Marc Andreessen: Why workers will be scarcer, not cheaper

1. 0:00 – 4:27

   Introduction to Marc Andreessen

   1. MAMarc Andreessen0:00

      If we didn't have AI, we'd be in a panic right now about what's gonna happen to the economy. We've actually been in a regime for fifty years of very slow technological change in the face of declining population growth. The timing has worked out miraculously well. We're gonna have AI and robots precisely when we actually need them. The remaining human workers are gonna be at a premium, not at a discount.

   2. LRLenny Rachitsky0:16

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

   3. MAMarc Andreessen0:21

      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.

   4. LRLenny Rachitsky0:30

      I spent a lot of time with the most cutting-edge AI-forward founders.

   5. MAMarc Andreessen0:34

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

   6. LRLenny Rachitsky0:38

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

   7. MAMarc Andreessen0:43

      Everybody wants to talk about job loss, but really what you wanna look at is task loss. The job persists longer than the individual tasks.

   8. LRLenny Rachitsky0:49

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

   9. MAMarc Andreessen0:54

      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.

   10. LRLenny Rachitsky1:18

       People aren't fully grasping how much is changing.

   11. MAMarc Andreessen1:20

       People who really want to improve themselves and develop their career should be spending every spare hour, in my view, at this point, talking to an AI, being like: "All right, train me up."

   12. LRLenny Rachitsky1:27

       [upbeat music] Today, my guest is Marc Andreessen, one of the most seminal figures in tech and in business. He invented the web browser, built the world's largest venture firm. He's also a multi-time founder and an investor in essentially every generational tech company, and is also one of the most clear-minded, lateral, and insightful thinkers about both the past and the future of technology. In this very special conversation, we chat about how unique and significant the moment that we are all living through right now is, what skills he's teaching his kids to thrive in the AI future, what happens to product managers, designers, and engineers in the coming years, where moats exist in AI, what the most AI-native founders are doing differently, and so much more. That is just scratching the surface of this very deep and important conversation. You are gonna 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 twenty incredible products, including a year free of Lovable, Replit, Bold, Gamma, n8n, Linear, Superhuman, Devon, PostHog, Descript, Whisperflow, Perplexity, Warp, Granola, Magic Pattern, Raycast, ChatPRD, Mobbin, and Stripe Atlas. Head on over to lennysnewsletter.com and click Product Pass. With that, I bring you Marc Andreessen after a short word from our sponsors. Today's episode is brought to you by DX, the developer intelligence platform designed by leading researchers. To thrive in the AI era, organizations need to adapt quickly. But many organization leaders struggle to answer pressing questions like: Which tools are working? How are they being used? What's actually driving value? DX provides the data and insights that leaders need to navigate this shift. With DX, companies like Dropbox, Booking.com, Adyen, and Intercom get a deep understanding of how AI is providing value to their developers and what impact AI is having on engineering productivity. To learn more, visit DX's website at getdx.com/lenny. That's getdx.com/lenny. If you're a founder, the hardest part of starting a company isn't having the idea, it's scaling the business without getting buried in back-office work. That's where Brex comes in. Brex is the intelligent finance platform for founders. With Brex, you get high-limit corporate cards, easy banking, high-yield treasury, plus a team of AI agents that handle manual finance tasks for you. They'll do all the stuff that you don't wanna do, like file your expenses, scour transactions for waste, and run reports, all according to your rules. With Brex's AI agents, you can move faster while staying in full control. One in three startups in the United States already runs on Brex. You can, too, at brex.com.

2. 4:27 – 6:52

   The historic moment we’re living in

   1. LRLenny Rachitsky4:28

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

   2. MAMarc Andreessen4:36

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

   3. LRLenny Rachitsky4:38

      I wanna start with just a big-picture question. I have a billion directions I wanna go, but I think this is gonna give us a little bit of a frame of reference. How big of a deal is the moment in time that we're living through right now?

   4. MAMarc Andreessen4:50

      This is a very, very historic time. I think twenty twenty-five was maybe the most interesting year in my entire career and, and probably life, and I think [chuckles] I, I would expect twenty twenty-six to exceed that.

   5. LRLenny Rachitsky5:00

      Wow, that says a lot.

   6. MAMarc Andreessen5:01

      Yeah, I've see- I've seen some stuff. So, um, it feels like two things are happening. One is the, the, the trust that a lot of people have had in kind of what you could describe as kind of legacy institutions around the world is, I, I think, in kind of full-scale collapse right now. By the way, there's a lot of data, data to support that. And so I think there's just- there's, there's, like, a lot of structures and orders and, uh, 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 kinda corresponding with that is the national and global conversation have become, like, let's say liberated. Um, and so, you know, this sort of incredible revolution that we have in, in kind of, uh, you know, what I would describe as freedom of speech, freedom of thought, um, ability for people to openly discuss things that maybe they couldn't discuss even a few years ago, you know, is just dramatically expanded, and I think that's, that's now on a, 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 U- 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, into the daylight and, and, and re-examined. And, 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 in upheaval, but you have AI as this kind of new technology that's gonna really affect things. And then you've got, you know, people, you know, citizens being able to fully participate, uh, being able to argue things out. And so it's, it's kind of like those three kind of big mega things are kind of all colliding, um, at the same time, and I, I think we're probably just the very beginning of all three of those. And, and those all feel like kind of, you know, historical, you know, moment shifts. It, 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, um, you know, kind of moments like that. It, it certainly feels like that.

   7. LRLenny Rachitsky6:48

      Good God. [chuckles] What a time to be alive.

3. 6:52 – 11:14

   The impact of AI on society

   1. MAMarc Andreessen6:52

      Yeah.

   2. LRLenny Rachitsky6:52

      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 gonna have on, say, the world or just people listening?

   3. MAMarc Andreessen7:03

      The AI thing at, I think at this point, I think it's pretty clear with the... With, you know, our technology hats on, that like, the, the stuff is really working now, right? And so there, there was this, you know, kind of, you know, when, when there was a ChatGPT moment, you know, three years ago. It was only, by the way, only three years ago, right, um, was the ChatGPT moment, and, and the, the big question was, all right, this, 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, there, you know, there's this kind of big question like, can you, can you harness this technology for reasoning, um, and for, you know, problem-solving in, in domains that like really matter, you know, medicine and science and, and, and law and, and, and, and so forth. Um, and, and, you know, it, it turns out the answer to that is yes, right? Um, and, you know, the, the, 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... I mean, you're seeing it all now. You know, it can actually, you know, AI is now developing new math theorems. Um, you know, there, there, you know, over the holiday break, you know, there's sort of the, what it feels like the AI coding thing, you know, really hit critical mass, uh, and the world's best, you know, the, the world's best programmers, right? Including, like Linus Torvalds, you know, for, 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, that's incredibly in- incredibly power- powerful, and I think we, we all, you know, kind of, I think, assume that AI now is gonna get really good at reasoning, um, in, in any domain in which there are verifiable answers. And so that, that, you know, that's gonna include like many very important domains. So, um, so like for the, the technology feels like it's, it's, it's moving fast and, and, and it's gonna be working really well. Um, I think the thing that is not well understood, it- I, I think a lot of people have a... I think, you 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 frame, or I think it's based on an incomplete understanding of, of the world that we live in, or the world that we've been living in for the last, you know, 80 years. And I, I would call out 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 in a time of great te- technological change. But actually, if you look for actually evidence of that, like in statist- in statistical evidence of that, analytical evidence of that, like you basically can't find it. Um, and in particular, um, economists have a way of measuring the rate of technological change in the economy, that is productivity growth, which, which, which we could talk about what that means, but basically, it's, it's a, it's, it's sort of the mathematical expression of the impact of technology, uh, 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, it's been very low. And in fact, the pace of productivity growth, like in the US, is, is running at like a half of what it w- in my lifetime, in our lifetimes, it's been running at about a half the pace, um, that it ran in, um, 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 we, you know, the, the, the AI thing is, is gonna hit, but it's hitting an environment in which we, we have actually had almost no technological progress in the actual economy for a very long time. So, so we could talk about that. And then there's this other, like, just incredible thing that's happening, which is the, the, you know, sort of the, the de- demographic collapse, right? It's sort of a, a Western phenomenon and an increasingly global phenomenon, which is, you know, the rate of reproduction of the human species is, 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, meaning that, you know, ma- many, many countries around the world, by the way, including China, [chuckles] which is a really big deal, um, are actually going to depopulate over the next century. Um, and so you have this kind of precondition that says there's actually been very little technol- technological progress happening in the world, um, and the world is going to depopulate. Um, and so AI is gonna enter the wor- a world in which those two things are true, and I think it's incr- 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 gonna need, you know, [chuckles] we're gonna need machines to do all the jobs that we're not gonna have people to do, because we're, we're literally gonna depopulate, we're gonna depopulate the planet over the next 100 years. And so I, I think the interplay of these factors i, i, is going to be much more interesting and, and frankly, more, more, more complex

4. 11:14 – 22:15

   AI’s role in education and parenting

   1. MAMarc Andreessen11:14

      than a lot of people have been thinking.

   2. LRLenny Rachitsky11:15

      I'm gonna follow this thread about kids. I know 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.

   3. MAMarc Andreessen11:26

      Yeah.

   4. LRLenny Rachitsky11:26

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

   5. MAMarc Andreessen11:31

      The way I think about this, uh, anyway, yeah, we, we have a 10-year-old, and so I, you know, and we, and we actually homeschool, and so we, we, we think a lot about this. Um, so I think the way to think about the impact of AI on, on people, on specifically people as individuals, I think it's, it's, it's actually, you know, there... A lot of people just focus on kind of this, you know, this kind of very, I would say, straightforward and/or overly simplistic view of just literally job gains, you know, job losses, which we can 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 gonna make them very good at doing things, right? And so it's gonna be a tool that's gonna sort of raise the average, kind of across the board. And, you know, look, you, you see that playing out already.... you know, anybody who's in a position where they need to, you know, write something or 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 there, there's sort of that aspect to it. And I think the, the, the, the way the education system writ large is gonna te- is gonna kinda teach AI is, is, is gonna be based, you know, uh, uh, 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, in coding right now, [lips smack] um, where the really great people are becoming, like, spectacularly [chuckles] great, right? Um, and so you, you, you, you, you just, you kinda use it, use the term, you think about, like, the super empowered individual, right? So the individual who is, like, really good, um, at coding, or really good at making movies, or really good at making songs, or really good at designing, you know, making art or, or whatever, whatever those things are, or, or, you know, or podcasting or, you know, hopefully venture capital. You know, if, if you're very good at it and you can really harness AI, y you can become spectacularly great, uh, and, and, like, super productive, right? And, you know, um, I'm sure you have a lot of friends in this, in this category, uh, as well, but, like, you know, co- co- the, the, the really, really good coders are experiencing this right now. My friends who are really good coders are like: "Oh, my God! All of a sudden, I'm not twice as good as I used to be. I'm, like, ten times as us- as good as I used to be." And so I, I think at the, uh, 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 gonna be really kinda deep in whatever it is they're gonna do, but they're gonna, they're gonna be deep in a way that's gonna let them fully use the power of AI to be not just great, but to be, like, spectacularly great? Um, and, and I think that, that, that's, that's gonna be the real... You know, that, that, 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.

   6. LRLenny Rachitsky13:53

      So what I heard there is essentially agency, this word that we see on Twitter all the time, is building a agency, them not waiting for someone to tell them what to do, figuring out what to do.

   7. MAMarc Andreessen14:01

      Yeah, yeah. So this, this, this, this thing with this, this term agency has become very, very, um, you know, very popular, um- [chuckles]

   8. LRLenny Rachitsky14:07

      [chuckles]

   9. MAMarc Andreessen14:07

      ... you know, certainly California for the last couple of years. It, it's really interesting 'cause it's, it's-- I had a lot of trouble with this early on 'cause I'm like: "Agency, okay, what are they talking about?" And what, what they're kinda talking about is, like, you know, initi- you know, initiative, you know, um, you know, willingness to, you know, [chuckles] you can just do things. Um, you know, uh, what is it? Uh, the, the... Samuel Berg has the great term, live player. Um, you know, you, you, you can be, like, a primary participant in events. And at, at first I was like: "Well, yeah, like, that's kind of obvious, right? Like, of course." And, and then, 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, 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 ours, 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, 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 or whatever, has gotten kind of more and more focused on over time. And it's like, yeah, it's like, no, you, you should actually... And, and again, especially at unit, uh, unit N equals one, like, of your kid, it's like... Oh, and, and look, there's, there's something to be had. We [chuckles] I just had this conversation with my 10-year-old last night, actually. I, I, I, I rolled out, uh, uh, the concept of, uh, you know, i, i, 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, uh, you know, kind of trying to keep [chuckles] him, to keep him with some level of structure in his life.

   10. LRLenny Rachitsky15:38

       [chuckles] Sorry.

   11. MAMarc Andreessen15:39

       And not just, and not just pure agency. But yeah, I mean, so and so look, you know, some rules are important and so forth. But yeah, no, look, there, there is, like, a huge pre-- 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. Um, and, you know, maybe, yeah, that, that has been maybe a little bit diminished in our culture over the last 30 years. It, it, it, uh, you know, it's, it's healthy. You know, that, that, you know, that, that there's now a term for that, that, that, that is coming back, back into vogue. And then, and then, and again, like, that's how I view AI for kids, is like, okay, AI should be the ultimate lever on the world for a kid with agency to be able to say: "Okay, I can actually be a primary contributor," right? Whether that's I can be a primary contributor in everything from, 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, I can fully participate in the world. I can really change things. And I, and I... That, that feel, the, the combination of that idea combined with this technology feels very healthy to me.

   12. LRLenny Rachitsky16:35

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

   13. MAMarc Andreessen16:38

       "And I'll move the world." Yeah, that's exactly right. Well, so it's actually funny you mentioned that. So the, the, um, the, uh, the, the early kind of scientists, including, like, Isaac Newton, were super obsessed with, with, uh, you know, this concept of alchemy, right? It's like, you know, they, you know, they de-- 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, 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, the, he, 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- it would be able to transmute the rare, you know, the common thing into the rare thing, le- lead into gold, and he never figured it out. And, you know, it's incredibly frustrating. Nobody ever figured that out. And now we literally, [chuckles] with AI, have a technology that transfers sand into thought. [laughing]

   14. LRLenny Rachitsky17:30

       Whoa!

   15. MAMarc Andreessen17:30

       Right?

   16. LRLenny Rachitsky17:31

       Just blew my mind.

   17. MAMarc Andreessen17:32

       Right. The, the, the most common thing in the world, which is sand, uh, converted into the most rare thing in the world, which is thought, right? And, and so AI is... It, it, it is, it is the, it is the philosopher's stone. Like, it, it, it is that. It, it actually is that, and it's just this incredibly powerful tool. Um, and, and that's where I, uh, that's where I get so excited. I mean, and, and again, this is what we're doing with our 10-year-old, which is like, all right, a primary thing that we wanna 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, [chuckles] right? Which is, uh, you know, which is to say AI. And that, that, and, 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 around, which is-... uh, the, you know, the more you're kind of plugged into stuff in Silicon Valley, the more, 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, and that's, that's certainly the mode that I would encourage parents to think about.

   18. LRLenny Rachitsky18:26

       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 on 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?

   19. MAMarc Andreessen18:42

       This is the challenge, and, and again, this, this kind of goes to how your, you know, kind of your original question, which is, e- education, there, there's two completely different ways to talk about and think about education. The way that's usually thought about and talked about is kind of at, at the level of like a, a, a nation, right? So, so, you know, it's, 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 gonna 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. Um, but then there's this other question, which is like, at n equals one, for an individual kid, like, what can you do with, with an individual kid? Um, and so I, I'll just give you kind of the ultimate, you know, kind of the ultimate answer to that question, which is, it's been known for centuries that the ideal way to teach, uh, a, a kid at the unit of n equals one, by far, the ideal way to do it is, is with one-on-one tutoring. Like, if you just have an individual kid, and the goal is to maximize the 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, [chuckles] right? Like, you know, many of the great kings and queens and s... you know, royal families and aristocrats and so forth, you know, uh, over, over the course of centuries, um, all, you know, kind of always had, uh, always had this approach. There's actually also statistical evidence, um, uh, analytical evidence that this is correct. Um, 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, a- and will take a kid from the fiftieth percentile to the ninety-ninth percentile, and that's one-on-one tutoring, right? [chuckles] So again, right, if you go back to, like, a- 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, 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. 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, an LLM about it, and they can ask an infinite number of questions, and they can get instantaneous feedback. Um, and in fact, you can even tell an LLM, 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. Um, okay, now quiz me. You know, do I actually understand this?" Like, people can just do this today, right? Um, 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, uh, to be able to say, "Okay, you know, my, my kid's probably still gonna go through traditional education system, but I'm gonna augment this with AI tutoring." Um, and of course, there, you know, and of course, there's gonna be tons of start-ups, right? And there already are, that are, that are gonna 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. Um, and so, you know, I think the, the broad answer might be a hybrid approach with schools plus one-to-one tutoring through AI. Um, there's also this great... You may have heard there's this great school, new private school system called Alpha, um, 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, a, there, there is a magic formula in here, um, that I think is gonna apply much more broadly. Um, and I, and, and it really, for parents interested in this, I... Now would be a great time to really

5. 22:15 – 30:15

   The future of jobs in an AI-driven world

   1. MAMarc Andreessen22:15

      start to think hard about that, um, and, and, and to look at the options.

   2. LRLenny Rachitsky22:17

      It's interesting because there's all this concern that young people, uh, jobs are not gonna 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 to learning today are gonna be, move so fast and learn so much more and, and where, where do you sit on this divide of, like, young people are in big trouble, or they're actually gonna be the ones winning in the end?

   3. MAMarc Andreessen22:37

      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 fifty years of very slow technological change in the economy. And so, you know, and again, like I said, it's like at, at a half the rate of the, of the, of the previous era and then a third of the rate of, like, a hundred years ago. And so w- w- 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 a, 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, right? At, at that rate of technological transformation, kids were able to, like, develop new careers into new areas of, of, of, 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, and, uh, and proliferated kind of during that period. Um, and so even if AI, like, triples the pace of economic change in the economy, it's gonna just translate to, like, a much higher rate of economic growth. It's gonna transfer- translate to a much higher r- 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, uh, that will happen, and that, then corresponding to that, there will be, you know, there, there will be hiring booms, I, you know, I quite honestly think, all over the place. And then again, go back to the, the, the other thing, which is like, this is all happening in the face of declining population growth and, and, and increasingly, population shrinkage. Um, and so human workers in many, many, many countries over the next, you know, ten, twenty, thirty years are going to be at more and more of a premium.... uh, literally because you're gonna 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 go-- is, is gonna reverse course on, on, on the rates of immigration that we've had for the last fifty years. It seems to be kind of a, a broad-based, you know, kind of thing happening, um, you know, kind of with ri-- you know, ri- rise of 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, k- 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 combine declining population with less immigration, you-- the, the remaining human workers are gonna be at a premium, not at a discount. Um, 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, um, actually means there's gonna be much less of this kind of dystopian, you know, no jobs thing. I, I just think it's probably totally off-base.

   4. LRLenny Rachitsky25:10

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

   5. MAMarc Andreessen25:24

      Yeah. Well, look, if we didn't have AI, we'd be in a panic right now about what's gonna happen to the economy.

   6. LRLenny Rachitsky25:29

      Mm.

   7. MAMarc Andreessen25:29

      [chuckles] Right? Uh, 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, 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, there are no new jobs, there are no new fields, there's no new d- there's no s- new source of consumer demand for spending on things. Um, and so you, you would, you would, you would be very worried about going into a period of, like, severe decline and stagnation. Um, and, you know, the, well, you know, essentially, you'd, you'd be looking at these, like, very dystopian scenarios of, like, an economy kind of self-euthanizing itself, uh, over time. Um, and, and you, so you'd be very worried about, like, the opposite of what everybody, you know, thinks that they're worried about. The only reason we're not worried about that is because we now know that we have the technology that can substitute for the lack of population growth and then, you know, also for the, for the lack of immigration that's likely. And so, you know, it, it, [chuckles] I would say the timing has worked out mira- miraculously well, in the sense that we're gonna 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 fun- uh, a fundamentally, a fundamentally good news story. Um, 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 ten, twenty, thirty, fifty percent 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. Um, 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, uh, 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 l- level of, 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 want 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, and that's the equivalent of a gigantic increase in wealth, right, across the society. Right, uh, uh, take it this way. This is actually worth talking about because pe- people, I think, get, get kind of sideways on, uh, uh, on this issue. So if AI is going to transform the economy as much as the, you know, whatever, utopians or dystopians [chuckles] or whatever, kind of think that it will, the necessary economic calculation of what happens is massive e- 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, which with much lower input costs. The result of that is you get gluts of goods and services in all the, those affected sectors. The result of those gluts is you get collapsing prices, right? The collapsing prices mean that the thing today that costs you one hundred dollars now costs you ten dollars and now costs you one dollar. 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, 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 because this, this, this, this, this incredible impact that AI is having. And so in this kind of utopian, dystopian scenario that people have, it's not-- there, there's no scenario in which, like, everybody's just poor. In fact, it's, 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, uh, like maybe we end up in that scenario. I mean, the, 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, to accommodate that, and maybe that'll happen." But the result of that is gonna be a much better news story than people think it's going to be. Um, and, and again, everything I've just described, by the way, is like just a very straightforward extrapolation on very basic economics. I'm not making any, like, bold predictions on what I just said. This is just like a straightforward mechanical process that, that, that, that plays itself out if you have higher rates of productivity growth, which are necessarily the results of higher gra- r- rates of technological growth. And so I think we're, I think we're looking at... A- a- 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 dys- the dystopians think it will be. I think it'll be more incremental for reasons we can discuss, but I think that incremental o- o- over, over, overwhelmingly, I think that process is going to be a good news process, and then even if it's much faster, it's also gonna be a good news process. It'll just be a good news process in the other way that I just described.

6. 30:15 – 35:35

   Marc's past predictions

   1. LRLenny Rachitsky30:15

      I love hearing optimism [chuckles] 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. Uh, okay, so one, you were right about the web [chuckles] and web browsers becoming important. You were right about software eating the world, check.... you, uh, in twenty eleven, you said that in ten years, we're gonna have five billion people using smartphones, and I believe the actual number ended up being six billion. [chuckles] You also, you had this debate with Peter Thiel that I came across, where you were debating whether technology has stopped progressing or if new technology will continue to emerge, and you were arguing, "There is progress. Progress will continue." And he, he was like, "No, I think we're done with cool technology." You were right. Uh, I imagine there are many more [chuckles] things you were right about. So, so again, I'm just-- I, I, [chuckles] I love hearing your predictions 'cause I feel like they're actually gonna turn out to be correct.

   2. MAMarc Andreessen31:15

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

   3. LRLenny Rachitsky31:20

      [chuckles] Delete them from the internet.

   4. MAMarc Andreessen31:22

      Yes.

   5. LRLenny Rachitsky31:22

      No web browser can discover them.

   6. MAMarc Andreessen31:24

      Yes. I have them nuked out of the internet archives so that they're ne- they're never seen again. Um, so, uh, you know, I, I'm wrong plenty of times also. Um, but yeah, I mean, look, I, I think, and, and, yeah, some, some of those I got right. By the, by the way, I will say on the, on the Peter one, I, I have come, I've come much more around to Peter's point of view.

   7. LRLenny Rachitsky31:39

      Hmm.

   8. MAMarc Andreessen31:39

      Um, 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. Um, and, and it actually goes to the kind of the discussion that we, the kind of conversation we just had, which is the, the, the, the real form of what Peter was arguing was we have lots of progress in bit-- we have lots of progress in bits, right? But we have, we have very little progress in atoms, right? Um, and that, and that's the real core of what he was arguing. And I think I, I, I think I, I was a little bit, I don't know, missing that, or kind of, you know, uh, kind of glossing that over a little bit, um, because I was so focused on making sure people understood, no, there actually is still progress happening in, um, in bits. But I think, you know, a lot of his critiques around the lack of progress in atoms is real, and, and again, this goes back to this thing of, like, in the la-- and he, you know, he's talked about this for a long time. In the last fifty years, there has just been very little technological innovation in most of the economy. There's been very little technological innovation, in particular, anything involving atoms. You know, there's been very little real-world technological change. There just, there just hasn't been. Like, the, the, the, the built world is just not that different today than it was fifty years ago. And if you... And again, if you contrast that, you know, if you, if, if you compare and contrast eighteen seventy to nineteen thirty, it was a dramatically different world. If you contrast nineteen thirty to nineteen seventy, it was a dramatically different world. If you contrast nineteen seventy to date, it's not that different, right? And, and look, you just see that you could just, like, walk around, and it's just like: Oh, yeah, there's a bunch of buildings that were built in, like, nineteen sixty, right? And there's a bridge that was built in, like, nineteen thirty, and there's a dam that was built in, like, nineteen ten, and there's a city that was founded in, you know, eighteen eighty. And, like, [chuckles] what have we done? [chuckles] Right, like, where are our new cities? Where are our new dams? Where, you know, where's, where's the California high-speed rail? Like, you know, you know, like, what's going on here? And so, like, I, I think he is, I, I think he is right about a lot of that. Um, again, this is also why I think that AI is not going to have as rapid an imp-- it's not going to be... Th- 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 just, there's so much about how the world works that's basically just, like, wrapped up in red tape. Like, y- bureaucratic process, rules, restrictions, um, you know, the, the, the, the politics, um, by the way, you know, unions, cartels, oligopolies. There, 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, uh, I mean, uh, let's take, let's take a great example, like A- AI's impact on the healthcare system. Like, uh, uh, by rights, AI is gonna have a dramatic impact on the healthcare system, and, and, and in very positive ways. But, you know, the, uh, large parts of the medical system today are, they are cartels, right? And so there's like a, there's the, the doctors are a cartel, and, like, nurses are a cartel, like, hospitals are a cartel. And then there's this push to, like, nationalise all the healthcare systems, and then you've got, you know, 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, [chuckles] 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 doctors' jobs? Well, in that case, we're gonna, we're gonna block it. So a- and I think a lot of consumers, by the way, you know, I don't... I, I, I see this in my life, and you, you'll probably see this in your life also, which is, you know, like, ChatGPT is, like, almost certainly a better doctor than your doctor today, but, like, ChatGPT can't get a license to practice medicine, right? So it, 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... Any- anyway, so Peter, Peter, I think, was very articulate and, and has been for a long time on, like, no, there are actually real structural impediments in the economy and in the political system that we have that actually prevent any, uh, uh, the rates of change that are anywhere near the rates 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 t- of the new magic technology of AI, maybe it causes us to revisit a lot of these assumption- 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.

   9. LRLenny Rachitsky35:26

      "It's time to build," somebody famously said. I, uh, in my calendar, I actually have that as my-- [chuckles] when I start to work, "It's time to build."

   10. MAMarc Andreessen35:33

       Yes.

   11. LRLenny Rachitsky35:33

       That's my block in the morning of the day.

7. 35:35 – 39:28

   The Mexican standoff of tech roles

   1. LRLenny Rachitsky35:35

      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 gonna disappear? Is one of these roles gonna 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?

   2. MAMarc Andreessen36:09

      Th- this, I think, is a really funny question. So the, 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 pointed at each other's heads.

   3. LRLenny Rachitsky36:23

      Mm-hmm.

   4. MAMarc Andreessen36:24

      Um, and then there's, if you watch, like, John Woo movies, he loves to have-- he does the three-way Mexican standoff-

   5. LRLenny Rachitsky36:28

      Mm

   6. MAMarc Andreessen36:28

      ... where you've got, like, a triangle, you know, pe- people and, like, the, you know, and of course, it's John Woo movie, is they've got, you know, guns in both hands. S- so they're all, each, each is aiming at the other two.

   7. LRLenny Rachitsky36:38

      Yeah.

   8. MAMarc Andreessen36:38

      ... um, and you've got this kind of standoff situation. And so the, the, the way I've been describing this is there's like a Mexican standoff happening between those three roles, between product manager, designer, and, and coder. Specifically the following, which is every coder now believes they can also be a product manager and a designer, right? Because they have AI. Every product manager thinks they can be a coder and a designer, and then every designer knows they can be a product manager, right, and a, and a coder. Right? And so people in each of those roles now, you know, know or believe that with AI, they, they don't need the other two roles anymore, right? They, they, they can do that 'cause they can have AI do that. And then, of course, and then, of course, there's the real irony, which is, you know, the all, the, the three- all three of them are gonna realize that AI can also be a better manager, right? [chuckles] S- so they're gonna, they're gonna be a- ai- aiming the guns up the org chart, but that, that, that's probably the, that, that, that's the next phase. And what I think is so fascinating about this Mexican, 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, like, 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, in, in those three jobs. Um, and so again, this, this goes back to the, the, the super empowered, this kind of idea of the super empowered individual, uh, where if, 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 gonna change in the future. You know, the, the- I need to under- 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, of, of, you know, coding bots. You know, you know, there's a, 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, how do I become a coder that also then harnesses AI so that I can also be a great product manager, and I, 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 a, be- also become a coder and also become a product manager? And then what you get is m- maybe the, maybe the, 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 thirty years or whatever. Uh, 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. Um, and then, and then, those people become incredibly valuable because then those are people who can actually, like [chuckles] you know, build and design, right, new products, right from scratch, which is, like, the, you know, the, which is, which is the most valuable thing. Uh, and so I, I think, I think that's, I think, I think that's the opportunity.

   9. LRLenny Rachitsky39:01

      Uh, so I love this answer. So what I'm hearing is essentially, uh, if you're amazing at any of these three roles, you will do well.

   10. MAMarc Andreessen39:08

       Number one, if you're amazing at these roles, that's great. But also, you... Pa- part of being amazing at these roles is also y- being, being able to fully harness the new technology, right? So i- if you're, if you're a master coder today, and you, you don't ever get to the point where you, where you figure out how to use AI to leverage your coding skills, you know, and, and, and do more, right, like, at some point, you are gonna hit an issue, right? Uh,

8. 39:28 – 42:15

   Adapting to changing job tasks

   1. MAMarc Andreessen39:28

      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, 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? The tasks changing. I mean, the, the, the, the clas- [chuckles] the classic ex- the classic example of tasks 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, the job of the secretary changed from sending out letters with stamps on them to, like, sending and receiving emails with the other admins. And then, and then the, the secretary would print out the email and bring it into the executive's office. And the executive officer would read the email on paper, scroll, scrawl the reply, um, and, and, and, and give, and give that message back to the secretary, who would go back and type it into the computer on, on, on, on, on his or her desk and, 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, that, you know, that, that, that great admins do. And then the, 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 own memos, which again, fifty years ago, they never, never would've 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 gonna happen in coding. The tasks are gonna change. It's what's going to happen in product management, the tasks are gonna change. Designer tasks are, are gonna change. And so the, the, the job can per- 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. Um, I can, 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 'cause I, 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, ten 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? It's like, whatever that, whatever that job is called, who even knows what it's gonna be? But it's gonna be incredibly important 'cause the people doing that job are gonna be orchestrating the AI. And so that, that, that's the track that the best people are going to be on. Um, and, and I think that that's

9. 42:15 – 44:50

   The shift to scripting languages

   1. MAMarc Andreessen42:15

      the thing to lead heart- lean hard into.

   2. LRLenny Rachitsky42:17

      I think people aren't fully grasping just specifically software engineering and how much that is changing. Like, it's pretty clear we're gonna 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 gonna be this artisanal experience of sitting there writing code, which is so crazy how much that job is gonna change.

   3. MAMarc Andreessen42:38

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

   4. LRLenny Rachitsky42:50

      Mm, no.

   5. MAMarc Andreessen42:52

      It referred to people.

   6. LRLenny Rachitsky42:53

      [chuckles]

   7. MAMarc Andreessen42:54

      Uh, right? So back before there were, like, electronic calculators or computers or any of these things, um, the way that you would actually do computing, the way that you would do calculating, like the way that an insurance company would calculate actuarial tables, or the military would, like, calculate, you know, I don't know, whatever, troop logistics, you know, formulas or whatever it was. The way that you would do it is you would actually have a room full of people. Um, and by the way, these were like big rooms. You could have hundreds or thousands or tens of thousands of people doing this, and you would actually br- you would actually figure out, you have somebody at the head of the room who was, 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, and those, th- that, that job title was those people were calculators, right? Um, 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, y- 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 in the machine code, and that's what programmers did. And then I still remember when, when scripting, you know, when scripting languages... You know, we developed JavaScript at Netscape, and then, you know, Python took off, and Perl and these other scripting languages. When scripting languages, you know, took off in the, in the, in the, in the, in the 2000s, there was this big fight in, 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, th- this, this, this whole craft of writing, writing, uh, you know, writing, writing C code. And, you know, these, these JavaScript or Python programmers are just

10. 44:50 – 51:37

    The importance of understanding code

    1. MAMarc Andreessen44:50

       doing this kind of lightweight thing. It doesn't even really count as, as coding. And of course, the answer is yes, it very much counted. And now most coding is done with the scripting languages, right? Um, which have... You see my point. The scripting languages have abstracted away, like, five layers of detail underneath that, that people used to do by hand, and they don't anymore. And then, and then there's-- and then to, to your point, like, AI coding is the next layer on that. AI coding actually abstracts away the process of actually writing the scripting code, right? And so in one sense, this is a really big deal for all the obvious reasons, but on the other hand, it's like, okay, this is the next layer of the task redefinition under the job of programmer, right? Now, [chuckles] 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 ten code bots, right? Coding bots that are running in parallel, right?" And, and literally, they sit there and they shift from browser, you know, browser to browser or terminal to terminal, and their, and their wa- their, their, their day jo- their day job now is kind of arguing with the AI bots to try to get them to, like, write the right code, right? And then, and then debug it, and, and fix the problems, and change, change, change the spec and, 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, you know, you asked about the ten, you know, our, our ten-year-old is, you know, super, super into computers and super into programming. And what I'm, what I'm telling... You know, he's, he's using Claude and ChatGPT, and 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 game, doing games. You know, he's sitting there, you know... It's hysterical, right? Because he's sitting there. It's a ten-year-old basically, who's, you know, spends two hours at dinner arguing with an AI for fun, right? Um, right, but, but what I'm telling him is, "No, look, you need to still fully understand and learn how to write and understand code, because the, 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, in the same way that somebody who's writing scripting language code does need to understand ultimately how the microprocessor works." Um, and so again, it's, it's kind of this upleveling 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, I look at that and I'm like, okay, now programmers are gonna be ten times, or a hundred times, or a thousand times more productive than they used to be, right? And, and that, and that is overwhelmingly a good thing. The, the, the, the, the, the tasks are definitely changing. The nature of the job is changing. Um, but are human beings going to be involved in, like, in, in the coding process and overseeing the, the AI bo- AI coding and all that? And, and the answer is, of course, absolutely, a hundred percent. Like, no question.

    2. LRLenny Rachitsky47:22

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

    3. MAMarc Andreessen47:24

       Oh, yeah, totally. Well, again, if you want to be one of these super... Look, look, if you just want to put your, like, self on autopilot and like, I can't be bothered, and I'm just gonna have AI write the code, and it's gonna generate whatever it does, and that's fine, and I, I'm gonna be, you know, I'm gonna be-- If, if, if the goal is to be a, a mediocre coder, [chuckles] then just let the AI do it. It's fine. The AI is gonna be perfectly good at generating infinite amounts of mediocre code. No problem, it's all good. If, if, 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 one hundred percent 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, of the chip, right? And, and, and the network and so forth. By the way, you also really deeply want to understand how the AI itself works, right? Because you wanna... Right, because 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, doesn't understand how it works, right? I mean, you're, you're always more productive if you know how the machine works, right, when you use the machine. And so, yeah, the, the super empowered individual on the other end of this that wants to do great things with the new technology, yes, you a hundred percent want to understand this thing all the way down the stack, because you want to be able to understand what it's giving you, right? And, and, and when something doesn't work or when something isn't right, you want to be able to really quickly understand why that is. Um, by the way, again, this goes back to education.... AI is your best friend in helping you learn all that, right? 'Cause it's like, oh, I need to understand, I don't know, like, this isn't fast enough. Um, I need to go- I need to figure out, as a coder, I need to figure out how to do a different approach to memory management or something. And you can be like, well, you know, shit, like I, you know, I don't quite know how to do that. Okay, AI, let's spend ten 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 the AI, where it's also helping you get better at the same time as it's doing a lot of work for you.

    4. LRLenny Rachitsky49:07

       By the way, I was gonna say, uh, I was a big Perl, uh, programmer. I was an engineer for ten years, and that was my, [chuckles] my language of choice.

    5. MAMarc Andreessen49:13

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

    6. LRLenny Rachitsky49:22

       For sure. 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?

    7. MAMarc Andreessen49:27

       Yeah, exactly.

    8. LRLenny Rachitsky49:27

       Yeah.

    9. MAMarc Andreessen49:27

       And of course-

    10. LRLenny Rachitsky49:28

        Yeah

    11. MAMarc Andreessen49:28

        ... 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, it swept the world. Uh, you know, most coding today happens in scripting languages. A- and then, by the way, the people alo- along the way, the people who really understood the scripting languages and the people who understood all the lower-level systems, they, they were the ones who were able to actually make the scripting languages actually work really well, right? And so that, that was, that was a great example of this kind of adaptation. And then, and then again, the result of that was, you know, a far higher number of people writing code with 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.

    12. LRLenny Rachitsky50:04

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

    13. MAMarc Andreessen50:10

        Well, that's funny because, of course, it was so notorious for being i- impossible to understand, so. [chuckles]

    14. LRLenny Rachitsky50:15

        [chuckles] How ironic.

    15. MAMarc Andreessen50:16

        Yeah.

    16. LRLenny Rachitsky50:17

        This episode is brought to you by Datadog, now home to Eppo, the leading experimentation and feature flagging platform. Product managers at the world's best companies use Datadog, the same platform their engineers rely on every day to connect product insights to product issues like bugs, UX friction, and business impact. It starts with product analytics, where PMs can watch replays, review funnels, dive into retention, and explore their growth metrics. Where other tools stop, Datadog goes even further. It helps you actually diagnose the impact of funnel drop-offs and bugs and UX friction. Once you know where to focus, experiments prove what works. I saw this firsthand when I was at Airbnb, where our experimentation platform was critical for analysing what worked and where things went wrong. And the same team that built experimentation at Airbnb built Eppo. Datadog then lets you go beyond the numbers with session replay. Watch exactly how users interact with heat maps and scroll maps to truly understand their behaviour. And all of this is powered by feature flags that are tied to real-time data, so that you can roll out safely, target precisely, and learn continuously. Datadog is more than engineering metrics. It's where great product teams learn faster, fix smarter, and ship with confidence. Request a demo at datadoghq.com/lenny. That's datadoghq.com/lenny.

11. 51:37 – 53:30

    The value of design in the AI era

    1. LRLenny Rachitsky51:39

       Coming back to these- 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 gonna be much more valuable in the future.

    2. MAMarc Andreessen51:54

       Yeah, that's right. And again, here, this, this is a great example. So the, the, again, the task level, the, the des- the, the, the, the task level of like design the perfect icon, [chuckles] right, is gonna be like, all right, the AI's gonna do that all day long. It's gonna give you a thousand icon designs, and it's gonna be great. Like, it's gonna be fantastic, like, whatever, you know, and there will still... By the way, there will still be some level of human icon design or whatever, but like the AI's gonna get really good at that. But like, what are we trying to do? Like, the, the, the, you know, kind of capital D design of like, all right, what is this thing for? And how does this... Yeah, how is this going to function in a world of human beings? And like, you know what, what's gonna... Is, is this gonna make people happy when they use it? Is it, it's gonna make people feel good about themselves? Um, is it gonna fit into the rest of their life? Is it gonna, you know, I don't know, challenge them in the right way? You know, all, all these kinds of higher level questions that the great designers have always thought about, like the, the, the job of designer, right, will involve much more of those higher level, m- more important components. And, and then again, with A- with AI doing a lot more o- o- of the underlying tasks. And so, you know, one way to think about it is, you know, I don't know, you, you think of like, I don't know, the world's best designers, you know, Jony Ive or whatever, it could be like, wow, like, if I'm a designer today, if I'm a twenty-five-year-old designer, and I, and I aspire to be, you know, Jony Ive in a decade, um, i- it's, it's- all of a sudden, I have a new path that I can use to kind of get to, to, to, to get there, which is I, you know, because Jo- Jony Ive did everything he did without AI. Now, you know, a young designer can just be like: "Wow, if I really harness AI in a decade, I'm gonna be like the best designer the world's ever seen, because it's not just gonna be me, it's gonna be me plus being so super empowered by this technology to be able to do so much more. Um, and then so much more of my time and attention is gonna be- is gonna be able to be focused on these higher level things that most, most designers never get to." And I think that, that's

12. 53:30 – 1:02:05

    The T-shaped skill strategy

    1. MAMarc Andreessen53:30

       gonna be another great example of that.

    2. LRLenny Rachitsky53:31

       So maybe what I'm hearing here is kind of this T-shaped strategy of be- if you want to be successful in any three of these roles-

    3. MAMarc Andreessen53:38

       Yeah

    4. LRLenny Rachitsky53:38

       ... be very, very, very good at that specific role, product management, engineering, design, and then get good enough at these other two roles.

    5. MAMarc Andreessen53:44

       Well, so I think that's great. I think that's really, really relevant. And then, you know, the Scott, you know, Scott Adamson firstly just passed away, um, you know, which, which is a, a real tragedy. But, um, I, I was always- I've always... I've referred for years to actually Scott's, uh, Scott Adams, he had this, uh, famous, um, he had this famous, uh, kind of career advice he would give people, which I, 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, um, "You know, I, I could"... He, he, he said, "You know, I could have been a pretty good cartoonist, um, 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 cartoonist who didn't understand business could have never written Dilbert, and then the world's best business people who didn't know how to do cartoons couldn't have done Dilbert. It took somebody who actually had both of those skills to be able to make Dilbert, right? Which is one of the most successful cartoons in history, right? And so, so the, the way Scott always described it was that, that, that from a career development standpoint, the, the additive effect of being good at two things is, like, more than double, right? Um, the additive effect of being good at three things is more than triple.... right? Um, be- because you, you, you become a super relevant specialist in the combination of the domains. Um, and, and you, you, like, you see this all, I mean, you see this all over, you know, you see this all over the economy. Y- yeah, I mean, you see this all over the economy, but I'll just, you know, give you an example, Hollywood. You know, just Hollywood as an example. Y- y- 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, and they can be very successful directors. But the superstars in the entertainment industry are the people who can write and direct, right? Uh, and, you know, they, they literally have a term for those. They call those auteurs, 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, [chuckles] it's just really funny, I've sp- I've 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 attack, 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 'cause the AI can write the script, and I don't need the actor anymore 'cause I can have AI actors." The writer is saying: Well, I don't need the director 'cause [chuckles] the AI 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 the AI direct the thing, I can have the AI write the thing, and I'm just gonna show up and do my performance, right? And so, so it's, it's, it's the same, [chuckles] it's the same kind of tri- triangular configuration. And again, what, 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 gonna have more people who can write and direct, or write and act, or direct and act, or do all three. And, and I think, y- you know, to your point, like your, your, your T-shaped thing, like I, I think that's gonna be true basically across the entire economy. And, 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. 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-powered individual. You're gonna be able to just perform, like, feats of magic, uh, for example, in terms of designing and building new products, you know, that, that 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.

    6. LRLenny Rachitsky57:06

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

    7. MAMarc Andreessen57:20

       Yeah, I think that's right. I think that's right. Uh, the, yeah, the combination... Yeah. Uh, le- um, uh, uh, my, my friend Larry Summers, uh, uh, had a, had a different version of the Scott Adams thing, which is he, he used to tell people, he said, "The key for, uh, career planning is," he said, "Don't be fungible."

    8. LRLenny Rachitsky57:33

       Mm.

    9. MAMarc Andreessen57:34

       Right? A- and, you know, that's a- he's an economist, and so that was economics speak. And what, and what that means is, what that means essentially is don't be replaceable. And so don't be a cog, right? So and, 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, if you have this, if you have this, yeah, to your... If you have this E or F, you know, lying 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, not only are you not fungible, like, you're actually massively important 'cause you're one of the only people in the world who can actually do that combination of things. Um, and yeah, that, that, your ability not to become one of those people is, like, just titanically enhanced, uh, with AI as compared to anything we've ever seen before.

    10. LRLenny Rachitsky58:14

        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.

    11. MAMarc Andreessen58:21

        Right.

    12. LRLenny Rachitsky58:21

        She can code and design. Oh, my God! And what I'm hearing here is this is what you need to become. You need to become really good at, at least two things. There- I think you used the term smokestack or something, where it's like PM over here, engineer, design. And what I'm hearing here is you need to get good at, at least two of these skills. The silos of these two roles are disappearing.

    13. MAMarc Andreessen58:37

        That's right, that's right. And again, I can't, I can't overstress the following for, for anybody listening to this. I- i- the thing about AI that I think people are just, like, not getting enough benefit out of yet is just it will teach you. [chuckles] 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. [chuckles] And so it's... I always feel like it's like, it's like people spend too much... I, I- it's one of these things where it's, like, so much focus on figuring how to use, like, a large language model. It's like: Okay, what am I gonna try to get it to do for me, right? Which is, of course, very important. But the other side of it is, what can I get it to teach me how to do, right? And it's, it's just as good at that, right? Um, and so, th- th- again, this is this level, this level of latent superpower. Like, you know, people who really wanna, like, improve themselves and, like, devi- 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, tell me, tell... 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's, it, it knows exactly how to do that. You know, ru- run me, you know, make me problems, you know, uh, make me assignments, then evaluate my results, right? And it, it will do, it will do that just as happily as it will do work, quote-unquote, for you.

    14. LRLenny Rachitsky59:43

        Two tricks I've heard along those lines. One is, uh, 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, uh, a couple 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?

    15. MAMarc Andreessen1:00:14

        Yeah, that's right. That's right. Yeah, look, on that first one, and this, again, this is what I'm doing with my ten-year-old. Yeah, look, if, if, if you ask an AI... Yeah, this is, this is a really good point. So if you ask an AI, "Write me this code," and then it, and then it does it, and 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, 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- do you even understand what to tell it to try to get it to do something different? But to your point, like, if you actually wa- if you actually watch what it's doing, um, and, and, and, and then you, and then you, you have the grounding, you know, kind of that leg of the, [chuckles] of your E or your F-... um, if you have that grounding, then you can be like: "Oh, I see what it's doing. I see where it made the mistake. I see where it went sideways." And then you're all of a sudden able to intervene and able to say, "No, no, that's not what I meant. Do this other thing," right? And so, and again, this is, this is, this, this is a big part of having, having the actual kind of, you know, synergistic relationship, um, is that you understand. And by the way, look, I mean, th- this is-- like, everything I'm saying is, you know, everything I-- everything that we're saying right now also is the same as if you're working with human beings, right? Like, you know, if you and I are colleagues, and I, you know, would ask you to do something, and you'd come back with something completely different, like, I, I do need to understand what was happening in your head, right? In order to, in order to be able to get... Give you, give you feedback, right? If I just tell you, "Oh, that's wrong," it, it doesn't-- like, noth- nothing happens. I need to actually understand. I need to have theory of mind, right? I need to understand what you were thinking in order to really give you the right feedback. Um, and so, and, and, you know, and again, the great thing with AI is AI will happily sit there and explain all day long why it's doing what it's doing. It'll, you know, it'll happily critique itself. [chuckles] You know, and you can do this... By the way, this is also a very fun thing, where you can have, have one AI critique the other AI, right? Um, which is another thing, which is like, you have one AI write the code, you have another AI debug the code. Um, and so you can actually use- you can play the AI's off against each other and get them to argue with each other. Um, and yeah, these, these are all, these are all the kinds of skills that are going to become, I think, incredibly valuable.

    16. LRLenny Rachitsky1:02:01

        I think people call those LLM councils.

    17. MAMarc Andreessen1:02:03

        Yes.

    18. LRLenny Rachitsky1:02:03

        Where they're talking to each other. [chuckles]

13. 1:02:05 – 1:05:58

    AI’s impact on founders and companies

    1. MAMarc Andreessen1:02:05

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

    2. LRLenny Rachitsky1:02:06

       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? Because 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.

    3. MAMarc Andreessen1:02:25

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

    4. LRLenny Rachitsky1:02:28

       Hmm.

    5. MAMarc Andreessen1:02:29

       But I have no, like, art skills. But as we're talking, I'm like, "Hmm, it might be time." [chuckles]

    6. LRLenny Rachitsky1:02:35

       [chuckles] Your time has come, Marc.

    7. MAMarc Andreessen1:02:36

       Yes.

    8. LRLenny Rachitsky1:02:38

       I want to pivot to founders, your maybe your bread and butter.

    9. MAMarc Andreessen1:02:41

       Sure.

    10. LRLenny Rachitsky1:02:41

        You spent a lot of time with the most cutting-edge AI forward founders. I'm curious what you see them do, how you see them... Some way they operate that's maybe blowing your mind about how the future of starting a company looks, how the future of AI-forward companies look.

    11. MAMarc Andreessen1:02:57

        Yeah. So this is a great, it's a very, you know, topical ti- topic that's all playing out in real time right now, um, on the, on the leading edge. So I, I think there's like three layers of it. And see, 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, um, is this a new capability that gets added to existing products, right? So all of a sudden, you've got, I don't know, an existing, you know, software business, and now you've got your, you know, PC version of it, and now you've got your iPhone version of it, and you just kind of keep on going. And, you know, you kind of add, the, the new technology kind of gets kind of added into the mix. Um, you know, it's kind of another ingredient into an, into an existing formula. And, 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, it didn't really, it didn't really redefine the, the software industry because people just went from using, you know, hard disks, using flash storage or something. Um, uh, 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. Um, right, and so, so sometimes you get the kind of... It's, it's additive to an existing thing. Sometimes you get the, actually, it redefines a, a, an entire product category, or redefines an industry. The actual com-- you know, in many cases, the companies themselves turn over. And so, so, so, you know, so there's sort of this question, and like, you know, an example, you just mentioned Nano Banana. So, like, a, a great example is there are, you know, there, there are these businesses like, you know, just take Adobe, like, you know, Photoshop has built a, whatever, forty-year franchise in image editing. Um, okay, is AI a sort of a feature now that gets added to Photoshop to be able to do AI-based image editing? Or, you know, do you just, like, stop editing images entirely because you're using Nano Banana, and your-- all images are just being generated, and it's just easier to just gen-- 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 answers, I think, will vary by domain. But, um, 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, 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've already talked about, but like, okay, if I'm a founder of a company and I've got, you know, if I have, you know, room in my budget for a hundred coders, you know, how do I get those coders to be super empowered AI coders not, you know, not the kind of coders I used to have? And if they're super empowered AI coders, then does that mean, you know, do I still need the hundred? Maybe now I only need ten. Or does that mean I still want a hundred, but now they're doing ten times more, right? And so the, you know, as you know, like, a lot of the best founders are, 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, [chuckles]

14. 1:05:58 – 1:08:33

    The concept of one-person billion-dollar companies

    1. MAMarc Andreessen1:05:58

       right? You know, does that change? And, and again, here you've got this concept of the super-powered individual, which is like, okay, um, 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 A- AI bots. And, and there's sort of this, you know, there's kind of this holy grail in our industry that's been running for a long time, which is like, can you have the- can you have, like, the one-person billion-dollar outcome? And, you know, we've had a few of those over the years. Bitcoin is probably the most spectacular example, you know, with Ethereum right behind it. Um, 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- some- something hits, and you just have a, you know, very small number of people associated with it. You know, but that said, you know, mo- 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, um, of a, um, uh, o- of having a company? And, and, you know, can you have a company that's, that's literally basically just all AI?... um, and so, and, and if you're doing so, you know, if you're doing anything in the real world, that's hard. But if you're doing software like that, that, that seems like it might be feasible in some cases. And then, you know, there's, like, the ultimate example of that, which is like, you know, can you have like AI-- can you have like autonomous, like AI economy stuff happening where you have like AI bots on the blockchain or something, you know, that are out, basically out there, like functioning as a, as a, as a business and like making money and just, you know, l- 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 the, the, you know, maybe that, maybe that's the, the final outlier result. We have, we have a few, uh, founders who are chasing that kind of thing. Um, so I, I would describe that as, I would describe that as kind of the, the, the latter, that the best founders are out.

    2. LRLenny Rachitsky1:07:36

       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. Uh, having run, running my newsletter, uh, 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 cons..., like filling out forms. Uh, and so I guess depends on do you have contractors? Does that count? As you know, like, what does it count- what does it mean to be a one person? But I'm just like, I can't see that happening.

    3. MAMarc Andreessen1:08:12

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

    4. LRLenny Rachitsky1:08:16

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

    5. MAMarc Andreessen1:08:19

       Yeah.

    6. LRLenny Rachitsky1:08:20

       I guess, I guess it counts. Okay.

    7. MAMarc Andreessen1:08:21

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

15. 1:08:33 – 1:14:39

    Debating AI moats and market dynamics

    1. MAMarc Andreessen1:08:33

       so.

    2. LRLenny Rachitsky1:08:33

       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?

    3. MAMarc Andreessen1:08:45

       My experience with like, really big technological transformations, and of course, I, I kind of lived 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 up front, where people kind of say, "Oh, it's therefore obvious that, you know, XYZ. It's therefore obvious that this kind of company is gonna be the company of the future, not that kind. It's obvious that this incumbent is gonna be able to adapt, and this other one isn't. It's, it's obvious that there's economic opportunity in this kind of start-up and not in these others. Um, it's obvious that the moats are gonna be in this area of the technology, but not in this other area." And, and there... And, you know, what everybody does is they, they kind of state those things with like, just an enormous amount of self-assurance, where they, they, you know, where they really sound like they have all the answers. And then, you know, what happens is this, these, these ideas kind of saturate the media, right? Because the, 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 gonna 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 confident statements people were making in the first ten or fifteen years, like, I would say, like, almost all of them are wrong, [chuckles] and generally, like, quite badly wrong. And so I just- I think the process, I think with massive- with, if there's gonna be a massive amount of technological change, it's gonna be like, I don't know, five or six layers of, like, structural change that will play out over time. And, and again, a lot, we've talked about a lot of this, but like, the, the, the implications of, 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 on the national level? How does this play out at the global level? You know, how does this inter- 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 gonna do? Um, you know, uh, and so it's just like, there's just-- there's, there are just a tremendous number of unknowns, like a, a, a, a, a very, very large number of unknowns. And I think it's just, like, really, really dangerous to prejudge these things. And so I'll just give, I'll just give... And it's just, I'll just run this as a thought experiment, and, you know, 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 moat, uh, 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 need, 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 NBA 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. Like, all of that translates to like, you know, okay, probably at the end of this, there's gonna be two or three companies that are gonna end up with like, you know, a hundred percent, you know, I don't know, whatever, fifty-fifty or thirty, thirty, thirty, or ninety, ten, one, or whatever it is, market share, and then they're gonna have whatever profitability they have, and it's gonna be a kind of a classic oligopoly and... Or, or maybe, you know, maybe one company's gonna win definitively, and it'll be, it'll be a monopoly. And that, and by the way, those outcomes have happened in software many times before, and so may- 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 ChatGPT, that like within basically a year to 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, and then there would additionally be open source that was basically the same, um, I would have been like: Wow, like, it, you know, the, 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 Cha- of GPT-3 coming out, there were, there were open source GPT-3s running on a fraction of the hardware, right? That were available for free. Um, and then there were, and then, you know, there were five... You know, now, now you've got, you know, in the ga- you know, fully in the game, you've got Google, and you've got Anthropic, and you've got xAI, and you've got Meta, and you've got, you know, all these other companies that are... And then DeepSeek and, you know, Kimi and all these other Chinese companies. Um, and so, like, even at the level of, like, LLMs or, you know, AI models, like, you can squint and make that argument either way. By the way, same thing at the level of apps, right?... it's like, you know, one school of thought is, you know, the apps, apps are not a thing 'cause, like, the model's just gonna do everything. Um, uh, but ano- another way of looking at it is no, actually, like, actually adapting the model is kind of the engine into a, i- into a domain involving human beings, um, 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, um, or coding. You know, no, you actually need, like, the application level is actually gonna matter enormously, and maybe the LLM's commoditized, and maybe the value goes to the apps. Um, and, and, and again, you can kinda squint either way on that one, and I, and I know very smart people who are on both sides of that argument. Um, and so I, I-- my honest answer on this is I think we're in a process of discovery over time, um, which is, you know, it's in the way I think about this kind of structurally is it's, it's a complex adaptive system. The technology itself, you know, provides one of the inputs. The legal and regulatory process, you know, is another input. Um, and, you know, actual individual choices made by entrepreneurs, um, you know, matter a lot. Um, you know, the economics matter a lot. Availability of investor capital varies over time. That matters a lot. Um, and this is a, this is a complex system, and so we, we actually don't know the, the outcomes on this yet, and, and we need to basically be-- we need to be open to surprises at the structural level, uh, uh, uh, of what happens. And of course, as a, [chuckles] as a VC, this is very exciting 'cause it means we, you know, we, we're doing this now. We should kind of make bets, uh, along every one of these strategies, um, and kind of see and, and, and see how this plays out. Uh, 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, hedge fund manager or something who has this all figured out, but I, I guess I would say if, if, if, if they exist, I haven't met them yet. [chuckles]

    4. LRLenny Rachitsky1:14:38

       [chuckles]

16. 1:14:39 – 1:18:05

    The rapid evolution of AI models

    1. LRLenny Rachitsky1:14:40

       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. A lot-- 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, the fastest-growing companies in the world.

    2. MAMarc Andreessen1:15:01

       Yeah, well, it's, it's like a little bit like, I don't know, I mean, even just like with, you know, uh, the, you know, the bit the... You know, this has been the, you know, the, the, the holiday if, you know, three years ago, it was the holiday of, of ChatGPT. This last, you know, month or whatever, has been the holiday of, of, uh, Claude, particularly Claude Code, right, for, for coding. But it's like, you know, it's, it's pretty amazing 'cause it's like, okay, there was Claude, which is, you know, obviously a great accomplishment, but then there's Claude Code, which is, which is an app, r- which is an app, right? [chuckles] So it's a, it's a Claude wrapper.

    3. LRLenny Rachitsky1:15:23

       Mm.

    4. MAMarc Andreessen1:15:24

       Right? It's a, you know, agent harness. Um, and then, um, and then they did this amazing thing where they came out with, uh, was it, uh, Coworker?

    5. LRLenny Rachitsky1:15:30

       Co- Cowork.

    6. MAMarc Andreessen1:15:31

       Cowork. Um, and, uh, and remember w- they said a Cowork, which is a Claude Code wrote Cowork in a week.

    7. LRLenny Rachitsky1:15:36

       Yeah, a week and a half. Yep, 100%.

    8. MAMarc Andreessen1:15:38

       Right. Well, and that's r- uh, and, and there's two ways of looking at that, which is like, wow, that's really impres-- I mean, obvio- obviously, that's really impressive that Claude Code was able to build Cowork in a week, in a week and a half. That's great. That's amazing. The other way to look at it is, Cowork was developed in a week and a half. [chuckles] Like, uh, like how, how much complexity could there be? How much of a varied entry can there be in something that was developed in a week and a half? And so, and, and, 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 val-- it's incredibly functional and 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, is sitting there being like: "Okay, obviously, we need to build, you know, an agent artist, and then obviously we need to build a cowork, you know, thing for, for, for regular people." And obvious, uh, you know, I, I don't, I, I'm not even saying I know anything, but just, like, obviously, they're all gonna do that, right? Um, and so, you know, how defensible is that? And, you know, in six months, y- you know, and, and we've seen this happen before. Like in s- is, is Claude Code gonna get lapped the same way that, you know, GitHub Copilot got lapped? You know, the, the history in the last three years has been ev- everything that looks like it's like the fundamental breakthrough gets, gets basically replicated and lapped very quickly. Like, uh, 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 drinks into 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. Um, and then, and then, you know, a- again, evidence of that is, you know, DeepSeek, you know, came out of left field and basically was like a, you know, a re-implementation of a lot of the ideas out of American big labs and, you know, and, and had some original ideas o- of its own. Um, but like, you know, wow, it wasn't that hard for, you know, some, you know, basically a 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 gonna be a huge breakthrough that's gonna be hard to replicate. And so, again, it's just like, I, I think we just need to... I don't know, my view is, uh, I, my view, myse- I need to put, like, a big discount on my forecasting ability on this one. Like, it, it- for me, it's much less interesting to try to say, "Okay, as a consequence, industry structure in five years is gonna be X. The big winner in the category is gonna be company Y. The big, you know, product killer app is gonna be Z." It's like I... Let's just say, I don't think I can predict that. Um, I, I think we're- I, I, I think a much, much better use of my time is, is being, being very flexible

17. 1:18:05 – 1:22:17

    Indeterminate optimism in venture capital

    1. MAMarc Andreessen1:18:05

       and adaptable at, at a time like this.

    2. LRLenny Rachitsky1:18:07

       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?

    3. MAMarc Andreessen1:18:23

       Yeah, so for, I mean, for us, yeah, for, for us, we, we obviously have a very, very deliberate strategy. [chuckles] One, one way to think about this, use the Peter Thiel for- you remember the Peter Thiel formulation of, uh, he said there's a two by two, there's optimism and pessimism, and then there's determinate, and, and is it indeterminate and, uh, indeterminate, uh, right? Um, and so, um, and he always argued l- like, there's- he always argued that, 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 i- an indeterminate optimist who thinks the world is going to be better but can't explain why, right? Like, some combination of things is going to happen to make the world be better, even if we don't know what those things are. And, and, you know, I think he, he at least historically would say like, that's, that's basically, you know, that, that, that, that risks at least being just like wishful thinking or deli- delusional thinking.... and what the world needs more is tr- determinant optimists, which are people who are like, "No, the world is going to be better 'cause 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, El- Elon is the de- determinant, determinant, determinant optimist," where it's like, "No, I'm going to build the electric car. [chuckles] I'm gonna do, you know, solar, and then I'm gonna do sp- you know, Mars," right? 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 he and I disagree on part of that, it would be I think the indeterminate optimism is a stronger phenomenon than at least I think he's historically represented it as, and I would put myself firmly in the indeterminate optimist category, and that's the strategy that we, that we have at a16z, which is a... And, 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 a16z or Silicon Valley is very, it's, it's actually very specific, which is, there are these extremely bright and capable people [chuckles] like Elon and many others, who are founders, right, and product- and, and, you know, kind of pr- product creators, right? And, and, and each of those individual people is a determinant optimist. Like, each of them, e- each of them individually has, like, a very strong view of what they're gonna 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 ten of those, we have a hundred and a thousand and then ten thousand of those. And the, 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 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. But and then, and then the right way to deal with that is to run as many experiments as possible and have as many smart people try to do as many interesting things as possible. Um, and so yeah, I would, I would put myself firmly on the side of the indeterminate optimist.

    4. LRLenny Rachitsky1:20:55

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

    5. MAMarc Andreessen1:21:01

       Yeah

    6. LRLenny Rachitsky1:21:01

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

    7. MAMarc Andreessen1:21:06

       Yeah. Yeah. No, that's right. That's right. I mean, look, the, the founders need to be deter- determinant optimists. Like, they need to have a very specific plan now. And, you know, look, the, the critique, the critique always, you know, the critique from the founders is, "Oh, you VCs have it easy, 'cause like you don't have to like... You don't actually have to commit, right? You don't actually have to, like, make-- You don't, you don't actually have to like, you know, you have to make the bed you lay in. You can, like, place multiple bets. You can 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." Um, you know, and there's, there's truth to that. You know, the counterargument on that is the founders get to run their companies, we don't. [chuckles] So, 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 determinant optimist is you actually get to, get to single-mindedly execute against that goal. And, and, and, you know, look, in the long run, who, who does history remember? History remembers Henry Ford, right? Not, you know, whoever was the, you know, whatever the seed investor who seeded Ford, Ford Motor Company, and, and, you know, ten other car companies have failed, right? Um, and so, you know, the determinant optimist is the per- you know, the founder, is the, the founder and the company builder and the engineer. I mean, these are the people who actually do the thing and, you know, deserve ninety-nine point nine nine nine nine% of the credit. But, uh, you know, having said that, I do, I do think there is a role for abs- having some indeterminate optimists in the, in the, uh, in, in the background, helping along the way and, and helping keep the whole, the

18. 1:22:17 – 1:30:00

    The concept of AGI and its implications

    1. MAMarc Andreessen1:22:17

       whole cycle going.

    2. LRLenny Rachitsky1:22:18

       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?

    3. MAMarc Andreessen1:22:29

       Yeah, so I've always kind of had a little bit of an is- I've, I've always kind of struggled with the concept of AGI, um, because it at least... Well, put it this way, there, there's, let's define terms, which is where I kind of struggle with it, which is, there's like the prosaic, there's the, there's the prosaic, uh, definition of AGI, and then there's, like, the, I don't know, cosmic definition. And the way I would describe it as... Well, let's start with the cosmic one. So the co- the cosmic one is basically the s- it's the singularity, right? Um, and so AGI is the, is the moment where you enter the singularity, which is to say the, 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. The AI, the AI is improving itself, and it's sort of raising, you know, so-called takeoff scenarios. You can see with this takeoff thing, where the AI is improving itself, and the machines are making decisions so much faster than people, and people are just sitting there watching the, the, the machine do its thing. You know, and I kind of described why I don't really, I don't really think that's- I don't, I don't think we live in that world. Like, uh, 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, um, the, the, the prosaic definition of AGI that at least I think the industry participants have kind of converged on, and tell me if you agree with this, is, uh, it's when the AI can do every economically relevant task as good as a person.

    4. LRLenny Rachitsky1:23:47

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

    5. MAMarc Andreessen1:23:56

       Okay, got, got it. Yeah, so it's maybe even a slightly reduced, a slightly reduced definition.

    6. LRLenny Rachitsky1:24:00

       Mm-hmm.

    7. MAMarc Andreessen1:24:00

       Um, and by the way, we're gonna- we're clearly getting close to that, if we're not already there.

    8. LRLenny Rachitsky1:24:03

       Mm.

    9. MAMarc Andreessen1:24:03

       And so on that one, I kind of feel like... So I kind of feel like the cosmic one overstates what's gonna 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. Uh, it, like, it, 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 the way we say that is, AGI always is, you know, the definition you gave, the definition I gave, it's kind of in com- it's always kind of relative in comparison to a human worker, right? And it's like, I don't know, like, human skill level caps out at a certain point, but that's because of the inherent, like, biological limitations of the human organism, right? Like, we're all, you know... Human, I'll give 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, uh, you know, uh, uh, uh, fluid intelligence. Uh, IQ, I think, tops out in, in humans as a species, it tops out around one sixty, right? Where at, at, at, like, one sixty, it, it's like Einstein level. Einstein, Feynman-

    10. LRLenny Rachitsky1:25:00

        In terms of IQ. Yeah.

    11. MAMarc Andreessen1:25:01

        In terms of IQ. Like, it just tops out at one sixty. The, the one sixty 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 best-selling 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, um, it would be probably one forty, um, is kind of the IQ that you're looking for there. Um, if you're looking for, like, a really good lawyer, it's probably one thirty. Um, if you're looking for, like, a really good, like, line manager in a business, it's probably one ten. Um, you know, if you're looking for, like, an accountant, like a small business accountant who's good at doing the books for small businesses, it's probably one oh five, right? 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, it's sort of that one ten to one sixty 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 one forty, one fifty, one sixty. But it's like, that's just- [chuckles] 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, and you already have people running these experiments to kind of do human equivalent, you know, kind of IQ, uh, um, uh, you know, for, for existing AI model. And by the way, existing AI models right now are kind of testing around the one thirty, one forty level, which means they're gonna get to the one sixty level, and they're, you know, they're arguably on the mass side, starting to get to the one sixty level now. But like I, I think we're gonna have AI models relatively quickly that are gonna be like one sixty, one eighty, two hundred, you know, two, two, two fifty, three hundred. 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 or greater than Einstein. But like, I think IQ, IQ of the machines is gonna exceed that of the humans. I think that's, that's really good. Um, 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, I think, you know, this is where Linus Torvalds, in particular, was like: "Yeah, okay. Like, this thing is starting to generate better code than I can." Okay, so now we're gonna have AI coders that are actually better coders than the best human coders. I think that's [chuckles] great. I think we're gonna have AI doctors that are better than the best human doctors. I think we're gonna have AI lawyers that are better than the best human lawyers, which actually is gonna be very interesting to see- [chuckles] ... uh, which we can talk about, which I think is also great. Um, 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, 'cause we've been capped by our own biology, and we're gonna get to experience what it's like when you have the capability at your fingertips that's actually better than human in these domains. Um, and so I, I, you, you see what I'm saying, which is like, each-- I think this idea of, like, human equivalent is just gonna be like a footnote. It's like: "Oh yeah, that was just on Tuesday," you know, in, in 2026, is when they hit that. And it kind of didn't matter because the, the next question was like: Okay, what are we gonna, what are we gonna-- what do we get to do in a world in which we're a- we actually have machines that are better than that, right? And so, so, so I think this is gonna be much more of an exploratory process for actually exceeding human capability than it's gonna be any sort of particular singular, singularity moment or whatever that happens, just, that just happens to coincide with the human threshold.

    12. LRLenny Rachitsky1:28:10

        Two hundred IQ. I, uh, just, like, that frame of reference is such a, a mind-expanding way to think about just how fast and how smart these things are gonna get and, and quickly.

    13. MAMarc Andreessen1:28:20

        Well, I don't know if you have this experience. I, I, I have this experience all the time. Well, two, two experiences I have all the time. One is just like, I've just like, like, I know I ought to be able to do this, but, like, I just can't... Like, it's gonna take too long. You know, I, I, I wanna write this thing, or I wanna, like, whatever, I wanna have this theory on this thing or have a plan or whatever, and it's just like, fuck, like-

    14. LRLenny Rachitsky1:28:41

        [chuckles]

    15. MAMarc Andreessen1:28:41

        ... I, I don't have the eight hours or, or by the way, the eight weeks or the eight years, [chuckles] 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, and I'm sure you have this. You get interested in something, you read ten books, and then you're like: "Shit, I forgot almost everything that I just read." Like, I, I-

    16. LRLenny Rachitsky1:29:00

        [chuckles]

    17. MAMarc Andreessen1:29:01

        ... I wish I could retain it all, but I can't. It's just like, I, you just have this, I, I sort of live in this kind of state of like end- almost frustration. I just, like, I, like, if I could just be smarter [chuckles] than I was, like, I'd be so much better at what I do, but I'm not. So, so, so, so there's that. And I, I don't know how often you have this, but I have this on a regular basis. It's just like, you know, I, I, you know, because of what we do, like, I know a bunch of people who I know for fucking sure are smarter than I am. And I know it because when I talk to them, I just find myself at a certain point... You know, it's like for the first half of the conversation, I'm just taking notes the entire time, and for the second half of the conversation, I'm just like: "Fuck," like, "fuck me."

    18. LRLenny Rachitsky1:29:33

        [chuckles]

    19. MAMarc Andreessen1:29:34

        "Like, this person is just smarter than I am, and they're just outthinking me, and they're gonna 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, I'm just not... Whatever that is, I'm not that." And so we're just so used to having those limitations, um, that the idea of having machines that work for us, that don't have those limitations, I, I just, I think that's much more exciting than people are giving it credit for.

19. 1:30:00 – 1:36:18

    Marc's media diet

    1. LRLenny Rachitsky1:30:00

       Oh, man, I could talk to you for, for hours, Marc. I'm thinking to close out the conversation, I want to ask about your media diet and your product diet.

    2. MAMarc Andreessen1:30:08

       Yep.

    3. LRLenny Rachitsky1:30:08

       You just talked about books, reading ten books. I, 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, I don't know, podcasts, newsletters, blogs, things like that, and then any books in particular?

    4. MAMarc Andreessen1:30:25

       Yeah, yeah. So what I read is basically, I mean, I, so I read basically three categories of things. So, like, in terms of, like, general media, um, it's basically I, I sort of, um, I always describe it as I have, like, a almost a perfect barbell strategy, um, [chuckles] which is I read X and I read old books, right? So it's basically either like, up to the minute, what's happening right now, um, or it's like a book that was written fifty years ago that has stood the test of time, and then, you know, where presumably there's something timeless in it. Um, and, and then it, it's sort of everything in the middle I'm always, like, much more skeptical about. And, and it's particular, it's, it's kind of what I already said, which is, I, 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, and by the way, you can, you can do this, just read last week's newspaper, right? I'd say we're, 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." [chuckles] Like-... [chuckles] None, the, none of what they predicted played out the way that they said that it would. N- 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 gonna 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, n- like, none of this happened. Like, I wish I had never read this. Like, oh, my God. Um, and then, you know, it's kind of the same thing with magazines. Like, go back and read old magazines, um, and just, like, the, the, the, the level of the, you know, the just the, the endless numbers of predictions that they make. Yeah, and, and kind of, you know, the problem with, you know, newspapers at least are going day to day. The thing with magazines is like every... It's like a, a week or month, you know, a kind of long cycle. And so it's even, you know, by the time an article even hits publication, it's, you know, it's, it's often out of, out, out of date. So I just, I just have, like, a big problem with kind of everything in the middle. Um, and so it, it's either, it's either, it's either of the moment or, or timeless. But then, yeah, you mentioned, like, newsletters. I mean, so the, the, the other thing, and, you know, this is maybe obvious, but I think it's probably still underrated, which is, uh, the actual practitioners in the field who are actually creating content, I think probably is still, like, dramatically under- underrated. Um, 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, we're used to being in this mass media kind of culture in which basically everything is mediated, right? Ev- everything got filtered through, like, TV interviews or, like, newspaper interviews or magazine interviews. And, and, you know, obviously now more and more, it's just, no, you actually want, like, smart people who are actually working on something, explaining themselves. And then you have, you know, you have new kinds of intermediation, like podcasts, that, that, that, that kind of open that up for people and make that possible. Um, and so yeah, like, domain practitioners are, um, you know, really great. I mean, it, it, just to state the obvious, in AI, you know, it's obviously your, your stuff, but also like, you know, Let- Lex, you know, Well, you know, the fact that, like, Lex Fridman can have, you know, the world's leading or, you know, whoever the... You know, any, any of you guys, you know, the small handful of you guys who have access to these people, you can have the world's, you know, kind of leading experts in the domain actually show up and... A- and by the way, it's, you know, as- and look, uh, 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, um, 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, and they want to explain it, and they want people to understand it. And everybody kind of enjoys that, and they get to contribute to kind of human knowledge by doing that, and they get ego gratification by doing that. Um, 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 ten years ago. So I, yeah, I do as much of that as I can do.

    5. LRLenny Rachitsky1:33:54

       And there's also just this culture in, 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.

    6. MAMarc Andreessen1:34:03

       Yeah. It's, uh, somebody said, uh, Silicon Valley is a company town, but the, the, the, the company is Silicon Valley.

    7. LRLenny Rachitsky1:34:09

       Mm. [chuckles]

    8. MAMarc Andreessen1:34:09

       Right? And, and, but, and again, at the, the level this goes again, is one of these great N equals one. At the level of N equals one is somebody... You know, and I've, I've run startups before. I've run companies before. Um, 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, you're in this, you're in this ecosystem that, that adapts, right? And channels talents and, and, and, and skill and knowledge and people into, 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. Um, at the level of, of, of just being in the ecosystem, to your point, like, yeah, it's a, it's an absolutely magical phenomenon. And by the way, like, you know, one of the, one of the... You know, for all the, for all the issues in Silicon Valley, um, you know, I think AI, I, I did the count once. I think AI is the ninth major technology platform in the history of Silicon Valley, right? The, and, you know, Silicon Valley is- Silicon Valley is still called Silicon Valley. We haven't made silicon here in decades, right? Uh, we used to actually... You know, it's called Silicon Valley because they used to make chips, right? They used to have the, like, the actual fabs were in Silicon Valley, and then they, and they designed them, and, and they made the chips. Um, and, and so, and that was, you know, wave one, starting in the 19th... You know, actually, that was like, actually, no, actually more or less like wave three or whatever, but, like, it was, you know, that was when the, the initia- the, the area was named, like in the 1950s. But now we're on like wave nine, right? Um, and, and the, the company town phenomenon, where the company is the industry, like the, the, the, 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, the, right, the indeterminate optimi- optimism of ecosystem, flexibility of the ecosystem meant that the, the, the, the Silicon Valley could, could morph, um, i- i- into all these categories. And again, a, maybe a, a testimony to indeterminate optimism.

    9. LRLenny Rachitsky1:35:58

       This reminds me of the meme of how we're all just wrappers over sand. Everything we're building is just wrapper over wrapper, wrapper, wrapper.

    10. MAMarc Andreessen1:36:03

        The wrapper thing is hysterical. Yeah, yeah, I'm a, I'm a software company. I'm a, I'm a, I'm a chip wrapper, right?

    11. LRLenny Rachitsky1:36:07

        [chuckles] Yeah.

    12. MAMarc Andreessen1:36:07

        Um, uh, yeah, I'm a, I'm a, I'm a, I'm a business application. I'm a database wrapper. Um, yeah, exactly. I'm a sander, and, yeah, you and I are, we're all now sand wrappers. [chuckles]

    13. LRLenny Rachitsky1:36:16

        Sand wrapper.

    14. MAMarc Andreessen1:36:17

        Perfect.

    15. LRLenny Rachitsky1:36:17

        Okay,

20. 1:36:18 – 1:39:24

    Favorite movies and AI voice technology

    1. LRLenny Rachitsky1:36:18

       one more question along the media diet. I asked your partner, Ben Horowitz, uh, what to talk to you about, uh, the Z in a16z, if people don't know him. And he said that you're really into movies these days.

    2. MAMarc Andreessen1:36:27

       Yeah.

    3. LRLenny Rachitsky1:36:28

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

    4. MAMarc Andreessen1:36:33

       Yeah, so the movie that blew my socks off, uh, last year, which I think is the best movie of the decade for sure, and maybe of the last, like, fifteen years, is this movie... Uh, 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. Um-

    5. LRLenny Rachitsky1:36:46

       Mm-hmm. Not heard of it.

    6. MAMarc Andreessen1:36:48

       Have you not heard of it? Okay, so, Ed, you're gonna really enjoy it. So I won't, I won't spoil too much of it. So i- at, at, at, at, at the surface level, the, the, the sp- 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 six hundred people. Um, and, um, there's a, uh, sheriff, uh, who's played by Joaquin Phoenix, who's like an old, crusty, basically right-winger. And then there's a, um, uh, there's a, uh, mayor, uh, played by Pedro Pascal, who's basically a young, hip progressive. And, uh, and then the movie starts, I think, in March of 2020, and so it starts when Covid first hits.... and then it sort of, as it plays out over the next few months, it, it then, it, it intersects and it, it sort of extends into the summer of twenty twenty. So, you know, kind of the, the George Floyd moment, and then the, you know, the, the protests and riots and kind of everything. So sort of the convergence of COVID and then the, um, and then the, uh, and then, and then the, uh, the, all the, um, all the BLM stuff. And, and, and, and then, um, it, it, and then, and, and then there's a third kind of element to it, which is, um, there's a company which is basically a loosely disguised version of Meta, if you read the backstory of it, which is building an AI data centre on the outskirts of town. So they kind of pull that in, uh, as sort of a thing that looms larger and larger over time. And then, um, the thing it really is great at is it really shows, um, you know, this is a small town in New Mexico, and so everybody in the town gets kind of fully wrapped up in all the COVID stuff, and they get fully wrapped up in all the BLM stuff, and they get fully wrapped up in all the, like, you know, tech anxiety stuff, but they're all experiencing it basically through the internet, right? Which, which, which is, which is, you know, what, what act- what actually happened, right? And so, so it, it's, it's... So, so the reason I love the movie so much is one, one, is it's the first movie that directly grapples with twenty twenty, of what happened in twenty twenty, and it just, like, fully, fully engages and grapples with, like, all the dynamics that were playing out in the country. But the other reason is, it's the first movie that does a really good job of showing what it, what it, what it was like, especially in that era, to live in a world in which there were things happening in the real world, and people were kind of experiencing events online, it, you know, like, in a way that was, like, very central in their lives, right? Um, and so it does, like, a really good job of pulling in, like, smartphones and social media, um, in a way that, um, uh, in a way that movies really, really, really struggle with, and then the whole thing comes together in an incredibly entertaining way. Um, and so, and I won't even say I, I, I won't even say I completely agree with the movie or whatever, and I, 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 it actually like to live like a human being in the twenty twenties in America, in a way that I think many other filmmakers, who are very talented, have just been very scared of touching. And, 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 'em. [chuckles]

    7. LRLenny Rachitsky1:39:19

       [chuckles] I can see why it's your favorite movie of the year.

    8. MAMarc Andreessen1:39:21

       It's great.

    9. LRLenny Rachitsky1:39:22

       [chuckles]

    10. MAMarc Andreessen1:39:22

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

    11. LRLenny Rachitsky1:39:23

        Oh,

21. 1:39:24 – 1:43:16

    Marc's product diet

    1. LRLenny Rachitsky1:39:24

       man. Okay, fi- final question. I wanna ask about your diet, uh, your product diet. Are there any products you use that maybe are less known that you love, that you wanna recommend? You can, you know, mention products you're investors in if, if you use them constantly.

    2. MAMarc Andreessen1:39:37

       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- who's your favorite children? So it's, it's really hard to, to, to, uh, to, uh, you know, to, to, to pull out specific ones. Um, but I'll, uh, you know, I'll, I'll talk about a few. Um, yeah, I mean, or just, I'll, just an observation. So one is my, my 10-year-old, um, I have my 10-year-old, my 10-year-old right now is 100% obsessed with Replit. Um, and, and by the way, it was not from me. Do you have kids?

    3. LRLenny Rachitsky1:40:00

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

    4. MAMarc Andreessen1:40:01

       Two and a half. Okay, so you haven't run into what I'm running into now, which is, whatever it is you do is not cool. [chuckles] Right? Like, it's two and a half, whatever Daddy does is, like, the coolest thing in the fucking world. I can tell you, by the time he's 10, whatever you do is, like, deeply uncool, right? And I'm, and I'm highly aware of that. Um, and so, like, if I mention, "Oh, yeah, we work on XYZ," you know, he's like, "Okay." Um, but when he discovers something, then, then it's cool, or when his friends tell him about it, it's cool. And so he, he, he, through no inter- 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 will sit and do it, and do it for hours. And so I'm, 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, uh, with, uh, Bad Rudy, which is, if you've seen it, it's a, it's the, uh, it's a foul-mouthed raccoon-

    5. LRLenny Rachitsky1:41:00

       [chuckles]

    6. MAMarc Andreessen1:41:00

       ... uh, a- a avatar, uh, on the, uh, in the, 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, just these just incredibly, like, uh, you know, i- intimate, emotional, you know, kind of voice experiences. Um, so I think the voice stuff is fantastic. I'm also super fascinated by all the voice input stuff. Um, and so, um, you know, Li- Limit, you know, Limitless Suite, uh, Limitless recently, um, kind of the company recently, um, uh, sold, but, um, you know, the, the, all the, the... I, I think, like, the pendants, the wearables, like, all that stuff is gonna be big, the meta glasses. Um, I, you know, I think there's gonna be a whole wearables revolution here. Um, I, I love the voice input stuff. Um, I have this app on my- there's this app on my phone now called WhisperFlow, um, which is a voice transcription, um, which works, like, staggeringly well. Um, uh, it, 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, I think the voice mode stuff is gonna be, is gonna be, uh, is, is gonna be really great.

    7. LRLenny Rachitsky1:42:10

       Uh, subscribers of my newsletter get a year free of Replit and WhisperFlow, so there we go. [chuckles] Uh, uh, what's the, what's the most memorable thing your son built with Replit?

    8. MAMarc Andreessen1:42:19

       Oh, well, so he's gotten super into Star Trek. Um, and so, uh, so far it's been, he's write- like, writing, like, Star Trek simulators. Um-

    9. LRLenny Rachitsky1:42:25

       Hmm.

    10. MAMarc Andreessen1:42:25

        So, uh, like, all the, uh, you know, all the, uh, uh, by Next Generation, they actually had a-

    11. LRLenny Rachitsky1:42:28

        Next Generation. Okay, I was gonna ask which. [chuckles]

    12. MAMarc Andreessen1:42:30

        Well, he like... We actually, we like them all. We watched the new Starfleet Academy last night-

    13. LRLenny Rachitsky1:42:33

        Mm, mm

    14. MAMarc Andreessen1:42:33

        ... which actually is quite, it actually is quite good. Um, but, uh, we, 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. 'Cause if you, if you watch the original series, they just had, like, basically, you know, knobs with lights, and the, they didn't re- 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, a UI design language. And so, uh, one of the, one of the fun things you can do vibe coding, is you can say, "Give me a Star Trek: Next Generation," you know, "user interface for, you know, whatever, this, that, or whatever," and it actually uses the, they call it, this is having a nerd-out, they call it LCARS, um, uh, de- design language. And, um, it'll, you know, it'll actually build you, like, Star Trek: Next Generation bridge consoles, um, using that design language but, you know, with your choice of, like, a Star Trek game, for example.

22. 1:43:16 – 1:44:34

    Closing thoughts and recommendations

    1. MAMarc Andreessen1:43:16

       Um, and so he's, he's gone crazy for that kind of thing.

    2. LRLenny Rachitsky1:43:19

       That sounds extremely delightful. You guys should, uh, open source or release that. Marc, I- like I said, I could talk to you for hours. Uh, well, you've got things to do. [chuckles] Uh, anything you wanna leave listeners with before we wrap up? Anything you wanna double down on or just leave listeners with?

    3. MAMarc Andreessen1:43:33

       Yeah, so a couple things. So one is, we got super lucky last week. Uh, Packy McCormick, uh, wrote the best piece ever written about us, actually, um, which he released, um, and so it's the best explanation of what we do, uh, and how we think, and so I, I would definitely recommend that. Um, 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, um, and, you know, and content. Um, and so I'd definitely recommend our YouTube channel, which I, I think has a lot of great stuff and is gonna be very exciting in the next year.

    4. LRLenny Rachitsky1:43:58

       Awesome. We'll link to that. I think it's just youtube.com/a16z, something like that, and you guys have great stuff.

    5. MAMarc Andreessen1:44:04

       Good.

    6. LRLenny Rachitsky1:44:04

       Marc, thank you so much for being here.

    7. MAMarc Andreessen1:44:06

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

    8. LRLenny Rachitsky1:44:08

       Bye, everyone. [upbeat music] Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode!

Episode duration: 1:44:35