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Marc Andreessen on Why This Is the Most Important Moment in Tech History

The a16z Show

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  • Three Colliding Historical Forces
    • Marc Andreessen argues 2025–2026 are historic because AI, institutional collapse, and geopolitical shifts collide.
    • These three simultaneous forces create complexity and opportunity unlike recent decades. Transcript: Marc Andreessen Is a very, very historic time. I think 2025 was maybe the most interesting year in my entire career and probably life. And I think I would expect 2026 to exceed that. Wow, that says a lot. Yeah, I’ve seen some stuff. So it feels like two things are happening. One is 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 think, in kind of full-scale collapse right now. By the way, there’s a lot of data to support that. And so I think there’s just, there’s like a lot of structures and orders and institutions that people have just relied on for a long time that have just proven to not be up for the challenge. And then kind of corresponding with that is the national and global conversation have become like, let’s say, liberated. And so, you know, this sort of incredible revolution that we have in kind of, you know, what I would describe as freedom of speech, freedom of thought, ability for people to openly discuss Things that maybe they couldn’t discuss even a few years ago, you know, is just dramatically expanded. And I think that’s now on a one-way train for just a much broader range of discourse. And then, you know, there’s also just these like incredibly massive geopolitical shifts that are happening. And obviously the U.S. Is changing a lot. Europe is changing a lot. China is changing a lot. Latin America, by the way, is changing a lot. Very dramatic, you know, events playing out down there right now. You know, kind of all over the world, like I think a lot of assumptions are being pulled out into the daylight and reexamined. And then it’s kind of the fact that all these things are happening at the same time, right? And so you’ve got all of these countries and industries, you know, where things are kind of increasingly upheaval, but you have AI as this kind of new technology that’s going to really Affect things. And then you’ve got, you know, people, you know, citizens being able to fully participate and being able to argue things out. And so it’s kind of like those three kind of big mega things are kind of all colliding at the same time. And I think we’re probably just the very beginning of all three of those. (Time 0:02:08)
  • AI Needs To Raise Productivity
    • Productivity growth in the U.S. has been low for decades, contrary to our perception of nonstop tech progress.
    • AI must raise productivity to offset demographic decline and prevent economic contraction. Transcript: Marc Andreessen Think at this point, I think it’s pretty clear with, you know, our technology hats on that, like, this stuff is really working now, right? And so there was this, you know, kind of, you know, when there was a ChetGPT moment, you know, three years ago. It was only, by the way, only three years ago, right, was the ChetGPT moment. And the big question was, all right, this is, like, incredibly fun and creative. And, like, we have machines now that can compose Shakespearean sonnets and rap lyrics. And, like, you know, this is amazing. But then there was, you know, there’s this question, like, can you harness this technology for reasoning and for, you know, problem solving in domains that, like, really matter, you Know, medicine and science and law and so forth. And, you know, it turns out the answer to that is yes, right? And, you know, the last 12 months and especially the last, even just the last three months have really proven that like AI can really do like, you know, you’re seeing it all now, you know, You can actually, you know, AI is now developing new math theorems, you know, they’re, you know, over the holiday break, you know, there’s sort of the, but it feels like the AI coding Thing, you know, really hit critical mass and the world’s best, the world’s best programmers, right, including like Linus Torvalds, you know, for the first time over the holiday break, Basically said, yeah, AI is now coding better than we can. And so that, you know, that’s incredibly, incredibly powerful. And I think we all, you know, kind of, I think, assume that AI now is going to get really good at reasoning in any domain in which there are verifiable answers. And so that, you know, that’s going to include like many very important domains. So, like, the technology feels like it’s moving fast it’s going to be working really well. I think this thing that is not well understood, I think a lot of people have a, I think 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 the world and changes everything. And I think that’s kind of the wrong frame, or I think it’s based on an incomplete understanding of the world that we live in or the world that we’ve been living in for the last, you know, 80 years. And I would call it 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 technological change. But actually, if you look for actually evidence of that, like in statistical evidence of that, analytical evidence of that, like you basically can’t find it. And in particular, economists have a way of measuring the rate of technological change in the economy that is productivity growth, which we could talk about what that means. But basically, it’s sort of the mathematical expression of the impact of technology on the economy. And productivity growth for the last 50 years has actually been very low, not very high. So we all feel like it’s been very high. There’s been lots of technological change. What’s actually happening is it’s been very low. And in fact, the pace of productivity growth, like in the U.S., is running at like a half of what it, in my lifetime, in our lifetimes, it’s been running at about a half the pace that it ran In between 1940 and 1970. And it’s been running at about a third the pace that it ran between about 1870 to about 1940. And so statistically, in the U.S., in the West, technology progress in the economy, technology impact in the economy has actually slowed way down. And so, you know, the AI thing is going to hit, but it’s hitting an environment in which we have actually had almost no technological progress in the actual economy for a very long time. (Time 0:04:22)
  • Raise Super‑Empowered Kids
    • Teach kids to become “super-empowered individuals” who deeply master a domain and harness AI.
    • Focus on agency, depth in one skill, and using AI to amplify that skill. Transcript: Marc Andreessen The way I think about this, and yeah, we have a 10-year and so, you know, we actually homeschool and so we think a lot about this. So I think the way to think about the impact of AI on people, on specifically people as individuals, I think it’s actually, you know, a lot of people just focus on kind of this, you know, This kind of very, I would say, straightforward and overly simplistic view of just literally job gains, you know, job losses, which we 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 going to make them very good at doing things, right? And so it’s going to be a tool that’s going to sort of raise the average kind of across the board. And, you know, look, you see that playing out already. You know, anybody who’s in a position where they need to, you know, write something or design something or write code or whatever, if they’re pretty good at it today, they use AI and all Of a sudden they’re very good at it. And so there’s sort of that aspect to it. And I think the way the education system at large is going to kind of teach AI is going to be based, you know, hopefully a lot on that. But then there’s this other thing that’s happening, which we’re also starting to see, and we’re really seeing it particularly in coding right now, where the really great people are Becoming, like, spectacularly great, right? And so you kind of use the term, you think about, like, the super empowered individual, right? So the individual who is like really good at coding or really good at making movies or really good at making songs or really good at designing, you know, making art or whatever, whatever Those things are, or, you know, or podcasting or, you know, hopefully venture capital, you know, if you’re very good at it and you can really harness AI, you can become spectacularly Great and like super productive. Right. And, you know, I’m sure you have a lot of friends in this in this category as well. But like, you know, the really, really good coders are experiencing this right now. My friends are really good coders. Like, oh, my God, all of a sudden, I’m not twice as good as I used to be. I’m like 10 times as good as I used to be. And so I think at the unit of like N equals one of like an individual kid, I think the question is kind of how do you get them in a position where they’re kind of this kind of super empowered Individual such that they’re going to be really kind of deep in whatever it is they’re going to do, but they’re going to be deep in a way that’s going to let them fully use the power of AI To be not just great, but to be like spectacularly great. (Time 0:08:50)
  • Use AI As A Personal Tutor
    • Augment traditional schooling with one-on-one AI tutoring to emulate Bloom’s two-sigma improvement.
    • Use AI to personalize learning, quiz students, and accelerate mastery affordably. Transcript: Marc Andreessen Is the challenge. And again, this kind of goes to how you’re, you know, kind of your original question, which is education. There’s two completely different ways to talk about, think about education. The way that’s usually thought about and talked about is kind of at the level of like a nation, right? So, so, you know, it’s like a national level issue or maybe a state level issue in the U.S., which is basically like, how do you educate all the kids? And of course, that’s incredibly important. And of course, you’re going to need like some level of large scale system, like the National K-12 School System or something like that, you know, in order to do that. But then there’s this other question, which is like at N equals one, for an individual kid, like what can you do with an individual kid? And so 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 a kid at the unit Of N equals one, by far, the ideal way to do it is with one-on tutoring. Like if you just have an individual kid and the goal is to maximize an individual kid, by far, you get the best results with one-on tutoring. And this is something that like every royal family knew in history. It’s something that every aristocratic class knew in history. There’s all these amazing examples. Alexander the Great was tutored by Aristotle. He took over the world, right? Like, you know, many of the great kings and queens, you know, royal families and aristocrats and so forth, you know, over the course of centuries, you know, kind of always had this approach. There’s actually also statistical evidence, analytical evidence that this is correct. There’s this, you know, massive question in the field of education, which is how do you improve educational outcomes? And basically it turns out it’s just, it’s very hard to improve educational outcomes, except there’s one method that always does it, which is called the, it’s called the Bloom Two Sigma Effect, which is there’s one method of education that routinely raises student outcomes by two standards of deviation. And we’ll take a kid from the 50th percentile to the 99th percentile, and that’s one-on tutoring, right? So again, if you go back to like, N equals one, you have a kid and a tutor, and they’re in this like, you know, very tight loop with each other, you know, where the kid is able to constantly Kind of be on the leading edge of what they’re capable of doing. And they can, you know, 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 tutoring 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 LLM about it and they can ask an infinite number of questions And they can get instantaneous feedback. And in fact, you can even tell an LLM, it’s like, you know, teach me how to do the following. And you can say, you know, wow, that’s like, I don’t quite understand what you’re saying, like, dumb it down for me a little bit. Okay, now quiz me, you know, do I actually understand this? Like, people can just do this today, right? And so, I think there’s this, like, massive opportunity for parents, you know, in many walks of life to be, you know, with a little bit of time and focus to be able to say, okay, you know, My kid’s probably still going to go through a traditional education system, but I’m going to augment this with AI tutoring. And of course, you know, and of course, there’s going to be tons of startups, right? And there already are that are going to try to build on all the products and services for this. Khan Academy, you know, on the nonprofit side has a big push to do this. And so, you know, I think the broad answer might be a hybrid approach with schools plus one-to tutoring through AI. There’s also this great, you may have heard, there’s this great private school system called Alpha, in which everything I just described is kind of the basis of their philosophy, which Is, you know, it’s a combination of in-person schools and teachers, but it’s also, you know, heavily based on AI and AI tutoring. And so I think there’s a magic formula in here that I think is going to apply much more broadly. And really, for parents interested in this, now would be a great time to really start to think hard about that and to look at the options. (Time 0:16:01)
  • Productivity Growth Creates Jobs
    • Faster productivity growth from AI will likely create more jobs and new fields, not mass permanent unemployment.
    • Historical industrial transitions show job churn accompanies net opportunity and growth. Transcript: Marc Andreessen Yeah, so the job substitution job loss thing is just it’s very reductive. I think it’s an overly simplistic model. And again, it goes back to what I said at the very beginning, which is we’ve actually been in a regime for 50 years of very slow technological change in the economy. And so, you know, and again, like I said, it’s like at a half the rate of the previous era, and then a third the rate of like 100 years ago. And so we’re coming out of this kind of phase where we’ve had like almost no technological progress in the economy. We’ve had remarkably little job churn as a result of that relative to any historical period. And so even if AI, like, ticks up, even if AI triples productivity growth in the economy, which would, like, be a massively big deal, it would take us back to the same level of job churn That was happening between 1870 and 1930. And if you go back and you read accounts of 1870 and 1930, people just thought the world was awash with opportunity, right? At that rate of technological transformation, kids were able to, like, develop new careers into new areas of the economy, building new kinds of products and services. I mean, you know, a huge part of everything in our modern world today was kind of invented and proliferated kind of during that period. And so even if AI like triples the pace of economic change in the economy, it’s going to just translate to like a much higher rate of economic growth is going to translate to a much higher Rate of job growth. And, you know, there’ll be some level of like task level and job level substitution that will take place, but that will be swamped by the macro effects of economic growth and innovation That will happen. (Time 0:19:55)
  • Be T‑Shaped And AI‑Fluent
    • Become T-shaped: be deep in one role (engineer, designer, or PM) and competent in the others, then leverage AI across them.
    • Use AI to expand your scope so you can build end-to-end products solo or in tiny teams. Transcript: Marc Andreessen This, I think, is a really funny question. So these three roles in particular, obviously, are kind of the central roles for building, you know, for tech companies. So the way I’ve been describing it is, you know, the concept of the Mexican standoff, right, which is the movie scene where the two guys have guns pointing at each other’s heads. And then there’s, if you watch like John Woo movies, he does the three-way Mexican standoff where you’ve got like a triangle, you know, people, and like, you know, and of course it’s John Woo movies, they’ve got, you know, guns in both hands. So they’re all, each is aiming at the other two. And you’ve got this kind of standoff situation. And so the way I’ve been describing this is there’s like a Mexican standoff happening between those three roles, between product manager, designer, and coder. Specifically the following, which is every coder now believes they can also be a product manager and a designer, right? Because they have AI. Every product manager thinks they can be a coder and a designer. And then every designer knows they can be a product manager, right? And a coder, right? And so people in each of those roles now, you know, know or believe that with AI, they don’t need the other two roles anymore, right? They can do that because they can have AI do that. And then, of course, there’s the real irony, which is, you know, all three of them are going to realize that AI can also be a better manager, right? So, they’re going to aim the guns up the earth chart, but that’s probably the next phase. And what I think is so fascinating about this Mexican staff is they’re actually all kind of correct, I think, right? Which is AI is actually a pretty good, you know, it’s actually now a really good coder. It’s actually now a really good designer. And it’s also a really good product manager, right? It’s actually good at doing all three of those things, or at least doing a lot of the tasks involved in those three jobs. And so, again, this goes back to this kind of idea of the super empowered individual, where if I’m a coder, like, you know, I mean, step one is like, I need to make sure that I really understand AI coding and like what that means and how coding is going to change in the future. You know, I need to understand, you know, specifically how to go from being a coder who writes code entirely by hand to being a coder who, you know, orchestrates, you know, a dozen instances Of, you know, coding bots. You know, there’s a change in the actual job of coding itself, which is happening right now. But the other part of it is, okay, how do I become that super part individual? How do I become a coder that also then harnesses AI so that I can also be a great product manager and I can also be a great designer, right? And then the same thing for the product manager, which is how do I make sure that I can now use coding tools? How do I make sure I can also do AI-based design? And the same thing for the designer, which is how do I use AI to also become a coder and also become a product manager? And then what you did is maybe those individual roles change. Like maybe those are not any more sort of stovepipe roles the way that, you know, they have been for the last 30 years or whatever. But what happens is the talented people in any of those roles become super empowered and they become good at doing all three of those things. And then those people become incredibly valuable because then those are people who can actually, like, you know, build and design, right, new products, right, from scratch, which Is like, you know, which is the most valuable thing. (Time 0:33:28)
  • Go Deep Even If AI Writes Code
    • Learn to write and understand code deeply even as AI generates code for you.
    • Use AI to teach and upskill; be able to evaluate, debug, and orchestrate code bots. Transcript: Marc Andreessen And then to your point, AI coding is the next layer on that. AI coding actually abstracts the way the process of actually writing the scripting code, right? And so in one sense, this is a really big deal for all the obvious reasons. But on the other hand, it’s like, okay, this is the next layer of the task redefinition under the job of programmer, right? Now, what’s the job of the programmer? It’s to your point. It’s not necessarily to write the code by hand, but what it is now is, all right, if you talk to the world’s best programmers today, what they’ll tell you is, oh, my job is I’m sitting there And I’m orchestrating 10 code bots, right? Coding bots that are running in parallel, right? And literally, they sit there and they shift from browser to browser or terminal to terminal. And their day job now is kind of arguing with the AI bots to try to get them to write the right code, right? And then debug it and fix the problems and change the spec and do all these things. And so now the job of the program is to argue with the coding bots. But if you don’t know how to write the code yourself, you don’t know how to evaluate what the coding bots are giving you, right? And so you asked about the 10-year he’s super into computers and super into programming. And what I’m, what I’m telling you, you know, he’s, he’s using Claude and ChatGPT, Copilot and all these things. And what I’m telling him is like, look, and by the way, he loves vibe coding. He’s on Replit all the time doing vibe coding, you know, doing games, doing games, you know, he’s sitting there, you know, it’s hysterical, right? Because he’s sitting there. It’s a 10 year old, basically, who’s, you know, spends two hours of dinner arguing with an AI for fun. But what I’m telling him is, no, look, you need to still fully understand and learn how to write and understand code because the coding bots are giving you code. If it doesn’t work or if it’s not doing what you expect or it’s not fast enough or whatever, you need to be able to understand the results of what the AI is giving you. In the same way that somebody who’s writing scripting language code does need to understand ultimately how the microprocessor works. And so again, it’s kind of this up-leveling of capability where you actually want the depths to be able to go down and be able to understand what this thing is actually doing, even if you’re Not spending your day actually doing that by hand. And again, I look at that and I’m like, okay, now programmers are going to be 10 times or 100 times or 1,000 times more productive than they used to be. And that is overwhelmingly a good thing. The tasks are definitely changing. The nature of the job is changing. (Time 0:42:24)
  • Design Value Shifts Upwards
    • AI will commoditize many low‑level design tasks but raise the value of high‑level taste, product purpose, and human fit.
    • Great designers will spend more time on strategy, emotion, and systemic fit than on routine artifacts. Transcript: Lenny Ritschitsky It feels like that’s a very hard skill to learn and to me tells me design is going to be much more valuable in the future yeah that’s right and again here this this is a great example so the Marc Andreessen Again the task level the the the the the task level of like design the perfect icon right is going to be like all right the ad’s going to do that all day long it’s the unescape you give you A thousand icon designs it’s going to be great like it’s going to be fantastic like whatever By the way, there will still be some level of human icon design or whatever, but Aya’s going To get really good at that. But what are we trying to do? Kind of capital D design of like, all right, what is this thing for? And how is this going to function in the world of human beings? And is this going to make people happy when they use it? Is this going to make people feel good about themselves? Is it going to fit into the rest of their life? Is it going to, I don’t know, challenge them in the right way? All these kinds of higher-level questions that the great designers have always thought about. The job of designer will involve much more of those higher-level, more important components. And then again, with AI doing a lot more of the underlying tasks. And so one way to think about it is, I don’t know, you think of it like, I don’t know, the world’s best designers, you know, Johnny Ive or whatever, you could be like, wow, like if I’m a designer Today, if I’m a 25-year designer and I aspire to be, you know, Johnny Ive in a decade, it’s all of a sudden I have a new path that I can use to kind of get there, which is, you know, because Johnny, I did everything he did without AI. Now, you know, a young designer can be like, wow, if I really harness AI in a decade, I’m going to be like the best designer the world’s ever seen. Because it’s not just going to be me. It’s going to be me plus being so super empowered by this technology to be able to do so much more. And then so much more of my time and attention is going to be able to be focused on these higher level things that most designers never get to. And I think that’s going to be another great example of that. (Time 0:47:43)
  • Three AI Layers For Founders
    • Founders face three AI layers: products redefined, jobs redefined, and company structure reimagined.
    • The most ambitious founders explore using AI to run very small or largely AI‑driven companies. Transcript: Marc Andreessen So this is a great, very, you know, topical topic. It’s all playing out in real time right now on the leading edge. So I think there’s like three layers of it and 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 does AI redefine the products themselves, right? And 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 entry capital is based on, which is, you know, okay, there’s a new technology that comes out. And, you know, maybe it’s the personal computer or the iPhone or the internet, or now it’s AI. And it’s like, all right, is this a new capability that gets added to existing products? Right. So all of a sudden you’ve got, I don’t know, an existing, you know, software business. And now you’ve got your 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 new technology kind of gets kind of added into the mix. You know, as kind of another ingredient to an existing formula. And of course, you know, a lot of new technologies are like that, right? You know, I don’t know when Flash, when Flash storage came out or something, you know, it didn’t really, it didn’t really redefine the software industry because people just went from Using, you know, hard disk using Flash storage or something. But when the internet came out, like basically old school on-prem software for the most part, you know, not entirely, but like a lot of it died and just got replaced by like web software, Right? And so sometimes you get the kind of, it’s additive to an existing thing. Sometimes you get the, actually it redefines, an entire product category redefines an industry, the actual, you know, in many cases, the companies themselves turn over. And so, you know, so there’s sort of this question and like, you know, an example, you just mentioned NanoBanana. So like a great example is there, you know, there are these businesses like, you know, just take Adobe, like, you know, Photoshop is built a whatever 40-year franchise in image editing. Okay, is AI a sort of a feature now that gets added to Photoshop to be able to do AI-based image editing? Or, you know, do you just like stop editing images entirely because you’re using Nano Banana and all images are just being generated and it’s just easier to just have AI generate a new Image than it is to try to edit an old one. And so I think, you know, there’s many areas of tech in which that question is being asked. And, you know, the answers I think will vary by domain. But, you know, obviously as a venture firm, we’re betting hard on many of these categories being totally reinvented. And a lot of the best founders are trying to figure out how to do that. So that’s kind of AI, you know, changing the definition of the product. I think the next layer is actually a lot of what we’ve already talked about, which is AI changing the jobs. And so it’s, you know, a lot of what we’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 10 or does that mean I still want a hundred, but now they’re doing 10 times more. Right. And so that, you know, as you know, like a lot of the best founders are working on that right now. And then I think the third shoe to drop hasn’t quite dropped yet but it’s it’s you know it’s kind of the big one which is like all right like the the the basic idea of having a company right You know does that change and and again here you’ve got this concept of the super powered individual which is like okay um you know can you have entire companies where you have basically The founder does right? Because what the founder is doing is like overseeing an army of AI bots. And there’s sort of this, you know, there’s kind of this holy grail in our industry that’s been running for a long time, which is like, can you have like the one person billion dollar outcome? And, you know, we’ve had a few of those over the years. Bitcoin is probably the most spectacular example, you know, with Ethereum right behind it, you know, which wasn’t quite one person, but, you know, a very small team. You know, you had, you know, kind of Instagram and WhatsApp that had very big outcomes with very small teams. You know, every once in a while, you get one of these things where you just, you know, something hits and you just have a, you know, very small number of people associated with it. You know, but that said, you know, most software companies obviously end up with, you know, huge numbers of employees. And so I think, you know, the most leading edge founders are thinking of like, okay, how do I reconstitute the actual very definition or idea of having a company? And, you know, can you have a company that’s literally basically just all AI? (Time 0:58:53)
  • AI Moats Are Still Evolving
    • Moats in AI are uncertain: models, apps, legal/regulatory barriers, and execution all matter and outcomes remain unclear.
    • Rapid replication, open source, and many labs mean structural surprises are likely. Transcript: Marc Andreessen My experience with like really big technological transformations, and of course I kind of lived this directly with the internet and I saw this happen, is the really big technological Transformations, they take a long time to play out. And there’s all of these structural implications that just kind of cascade out over time. And then there’s kind of this, there’s this like rush to judgment upfront where people kind of say, oh, it’s therefore obvious that, you know, it’s therefore obvious that this kind Of company is going to be the company of the future, not that kind. It’s obvious that this incumbent’s going to be able to adapt, and this other one isn’t. It’s obvious that there’s economic opportunity in this kind of startup and not in these others. It’s obvious that the moats are going to be in this area of the technology, but not in this other area. And, you know, what everybody does is they kind of state those things with, like, just an enormous amount of self-assurance, where they, you know, where they really sound like they Have all the answers. And then, you know, what happens is these ideas kind of saturate the media, right? Because the media naturally prizes like definitive answers over open questions. Because, you know, you want, you know, like when CNBC is like booking guests, they want a guest who’s going to come on and say, yes, this is the way it’s going to be, X. Not like, you know, I think that’s a really good question. And let’s like debate it from like eight different angles. And what I found is if you look back on those predictions a few years later, and you can do this, by the way, if you pull up like coverage of the internet from like 1993 through like 1997, Or even through like, for that matter, even through like 2005 or 2010, and you look at like the kinds of confidence statements people were making in the first 10 or 15 years, like I would Say like almost all of them are wrong. Again, generally like quite badly wrong. And so I just, I think the process, I think with massive, if there’s going to be a massive amount of technological change, it’s going to be like, I don’t know, five or six layers of like Structural change that will play out over time. And then again, we’ve talked about a lot of this, but like the implications on like, what are the definition of products? What are the definitions of companies? What are the definitions of jobs? What are the definitions of industries? How does this play out at the national level? How does this play out at the global level? You know, how does this, by the way, how does this intersect with politics? How does this intersect with, you know, unions? How does this intersect with, you know, war? You know, what’s China going to do? You know, and so it’s just like, there’s just, there’s, there are just a tremendous number of unknowns, like a very, very large number of unknowns. And I think it’s just like really, really dangerous to prejudge these things. And so I’ll just give it, I’ll just give it, and it’s just, I’ll just run this as a thought experiment, you know, so you can see what you think on this, but it’s like, you know, like do, do Models, are AI models themselves, like, defensible? Like, is there a move on AI models? And on the one hand, you’d be like, wow, it certainly seems like there is or should be because, like, if something takes, you know, billions of dollars to build, and you need, you know, You need this, like, incredible critical mass of, like, compute data, and there’s only a certain number of engineers in the world that know how to do this, and, you you know, they are Getting paid like NBA stars. And, you know, and then these companies have to deal with all these, like, crazy, you know, political issues and press issues and reputational stuff and regulatory and legal. There’s going to be two or three companies that are going to end up with like, you know, 100%, you know, I don’t know, whatever, 50-50 or 30-30 or 90-10 or whatever it is, market share. And then they’re going to have whatever probability they have. And it’s going to be a kind of a classic oligopoly and or maybe, you know, maybe one company’s going to win definitively and it’ll be a monopoly. And that, and by the way, those outcomes have happened in software many times before. And so maybe that will be the outcome. You know, the other side of it is, you know, if you had told me three years ago, you know, that in the, you know, kind of Christmas of ChatGPT, that like within basically a year to year and A half, there would be, you know, five other American companies that would have basically, you know, exactly capable products. And then there would be another five companies out of China that would have exactly capable products. And then there would additionally be open source that was basically the same, I would have been like, wow, like, you know, the thing that seemed like it was black magic all of a sudden, You know, has become like commoditized really fast. You know, which by the way is exactly what happened, right? Like, you know, within a year of GPT-3 coming out, there were open source GPT-3s running on a fraction of the hardware, right? They were available for free. And then there were, and then, you know, there were five, you know, now you’ve got, 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 all these other companies and then DeepSeek and Kimmy and all these other companies. And so even at the level of LLMs or AI models, you can squint and make that argument either way. By the way, same thing at the level of apps, right? It’s like one school of thought, the apps are not a thing because like the model is just going to do everything. But another way of looking at it is no, actually, like actually adapting the model is kind of the engine into a domain involving human beings, where you need to like actually have it fit For purpose to be able to function in the medical industry or the legal industry or whatever, or coding, you know, no, you actually need, like, the application level is actually going To matter enormously. And maybe the LLM is commoditized and maybe the value goes to the apps. And again, you can kind of squint either way on that one. And I know very smart people who are on both sides of that argument. And so my honest answer on this is I think we’re in a process of discovery over time, which is, you know, the way I think about this kind of structurally is it’s a complex adaptive system. The technology itself, you know, provides one of the inputs, the legal and regulatory process, you know, is another input. You know, actual individual choices made by entrepreneurs, you know, matter a lot. You know, the economics matter a lot. Availability of investor capital varies over time. That matters a lot. And this is a complex system. And so we actually don’t know the outcomes on this yet. And we need to basically be, we need to be open to surprises at the structural level of what happens. (Time 1:04:41)
  • Bet Broad But Back Determined Founders
    • As an investor, fund many bets and back determined founders while staying flexible in uncertain technological change.
    • Favor founders who are concrete, ambitious, and prepared to execute specific plans. Transcript: Marc Andreessen So for us, yeah, for us, we obviously have a very deliberate strategy. One way to think about this is the Peter Thiel, you remember the Peter Thiel formulation of, he said there’s a two by two, there’s optimism and pessimism. And then there’s determinant and is it indeterminate and indeterminate, right? And so, and he always argued like there’s, he always argued that like Silicon Valley is characterized by too much, what he calls indeterminate optimism. And what he always described, what he meant by that is basically, I think the way he would describe it is an indeterminate optimist who thinks the world is going to be better but can’t Explain why. Right? 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 I think he at least historically would say like that’s basically, you know, that risks at least being just like wishful thinking or delusional thinking. And what the world needs more is determinant optimists, which are people who are like, no, the world is going to be better because I’m going to do this specific thing, right? And he would classify, for example, Elon, you know, he would sort of maybe say, you know, VCs are indeterminate optimists. And then he would say, you know, Elon is the determinant optimist where it’s like, no, I’m going to build the electric car. I’m going to, you know, solar, and then I’m going to, you know, Mars, you know, right. I mean, these very concrete things. And I think there’s a lot, I think there’s a lot to Peter’s framework, but the way I would describe it is I think maybe, you know, if you 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 have at A16Z, which is, and the reason for that is it’s not, hopefully it’s not so much wishful thinking, it’s more, no, what the indeterminate optimism Of venture capital or the indeterminate optimism of A16Z or Silicon Valley is very, it’s actually very specific, which is there are these extremely bright and capable people like Elon and many others who are founders, right? And product, and you know, kind of product creators, right? And each of those individual people is a determinant optimist. Like each of them individually has like a very strong view of what they’re going to do. But the great virtue of the capitalist system, the great virtue of the American economy, the great virtue of Silicon Valley is we don’t just have one of those and we don’t just have 10 Of those. We have 100 and 1,000 and then 10,000 of those. 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 nature of, you know, the nature of the future is like, we just don’t know all the answers and that’s okay. And then the right way to deal with that is to run as many experiments as possible and have as many people try to do as many interesting things as possible. (Time 1:14:19)
  • AGI Is A Spectrum, Not A Single Event
    • AGI definitions differ: cosmic singularity versus machines matching top economically relevant human tasks.
    • Andreessen expects AI to exceed human ability and sees that as broadly positive. Transcript: Marc Andreessen Yeah, so I’ve always kind of had a little bit of an issue. I’ve always kind of struggled with the concept of AGI because at least, well, let’s define terms, which is where I kind of struggle with it, which is there’s like the prosaic, there’s The prosaic definition of AGI. And then there’s like the, I don’t know, cosmic definition. And the way I would describe it as, well, let’s start with the cosmic one. So the cosmic one is basically the singularity, right? And so AGI is the moment where you enter the singularity, which is to say where the world fundamentally changes. And like 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 is improving itself and it’s sort of racing, you know, so-called takeoff scenarios. You can see at this takeoff thing where the AI is improving itself and the machines are making decisions so much faster than people and people are just sitting there watching the machine Do its thing. You know, and I kind of described, I don’t really, I don’t really that’s, I don’t think we live in that world. Like whether you could call that utopian or dystopian, like I don’t think we’re lucky or unlucky enough to live in that world. We could debate that. We can talk about that more. But the prosaic definition of AGI that at least I think the industry purchase of bits have kind of converged on and tell me if you agree with this is, it’s when the AI could do every economically Relevant task as good as a person. Lenny Ritschitsky The way the co-founder of Anthropic put it is like a basket of the most valuable economic tasks. So it’s like 10, 15, not every single economically valuable task. Marc Andreessen Okay, got it. Yeah, so it’s maybe even a slightly reduced definition. And by the way, you’re clearly getting close to that if we’re not already there. And so on that one, I kind of feel like the cosmic one overstates what’s going to happen. And then I kind of feel like the kind of AGI definition that you just gave, I think it kind of understates what’s going to happen. Like it’s almost too reductionist. And the reason for that is I don’t think there’s any reason to assume that human skill level is the cap on anything. Right. And so we always say that as AGI always is, you know, the definition, you gave the definition, I gave it. It’s kind of, it’s always kind of relative in comparison to a human worker, right? And it’s like, I don’t know, like human skill level caps out at a certain point, but that’s because of the inherent like biological limitations of the human organism, right? Like we’re all, you know, human IQ, you know, kind of what they call fluid intelligence or the sort of G factor of kind of, you know, fluid intelligence. IQ, I think, tops out in humans as a species. It tops out around 160, right? Where at like 160, it’s like Einstein level. Einstein, Feynman. In terms of IQ. In terms of IQ. Like, it just tops out at 160. The 160 IQ people are the ones who come up with new physics. There’s only a small handful of those. Generally speaking, when we run into somebody in the world who’s like incredibly smart, who’s like a best-selling author or like one of the world’s best, I don’t know, research scientists Or one of the world’s best doctors, you know, whatever, it would be probably 140 is kind of the IQ that you’re looking for there. If you’re looking for like a really good lawyer, it’s probably 130. If you’re looking for, like, a really good, like, line manager in a business, it’s probably 110. You know, if you’re looking for, like, an accountant, like a small business accountant who’s good at doing the books for small businesses, it’s probably 105, right? And so the kind of scope of, like, impressive human, you know, the ability of the human organism to do intellectually impressive things, you know, it’s sort of that 110 to 160 is kind Of the spectrum. And, you know, good news is there’s a lot of those people running around, but like, there’s not that many at 140, 150, 160. But it’s like, that’s just, that’s like the limitations of what can fit in here, right? And it’s like, there’s no theoretical limit on where this goes if you release the limitations of human biology, right? And so can you have a, and you already have people running these extremists to kind of do human equivalent, you know, kind of IQ, you know, for existing AM model. And by the way, existing AM models right now are kind of testing around the 130, 140 level, which means they’re going to get to the 160 level. And they’re, you know, they’re arguably on the mass size starting to get to the 160 level now. But like, I think we’re going to have AM models relatively quickly that are going to be like 160, 180, 200, you know, 250, 300. By the way, and I think that’s great, right? Like, I feel as great about that as I do about the fact that we occasionally get an Einstein, right? It’s like, would the world be better off or worse off with more or fewer Einsteins? And the answer is, of course, the world would be better off with more Einsteins. And of course, the world would be better off with machines that have IQ, you know, more IQ like Einstein or greater than Einstein. But like, I think IQ of the machines is going to exceed that on the humans. I think that’s really good. And then the performance, you know, again, it goes back to the AI coding thing that’s happening. The performance against task is going to get better also. (Time 1:18:26)
  • Kid Builds Star Trek Simulators
    • Marc shares his 10‑year‑old’s obsession with Replit and vibe coding for Star Trek simulators.
    • The child builds UIs and games using AI tools, illustrating early AI fluency in kids. Transcript: Marc Andreessen I mean, or just, I’ll just observation. So one is my 10-year My 10-year my 10-year right now is 100% obsessed with Replit. And by the way, it was not from me. Do you have kids? I do, I have one two and a half year old. Two and a half, okay. So you haven’t run into what I’m running into now, which is whatever it is you do is not cool. Right, like it’s 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. And so like, if I mentioned, oh yeah, we work on XYZ, you know, he’s like, okay. 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,, through no interference on my part, discovered Replit about three months ago and discovered Vibe Coding and is like completely obsessed with Vibe Coding games and all Kinds of things. And like literally we’ll do it for hours. And so I’m seeing that phenomenon play out, which is super fun. That’s one. Two is I am just completely in love with all the AI voice stuff. I think it’s just absolutely amazing, hysterical. My favorite party trick at dinner parties now is to pull out Grok with Bad Rudy, which is, as you’ve seen, it’s a foul-mouthed raccoon avatar in the Elon’s Grok app. So I think that’s super fun. We have this company, Sesame,, you know, they, they went viral last year for this, uh, you know, these, these, these just incredibly like, uh, you know, intimate, emotional, you know, Kind of voice experiences. Um, so I think the voice stuff is fantastic. I’m also super fascinated by all the voice input stuff. Um, and so, um, you know, one of the most sweet, uh, one of the most recently, uh, kind of like that company recently, uh, uh, sold, but, um, you know, that all think like the pendants, the Wearables, like all that stuff is going to be big. The metaglasses, I think there’s going to be a whole wearables revolution here. I love the voice input stuff. I have this app on my, there’s this app on my phone now called Whisperflow, which is voice transcription, which works like staggeringly well. It’s like incredible. It’s like a voice transcription function, but you can actually talk to the AI model while you’re doing voice transcription. So you can kind of, it kind of understands when you’re telling it, no, no, you know, I want bullet points over there and I want this and that. And it understands that you’re not telling it to type in the words, I want bullet points. It just actually understands that you want bullet points. And so like, that’s a great example of a super useful thing. And so I think the voice mode stuff is going to be really great. Subscribers of my newsletter get a year free of Replit and Whisperflow. Lenny Ritschitsky So there we go. What’s the most memorable thing your son built with Replit? Marc Andreessen Oh, so he’s gotten super into Star Trek. And so far, he’s writing Star Trek simulators. So like all the, you know, all the, by Next Generation, they actually had… Lenny Ritschitsky Next Generation, okay, I was going to ask, which… Marc Andreessen Well, actually, we like them all. We watched the new Starfleet Academy last night, which actually is quite good. (Time 1:35:45)