there will be hiring booms, you know, I quite honestly, like all over the place. And then again, go back to the, the other thing, which is like, this is all happening

• 24:06Speaker 00

  in the face of declining population growth and increasingly population shrinkage. And so human workers in many, many, many countries over the next, you know, 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. You know, we don't really want to get into, you know, politics particularly, but it does feel like the world broadly is going, is going to reverse course on, on, on the rates of immigration that we've had for the last 50 years. It seems to be kind of a broad-based, you know, kind of thing happening, you know, kind of with, you know, rise in nationalism, you know, concerns about the rate of immigration and immigration historically in countries like the US, you know, it's, it's kind of ebbed and flowed over time, based on kind of how, you know, kind of how the, the national mood shifts. And so if you sort of combine in a country like the US or any country in Europe, if you

• 24:48Speaker 00

  combine declining population with less immigration, you, the, the remaining human workers are going to be at a premium, not at a discount. And so I think, I think that combination of kind 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 kind of dystopian, you know, no jobs thing, I just think is probably totally off base.

• 25:10Lenny Rachitsky?

  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 kind of just works out, does population decrease, you know, like all these kind of have to line up for there not to be this massive job loss with AI?

• 25:24Speaker 00

  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, what we'd be staring at is a future of depopulation and like depopulation without new technology would just mean that the economy shrinks. Right, so it would mean that the economy kind of itself kind of shrinks over time. You know, the opportunity diminishes, there are no new, there are no new jobs, there are no new fields. There's no, there's no new source of consumer demand for spending on things. And so you would, you would, you would be very worried about going into a period of like severe decline of stagnation. And you know, you know, essentially you'd be looking at these like very dystopian scenarios of like an economy kind of self euthanizing itself. Uh, over time, um, and you, it's, you'd be very worried about like the opposite of what everybody, you know, thinks that they're worried about.

• 26:08Speaker 00

  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 then, you know, also for the, for the lack of immigration is likely. And so, you know, I would say the timing has worked out miraculously well in the sense of we're going to have AI and robots precisely when we actually need them. Uh, to keep the economy from actually shrinking. Um, and, and I just think like that, that's just like a fundamentally, a fundamentally good news story. To get to the mass job loss thing that people are worried about, um, on the other side of things, you know, you have to, you'd have to look at like far, far, far. Higher rates of productivity growth, you'd have to look at rates of productivity growth that are 10, 20, 30, 50% a year. You know, something like that, which are, you know, orders of magnitude higher than we've ever had in any, in any economy in the history of the planet.

• 26:51Speaker 00

  You know, it's possible that we get that. I mean, look, I'm, you know, I, I have my utopian kind of, you know, kind of, you know, temptation. Along with everybody else, if, if AI like radically transforms everything overnight, then maybe you, you know, let's, let's play out the kind of utopian scenario. Uh, you get to a much higher level of, of productivity growth, you get to a much higher level of technological change. Corresponding to that, you'll have a massive economic boom, uh, you'll have a, you know, massive growth in the economy. Uh, and then corresponding with that, you'll have a collapse in prices. Um, and so the price of goods and services that are, that are, that are sort of, you know, whatever you're going to call it, 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.

• 27:33Speaker 00

  And that's the equivalent of a gigantic increase in wealth, right, across the society. Right, think of it this way, this is actually worth talking about because people I think get kind of sideways on this issue. So if AI is going to transform the economy as much as the, you know, whatever, utopians or dystopians or whatever kind of thing 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, right? So you're substituting in AI for human workers or whatever, and as a consequence, you get like 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 effective sectors.

• 28:18Speaker 00

  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, right? The development of new fields. Everybody's like materially like much better off very quickly. And then, by the way, if you, to the extent that you do have unemployment coming out the other side of that, it's now much cheaper to provide the kind of social safety net to prevent people from being immiserated, right? Because the prices of all the goods and services that like a welfare program has to pay from, they're all collapsing, right? And so the price of healthcare collapses, the price of housing collapses, the price of education collapses, the price of everything else collapses.

• 29:01Speaker 00

  Because this incredible impact that AI is having. And so in this kind of utopian dystopian scenario that people have, it's not, there's no scenario in which like 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, you know, for some reason can't find a job. And so like, like maybe we end up in that scenario. I mean, the kind of 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 like just a very straightforward extrapolation of very basic economics. I'm not making any like bold predictions of what I just said.

• 29:43Speaker 00

  This is just like a straightforward mechanical process that plays itself out if you have higher rates of productivity growth, which are necessarily the results of higher rates of technological growth. And so I think we're, I think we're looking at, and to be clear, I think we're looking at a world that's not like 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 is overwhelmingly, I think that 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 described.

• 30:14Lenny Rachitsky?

  I love hearing optimism and good news. I will also add that you've been, 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. You, 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, you had this debate with Peter Thiel that I came across where you were debating whether technologies stop progressing or if new technology will continue to emerge.

• 30:58Lenny Rachitsky?

  And you were arguing there's 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, so again, I'm just, I love hearing your predictions because I feel like they're actually going to turn out to be correct.

• 31:15Speaker 00

  So I should start by saying I've been wrong about tons of things, but you know, I buried those out back behind the shed.

• 31:21Lenny Rachitsky?

  Delete them from the internet. No web browser can discover them.

• 31:24Speaker 00

  I have them nuked out of the internet archives so that they're never seen again. So, you know, I'm wrong plenty of times also. But yeah, I mean, look, I think, yeah, some of those I got right. By the way, I will say on the, on the Peter one, I have come, I've come much further around to Peter's point of view. I would probably argue that one like quite a bit differently today than I did. And I would give his view, I think, I think a lot more credit. And it actually goes to kind of the discussion that we, the kind of conversation we just had, which is the, the real form of what Peter was arguing was we have lots of process and bit, we have lots of progress in bits, right? But we have, 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 kind of glossing that over a little bit because I was so focused

• 32:07Speaker 00

  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 Adams is real. And again, this goes back to this thing of like, and 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 Adams. 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 contrast that, 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 today, it's not that different. Right? And look, you just see that, you could just like walk around and it's just like,

• 32:51Speaker 00

  oh yeah, there's a bunch of buildings that were built in like 1960, right? And there's a bridge that was built in like 1930, and there's a dam that was built in like 1910, and there's a city that was founded in 1880. And like, what have we done? Where are our new cities? Where are our new dams? Where's the California High Street Rail? Like, 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 kind of utopian or dystopian view of like, everything changes overnight. I think it just kind of can't happen because of the reasons that Peter articulates, which is there's so much about how the world works that's basically just like wrapped

• 33:35Speaker 00

  up in red tape. Like bureaucratic process, rules, restrictions, the politics, by the way, unions, cartels, oligopolies. There's all these structures in the world that are kind of economic or political or regulatory structures that basically prevent things from changing. And so, I mean, let's take a great example, like AI's impact on the healthcare system. Like, by rights, AI is going to have a dramatic impact on the healthcare system and in very positive ways. But, you know, a large parts of the medical system today, they are cartels, right? And so, there's like, the doctors are a cartel, and like nurses are a cartel, like hospitals are a cartel. And then there's this push to like nationalize

• 34:18Speaker 00

  all the healthcare systems. And then you've got a government monopoly, right? And it's like, and, and, and guess what cartels and monopolies don't like is they don't like, like rapid change. Right. Um, and so, you know, you show up as a kid and you're like, wow, I've got like this new technology to do like AI medicine. And they're like, oh, well, does it threaten doctor jobs? Well, in that case, we're gonna, we're gonna block it. So. And I think a lot of consumers, by the way, you know, I don't, I, I see this in my life and you, you'll probably see this in your life also, which is, you know, like chat GPT is like almost certainly a better doctor than your doctor today. But like chat GPT 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, you know, perform procedures, right? And so there, there, there are these, anyway, so Peter, Peter, I think was very articulate and has been for a long time on like,

• 35:01Speaker 00

  no, there are actually real structural impediments in the economy and in the political system that we have that actually prevent any, the race of change that are anywhere near the race of change that people had in the past. And, and you can maybe say optimistically, you know, maybe the presence of it, of the new, of the new magic technology of AI. Maybe it causes us to revisit a lot of these assumptions, assumptions for the first time in decades to really say, okay, 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.

• 35:26Lenny Rachitsky?

  It's time to build, somebody famously said. I, uh, in my calendar, I actually have that as my, when I start to work, it's time to build. That's my block in the morning of the day. Thank you for that. Okay. I love, I love the way you go from just like macro to just like N of one. And I want to go to N of one. A lot of the listeners of this podcast are product managers, they're engineers, they're designers, they're not a lot of, there's a lot of founders, but there's also a lot of non-founders. There's a lot of people building product that aren't founders. And, uh, 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 just the future of these three very specific roles? Product manager, engineer, designer?

• 36:09Speaker 00

  This, I think is really funny question. So the, the, these three roles in particular, obviously are kind of the central roles for, for building, you know, for tech companies. So the way I've been describing it is, you know, you know, the concept of the Mexican standoff, right? Which is the, the movie scene where the, you know, the two guys have guns pointing each other's heads. Um, and then there's, if you watch like John Woo movies, he loves to have, he does the three-way Mexican standoff where you've got like, a triangle, you know, people and like, you know, and of course it's John Woo movie is they've got, you know, guns in both hands. So they're all, each is aiming at the other two.

• 36:38Lenny Rachitsky?

  Yeah.

• 36:38Speaker 00

  And you've got this kind of standoff situation. And so the way I've been describing this is there's like a Mexican standoff happening between those three roles, between product manager, designer and coder. Specifically of 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, right? And a coder. Right. And so people in each of those roles now, you know, 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, and then, of course, there's the real irony, which is, you know, all three of them are going to realize that AI can also be a better manager. Right? So they're gonna, they're gonna aim the guns up the orange chart. But that's probably, that's the next phase.

• 37:23Speaker 00

  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, you know, it's now, 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 empowered, this kind of idea of the super empowered individual. Where if I'm a coder, like, you know, I mean, step one is like, I need to make sure that I really understand AI coding and like what that means and what, how coding is going to change in the future. You know, that I need to understand, you know, specifically how to go from being a coder who writes code entirely by hand to being a coder who, you know, orchestrates, you know, a dozen instances of, you know, coding bots.

• 38:05Speaker 00

  You know, there's a change in the actual job of coding itself, which is, which is happening right now. But the other part of it is, okay, how do I become that super powered 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, you know, do AI, AI based design? And the same thing for the designer, which is how do I use AI to be, also become a coder and also become a product manager? And then what you get is maybe the, maybe the, those individual roles change. Like maybe those are not anymore sort of stovepipe roles of the way that, you know, 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 powered and they become good at doing all three of those things.

• 38:48Speaker 00

  And then, and then those people become incredibly valuable because then those are people who can actually like, you know, build and design, right, new products, right, from scratch, which is like, you know, which is, which is the most valuable thing. And so I think, I think that's, I think, I think that's the opportunity.

• 39:01Lenny Rachitsky?

  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.

• 39:07Speaker 00

  Number one, if you're amazing at these roles, that's great. But also, you, part of being amazing in these roles is also being, being able to fully harness the new technology, right? So if you're, if you're a master coder today, and you, you don't ever get to the point where you, you figure out how to use AI to leverage your coding skills and, and do more, right? Like, 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, and then what, what, 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, is task, task loss, right? Tasks changing. I

• 39:50Speaker 00

  mean, the, the, the, the classic, the classic example of task changing, classic example of task changing was, once upon a time, executives never used typewriters or personal computers themselves, right? You know, if you were a vice president of a company in 1970 or whatever, you did not have like a typewriter or a computer on your desk typing things. You had a secretary who you dictated memos to, right? And then there, and then there was this change where like email started to show up. And what would happen was, the job of the secretary then went from, you know, it went from, you know, the, the, the job of the secretary changed from sending out letters with stamps on them to like sending or receiving emails with the other admins. And then, and then the secretary would print out the email and bring it into the executive's office. And the executive office would read the email on paper, scroll, scroll the reply, and, and, and give, and give that message back to the secretary, who

• 40:34Speaker 00

  would go back and type it into the computer on, on, on, 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. You know, they're travel planning and orchestrating events and like doing all these other things, you know, that, you know, that, that, that great admins do. And then, and then the task, the task set ironically of the executive has expanded to do actually more of the clerical work themselves, actually like sit there and like type their memos, which again, 50 years ago, they never, never would have done that. And so the executive job still exists, the secretary job still exists, uh, but the tasks have changed. And, and I think that's like a great example of what's going to happen in coding. The tasks are going to change. It's what's got product management, the tasks are going to change. Designer tasks are going to change.

• 41:18Speaker 00

  And so the, the, the job can, the job persists longer than the individual tasks. And then as the tasks change enough, then that's when the jobs change. And so at the, at the level of an individual, you kind of want to think of like, okay, I have this job. The job is a bundle of tasks. I need to be really good at making sure that I can like swap the tasks out. Right. I can, I can really adapt, use the new technology, you know, get really good at AI coding, for example, uh, like, you know, and then, and then you want to kind of 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. So you want to kind of pick up more and more scope as you do that. And then, you know, 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?

• 42:02Speaker 00

  It's like whatever that, 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, that's the track that the best people are going to be on. Um, and, and I think that, that's the thing to lean hard, lean hard into.

• 42:16Lenny Rachitsky?

  I think people aren't fully grasping just specifically software engineering and how much that is changing. Like 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 this is where it's 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.

• 42:38Speaker 00

  Yeah. So again, here I go back and again, pardon maybe the history lesson, but like I go back like coding. So the first, uh, you know, do you know that the original definition of the, of the term calculator? Do you know what that referred to?

• 42:50Lenny Rachitsky?

  Hmm, no.

• 42:52Speaker 00

  It referred to people. Right, so back before there were like 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 insurance company would calculate actuarial tables or the military would like calculate, you know, I don't know, whatever 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 like big rooms. You can have hundreds or thousands or tens of thousands of people doing this. And you would actually, you would actually figure out, you have somebody at the head of the room who is like responsible for like 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 those, that, that job title was those people were calculators.

• 43:34Speaker 00

  Right? And so we've gone from a world in which you literally have people doing mathematical equations by hands, by hand. Then we got the first computers. The first computers, of course, didn't have programming languages, right? They, 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, you know, there's still people, you know, kicking, you know, today who, you know, whose job as a programmer was to like deal with 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 like with some level of like English kind of added to it. And then the best programmers did assembly language. And then, you know, when I was coming up, it was higher level languages like C that compiled into machine code. And that's what programmers did.

• 44:19Speaker 00

  And then I still remember when, when scripting, you know, when scripting languages, you know, we developed JavaScript and Netscape and then, you know, Python took off and Perl and these other scripting languages. The scripting languages, you know, took off in the, in the, in the, in the, in the 2000s. There was this big fight, you know, in the technical community, which is, is scripting real programming or not? Right? Cause it's, it's like, it's kind of cheating, right? Because real programmers write code that compiles to machine code. And like real programmers like do like memory management themselves and they do all, you know, they get this, this, this whole craft of writing, writing, you know, writing, writing C code. And you know, these JavaScript or Python programmers are just doing this kind of 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 like five layers of detail underneath that

• 45:03Speaker 00

  that people used to do by hand and they don't anymore. And then, and then there's, and then your point, like AI coding is the next layer on that. AI coding actually abstracts the way 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, you know, 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, you know, browser to browser or terminal to terminal. And they're, and they're, they're, they're, they're, their day, their day job now is kind of arguing with

• 45:47Speaker 00

  AI bots, trying to get them to like, write the right code. Right? And then, and then debug it and fix the problems and change, change this back and do all these things. And so now, now the job of the programmer is to argue with the coding bots. But like, 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 know, you asked about the 10, you know, our 10 year old is, you know, super into computers and super into programming. And what I'm, what I'm telling you, and he's, he's using Claude and ChatGPT, Copilot and all these things. And what I'm telling him is like, look, and by the way, he loves vibe coding. He's on Replit all the time doing vibe coding, you know, doing games, you know, he's sitting there, you know, it's hysterical, right? Because he's sitting there. It's a 10 year old, basically, who's, you know, spends two hours at dinner arguing with an AI for fun. Right? Right. But, but what I'm telling him is no, look, you need

• 46:28Speaker 00

  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, like 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 doesn't need to understand ultimately how the microprocessor works. And so again, it's kind of this up-leveling of capability where you actually want the depth 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 1,000 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.

• 47:11Speaker 00

  But are human beings going to be involved in like in the coding process and overseeing the AI coding and all that? And the answer is of course, absolutely 100%. Like no question.

• 47:21Lenny Rachitsky?

  So you're in the camp of still learn to code, still a valuable skill.

• 47:24Speaker 00

  Oh yeah, totally. Well, again, if you want to be one of these super, look, if you just want to put your like self in autopilot and like, 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, you know, 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 like really matter, then yeah, you 100% want to still be, 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.

• 48:06Speaker 00

  By the way, you also really deeply want to understand how the AI itself works. Right? Cause you want to, right? Cause if people understand how the AI works are able to, they're clearly able to get more value out of it than somebody who doesn't understand how it works. 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 a new technology, yes, you 100% 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, like, this isn't fast enough. I need to figure out as a coder, I need to figure out how to do a different approach to memory management or something.

• 48:52Speaker 00

  And you'd be like, well, you know, shit, like, I, you know, 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 like incredibly synergistic relationship with AI, where it's also helping you get better at the same time that's doing a lot of work for you.

• 49:07Lenny Rachitsky?

  By the way, I was gonna say, I was a big Pearl programmer. I was an engineer for 10 years and that was my, my language of choice.

• 49:13Speaker 00

  You, do you remember, I don't know, when you were doing it, but do you, do you remember that at the, at least early on, you remember, did you, did you ever hit this where like, C coders were like looking down their nose at you being like, eh?

• 49:23Lenny Rachitsky?

  For sure, it's like, this is so slow, it's not gonna scale. What are you, what are you spending all your time on this thing?

• 49:27Speaker 00

  Yeah, exactly. And of course, you know, and again, it was sort of this thing where, you know, they were, they were sort of correct, which is at the beginning, it wasn't, you know, fast enough or whatever. By the end, they were definitely wrong, right? Which is it got much better, much faster and, you know, it swept the world. You know, 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 actually work really well. Right? And so that was, that was a great example, this kind of adaptation. And then, again, the result of that was, you know, a far higher number of people writing code in scripting languages than were ever writing code with lower level languages. And I, I think this will just kind of be a more dramatic version of that.

• 50:04Lenny Rachitsky?

  I love that Pearl was designed by a linguist. I don't know if you remember that part? And that's what made it so nice to, to code with.

• 50:10Speaker 00

  Well, that's funny because of course it was so notorious for being impossible to understand, so.

• 50:15Lenny Rachitsky?

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• 50:57Lenny Rachitsky?

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• 51:39Lenny Rachitsky?

  Coming back to this kind of triad, the other element that I hear more and more of is just as 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.

• 51:54Speaker 00

  Yeah, that's right. And again, here, this is a great example. So again, the task level, the task level of like, 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. Like, it's going to be fantastic, whatever, you know? And there will still, by the way, there will still be some level of human icon design or whatever. But like, AI is going to get really good at that. But like, what are we trying to do? Like, the, you know, kind of capital D design of like, all right, what is this thing for? And how does this, how is this going to function in a world of human beings? And like, you know, what's going to, is this going to make people happy when they use it? It's going to make people feel good about themselves. Is it going to fit into the rest of their life? Is it going to, you know, I don't know, challenge them in the right way?

• 52:36Speaker 00

  You know, all these kinds of higher level questions that the great designers have always thought about, like 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, you know, one way to think about it is, you know, I don't know, you think of like, I don't know, the world's best designers, you know, Johnny Ive or whatever. You could be like, wow, like, if I'm a designer today, if I'm a 25 year old designer and I aspire to be, you know, Johnny Ive in a decade. It's all of a sudden I have a new path that I can use to kind of get to get there, which is I, you know, because Johnny, I did everything you did without AI. Now, you know, a young designer can be like, wow, if I really harness AI in a decade, I'm going to be like 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

• 53:21Speaker 00

  able to do so much more. And then so much more of my time and attention is going to be, is going to be able to be focused on these higher level things that most, most designers never get to. And I think that that's going to be another great example of that.

• 53:31Lenny Rachitsky?

  So maybe what I'm hearing here is kind of this T-shaped strategy of the, 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.

• 53:44Speaker 00

  Well, so I think that's great. I think that's really, really relevant. And then, you know, Scott, you know, Scott Adams unfortunately just passed away, you know, which is, which is a real tragedy, but I was always, I referred for years to actually Scott's, Scott Adams, he had this famous, he had this famous kind of career advice he would give people, which I think makes a lot of sense, which, which, which dovetails with what you're saying, which is he, he used to say, he used to say, it's like, look, he said, you know, I could, he said, you know, I could have been a pretty good cartoonist, or I could have been like pretty good at business, but the fact that I was a cartoonist who understood business made me like 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.

• 54:27Speaker 00

  It took somebody who actually had both of those skills to be able to make Dilbert, right? Which is one of the most successful cartoons in history. Right? And so, so the way Scott always described it was that, that, from a career development standpoint, that additive effect of being good at two things is like more than double. Right? The additive effect of being good at three things is more than triple. Right? Because you, you, you become a super relevant specialist in the combination of the domains. And you, like, you see this all, I mean, you see this all over, you know, you see this all over the economy, I mean, you see this all over the economy, but I'll, you know, give you an example, Hollywood, yeah, just Hollywood as an example. You know, 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.

• 55:13Speaker 00

  Right? And you know, they they don't have a term for those. They call those auturs, right? And that's, you know, th- those are the people who are like the real creative forces that move the field. And so, and so again, and by the way, Hollywood, it's just really funny. It's been, it's been spending a lot of time talking to Hollywood people about AI. Hollywood has the same Mexican standoff going, um, right now that we, 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 AI can do the actors. And the actor is saying, I don't need either one of these guys. I can have AI direct the thing. I can have the AI write the thing and I'm just going to show up and do my performance. Right? And so, so it's, it's, it's the same, it's the same kind of tri- triangular configuration.

• 55:57Speaker 00

  And again, what's, what's great about it is they're all correct, right? E- each person in each of those three fields is going to be able to expand laterally and pick up those other, tho- 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, and I think, you know, to your point, like your, your T-shift thing, like I, I think that's going to be true basically across the entire economy. And, and if you think about the T, it's, you know, if you think about the T configuration, it's like, yeah, the, the breadth, 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 the, the, 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?

• 56:38Speaker 00

  But like, if you're like super deep on coding and you can use AI to do design and you can use AI to do product management, right? That, that's your T right there. And, and you're a triple threat at the top of the T, but with this level of technical grounding underneath that. And I mean, at that point, you're, again, you're the super empowered individual. You're going to be able to just perform like feats of magic. Uh, for example, in terms of designing and building your products, you know, the, the people in my generation couldn't have even dreamed of. And so I, I, I think, I think that this is a universal kind of theory that I think can, can apply across the entire economy.

• 57:06Lenny Rachitsky?

  I'm going to invent a new framework right now. Okay, forget the T framework. Uh, I'm picturing an F sideways or an E. Or there's three, two or three. I don't know, downward parts. And so what I'm hearing is get good at least two.

• 57:20Speaker 00

  Yeah, I think 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, you know, that's he's an economist. And so that was economics speaking. And what that means is, 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, if you're, if you're quote unquote, you know, again, just a designer, just a product manager, just a coder, like then in theory, you can be swapped in or out. But if you have this, if you have this, yeah, if you have this E or F, you know, laying on its side kind of thing. And if you have, if you have this combination of things, it's actually quite rare, then all of a sudden you're not fungible. Not only you're not fungible, like you're actually massively important because you're one of the only people in the world

• 58:05Speaker 00

  who can actually do that combination of things. And yeah, that, that your ability not to become one of those people is like

• 58:11Speaker 00

  titanically enhanced with AI as compared to anything we've ever seen before.

• 58:14Lenny Rachitsky?

  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. 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.

• 58:37Speaker 00

  That's right. That's right. And again, I can't, I can't overstress the following for anybody listening to this. The thing about AI that I think people are just like not getting enough benefit out of yet is just it will teach you.

• 58:49Speaker 00

  Like, this is amazing. Like, there's never been a technology before where you can ask it, like, teach me how to do this thing. So it's, I always feel like it's like, it's like people spend too much, it's one of these things where it's like so much focus on figuring out how to use like a large language model is like, okay, what am I going to try to get it to do for me? Right? Which is first 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 is this level, this level of latent superpower. Like, you know, people who really want to like improve themselves and like develop their career should be spending every, every spare hour, in my view, at this point, talking to an AI, being like, all right, train, train me up, like, help, help, super empower me. Tell me how to, you know, train, train me, train me how to be, you know, I'm a coder. Train me how to be a product manager. It will happily do that. It knows exactly how to do that.

• 59:35Speaker 00

  You know, run me, you know, make me problems, you know, make me assignments, then evaluate my results. Right? And it will, it will do that just as happily as it will do work, quote unquote, for you.

• 59:43Lenny Rachitsky?

  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 like 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 unstuck 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?

• 1:00:14Speaker 00

  Yeah, that's right. That's right. Yeah, look, on that first one, and this, again, this is what I'm doing with my 10 year old. Yeah, look, if, if, if you ask me, yeah, this is a really good point. So if you ask an AI, I know, write me this code, and then, and then it doesn't, it comes back and it doesn't work right. Like, if, if all you know is like, single function I asked it and it gave me back something that's not good, like, well, what do you, like, what do you even do with that? Right? Like, you, you don't understand why it gave you that result? Do you really understand it even what, you even understand what to tell it to try to get it to do something different? But to your point, like, if you actually watch, if you actually watch what it's doing, and, and, and then, and then you, you have the grounding, you know, kind of that leg of the, 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 to do this other thing.

• 1:01:00Speaker 00

  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. 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 do need to understand what was happening in your head. Right? In order to, in order to be able to get, do you need to give you, 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. 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.

• 1:01:44Speaker 00

  You know, and 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 could play the AIs 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.

• 1:02:01Lenny Rachitsky?

  I think people call those LLM councils.

• 1:02:03Speaker 00

  Yes.

• 1:02:03Lenny Rachitsky?

  That they're talking to each other.

• 1:02:05Speaker 00

  Yeah, that's right. That's right.

• 1:02:06Lenny Rachitsky?

  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.

• 1:02:24Speaker 00

  So my, my true confession is I've always kind of wanted to be a cartoonist.

• 1:02:29Speaker 00

  But I have no like art skills. But as we're talking, I'm like, hmm, it might be time.

• 1:02:35Lenny Rachitsky?

  Your time has come, Mark.

• 1:02:36Speaker 00

  Yes.

• 1:02:37Lenny Rachitsky?

  I want to pivot to founders, your, maybe your bread and butter. You spend 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 a company looks? How the future of AI forward companies look?

• 1:02:56Speaker 00

  Yeah. So this is a great, it's a very, you know, topical topic. It's all playing out in real time right now, um, on, 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. Um, and, 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, um, 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?

• 1:03:34Speaker 00

  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. Um, you know, it's kind of another ingredient into, into an existing formula. And of course, you know, a lot of new technologies are like that, right? Um, you know, I don't know when, I don't know, when flash, when flash storage came out or something, you know, P P didn't really, you didn't really redefine the, the software industry because people just went for using, you know, hard disk, using flash storage or something.

• 1:04:06Speaker 00

  But when the internet came out, like basically old school on-prem software, for the most part, you know, not, 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 of like, you know, an example, you just mentioned Nano Banana. So like a, 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?

• 1:04:46Speaker 00

  Or, you know, do you just like stop editing images entirely because you're using Nano Banana and your, 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, edit an old one. And so. I think, you know, there's many areas of, of tech in which that question is being asked and, you know, the answer is, I think, will vary by domain, but. Uh, you know, obviously as, as a venture firm, we're, 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. Um, 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.

• 1:05:29Speaker 00

  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. Or does that mean I still want 100, but now they're doing 10 times more? Right? And so that, 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, 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.

• 1:06:12Speaker 00

  And then 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, you, 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. Um, and so I, I think, you know, so, so the, the most leading edge founders are thinking of like, okay, how, how do I reconstitute the actual very definition or idea?

• 1:06:55Speaker 00

  Of a, um, uh, uh, 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 if you're doing, 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 AI? 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, as a business and like making money and just, you know, literally where the, the AI does all the work itself. And just get, you know, issues me dividends. And so maybe, maybe, you know, maybe that, that, you know, maybe that, maybe that's the, 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 latter that the best founders are on.

• 1:07:36Lenny Rachitsky?

  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 like 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.

• 1:08:12Speaker 00

  Yeah, I mean, look, Bitcoin's Satoshi pulled it off.

• 1:08:15Lenny Rachitsky?

  But like, you know, the open source community, you know, like, does that count? I don't know. I guess, I guess it counts. Okay.

• 1:08:21Speaker 00

  Yeah, exactly. Right. So, yeah, that, that, that, yeah. And I would say I don't propose to have answers here, but more just like, the smartest people I know are, are many of the, many of the smartest people I know are thinking hard about this.

• 1:08:33Lenny Rachitsky?

  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?

• 1:08:44Speaker 00

  My experience with like really big technological transformations, and of course, 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 is 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.

• 1:09:30Speaker 00

  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, 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've 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 were wrong. Again, generally like quite badly wrong.

• 1:10:14Speaker 00

  And so I just, I think the process, I think with massive, with, if 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 then again, a lot, we've talked about a lot of this, but like it, it, the implications on like, what are the definition of products? What are the definitions of companies? What are the definitions of, of jobs? What are the definitions of industries? How does this play out at the national level? How does this play out at the global level? You know, how does this intersect? 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? Um, you know, uh, and so it's just like, there's just, there's, they're just a tremendous number of unknowns. Like a, a, a, a, a very, very large number of unknowns.

• 1:10:58Speaker 00

  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, 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. Um, and you, you know, you need this like incredible critical mass of like compute and 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 MBA stars. Um, 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.

• 1:11:39Speaker 00

  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, a hundred percent, you know? I don't know, whatever 50, 50 or 30, 30, 30 or 90, 10, 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, 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, um, you know, that in the, uh, you know, kind of Christmas of chat GPT, that like within basically a year, a year and a half, there would be, you know, five other American companies that would have basically, you know, exactly capable products. Um, and then there would be another five companies out of China that would have exactly capable products.

• 1:12:24Speaker 00

  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 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 G P T three coming out, there were their open source G P T threes running on a fraction of the hardware. Right? They 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 X A I 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 L L M's or, you know, A I models, like, you could squint and make that argument either way. By the way, same thing at the level of apps, right?

• 1:13:08Speaker 00

  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 L L M's commoditized and maybe the value goes to the apps. And, and again, you can kind of squint either way on that one. And I, 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, it's in the way I think about this kind of structurally is it's a complex adaptive system.

• 1:13:50Speaker 00

  The technology itself, you know, provides one of the inputs, the legal and regulatory process, you know, is another input. In, 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, we actually don't know. The, 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 V C, this is very exciting because it means we, you know, we, we're doing this now, we should kind of make bets. Along every one of these strategies and kind of see, and, and see how this plays out.

• 1:14:28Speaker 00

  And I would just say like, there may be like one, I don't know, there may be like one particularly brilliant, I don't know, edge fund manager or something who has this all figured out, but I, I guess I would say if, if, if they exist, I haven't met them yet.

• 1:14:38Lenny Rachitsky?

  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.

• 1:15:01Speaker 00

  Yeah, well, it's like a little bit like, I don't know. I mean, even just like, you know, the, you know, the, you know, this has been the, you know, the, the holiday. If, you know, three years ago was the holiday of chat GPT. This last, you know, month or whatever has been the holiday of Claude, particularly Claude code, right? For, for coding. But it's like, you know, it's, 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, uh, coworker.

• 1:15:30Lenny Rachitsky?

  Cowork.

• 1:15:31Speaker 00

  Cowork. And, uh, and remember what they said at coworker, which is a Claude code, work coworker in a week?

• 1:15:36Lenny Rachitsky?

  Yeah, a week and a half, yep, a hundred percent.

• 1:15:39Speaker 00

  Well, and that's, and there's two ways of looking at that, which is like, wow, that's really impressive. I mean, obviously, that's really impressive that Claude code was able to build coworker in a week, in a week and a half. That's great. That's amazing. The other way to look at it is coworker was developed in a week and a half. Like, like how, how much complexity could there be? How much of varied entry can there be in something that was developed in a week and a half? And so, and, and then, you know, and then again, it's this, 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 are 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, I'm sure you'd have to expect to sit in there being like, okay, obviously we need to build, you know, an agent artist.

• 1:16:23Speaker 00

  And then obviously we need to build a coworker, you know, thing for, for, for regular people. And obvious, you know, I, I don't, I don't understand 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 in six, is, is Claude code going to get lapped the same way that, you know, GitHub 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 lap each other on a regular basis, but you know, there, there's not a lot of proprietary anything at this point.

• 1:17:07Speaker 00

  Um, 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, you know, your re-implementation of a lot of the ideas on the American big labs and, you know, and had some original ideas of, of its own. Um, 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. Um, and they're, you know, incredibly bright and creative people. Um, 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, I think we just need to, I don't know, my views, I, 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,

• 1:17:51Speaker 00

  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, I was gonna say, I don't think I can predict that. Um, I, I think, I, I think a much, much better use of my time is it being, being very flexible and adaptable at a time like this.

• 1:18:07Lenny Rachitsky?

  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?

• 1:18:22Speaker 00

  Yeah. So for, I mean, for us, yeah, for, for us, we have, we obviously have a very deliberate strategy. One, one way to think about this is the Peter Thiel, 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 determinate and, and is it indeterminate and indeterminate, uh, right? Um, and so, um, and he always argued like there's, he always argued like Silicon Valley is characterized by in 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

• 1:19:06Speaker 00

  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 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, you know, he, 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 determinate, determinate, determinate 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 do, you know, Mars, you're right? And I mean, these very concrete things. And I, 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, 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.

• 1:19:50Speaker 00

  And that's the strategy that we, that we have at A60Z, which is, and, and the reason for that is, is it's, 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 A60Z 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, and you know, kind of product creators, right? And, and each of those individual people is a determinate optimist, like each of them, each of them individually has like a very strong view of what they're going to do. 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, and we don't just have 10 of those, we have 100 and 1,000, and then 10,000 of those.

• 1:20:33Speaker 00

  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 it 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, try people try to do as many interesting things as possible. Um, and so, yeah, I would, I would put myself firmly on the side of the indeterminate optimist.

• 1:20:55Lenny Rachitsky?

  I mean, uh, I'm wondering if the answer to the question of what you look for now more and more is this determinate optimistic founder?

• 1:21:01Speaker 00

  Yep.

• 1:21:02Lenny Rachitsky?

  That has this massive ambition and is actually working on achieving it.

• 1:21:06Speaker 00

  Yeah, no, that's right. That's right. I mean, look, the founders need to be determined and optimists. Like, they need to have a very specific plan now. And, you know, look, the critique, the critique always, you know, the critique from the founders is, oh, UVCs have it easy because 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 bet you lay in, you can like place multiple bets, you can operate as 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 kind of 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. And so, you know, the great virtue of being a determinate 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?

• 1:21:50Speaker 00

  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 determinate optimist is the, you know, the founder is the founder and the company builder and the engineer, and these are the people who actually use the same and, you know, deserve 99.9999% of the credit. But, you know, having said that, I do think there is a role for having somebody determinate optimist in the, in the background, not helping along the way and helping keep the whole, the whole cycle going.

• 1:22:17Lenny Rachitsky?

  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?

• 1:22:29Speaker 00

  Yeah, so I've always kind of had a little bit of, I've always kind of struggled with the concept of AGI. Because it, at least, well, there's those 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've described 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, is the moment where you enter the singularity, which is to say that where the world fundamentally changes and like the, the rules of the old world are gone, we're now 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.

• 1:23:13Speaker 00

  The AI is improving itself and it's sort of racing, you know, so-called takeoff scenarios. You see this takeoff thing. Where the AI is improving itself and the machines are making decisions so much faster than people. People are just sitting there watching the 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 the prosaic definition of AGI that at least I think the industry purchase has kind of converged on. And tell me if you agree with this is it's when the AI can do every economically relevant task as good as a person.

• 1:23:47Lenny Rachitsky?

  The way the co-founder of Anthropic put it is like a basket of the most valuable economic tasks. So it's like 10, 15, not every single economically valuable task.

• 1:23:56Speaker 00

  Okay, got, got it. Yeah, so that's maybe even a slightly reduced, slightly reduced definition. And by the way, we're going to, you're 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 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, so we said that as AGI always is, you know, the definition you gave, the definition I give, 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.

• 1:24:41Speaker 00

  Right? Like, we're all, you know, human, I gave you an example, human IQ, human IQ, you know, kind of what they call fluid intelligence or the the sort of G factor of kind of, you know, of fluid intelligence. IQ, I think tops out in humans as a species, it tops out around 160. Right? Where at like 160, it's like Einstein level, Einstein find the

• 1:24:59Lenny Rachitsky?

  Terms of IQ.

• 1:25:00Speaker 00

  In terms of IQ, like, you just tops out at 160. The 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 is 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 is probably 105, right?

• 1:25:41Speaker 00

  And so the the kind of scope of like impressive human, you know, the the the 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, and you already have people running these experiments to kind of do human equivalent, you know, kind of IQ, uh, you know, for for existing AI model. And by the way, existing AI 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 mast size starting to get to the 160 level now.

• 1:26:26Speaker 00

  But like, I, I think we're going to have AI 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, I feel, 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, greater than Einstein. But like, I think IQs, IQ of the machines is going to exceed that of the humans. I think that's, that's really good. And then the performance, you know, again, it goes back to like the AI coding thing that's happening. The performance against task is going to get better also. Like, I think, you know, this is where Linus Stravolts 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

• 1:27:11Speaker 00

  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, very interesting to see, uh, 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 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.

• 1:27:49Speaker 00

  And it kind of didn't matter because the next question is like, okay, what are we going to, what are we going to, 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.

• 1:28:10Lenny Rachitsky?

  200 IQ. I, just like that frame of reference is such a mind expanding way to think about it and just how fast and how smart these things are going to get and quickly.

• 1:28:20Speaker 00

  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 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 wish I could retain it all, but I can't.

• 1:29:03Speaker 00

  It's just like, you just have this, I sort of live in this kind of state of like, almost frustration. I was 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've just taken notes the entire time. And 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 outthinking me and they're going to keep outthinking me and I just can't. And I'm just like, all right, goddamn it. Like, I gotta go home and I gotta like, have a drink because I'm just not, you know, I'm just not, whatever that is, I'm not that.

• 1:29:46Speaker 00

  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. I think that's much more exciting than people are giving your credit for.

• 1:29:59Lenny Rachitsky?

  Oh, man. I could talk to you for hours, Mark. I'm thinking to close out the conversation. I want to ask you 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 audiobooks now all the time. So in terms of media diet, what are 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?

• 1:30:25Speaker 00

  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, it's basically, I sort of, I always describe it as I have like a, almost a perfect barbell strategy,

• 1:30:38Speaker 00

  which is I read X and I read old books.

• 1:30:41Speaker 00

  Right? So it's basically either like up to the minute what's happening right now, or it's like a book that was written 50 years ago that has stood the test of time. And then, you know, presumably there's something timeless in it. And then it's sort of everything in the middle. I'm always like much more skeptical about. And in 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's newspaper. Right? And just go back and read it and be like, oh, my God. Like, none of this happened. Like, none, none of what they predicted played out the way that they said that it would.

• 1:31:25Speaker 00

  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. They couldn't know. And so they were making predictions and forecasts and so forth based on like not having any 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. Like, 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. And kind of, you know, the problem with, you know, newspapers, at least they're going day to day. The thing with magazines is like every, it's like a week or a 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.

• 1:32:09Speaker 00

  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 underrated. And I think this is a huge part of like the Substack 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

• 1:32:52Speaker 00

  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, Lex, you know, like, 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 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, 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, 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, they, they fundamentally like want to express what they do 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

• 1:33:37Speaker 00

  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 do.

• 1:33:54Lenny Rachitsky?

  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.

• 1:34:03Speaker 00

  Yeah. It's, uh, somebody said Silicon Valley is a company town, but the, the, the company is Silicon Valley. Right. And again, at the level, this goes to, again, there's one of these great N equals one. At the level of N equals one, as somebody, you know, and 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, because, 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. Because you get to hire people with all these skills and experiences. Right. And you're in this, you're in this ecosystem that it that it acts, right? And channels talents and, and, and scale and knowledge and people into, into the new fields. And so, you know, so there, you know, there's kind of the push and pull of that at the level of just being an individual, individual CEO. At the level of, of just being in the ecosystem, to your point, like, yeah, it's, it's an absolutely magical phenomenon.

• 1:34:48Speaker 00

  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. You know, I think AI, I did the comment 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. We used to actually, you know, it's called Silicon Valley because they used to make chips. Right. And this stuff that like the actual fabs were in Silicon Valley. And then they, and they designed them and they made the chips. And, and so, and that was, you know, wave one, starting in the 19, actually, that was like, actually, no, actually more was like wave three or whatever. But like, it was, you know, that was when the, the, the area was named like in the 1950s. But now we're on like wave nine, right? And, and the, the company town phenomenon where the company is the industry.

• 1:35:31Speaker 00

  Like 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 2000s, they're going to do the smartphone. In the 2010s, they're going to do the cloud. In the 2020s, they're going to do AI. It, it just, the, the, the, right, the indeterminate optimism, optimism of ecosystem, flexibility of ecosystem. Met that the, the Silicon Valley could, could morph into all these categories. And again, maybe a testimony to indeterminate optimism.

• 1:35:58Lenny Rachitsky?

  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.

• 1:36:03Speaker 00

  The wrapper thing is hysterical. Yeah, yeah. I'm a, I'm a software company and I'm, I'm a chip wrapper, right? Yeah, I'm a, I'm a, I'm a, I'm a business application. I'm a database wrapper. Yeah, exactly. I'm a sand, I'm a, yeah, you and I are, we're all now sand wrappers. Perfect.

• 1:36:17Lenny Rachitsky?

  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 I don't know, any movies, any movies you're really into these days? Any movies you've absolutely loved recently?

• 1:36:33Speaker 00

  Yeah, so the movie that blew my socks off last year, which I think is the best movie of the decade for sure, 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.

• 1:36:47Lenny Rachitsky?

  Not heard of it.

• 1:36:48Speaker 00

  Have you not heard of it? Okay, so you're gonna really enjoy it. So I won't spoil too much of it. So 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 like 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. And so it starts when COVID first hits. And then it sort of, as it plays out over the next few months, it then intersects and it sort of extends into the summer of 2020.

• 1:37:31Speaker 00

  So, you know, kind of the George Floyd moment, and then the protests and riots and kind of everything. So sort of the convergence of COVID and then the, and then the, and then, and then the, all the BLM stuff. And then, and then, and then there's a third kind of 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 kind of pull that in, as sort of a thing that looms larger and larger over time. And then the thing it really is great at is it really shows, 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?

• 1:38:15Speaker 00

  Which which is which is, you know, what what actually what actually happened, right? And so so it's it's so. So the reason I love the movie so much is 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 was like, especially in that era to live in a world in which there were things happened 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? And so it does like a really good job of pulling in like smartphones and social media in a way that in a way that movies really, really, really struggle with. And then the whole thing comes together in an incredibly entertaining way. And so and I won't even say I won't even say I completely agree with the movie or whatever.

• 1:38:59Speaker 00

  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 and this guy, for some reason, he's just like, yeah, I'm just gonna find all the third rails and I'm just gonna like fucking grab them.

• 1:39:19Lenny Rachitsky?

  I can see what's your favorite movie.

• 1:39:21Speaker 00

  It's great. It's great. It's great. Everybody should see it.

• 1:39:23Lenny Rachitsky?

  Oh, man. OK, final question I want to ask about your private 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.

• 1:39:37Speaker 00

  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 really hard to to to, you know, to to pull out specific ones. But I'll, you know, I'll talk about a few. Yeah, I mean, or just I'll just observation. So one is my 10 year old. I have my 10 year old. My 10 year old right now is 100% obsessed with Replit. And by the way, it was not from me. Do you have kids?

• 1:40:00Lenny Rachitsky?

  I do. I have one two and a half year old.

• 1:40:01Speaker 00

  Two and a half. OK, so you haven't run into what I'm running into now, which is whatever it is you do is not cool.

• 1:40:07Speaker 00

  Right, like it's two and a half. Whatever daddy does is like the coolest thing in the fucking world.

• 1:40:12Speaker 00

  I can tell you by the time he's 10, whatever you do is like deeply uncool, right? And I'm, 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. Uh, 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 literally was, uh, do it, uh, do it for hours. And so I'm, I'm seeing that phenomenon play out, uh, which is super fun. Um, uh, that's one. Two 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, Grok,

• 1:40:54Speaker 00

  uh, with, uh, Bad Rudy, which is, if you've seen it's, uh, it's the, uh, it's a foul mouth raccoon, uh, a, a avatar, uh, on the, uh, in, uh, in the Elon's Grok 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, these, these, that these just incredibly like, uh, you know, intimate, emotional, you know, kind of voice experiences. Uh, 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, let, you know, one of the sweet, uh, one of the sweetest recently, uh, company recently, uh, sold, but, um, you know, that, that all the, the, I, I think like dependence, the wearables, like all that stuff is going to be big. The meta glasses. Uh, I, you know, I think there's going to be a whole wearables revolution here. Um, I, I love the voice input stuff.

• 1:41:38Speaker 00

  I have this app on my, there's this app on my phone now called Whisper Flow, um, which is, uh, voice transcription, um, which works like staggeringly well. Um, uh, it's like incredibly, it's like a voice transcription function, but you can actually talk to the AI 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, is gonna be really great.

• 1:42:09Lenny Rachitsky?

  Subscribers of 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?

• 1:42:18Speaker 00

  Oh, so he's gotten super into Star Trek. And so, so far, it's been, he's like writing like Star Trek simulators. So like all the, you know, all the, by Next Generation, they actually had a-

• 1:42:28Lenny Rachitsky?

  Next Generation, okay, I was gonna ask which-

• 1:42:30Speaker 00

  Well, he like, we'd actually, we like them all. We watched the new Starfleet Academy last night, which actually is quite, is actually is quite good. But we watched the original, you know, we watched, we watched them all. But it was in Next Generation where they actually developed an actual design language for the computers. Because if you, if you watched 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 called it, this is having a nerd out, they called it LCARS, design language. And it'll, you know, it'll actually build you like Star Trek Next Generation bridge cosh holes using that design language, but you know, with your choice of like a Star

• 1:43:15Speaker 00

  Trek game, for example. And so he's, he's gone crazy for that kind of thing.

• 1:43:18Lenny Rachitsky?

  That sounds extremely delightful. You guys should open source or release that. Mark, I, 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?

• 1:43:32Speaker 00

  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, you know, great team of folks now. We're putting a lot of effort ourselves into video and, you know, 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.

• 1:43:58Lenny Rachitsky?

  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.

• 1:44:05Speaker 00

  Awesome. Thank you for having me. I really, I really appreciate it.

• 1:44:08Lenny Rachitsky?

  Bye, everyone.

• 1:44:10Lenny Rachitsky?

  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 Lenny's Podcast dot com. See you in the next episode.