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

22:17It's interesting because there's all this concern that young people, jobs are not going to be there for them. AI is replacing them. On the flip side, there's what you're describing here. It feels like people coming in learning today are going to move so fast and learn so much more. Where do you sit on this divide of young people are in big trouble or they're actually going to be the ones winning in the end? Yeah. So the job substitution, job loss thing is just, 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.

22:49And 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 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.

23:22At that rate of technological transformation, kids were able to develop new careers into new areas of the economy, building new kinds of products and services. I mean, a huge part of everything in our modern world today was kind of invented and proliferated during that period. And so even if AI triples the pace of economic change in the economy, it's going to just translate to a much higher rate of economic growth. It's going to translate to a much higher rate of job growth. And there'll be some level of task level and job level substitution that will take place, but that will be swamped by the macro effects of economic growth and innovation that will happen.

23:57And corresponding to that, there will be hiring booms, I quite honestly think, all over the place. And then again, go back to the other thing, which is like this is all happening in the face of declining population growth and increasingly population shrinkage. And so human workers in many, many, many countries over the next, you know, 10, 20, 30 years are going to be at more and more of a premium, literally because you're going to have shrinking population levels. You know, we don't really want to get into, you know, politics particularly, but it does feel like the world broadly is going to reverse course on the rates of immigration we've had for the last 50 years.

24:30It seems to be kind of a broad-based, you know, kind of thing happening, you know, kind of what's, you know, rise of nationalism, you know, concerns about the rate of immigration and immigration historically in countries like the U.S., you know, it's kind of ebbed and flowed over time based on kind of how, you know, kind of how the national mood shifts. And so if you sort of combine in a country like the U.S. or any country in Europe, if you combine declining population with less immigration, the remaining human workers are going to be at a premium, not at a discount. And so I think that combination of kind of faster productivity growth, faster economic grows and then slower population growth and less immigration actually means there's going to be much less of this kind of dystopian, you know, no jobs thing.

25:08I just think it's probably totally off base. That is extremely interesting. So what I'm hearing is you're not super worried about job loss. Is the key here that the timing kind of just works out? Does population decrease? You know, like all these kind of have to line up for there not to be this massive job loss with AI? Yeah. Well, look, if we didn't have AI, we'd be in a panic right now about what's going to happen to the economy, right? Because what we would be staring at is a future of depopulation. And like depopulation without new technology would just mean that the economy shrinks, right?

25:38So it would mean that the economy kind of itself kind of shrinks over time, you know, the opportunity diminishes, there are no new jobs, there are no new fields, there's no new source of consumer demand for spending on things. And so you would be very worried about going into a period of like severe decline of stagnation. And, you know, Well, essentially, you'd be looking at these very dystopian scenarios of an economy kind of self-euthanizing itself over time. And so you'd be very worried about the opposite of what everybody thinks that they're worried about. The only reason we're not worried about that is because we now know that we have the technology that can substitute for the lack of population growth and then also for the lack of immigration this likely.

26:16And so I would say the timing has worked out miraculously well in the sense that we're going to have AI and robots precisely when we actually need them to keep the economy from actually shrinking. And I just think that's just a fundamentally good news story. To get to the mass job loss thing that people are worried about on the other side of things, you'd have to look at far, far, far higher rates of productivity growth. You'd have to look at rates of productivity growth that are 10, 20, 30, 50 % a year, something like that, which are orders of magnitude higher than we've ever had in any economy in the history of the planet.

26:49it. It's possible that we get that. I mean, look, I have my utopian kind of temptation along with everybody else. If AI radically transforms everything overnight, then maybe let's play out the kind of utopian scenario. You get to a much higher level of productivity growth. You get to a much higher level of technological change. Corresponding to that, you'll have a massive economic boom. You'll have a massive growth in the economy. And then corresponding with that, you'll have a collapse in prices. And so the price of goods and services that are sort of, you know, what are we going to call it, affected by or commoditized by AI, the prices of those goods and services will collapse, right?

27:28It'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? Take it this way. This is actually worth talking about because people, I think, get kind of sideways on this issue. So if AI is going to transform the economy as much as the, you know, whatever, utopians or dystopians or whatever kind of think that it will, the necessary economic calculation of what happens is massive productivity growth. The consequence of massive productivity growth, what that literally means mechanically is more output requiring less input, right?

28:03So you get more economic output for less input, right? So you're substituting in AI for human workers or whatever. And as a consequence, you get like this massive boom in output with much lower input costs. The result of that is you get gluts of goods and services in all those effective sectors. The result of those gluts is you get collapsing prices, right? The collapsing prices mean that the thing today that costs you$100 now costs you$10 and now costs you$1. That's the equivalent of giving everybody a giant raise, right? Because now they have all this additional spending power. That additional spending power then translates to economic growth, right?

28:36The development of new fields. Everybody's like materially like much better off very quickly. And then by the way, to the extent that you do have unemployment coming out the other side of that, it's now much cheaper to provide the kind of social safety net to prevent people from being immiserated, because the prices of all the goods and services that a welfare program has to pay from, they're all collapsing. And so the price of healthcare collapses, the price of housing collapses, the price of education collapses, the price of everything else collapses, because of this incredible impact that AI is having.

29:04And so in this kind of utopian, dystopian scenario that people have, there's no scenario in which everybody's just poor. In fact, it's quite the opposite, which is everybody gets a lot richer because prices collapse. And then it's actually much easier to pay for the social safety net for the people who, you know, for some reason can't find a job. And so like, like maybe we end up in that scenario. I mean, the kind of optimistic part of me says, yeah, maybe AI is that powerful and maybe the rest of the economy can actually change to accommodate that. And maybe that'll happen. But the result of that is going to be a much better news story than people think it's going to be.

29:35And again, everything I've just described, by the way, it's like just a very straightforward extrapolation of very basic economics. I'm not making any like bold predictions of what I just said. This is just like a straightforward mechanical process that plays itself out if you have higher rates of productivity growth, which are necessarily the results of higher rates of technological growth. And so I think we're looking at, and to be clear, I think we're looking at a world that's not like radically transformed the way that maybe the utopians think that it will be or the dystopians think it will be.

30:00I think it'll be more incremental for races we can discuss. But I think that incremental, overwhelmingly, I think that process is going to be a good news process. And then even if it's much faster, it's also going to be a good news process. It'll just be a good news process in the other way that I just described. I love hearing optimism and good news. I will also add that you've been, I was researching you ahead of this chat and you've been right so many times about where the world is heading. That's why I'm especially excited to talk to you. I'll give you a short list. I imagine there are many more things.

30:29Okay. So one, you were right about the web and web browsers becoming important. You were write about software eating the world. Check. In 2011, you said that in 10 years, we're going to have 5 billion people using smartphones. And I believe the actual number ended up being 6 billion. You also had this debate with Peter Thiel that I came across where you were debating whether technology has stopped progressing or if new technology will continue to emerge. And you were arguing there's progress. Progress will continue. And he was like, no, I think we're done with cool technology. You were right. I imagine there are many more things you were right about.

31:09So again, I love hearing your predictions because I feel like they're actually going to turn out to be correct. So I should start by saying I've been wrong about tons of things, but I buried those out back behind the shed. Delete them from the internet. No browser can disguise them. I have them nuked out of the internet archives so that they know they're never seen again. So, you know, I'm wrong plenty of times also. But yeah, I mean, look, I think, yeah, some of those are right. By the way, I will say on the Peter one, I have come much more around to Peter's point of view. I would probably argue that one like quite a bit differently today than I did.

31:42And I would give his view, I think, a lot more credit. And it actually goes to kind of the discussion that we did, the kind of conversation we just had, which is the real formal what Peter was arguing was we have lots of process in bits. We have lots of progress in bits, right? But we have very little progress in atoms, right? And that's the real core of what he was arguing. And I think I was a little bit, I don't know, missing that or kind of glossing that over a little bit because I was so focused on making sure people understood, no, there actually is still progress happening in bits. But I think a lot of his critiques around the lack of progress in atoms is real.

32:14And again, this goes back to this thing of like, and he's talked about this for a long time. In the last 50 years, there has just been very little technological innovation in most of the economy, there's been very little technological innovation in particular, anything involving atoms. There's been very little real world technological change. There just hasn't been. The built world is just not that different today than it was 50 years ago. And again, if you contrast that, if you compare and contrast 1870 and 1930, it was a dramatically different world. If you contrast 1930 and 1970, it was a dramatically different world.

32:45If you contrast 1970 and today, it's not that different. And look, you just see that you could just walk around and it's just like, oh yeah, there's a bunch of buildings that were built in like 1960, right? And there's a bridge that was built in like 1930, and there's a dam that was built in like 1910, and there's a city that was founded in, you know, 1880. And like, what have we done? Where are new cities? Where are new dams? Where's the California high-speed rail? Like, you know, what's going on here? And so like, I think he is right about a lot of that. Again, And this is also why I think that AI is not going to have as rapid an impact.

33:22It's not going to be, again, this kind of utopian or dystopian view of like everything changes overnight. I think it just kind of can't happen because of the reasons that Peter articulates, which is there's just there's so much about how the world works. It's basically just like wrapped up in red tape, like bureaucratic process, rules, restrictions, you know, the politics. by the way, you know, unions, cartels, oligopolies, there's all these structures in the world that are kind of economic or political or regulatory structures that basically prevent things from changing. And so, I mean, let's take a great example, like AI's impact on the healthcare system.

34:00Like by rights, AI is going to have a dramatic impact on the healthcare system and in very positive ways. But, you know, large parts of the medical system today are, they are cartels, right? And so there's like, there's the doctors are a cartel and like nurses are a cartel, like hospitals are a cartel. And then there's this push to like nationalize all the healthcare systems. And then you've got, you know, then you've got a government monopoly, right? And it's like, and guess what cartels and monopolies don't like is they don't like like rapid change, right? 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.

34:31And they're like, oh, well, does it threaten Dr. Jobs? Well, in that case, we're going to, we're going to block it. So, and I think a lot of consumers, by the way, you know, I don't, I see this in my life and you'll probably see this in your life also, which is, you know, like ChetGPT is like almost certainly a better doctor than your doctor today, but like ChetGPT can't get a license to practice medicine, right? So it can't substitute for a doctor. It can't prescribe medications, right? It can't, you know, perform procedures, right? And so there are these, anyway, so Peter, I think was very articulate 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, the race of change that are anywhere near the race of change that people had in the past.

35:11And you can maybe say optimistically, you know, maybe the presence of the new magic technology of AI, maybe it causes us to revisit a lot of these assumptions for the first time in decades to really say, 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. It's time to build, somebody famously said. In my calendar, I actually have that as my, when I start to work, it's time to build. That's my block in the morning of the day. Thank you for that. Okay. I love the way you go from just like macro to just like end of one.

35:40And I want to go to end of one. A lot of the listeners of this podcast are product managers, they're engineers, they're designers. There'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 obviously, a lot of people are worried about where their career is going. Is one of these roles going to disappear? Is one of these roles going to do really well? How do I stay up to date? You're close with a lot of teams, a lot of product teams. What's your sense of just the future of these three very specific roles, product manager, engineer, designer?

36:09This, 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, you know, the two guys have guns pointing at each other's heads. And then there's, if you watch like John Woo movies, he loves to have, he does the three-way Mexican standoff, where you've got like a triangle, you know, people and like, you know, John Woo movie, they've got guns in both hands. So they're all, each is aiming at the other two.

36:38And 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 and a coder. And so people in each of those roles now know or believe that with AI, they don't need the other two roles anymore.

37:07They can do that because they can have AI do that. And then of course, there's the real irony, which is all three of them are going to realize that AI can also be a better manager. So they're going to be aiming the guns up the work chart, but that's probably with it. That's 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.

37:41And 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 what that means and how coding is going to change in the future. I need to understand specifically how to go from being a coder who writes code entirely by hand to being a coder who orchestrates a dozen instances of coding bots. There's a change in the actual job of coding itself, which is happening right now. But the other part of it is, okay, how do I become that super 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?

38:20And 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 changed. Maybe those are not any more sort of stovepipe roles as the way that they have been for the last 30 years or whatever. But what happens is that the talented people in any of those roles become super empowered and they become good at doing all three of those things.

38:48and 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. And so I think that's the opportunity. I love this answer. So what I'm hearing is essentially, if you're amazing at any of these three roles, you will do well. Number one, if you're amazing at these roles, that's great. But also part of being amazing in these roles is also being able to fully harness the new technology, right? So if you're a master coder today and you don't ever get to the point where you figure out how to use AI to leverage your coding skills and do more, at some point you are going to hit an issue.

39:27Here'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 then the way the economists think about it is a job is a bundle of tasks. and everybody wants to talk about job loss, but really what you want to look at is task loss, right? Tasks changing. I mean, the classic example of task changing was once upon a time, executives never used typewriters or personal computers themselves, right? If you were a vice president of a company in 1970 or whatever, you did not have like a typewriter or a computer on your desk typing things.

40:07You had a secretary who you dictated memos to, right? And then there was this change where emails started to show up. And what would happen was the job of the secretary then went from, it went from the job of the secretary changed from sending out letters with stamps on them to sending or receiving emails with the other admins. And then the secretary would print out the email and bring it into the executive's office. And the executive office would read the email and paper, scroll the reply, and give that message back to the secretary, who would go back and type it into the computer on his or her desk and send it as an email.

40:38Fast forward to today, none of that happens. Now executives just do all their own email. They still have secretaries or admins, but they're now doing different tasks. They're travel planning and orchestrating events and doing all these other things that great admins do. And then the task set, ironically, of the executive has expanded to do actually more of the clerical work themselves, actually sit there and type their memos, which again, 50 years ago, they never would have done that. And so the executive job still exists, the secretary job still exists, but the tasks have changed. And I think that's like a great example of what's going to happen in coding.

41:14The tasks are going to change. Product management, the tasks are going to change. Designer tasks are going to change. And so the job persists longer than the individual tasks. And then as the tasks change enough, then that's when the jobs change. And so 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 really adapt, use the new technology, you know, get really good at AI coding, for example. You know, and then you want to kind of add skills.

41:45I can also get really good at design. I can also get really good at product management because I've got this new tool. So you want to kind of pick up more and more scope as you do that. And then, you know, 10 years from now, is your job title coder or coder designer product manager? Or is it just I build products? Or is it just I tell the AI how to build products? It's like, whatever that job is called, who even knows what it's going to be. But it's going to be incredibly important because the people doing that job are going to be orchestrating the AI. And so that's the track that the best people are going to be on.

42:13And I think that's the thing to lean hard into. I think people aren't fully grasping just specifically software engineering and how much that is changing. It's pretty clear we're going to be in a world soon where engineers are not actually writing code, which I think a year ago we would not have thought. And now it's just clearly this is where it's heading. It's like, there's going to be this artisanal experience of sitting there writing code, which is so crazy how much that job is going to change. Yeah. So again, here I go back and again, pardon maybe the history lesson, but like I go back like, so the first, you know, do you know the original definition of the term calculator?

42:49Do you know what that referred to? No. I referred to people, right? So back before there were like electronic calculators or computers or any of these things, the way that you would actually do computing, the way that you would do calculating, like the way that an insurance company would calculate actuarial tables or the military would like calculate, you know, I don't know, whatever troop logistics formulas or whatever it was, the way that you would do it is you would actually have a room full of people. And by the way, these are like big rooms. You can have hundreds or thousands or tens of thousands of people doing this.

43:18And you would actually, you would actually figure out, you have somebody at the head of the room who was responsible for whatever the mathematical equation was, and then they would parcel out the individual mathematical calculations to people sitting at desks who were doing them all by hand. And that job title was those people were calculators. And so we've gone from a world in which you literally have people doing mathematical equations by hand. Then we got the first computers. The first computers, of course, didn't have programming languages. They only had machine code. So the first computers were programmed with ones and zeros.

43:50And so the task of the programmer became do the ones and zeros. And then that became punch cards. And you can still, there's still people kicking today whose job as a programmer was to build the punch cards. And then you got actually this big breakthrough, which was called assembly language, which was basically the way to do machine code, but with some level of English added to it. And then the best programmers did assembly language. And then when I was coming up, it was higher level languages like C that compiled into machine code. And that's what programmers did. And then I still remember when scripting languages, we developed JavaScript at Nescape, and then Python took off, and Perl and these other scripting languages.

44:26When scripting languages took off in the 2000s, there was this big fight in the technical community, which is, is scripting real programming or not? Because it's kind of cheating, right? Because real programmers write code that compiles to machine code, and real programmers do memory management themselves, and they do this whole craft of writing C code. And these JavaScript or Pythagic programmers are just doing this kind of lightweight thing. It doesn't even really count as coding. And of course, the answer is yes, it very much counted. And now most coding is done with the scripting languages, right?

44:57Which 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 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, OK, 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.

45:29But what it is now is, all right, now, you know, if you talk to the world's best programmers today, what they'll tell you is, oh, my job is I'm sitting there and I'm orchestrating 10 code bots, right? Coding bots that are running in parallel. Right. And literally, they sit there and they shift from browser, you know, browser to browser, 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 programmer is to argue with the coding bots.

45:57But like, if you don't know how to write the code yourself, you don't know how to evaluate what the coding bots are giving you, right? And so, you know, you asked about the 10, you know, our 10-year-old is, you know, super into computers and super into programming. And what I'm telling, you know, 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 games. You know, he's sitting there, you know, it's hysterical, right? Because he's sitting there.

46:20It's a 10-year-old basically who spends two hours of dinner arguing with an AI for fun, right? Right, but what I'm telling him is, no, look, you need to still fully understand and learn how to write and understand code because the coding bots are giving you code. If it doesn't work or if it's not doing what you expect or it's not fast enough or whatever, like you need to be able to understand the results of what the AI is giving you, right? In the same way that somebody who's writing scripting language code 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 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.

46:57And, 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. But are human beings going to be involved in like in the coding process and overseeing the AI coding and all that? And the answer is, of course, absolutely. One hundred percent. No question. So you're in the camp of still learning to go still a valuable skill. Oh, yeah, totally. Well, again, if you want to be one of these super look, look, if you just want to put your like self on autopilot and like I can't be bothered and I'm just going to have AI write the code and it's going to generate whatever it does.

47:34And that's fine. And I'm going to be you know, I'm going to be if the goal is to be a mediocre coder, then just let the AI do it. It's fine. The AI is going to be perfectly good at generating infinite amounts of mediocre code. No problem. It's all good. If the goal is I want to be one of the best software people in the world and I want to build new software products and technologies that really matter, then yeah, you 100 % want you still, 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.

48:01You want to deeply understand what's happening at the level of the chip, right, and the network and so forth. By the way, you also really deeply want to understand how the AI itself works, right? Because people who understand how the AI works are able to, they're clearly able to get more value out of it than somebody who doesn't understand how it works. I mean, you're always more productive if you know how the machine works, right, when you use the machine. And so, yeah, the super empowered individual on the other end of this that wants to do great things with the new technology, yes, you 100 % want to understand this thing all the way down the stack.

48:29Because you want to be able to understand what it's giving you, right? And when something doesn't work or when something isn't right, you want to be able to really quickly understand why that is. By the way, again, this goes back to education. AI is your best friend at helping you learn all that, right? Because it's like, oh, I need to understand. I don't know, this isn't fast enough. I need to figure out as a coder, I need to figure out how to do a different approach to memory management or something. And you can be like, well, shit, I don't quite know how to do that. Okay, AI, let's spend 10 minutes.

48:57Teach me how to do this, right? Teach me what this all means, right? So all of a sudden, you have this incredibly synergistic relationship with AI, where it's also helping you get better at the same time that's doing a lot of work for you. By the way, I was going to say, I was a big Perl programmer. I was an engineer for 10 years, and that was my language of choice. Do you remember, I don't know when you were doing it, but do you remember, at least early on, did you ever hit this where C coders were looking down their nose at you being like, eh? For sure. For sure. It's like, this is so slow. It's not going to scale.

49:25What are you spending all your time on this thing? Yeah, exactly. And of course, and again, it was sort of this thing where they were sort of correct, which is at the beginning, it wasn't fast enough or whatever. By the end, they were definitely wrong, which is it got much better, much faster. And it swept the world. Most coding today happens in scripting languages. And then, by the way, along the way, the people who really understood the scripting languages and the people who understood all the lower level systems, they were the ones who were able to actually make the scripting languages actually work really well.

49:52And so that was a great example of this kind of adaptation. And again, the result of that was a far higher number of people writing code with scripting languages than were ever writing code with lower level languages. And I think this will just kind of be a more dramatic version of that. I love that Perl was designed by a linguist. I don't know if you remember that part. And that's what made it so nice to code with. Well, that's funny because, of course, it was so notorious for being impossible to understand. How ironic. This episode is brought to you by Datadog, now home to Epo, the leading experimentation and feature flagging platform.

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51:02And the same team that built the 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 behavior. 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 slash Lenny. That's datadoghq.com slash Lenny. Coming back to this kind of triad, the other element that I hear more and more of is the skill of taste and design and user experience.

51:47It feels like that's a very hard skill to learn. And to me, it tells me design is going to be much more valuable in the future. Yeah, that's right. And again, here, this is a great example. So, again, the task level of like design the perfect icon, right, is going to be like, all right, Aya's going to do that all day long. It's going to give you a thousand icon designs. It's going to be great. Like it's going to be fantastic. 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 Aya's going to get really good at that.

52:15But like what are we trying to do? Like, you know, 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.

52:49And so, you know, one way to think about it is, you know, I don't know, you think of like, I don't know, the world's best designers, you know, Johnny Ive or whatever, you could be like, wow, like, if I'm a designer today, if I'm a 25 year old designer, and I aspire to be, you know, Johnny Ive in a decade, it's all of a sudden, I have a new path that I can use to kind of get to get there, which is, you know, because Johnny Ive 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.

53:18It'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 is going to be able to be focused on these higher level things that most designers never get to. And I think that's going to be another great example of that. So maybe what I'm hearing here is kind of this T-shaped strategy of, if you want to be successful in any three of these roles, be very, very, very good at that specific role, product management, engineering, design, and then get good enough at these other two roles. Well, so I think that's great.

53:46I think that's really, really relevant. And then, you know, Scott Adams, unfortunately, just passed away, you know, which is a real tragedy. But I was always I referred for years to actually Scott's Scott Adams. He had this famous he had his famous kind of career advice he would give people, which I think makes a lot of sense, which which which dovetails with what you're saying, which is he he used to say used to say it's like, look, he said, you know, I could he said, you know, I could have been a pretty good cartoonist or I could have been like pretty good at business. But the fact that I was a cartoonist who understood business made me like spectacularly great at making Dobert.

54:20right 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 way Scott always described it was that that that from a career development standpoint that 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?

54:48Because you become a super relevant specialist in the combination of the domains. And you see this all over the economy, but I'll give you an example. Hollywood, just Hollywood as an example. There are a lot of writers who can't direct a movie, and they can be very successful writers. There are a lot of directors who can't write a movie, and they can be very successful directors. But the superstars in the entertainment industry are the people who can write and direct, right? And they don't have a term for those, they call those auteurs. And those are the people who are like the real creative forces that move the field.

55:20And so again, and by the way, Hollywood, it's just really funny, I've been spending a lot of time talking to Hollywood people about AI. Hollywood has the same Mexican standoff going right now that we described in tech, except in Hollywood, for example, for filmmaking, it's the director, it's the writer and the actor. Right? Because the director is now thinking, wow, I don't need the writer anymore because AI can write the script, and I don't need the actor anymore because I can have AI actors. The writer is saying, well, I don't need the director because I can direct the movie and I can do the actors.

55:46And 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 going to show up and do my performance. Right? And so it's the same kind of triangular configuration. And again, what's great about it is they're all correct. Right? Each person in each of those three fields is going to be able to expand laterally and pick up those additional skills. And then as a consequence, you're going to have more people who can write and direct or write and act or direct and act, or do all three. And I think, to your point, your T-shaped thing, I think that's going to be true basically across the entire economy.

56:19And if you think about the T configuration, it's like, yeah, the breadth, the top of the T is like, how many individual domains are you familiar enough with to be able to use the AI tools to be able to do really good work? And then this part of the T is how deep can you go in at least one of those domains so that you really, really deeply know what you're doing. But if you're super deep on coding and you can use AI to do design and you can use AI to do product management, that's your T right there. And you're a triple threat at the top of the T, but with this level of technical grounding underneath that.

56:50And I mean, at that point, again, you're the super empowered individual. You're going to be able to just perform like feats of magic, for example, in terms of designing and building your products that people in my generation couldn't have even dreamed of. And so I think that this is a universal kind of theory that I think can apply across the entire economy. I'm going to invent a new framework right now. Okay, forget the T framework. I'm picturing an F sideways or an E, where there's three, two or three, I don't know, downward parts. And so what I'm hearing is get good at least two. Yeah, that's right.

57:21I think that's right. Yeah, the combination. Yeah. My friend Larry Summers had a different version of the Scott Adams thing, which is he used to tell people, he said, the key for career planning is he said, don't be fungible, right? And he's an economist, and so that was economics-speaking. And what that means is, what that means essentially is don't be replaceable. And so don't be a cog, right? And what that meant was don't just be one thing, right? So if you're quote-unquote, you know, again, just a designer, just a product manager, just a decoder, like then in theory, you can be swapped in or out.

57:52But if you have this, yeah, if you have this E or F, you know, laying on the side kind of thing, and if you have this combination of things that's actually quite rare, then all of a sudden you're not fungible. Not only you're not fungible, you're actually massively important because you're one of the only people in the world who can actually do that combination of things. And yeah, your ability to not become one of those people is like titanically enhanced with AI as compared to anything we've ever seen before. This is so interesting because I've worked with people that are good at these two skills, and they were always called unicorns at the company.

58:21She can code and design. Oh my God. And what I'm hearing here is this is what you need to become. You need to become really good at at least two things. I think you used the term smokestack or something where it's like PM over here, engineer design. And what I'm hearing here is you need to get good at at least two of these skills. The silos of these two roles are disappearing. That's right. That's right. And again, I can't overstress the following for anybody listening to this. The thing about AI that I think people are just like not getting enough benefit out of yet is just it will teach you.

58:50Like, this is amazing. Like, there's never been a technology before where you can ask it, like, teach me how to do this thing. So I always feel like it's like people spend too much. It's one of these things where it's like so much focus on figuring out how to use like a large language model. It's like, okay, what am I going to try to get it to do for me? Right? Which is of course very important. But the other side of it is what can I get it to teach me how to do? Right? And it's just as good at that. Right? And so again, this is this level of latent superpower. Like, you know, people who really want to like improve themselves and like develop their career should be spending every spare hour, in my view, at this point, talking to an AI, being like, all right, train me up.

59:27Super empower me. I'm a coder. Train me how to be a product manager. It will happily do that. It knows exactly how to do that. Run me, make me problems, make me assignments, then evaluate my results. It will do that just as happily as it will do work, quote unquote, for you. Two tricks I've heard along those lines. One is to watch the output, what the agent is doing and thinking as it's doing the work. So if you're not an engineer, just sit there and watch it think and make decisions. And it's almost become this 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.

1:00:02And the other is a couple of podcast guests have mentioned this. When you get stuck and then you figure out how to unstuck yourself, you ask it, what could I have done differently? What could I have said that would have avoided this error in the first place? Yeah, that's right. That's right. Yeah. Look on that first one. And this again, that's what I'm doing with my 10 year old. Yeah, look, if you ask an AI, yeah, this is a really good point. So if you ask an AI, I don't know, write me this code, and then it doesn't, it comes back, and it doesn't work right. Like, if all you know is, like, single function, I asked it, and it gave me back something that's not good, like, well, what do you even do with that, right?

1:00:33Like, you don't understand why it gave you that result. Do you really understand it even what to, you even understand what to tell it to try to get it to do something different. But to your point, like, if you actually watch what it's doing, and then you have the grounding, you know, kind of that leg of your E or your F, if you have that grounding, then you can be like, oh, I see what it's doing. I see where it made the mistake. I see where it went sideways. And then you're all of a sudden able to intervene and able to say, no, no, that's not what I meant to do this other thing. Right. And so, and again, this is a big part of having the actual kind of, you know, synergistic relationship is that you understand.

1:01:06And by the way, look, I mean, this is like everything I'm saying is, you know, everything that we're saying right now also is the same as if you're working with human beings, right? Like, you know, you and I are colleagues and I would ask you to do something, you'd come back with something completely different. Like, I do need to understand what was happening in your head, right, in order to be able to get – you need to give you feedback, right? If I just tell you, oh, that's wrong, it doesn't, like nothing happens. I need to actually understand – I need to have theory of mind, right? I need to understand what you were thinking in order to really give you the right feedback.

1:01:33And so – and, you know, and again, the great thing with AI is AI will happily sit there and explain all day long why it's doing what it's doing. It'll, you know, it'll happily critique itself. You know, you can do this, by the way, this is a very fun thing where you can have one AI critique the other AI, right? Which is another thing, which is like, you have one AI write the code, you have another AI debunk the code. And so you can actually use, you can play the AIs off against each other and get them to argue with each other. And yeah, these are all the kinds of skills that are going to become, I think, incredibly valuable.

1:02:01I think people call those LLM councils. Yes. They're talking to each other. Yeah, that's right. That's right. I do feel like Like if I were like, I have no design background. I've always wanted to design. I've always wanted to be a great designer. It feels like that's the hardest one to learn of all these three by just watching and talking, right? Because there's a lot of exposure hours as folks have used this term, just like, how do you learn to be a great designer? That feels like that's going to be really hard and valuable. So my true confession is I've always kind of wanted to be a cartoonist.

1:02:29But I have no like art skills. But as we're talking, I'm like, hmm, it might be time. Your time has come, Mark. Yes. I want to pivot to founders. Maybe you're bread and butter. You spend a lot of time with the most cutting edge AI forward founders. I'm curious what you see them do, how you see them. Some way they operate that's maybe blowing your mind about how the future of starting a company looks, how the future of AI forward companies look. Yeah. So this is a great, very topical topic that's all playing out in real time right now on the leading edge. So I think there's like three layers of it.

1:03:05Let's 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 kind of thing that happens with technology transitions. And this is kind of what a lot of venture capital is based on, which is, okay, there's a new technology that comes out. And maybe it's the personal computer or the iPhone or the internet, or now it's AI. And it's like, all right, is this a new capability that gets added to existing products? Right. So all of a sudden you've got, I don't know, an existing, you know, software business.

1:03:38And 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, it's kind of another ingredient to an existing formula. And, of course, you know, a lot of new technologies are like that. Right. You know, I don't know when I don't know when flash when flash storage came out or something, you know, it didn't really. You didn't really redefine the software industry because people just went from using hard disk using flash storage or something.

1:04:06But when the internet came out, like basically old school on-prem software for the most part, 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, in many cases, the companies themselves turn over. And so, you know, so there's sort of this question, like, you know, an example, you just mentioned Nano Banana. So like a great example is there, you know, there are these businesses like, you know, just take Adobe, like, you know, Photoshop is built a whatever 40 year franchise in image editing.

1:04:39Okay, 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 woman. 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 reinvested and a lot of the best founders are trying to figure out how to do that.

1:05:12So 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 a lot of what we've already talked about. But like, OK, if I'm a founder of a company and I've got, you know, if I have room in my budget for 100 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 100? Maybe now I only need 10. or does that mean I still want 100, but now they're doing 10 times more, right?

1:05:44And so, you know, as you know, like a lot of the best founders are working on that right now. And then I think the third shoe to drop hasn't quite dropped yet, but it's, you know, it's kind of the big one, which is like, all right, like the basic idea of having a company, right? You know, does that change? And again, here you've got this concept of the super powered individual, which is like, okay, you know, can you have entire companies where you have basically the founder does everything? Because what the founder is doing is overseeing an army of AI bots. And there's sort of this, 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 one person billion dollar outcome?

1:06:20And we've had a few of those over the years. Bitcoin is probably the most spectacular example, with Ethereum right behind it, which wasn't quite one person, but a very small team. You had kind of Instagram and WhatsApp that had very big outcomes with very small teams. every once in a while you get one of these things where something hits and you just have a very small number of people associated with it. But that said, most software companies obviously end up with huge numbers of employees. And so I think the most leading edge founders are thinking of like, okay, how do I reconstitute the actual very definition or idea of having a company?

1:06:57And can you have a company that's literally basically just all AI? And if you're doing anything in the real world that's hard. But if you're doing software like that, that seems like it might be feasible in some cases. And then, you know, there's like the ultimate example of that, which is like, you know, can you have like, can you have like autonomous like AI economy stuff happening where you have like AI bots in the blockchain or something, you know, that are basically out there like functioning as a business and like making money and just, you know, literally where the AI does all the work itself and just get, you know, issues with dividends.

1:07:25And so maybe, you know, maybe that, you know, maybe that, maybe that's the final outlier result. We have, we have a few founders who are chasing that kind of thing. So I would describe that as kind of the ladder that the best founders are on. Super interesting. This whole idea of a one-person billion-dollar company, I think it depends on your definition of what this is, like an outcome I could see. Having run my newsletter as one person with some contractors, there's so many little annoying things that I have to deal with, with just support tickets and issues and bugs. And it's hard for me to imagine, actually, a one-person billion-dollar company, even if AI is handling so much of your support because there's just so many random edge cases that I'm just constantly filling out forms.

1:08:04And so I guess it depends on do you have contractors? Does that count? What does it mean to be a one person? But I'm just like, I can't see that happening. Yeah. I mean, look, Bitcoin, Satoshi pulled it off. But the open source community, does that count? I don't know. I guess it counts. Okay. Yeah, exactly. Right. So yeah. I would say I don't propose to have answers here, but more just like the smartest people I know are, many of the smartest people I know are thinking hard about this. Yeah. What do you think about moats? A big question constantly in AI, you know, the fact that everything's changing, just what's your guys' thesis on moats in AI?

1:08:42Is that even a thing? Do you care? 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 growth 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 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 going to be the company of the future, not that kind.

1:09:13It's obvious that this incumbent is going to be able to adapt and this other one isn't. It's obvious that there's economic opportunity in this kind of startup and not in these others. It's obvious that the moats are going to be in this area of the technology, but not in this other area. And, 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.

1:09:40Because, 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, generally like quite badly wrong.

1:10:13And 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 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?

1:10:42How 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 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, and 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 AI models, themselves, 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 know, you need this like incredible critical mass of like compute data and there's only a certain number of engineers in the world that know how to do this and, you know, they are getting paid like NBA stars and, you know, and then these companies have to deal with all these like crazy, you know, political issues and press issues and reputational stuff and regulatory and legal.

1:11:39Like all of that translates to like, you know, okay, probably at the end of this, there's going to be two or three companies that are going to end up with like, you know, 100%, you know, I don't know, whatever, 50-50 or 30-30-30 or 90-10-1 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. Or maybe, you know, maybe one company is going to win definitively. It will be a monopoly. And by the way, those outcomes have happened in software many times before. And so maybe that will be the outcome. And, 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.

1:12:20And 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, in the game, you know, fully in the game, you've got Google and you've got Anthropic and you've got XAI and you've got Meta and you've got, you know, all these other companies that are, and then DeepSeek and, you know, Kimmy and all these other companies.

1:12:57companies. 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 is apps are not a thing because the model is just going to do everything. But another way of looking at it is no, actually adapting the model is kind of the engine into a domain involving human beings where you need to actually have it fit for purpose to be able to function in the medical industry or the legal industry or whatever, or coding. No, you actually need, like, the application level is actually going to matter enormously.

1:13:31And 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 is another input. Actual individual choices made by entrepreneurs matter a lot. The economics matter a lot. Availability of investor capital varies over time.

1:14:06That matters a lot. And this is a complex system. And so we actually don't know the outcomes on this yet. And we need to be open to surprises at the structural level of what happens. And of course, as a VC, this is very exciting because it means we're doing this now. We should kind of make bets along every one of these strategies and kind of see how this plays out. And I would just say, like, there may be like one, I don't know, there may be like one particularly brilliant, I don't know, edge fund manager or something who has this all figured out. But I guess I would say if they exist, I haven't met them yet.

1:14:39So 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, OK, there's no way OpenAI will lose this lead. clearly we're seeing a lot of competition. GPT wrapper point is really great. It was such a derogatory term. I don't know, a year ago, just like you're just GPT wrapper. Now it's like the companies that are the biggest companies, fastest growing companies in the world. Yeah, well, it's like a little bit like, I don't know, I mean, even just like with, you know, this has been the holiday, if three years ago was the holiday of ChadGPT, this last month or whatever has been the holiday of particularly Claude Code, right, for coding.

1:15:14But it's like, it's pretty amazing because it's like, okay, there was Claude, which was obviously a great accomplishment, But then there's Cloud Code, which is an app, right? It's a Cloud wrapper, right? It's, you know, agent harness. And then they did this amazing thing where they came out with, was it a co-worker? Co-work. Co-work. And remember what they said of co-work, which is a Cloud Code wrote co-work in a week? Yeah, a week and a half, yeah, 100%. Well, and there's two ways of looking at that, which is like, wow, that's really impressive. I mean, obviously, that's really impressive that Cloud Code was able to build co-work in a week and a half.

1:15:47That's great. That's amazing. The other way to look at it is, co-work was developed in a week and a half. Like 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 then, you know, and then again, it's this push and this pull thing where it's like, wow, it's incredibly functional, incredibly valuable. And people are 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 every other model company, I'm sure you'd have to expect to sit in there being like, okay, obviously we need to build an Asian artist.

1:16:23And then obviously we need to build a co-work thing for regular people. And obviously, I don't even say I know anything, but just like obviously they're all going to do that. And so how defensible is that? And in six months, and we've seen this happen before, like is quad code going to get lapped the same way that GitHub Copilot got lapped? You know, the history in the last three years has been everything that looks like it's like the fundamental breakthrough gets basically replicated in labs very quickly. Like many of the smartest people I know in the field, when I really kind of talk to them, kind of get a couple drinks in them, they're like, yeah.

1:16:54They're basically, you know, one theory is like there really aren't any secrets among the big labs. Like the big labs kind of all have the same information and they kind of have all the same knowledge and they're kind of, they lab each other on a regular basis, but there's not a lot of proprietary anything at this point. and then you know again evidence of that is you know DeepSeek you know came out of left field and basically was like a you know re-implementation of a lot of the ideas of the American big labs and you know and had some original ideas of its own but like you know wow it wasn't that hard for you know some you know basically hedge fund in China to do it and so like how much defensibility is there but on the other side of it you've got wow these big labs are now paying you know individual engineers like they're rock stars and they're you know incredibly bright and creative people and maybe there's a dozen nascent ideas in any one of these labs that it's actually going to be a huge breakthrough that's going to be hard to replicate.

1:17:41And so again, it's just like, I think we just need to, I don't know, my view of myself, I need to put like a big discount on my forecasting ability on this one. Like for me, it's much less interesting to try to say, okay, as a consequence, industry structure in five years is going to be X. The big winner in the category is going to be company Y. The big product killer app is going to be Z. It's like, I don't think I can predict that. I think a much, much better use of my time is being very flexible and adaptable at a time like this. So with all this in mind, do you feel like there's something you're paying attention to more to help you decide, okay, this is where we want to place our bet?

1:18:14Or is the answer essentially the strategy you guys have, which is place a lot of bets? You guys raised the largest fund in history. Is that the way you win in this world? Yeah. So, I mean, 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 Silicon Valley is characterized by too much what he calls indeterminate optimism, right?

1:18:48And 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 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 that's basically, 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.

1:19:19And he would classify, for example, Elon, he would sort of maybe say, VCs are indeterminate optimists. And then he would say, Elon is the determinant optimist, where it's like, no, I'm going to build the electric car. I'm going to do solar, and then I'm going to do Mars. I mean, these very concrete things. I think there's a lot to Peter's framework, but the way I would describe it is I think maybe if you and I disagree 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.

1:19:54The reason for that is, hopefully, it's not so much wishful thinking, it's more, no. The indeterminate optimism of venture capital or the indeterminate optimism of basic EZ or Silicon Valley is actually very specific, which is there are these extremely bright and capable people like Elon and many others who are founders, right? And product creators, right? And each of those individual people is a determinate optimist. Each of them individually has a very strong view of what they're going to do. 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.

1:20:30We have a hundred and a thousand and then 10 ,000 of those. And the way to optimize the outcome is to have as many of those as possible, be as good as possible, run as hard as possible. And then just the nature of the future is like, we just don't know all the answers and that's okay. And the right way to deal with that is to run as many experiments as possible and have as many people try to do as many interesting things as possible. And so, yeah, I would put myself firmly on the side of the indeterminate optimist. I mean, I'm wondering if the answer to the question of what you look for now more and more is this determinant, optimistic founder that has this massive ambition and is actually working on achieving it.

1:21:06Yeah, no, that's right. That's right. I mean, look, the founders need to be determined and optimists. Like they need to have a very specific plan. Now, look, the critique of the critique always, you know, the critique from the founders is, oh, you VCs have it easy because like you don't have to like you don't actually have to commit. Right. You don't actually have to like make you don't 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.

1:21:30You know, and there's there's truth to that. You know, the counter argument on that is the founders get to run their companies. we don't. So, you know, we don't 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 single-mindedly execute against that goal. And, you know, look, in the long run, who does history remember? History remembers Henry Ford, right? Not, you know, whoever was, you know, whatever the seed investor who seeded Ford Motor Company and, you know, 10 other car companies have failed, right? And so, you know, the determinant optimist is the, you know, the founder is the founder and the company builder and the engineer.

1:22:04I mean, these are the people who actually do this thing and deserve 99.9999 % of the credit. But having said that, I do think there is a role for having some independent optimists in the background, helping along the way and helping keep the whole cycle going. Do you think about AGI in shifting your investment thesis? As we approach AGI and hit AGI as an investor, how do you think about your investment thesis changing? Yeah, so I've always kind of had a little bit of an issue. I've always kind of struggled with the concept of AGI because it at least, well, 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.

1:22:46And then there's like the, I don't know, cosmic definition. And the way I would describe it as, well, let's start with the cosmic one. So the cosmic one is basically, it's the singularity, right? And so AGI is the moment where you enter the singularity, which is to say that where the world fundamentally changes and like the rules of the old world are gone. We're not operating in a new domain. And then, you know, the kind of the full definition of singularity is like, it's a world in which, you know, human judgment is no longer really relevant because the, you know, you get this self-improvement loop.

1:23:12The 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. And I kind of described why I don't really think that's, I don't think we live in that world. Whether you could call that utopian or dystopian, I don't think we're lucky or unlucky enough to live in that world. We could debate that. We could talk about that more. But the prosaic definition of AGI that at least I think the industry participants have kind of converged on, and tell me if you agree with this, is it's when the AI could do every economically relevant task as good as a person.

1:23:46The 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. 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.

1:24:24Right. And so we always say that as AGI always is, you know, the definition you gave, the definition I give. It's kind of it's always kind of relative in comparison to a human worker. Right. And it's like, I don't know, like human skill level caps out at a certain point. But that's because of the inherent like biological limitations of the human organism. Right. Right? Like we're all, you know, human, I give you an example, human IQ, human IQ, you know, kind of what they call fluid intelligence or the sort of G factor of kind of, you know, fluid intelligence. IQ, I think tops out in humans as a species, it tops out around 160.

1:24:56Right? Where at like 160, it's like Einstein level, Einstein, Feynman. In terms of IQ. In terms of IQ. Like you just tops out at 160. The 160 IQ people are the ones who come up with new physics. There's only a small handful of those. The generally speaking, when we run into somebody in the world who's like incredibly smart, who's like a bestselling author or like a, you know, one of the world's best, I don't know, research scientists or one of the world's best doctors, you know, whatever, it would be probably 140 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.

1:25:29If you're looking for like a really good like line manager in a business, it's probably 110. You know, if you're looking for like an accountant, like a small business accountant who's good at doing the books for small businesses is probably 105, right? 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?

1:26:01And it's like, there's no theoretical limit on where this goes if you release the limitations of human biology, right? And so can you have a, and you already have people running these experiments to kind of do human equivalent, you know, kind of IQ, 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.

1:26:34By the way, and I think that's great, right? I feel as great about that as I do about the fact that we occasionally get an Einstein, right? 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, more IQ like Einstein or greater than Einstein. But I think IQ of the machines is going to exceed that on humans. I think that's really good. And then the performance, again, it goes back to the AI coding thing is happening, the performance against task is going to get better also.

1:27:02Like, I think, you know, this is where Linus Storvalds in particular is like, yeah, okay. Like, this thing is starting to generate better code than I can. Okay, so now we're going to have AI coders that are actually better coders than the best human coders. I think that's great. I think we're going to have AI doctors that are better than the best human doctors. I think we're going to have AI lawyers that are better than the best human lawyers, which actually is going to be very interesting to see, which I think is also great. And so, like, I don't think there's a, I think we're used to living in a world where we just don't understand how good good can get because we've been capped by our own biology.

1:27:31And we're going to get to experience what it's like when you have the capability at your fingertips that's actually better than human in these domains. And so you see what I'm saying, which is like, I think this idea of like human equivalent is just going to be like a footnote. It's like, oh yeah, that was just on Tuesday, you know, in 2026 is when they hit that. And it kind of didn't matter because the next question was like, okay, what are we going to, what are we going to, What do we get to do in a world in which we actually have machines that are better than that? Right? And so I think this is going to be much more of an exploratory process for actually exceeding human capability than it's going to be any sort of particular singularity moment or whatever that just happens to coincide with the human threshold.

1:28:10200 IQ. Just like that frame of reference is such a mind-expanding way to think about just how fast and how smart these things are going to get and quickly. Well, I don't know if you have this experience. I have this experience all the time. I have two experiences I have all the time. One is just like, I know I ought to be able to do this, but I just can't, it's going to take too long. I want to write this thing or I want to have this theory on this thing or I have a plan or whatever. And it's just like, fuck, I don't have the eight hours or by the way, the eight weeks or the eight years. And I just don't know enough yet.

1:28:49And 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, after you have this, you get interested in some of your written books and then you're like, shit, I forgot almost everything that I just read. Like, I wish I could retain it all, but I can't. It's just like, you just have this, I sort of live in this kind of state of like endless frustration. I was just like, I, like if I could just be smarter than I was, like, I'd be so much better at what I do, but I'm not. So, so, so there's that. And I don't know how often you have this, but I have this on a regular basis.

1:29:16It's just like, you know, I, you know, because of what we do, like, I know a bunch of people who I know for fucking sure are smarter than I am. And I know it because when I talk to them, I just find myself at a certain point, you know, it's like for the first half of the conversation, I'm just taking notes the entire time. And for the second half of the conversation, I'm just like, fuck, like, fuck me. Like this person is just smarter than I am. And they're just out thinking me and they're going to keep out thinking me. And I just can't. And I'm just like, all right, God damn it. Like, I got to go home and I got to like have a drink because I'm just not, you know, I'm just not, whatever that is, I'm not that.

1:29:46And so we're just so used to having those limitations that the idea of having machines that work for us that don't have those limitations, I think that's much more exciting than people are giving you credit for. Oh man, I could talk to you for hours, Mark. I'm thinking to close out the conversation, I wanna ask about your media diet and your product diet. You just talked about books, reading 10 books. I think you famously read constantly. I saw an interview with you where you're just like, AirPods changed my life. I'm just listening to audio books now all the time. So in terms of media diet, what are you reading?

1:30:19what 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? Yeah, yeah. So what I read is basically, I mean, I would say I read basically three categories of things. So like in terms of like general media, it's basically, I sort of, I always describe it as I have like a almost a perfect barbell strategy, 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, or it's like a book that was written 50 years ago that has stood the test of time.

1:30:49And then, you know, we're presumably there's something timeless in it. And then it's sort of everything in the middle I'm always like much more skeptical about. And in particular, it's kind of what I already said, which is I think if you go back and you read old, nobody ever does this. It's actually really funny. Nobody ever does this. There's no market for it. But if you go back and you read old newspapers, and by the way, you can do this. Just read last week's newspaper, right? I'd say we're taping on Friday. So read last Friday's newspaper, right? And just go back and read it and be like, oh my God, like none of this happened like not that none of what they predicted played out the way that they said that it would none of this turned out to actually be that like relevant or correct like they didn't understand like you know they by the way they had no view what was gonna happen this week then they couldn't know and so they were making predictions and forecasts and so forth based on like not having information but it's just like wow like you know like none of this happened like i wish i had never read this like oh my god um and then you know it's kind of the same thing with magazines.

1:31:44They go back and read old magazines and just like the level of, you know, just the endless numbers of predictions that they make. And kind of, you know, the problem with, you know, newspapers at least are going day to day. The thing with magazines is like every, it's like a week or month, you know, kind of a long cycle. And so it's even, you know, by the time an article even hits publication, it's often out of date. So I just, I just have like a big problem with kind of everything in the middle. And so it's either, it's either, it's either of the moment or timeless. But then, yeah, you mentioned like newsletters.

1:32:10I mean, And so the other thing, and this is maybe obvious, but I think it's probably still underrated, which is the actual practitioners in the field who are actually creating content, I think, probably is still dramatically underrated. And I think this is a huge part of the Substack phenomenon and the newsletter phenomenon and the podcast phenomenon is direct exposure to the people who are actually principals in the field who actually know what they're talking about is probably still dramatically underrated. And I think, again, the reason for that is we're used to being in this mass media kind of culture in which basically everything is mediated, right?

1:32:39Everything got filtered through TV interviews or newspaper interviews or magazine interviews. And obviously now more and more, it's just, no, you actually want smart people who are actually working on something explaining themselves. And then you have new kinds of intermediation, like podcasts that kind of open that up for people to make that possible. And so, yeah, domain practitioners are really great. I mean, just to state the obvious in AI, it's obviously your stuff. but also like, you know, the fact that like Lex Friedman can have, you know, the world's leading or, you know, whoever the, you know, any of you guys, you know, there's a small handful of you guys who have access to these people.

1:33:12You could have the world's, you know, kind of leading experts in the domain actually show up. And by the way, it's, you know, it looks, the critique always is, you know, people talk their book, like if I'm running a startup or whatever, I'm just selling. But it's like, and there's always a little bit of that. But it's also, you know, my experience is people love to talk about what they do. And, you know, they fundamentally like want to express what they do 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.

1:33:40And so I think there's just actually just tremendous amounts of alpha in listening to the world's leading experts in the space who actually just like show up and talk about what they're doing. And of course, like the world is awash in that today in a way that it wasn't as recently as 10 years ago. So I, yeah, I do as much of that as I can too. And there's also just this culture in tech, Silicon Valley in particular, of sharing, of not trying to keep these secrets. Everyone on LinkedIn is always like, how is this free? Like, it's just the way it works. Yeah. Somebody said Silicon Valley is a company town, but the company is Silicon Valley, right?

1:34:10And again, at the level, this goes, again, there's one of these great N equals one. At the level of N equals one is somebody, you know, and I've run Stratus before. I've run companies before. At the level of N equals one of like running a company, that's just a giant pain in the fucking butt. Like, because, you know, your secrets are walking out the door and your employees are walking out the door and the whole thing sucks. But, you know, the other side of it is you also benefit from that, right? Because you get to hire people with all these skills and experiences, right? And you're in this ecosystem that adapts, right?

1:34:34And channels talents and skill and knowledge and people into the new fields. And so, you know, there's kind of the push and pull of that at the level of just being an individual CEO. At the level of just being in the ecosystem, to your point, like, yeah, it's an absolutely magical phenomenon. And by the way, like, you know, one of the, you know, for all the issues in Silicon Valley, you know, I think AI, I did the comment once. I think AI is the ninth major technology platform in the history of Silicon Valley. Silicon Valley is still called Silicon Valley. We haven't made Silicon here in decades.

1:35:08It's called Silicon Valley because they used to make chips. The actual fabs were in Silicon Valley, and then they designed them, and they made the chips. And that was wave one starting in the 19th. Actually, that was actually more or less wave three or whatever. But that was when the area was named in the 1950s. But now we're on like wave nine, right? And the company town phenomenon where the company is the industry, like the indeterminate optimism. Nobody had to sit and plan and say, okay, in the 1990s, Silicon Valley is going to do the internet. In the 2000s, they're going to do the smartphone.

1:35:41In the 2010s, they're going to do the cloud. In the 2020s, they're going to do AI. It's just the indeterminate optimism of the ecosystem, the flexibility of the ecosystem method that Silicon Valley could morph into all these categories. And again, maybe a testimony to indeterminate optimism. This reminds me of the meme of how we're all just wrappers over sand. Everything we're building is just wrapper over wrapper, wrapper, wrapper. The wrapper thing is hysterical. Yeah, I'm a software company. I'm a chip wrapper, right? Yeah, I'm a business application. I'm a database wrapper. Yeah, exactly. I'm a sand rapper.

1:36:13Yeah, you and I, we're all now sand rappers. Sand rappers. Perfect. Okay. One more question along the media diet. I asked your partner, Ben Horowitz, what to talk to you about. The Z in A16Z, if people don't know him. And he said that you're really into movies these days. And so I don't know, any movies, any movies you're really into these days? Any movies you've absolutely loved recently? Yeah. So the movie that blew my socks off last year, which I think is the best movie of the decade for sure, maybe of the last 15 years, is this movie. Unfortunately, it's one of these things. Not a lot of people have seen it, but I would highly encourage it.

1:36:45It's called Eddington. I've not heard of it. Have you not heard of it? Okay. So, Eddie, you're going to really enjoy it. So, I won't spoil too much of it. So, at the surface level, the following spoils nothing. At the surface level, it's set in a small town in New Mexico called Eddington, which is a small town of about 600 people. And there is a sheriff who's played by Joaquin Phoenix, who's like an old, crusty, basically right-winger. And then there is a mayor, played by Pedro Pascal, who is basically a young, hip progressive. And then the movie starts, I think, in March of 2020. And so it starts when COVID first hits.

1:37:24And then it sort of plays out over the next few months. It then intersects. And it sort of extends into the summer of 2020. So, you know, kind of the George Floyd moment and then the protests and riots and kind of everything. So it started the convergence of COVID and then all the BLM stuff. And then there's a third kind of element to it, which is there's a company which is basically a loosely disguised version of Meta, if you read the backstory of it, which is building an AI data center on the outskirts of town. So they kind of pull that in as sort of a thing that looms larger and larger over time.

1:37:58time. And then the thing it really is great at is it really shows, you know, this is a small town in New Mexico. And so everybody in the town gets kind of fully wrapped up in all the COVID stuff, and they get fully wrapped up in all the BLM stuff, and they get fully wrapped up in all the, like, you know, tech anxiety stuff. But they're all experiencing it basically through the internet, right? Which is, you know, what actually happened, right? And so the reason I love the movie so much is one is it's the first movie that directly grapples with 2020, of what happened in 2020, and that just like fully, fully engages and grapples with like all the dynamics that were playing out in the country.

1:38:30But the other reason is it's the first movie that does a really good job of showing what it was like, especially in that era to live in a world in which there were things happening in the real world and people were kind of experiencing events online, you know, like in a way that was like very central in their lives, right? And so it does like a really good job of pulling in like smartphones and social media in a way that 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 won't even say i completely agree with the movie or whatever and i think the director of the movie and i would probably disagree about a lot but he really tries hard to like really grapple with like what it is actually like to live like a human being in the 2020s in america in a way that i think many other filmmakers who are very talented have just been very scared of touching and and this guy for some reason he's just like yeah i'm just going to find all the third rails and i'm just going to like fucking grab them i can see why that's your favorite movie.

1:39:21It's great. It's great. It's great. Everybody should see it. Oh, man. Okay. Final question. I want to ask about your product diet. Are there any products you use that maybe are less known that you love, that you want to recommend? You can mention products your investors in if you use them constantly. I mean, we have so many that it's really hard to, you know, I always feel it's like, you know, here's your favorite children. So it's really hard to pull out specific ones, but I'll talk about a few. I mean, just observations. So one is my 10-year-old. My 10-year-old right now is 100 % obsessed with Replit.

1:39:56And 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? 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 mentioned oh yeah we work on xyz you know he's like okay uh but when he discovers something then then it's cool or when his friends tell him about it it's cool and so he he he threw no interference on my part uh discovered replet 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 we'll sit and do it do it for hours and so i'm seeing that phenomenon play out uh which is super fun um uh That's one.

1:40:43Two 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, if 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, that went viral last year for this. just incredibly intimate, emotional kind of voice experiences. So I think the voice stuff is fantastic. I'm also super fascinated by all the voice input stuff. And so a company recently sold. But I think the pendants, the wearables, all that stuff is going to be big.

1:41:32The 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 phone now called Whisperflow, which is voice transcription, which works staggeringly well. It's like a voice transcription function, but you can actually talk to the AM model while you're doing voice transcription. So it kind of understands when you're telling it, no, no, I want bullet points over there and I want this and that. And it understands that you're not telling it to type in the words, I want bullet points. It just actually understands that you want bullet points.

1:42:03And so that's a great example of a super useful thing. and so i i think the voice mode stuff is going to be is going to be uh it's going to be really great uh subscribers of my newsletter get a year free of replet and whisper flow so there we go uh uh what's the what's the most memorable thing your son built with replet oh well so he's gotten super into star trek um and so uh so far it's been he's right like writing like star trek simulators um so uh like all the uh you know all the uh uh by next generation they actually had generation okay i was gonna ask which well he like we actually like them all we watched the new Starfleet Academy last night, which actually is quite good.

1:42:35But we watched the original, you know, we watched them all. But it was in Next Generation where they actually developed an actual design language for the computers. Because if you watch the original series, they just had like basically, you know, nods with lights. And they didn't really, you know, they just like were like, you know, fucking around on set and trying to pretend they were doing it. But by Next Generation, they actually had designed, they actually had a UI design language. And so one of the fun things you can do vibe coding is you can say, give me a Star Trek Next Generation, and, you know, user interface for, you know, whatever, this, that, or whatever.

1:43:02And it actually uses the, they call it, this is Evan a nerd now, they call it Elkars design language. And, you know, it'll actually build you like Star Trek Next Generation British consoles using that design language, but, you know, with your choice of like a Star Trek game, for example. And so he's going crazy for that kind of thing. That sounds extremely delightful. You guys should open source to release that. Mark, like I said, I could talk to you for hours. Well, you've got things to do. Anything you want to leave listeners with before we wrap up? Anything you want to double down on or just leave listeners with?

1:43:33Yeah, so a couple things. So one is we got super lucky last week. Paki McCormick wrote the best piece ever written about us, actually, which he released. And so it's the best explanation of what we do and how we think. And so I would definitely recommend that. And then, you know, we're putting a lot. We have a great team of folks now. We're putting a lot of effort ourselves into video and content. And so I definitely recommend our YouTube channel, which I think has a lot of great stuff and is going to be very exciting in the next year. Awesome. We'll link to that. I think it's just youtube.com slash A16Z, something like that.

1:44:02And you guys have great stuff. Mark, thank you so much for being here. Awesome. Thank you for having me. I really, I really appreciate it. Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny's podcast.com. See you in the next episode.