The New Economics of AI | Martin Casado & Steven Sinofsky

23:50But I still think this kind of notion of economic need is very important. Oh, yeah, yeah. Right. So, for example, Bletsley Park was about cracking a code for a war. And so, like, there's this effort that created innovation that the outcome was, you know, winning World War II. ENIAC, we were trying to do nuclear, not just research, but, like, innovation, you know, in terms of a war effort. And so we needed to, like, calculate integrals, and we were doing it by hand. And so at that point, these things were lauded as, like, saving humanity. Everybody was super excited. All the physicists loved computers and used computers.

24:32And for me, the thing about the current solving math is I don't know what that thing on the other side is. And I do think we've had that in the past. Well, we did. We had the alpha-go moment. It was the same thing. Like we did a podcast, not in this room, but. But even before AlphaGo, we had like, remember when Chats were bitten in chests? We had the IBM chess thing. And Frank, Chad and I, we did this podcast at AlphaGo. And we had to try to make people understand like why it was a good idea. And I think it's actually pretty reasonable for us to ask the question, which is there's things that these things solve.

25:06And, you know, there's a lot of utility and value in that. And like that's going to move things forward. And when that tends to happen, people tend to be excited and get behind it. And there's these things you solve. I think people don't have as positive a view. And I would submit that's because it's almost like solving the problem had become the end as opposed to the actual end. And so we should maybe all step back and be like, if you're really sad about something being solved, maybe it wasn't worth working on to begin with. Right. And you're like doing the Sandman doll and you're taking her peace or something.

25:42But that's not moving the economy forward. Right. Well, we're both – look, we're systems people, but I'm actually an apps person. I know you're apps. So, yeah, we're both systems people. But I, of course, absolutely think that the wave that matters are apps. And, of course, the internet also – this same problem happened in 1995 and 96 with the internet, which was – it was very exciting. But most people just sat around saying, I don't know what that does for me. Look, there's a great book out now called Steve Jobs in Exile, which I absolutely think is required reading if you're listening to this podcast.

26:18So, Cain wrote the book, but it's with Catmull at Pixar and with Dana Lewin, who was at Steve's, super good friend, and was also at Microsoft. They all – this book is just fantastic because it explains all of – it encapsulates all of this notion of, like, building things that people actually need and solve problems. But it pointed out very clearly, you know, the Next was actually the machine that Tim Berners-Lee used to write the HTTP protocol. Right. So he actually. A Next machine? And he used the Next machine. That's interesting. And it's super interesting. Computer mechanics. Because nobody knew what this machine was for or what it did.

26:54But then he built that and still nobody knew what the machine was for or what it did. Because he's like, well, it's to find the phone numbers and the other researchers and to share papers. And I think there was a great example of a company, a Seattle-based company, that was called Cyber Pizza. And this was like a dot-com thing that didn't even make it to the 2000, I think. But the idea was it was basically on DoorDash for pizza, only pizza. And they would basically, you would order, and then they would figure out a pizza place near you and send the pizza. That was the launch demo for the next on stage.

27:28They did that, and they had actually pizzas in the back in case it didn't work. And I should say for next step or open step. But the idea was that that was showing what you could do with it. And literally the reaction was like, wow, that's really cool. But have you heard of the telephone? Yeah, yeah, yeah. Your point is not everything we've known how to use. So I'm a little focused on like there was a solution on the other side that people are going for. You're making your point that there's a lot of platforms that get built where that's not clear, but clearly. Well, the spreadsheet was like, I will show, here's probably one of my last visual aids for today.

28:03But like the word processor came out and this is in 1982. And people are using it on Apple II computers and this new kind of computer called CPM, which is the origin of DOS. And people were like, I don't understand why you can just type. And the people, once you use the computer, the idea of typing really, really didn't work anymore. And so some people at Lawson. Well, you have to show it now. Yeah, I will. I'm just like building up. But these people at Harvard Law School, they brought in the first laptop. So that's the first laptop. Weren't those called luggables? Well, no, this was just called, this was literally just called an Osborne and it was the only one.

28:41So as a guess, Eric, you're a kid. How long was the battery life in this? Not long. There was no battery. This giant case that weighed 25 pounds, there's no battery in it. It just plugged in. But that was a trick question. Because every time I've ever vlogged mine out, I have mine from college. Like, people are like, well, how long does the battery last? And so it's literally the size of a sewing machine. It's bigger than a legal carry-on ever was. And that was my college computer. But in my senior year of high school, it got banned from Harvard Law School. So someone showed up to do their exams.

29:19So at Harvard, they used to bring your typewriter to exams. Because that way, the professor could read it. And two kids brought computers in. One brought an Apple II, and one brought the Osborne. And the school banned them. They just said this is, and for all, every reason you could read, and I have the Time Magazine articles and the New York Times, every article you could read reads like, don't use the graphing calculator, don't listen to rap music, or don't read, don't know jazz, or the arguments that are going on now. Three years ago, I tried to get Cornell to use AI in freshman writing. When the first, and, and they just stopped talking to me.

30:00Wow. And the, but here's the irony of that. My freshman year, when I had this computer, I was, of course, the only person in my 90 person dorm with a computer. And, and I had to get permission from the dean to use it to write my papers for freshman English. This is the fall of 1983. And so, and so that's exactly where we are now on all of this stuff and this, this whole, and it, but the thing is, it's a, you can also think of it as a level of abstraction because like no one's going to college now without a computer. Like... Can I just put... Yeah, no, put... So I agree with you, but let me... Right, right.

30:35Every once in a while I'm like, well, maybe it's a little different. So here'd be the argument. I don't think of the history of computer science that I can recall. Have we ever abdicated actual reasoning or logic? It's always been a resource, right? It's been like compute never can storage and like that's what you're providing. And then the human is like putting in the high-level thing and then it's using the compute network and storage to like calculate the answer. But like all of the kind of initial setup we're providing. Wherein, and I guess maybe it's not true for the internet, but now I feel like you're actually abdicating thinking in a way where you're like, tell me the answer.

31:18Like I'm not even really sure what the question is. Right, right. And again, like I think maybe you could say, well, Google was kind of like that too, but it was still very much a social thing and not very much. And so it does feel like that's a little different than just going up in abstractions because going up in abstractions, you still tend to have like a deterministic system that's a higher level of abstraction that like you have a computer and like it's the human being that's kind of defining everything about the problem statement. It feels a little different. Well, it definitely feels different.

31:43Here's, I also think for me, graphing calculators felt different. To me, graphing calculators felt like cheating. And because, you know, the test question was make a graph. And so that's what's going on right now is that the capabilities match the test question. Now, getting us full circle to what we were talking about, about computers and mathematicians, my freshman year, also a new product, a new thing came out, and it was Maxima, which was the MIT symbolic math package. And so this was a way you could literally type in like an integral into a computer. I remember the first time I saw Mathematica, I'm like, this stuff in black.

32:21So Maxima is, you know, machine-aided, what was it, machine-aided computation symbolic math, I think was the, and that was the lab at MIT started in the late 60s, early 70s. And that had started to sweep through. So my freshman engineering class, we had a version of it that ran on IBM PC. It was called MuMath. and like you could, like, we got our calculus homework, you marched over to the engineering library, checked out a PC disk, and then just typed in the answers to. So you don't think that. And that was cheating. Let me just push on just a little bit. Because I tend to agree, but every once in a while I have like moments of doubt.

33:00So I don't remember writing programs where you actually abdicate logic. Like, if I'm writing a program, I'll like, whatever, I'll use a cloud database, I'll use storage, I'll use networking, you know, whatever it is. But like correctness and logic for the program is under the programmer's control. Maybe I'll use a third-party library. But again, like I'm choosing the library. I know the inputs. I know the outputs. And I feel like we're entering this realm where you're actually abdicating logic to a third party. You're like, tell me the answer. So maybe that's just a higher level of attraction.

33:35It feels a little different to me. Yeah, no, look, that's the debate. I'm like, I'm all in on the debate. Like here's an example of that, a Stanford example. So during the AI winter that was the 80s, Stanford, the biggest— One of the AI winters. Stanford was—and we have a podcast on that from 15 years ago. One of the AI—one of the biggest things at Stanford was to combine new AI with the medical school. And so there were all of these projects to do, like, medical diagnosis, chemotherapy kind of stuff. I worked on one that was doing organic synthesis with a team at Harvard. And all of those were sort of the earliest, like, let me turn over the decision-making.

34:16In fact, that's the whole era of the 80s and computers were the dawn of what they used to call expert systems. I remember then. And so expert systems were the first time we got a taste of this debate. I remember very well. Your classes were all this. It just didn't work. Well, your classes were mostly about, like, you had a bunch of classes on this stuff. A bunch of guests on expert systems. I've had to build expert systems. Right, right. I've written a lot of prologue. Exactly. So I very much understand it. I just thought like that never really worked. Right. So the big difference is that stuff was working.

34:50But now it's work. And we're abdicating logic. So it's interesting because to compare and contrast. And even in the case of prologue, you're kind of like for these declares. You're coding it. It's algorithmic. You're still providing the end state. And it's just like finding a way to get to the end state. Where here, like you're almost asking what the end state should be. So it just feels a little different. And it's especially, so I agree. Like, I love having this debate because I think so much of it boils down to the concern and the willies that you get thinking about it. Yeah. It's actually because of the context we're in.

35:23Yeah. And, like, because, you know, think about, like, we have all this stuff going on where people don't want to build data centers. Yeah. But, like, two years ago, people were, like, beating each other to, please, governors were racing to have data centers built. Or, you know, 10 years ago, like build a car factory in our state, the one that billows smoke and is really hard labor. And so the context really matters to these discussions. You can't separate them from – Right. But I said – I just want to go back to this later. Yeah. And I don't mean to – I just think – so your and my entire career has been moving up layers of stack.

35:57But like there's always a computer layer of stack. Yeah, yeah. You can always map it down to like the next layer in basically a deterministic way. Yeah, yeah. There's higher levels of like compute abstractions. This is the first time it feels like a different layer of the stack. Like maybe this is like really is the next abstraction, which is more of a human level abstraction, which doesn't map directly and so is actually different. So it may like, I think, you know, whatever it is, starting with like, you know, transistor logic and then going to compute and then going to like hardware and then going to OSs and then going to like applications and then going to platforms.

36:32Like you've been moving up the stack that way. It could be the case that like we're at a layer where like we have to rethink fundamentals. We do because it feels a lot different to me than just like this is the next layer. The big difference is, and we can argue, you can argue this or debate it or label it, either side, which is we actually are making the leap from calculating to imperative programming, which is where we've been and where everybody is, to now, and then we were in this, for a brief time, we were in this mode where basically the data really determined the program. And that was the first recognition, all of the inference and everything.

37:07And now we're at this where it's arbitrary, it's random, and it's statistical. Right, so the way that I think about it is the following. So imperative programming, you know all of the steps, so you write the recipe, and it follows the steps. Okay, then there's declarative programming. Declarative programming is you know the end state. Which is this prologgy kind of thing for people to follow the world. Or data log. Right, right. Or SQL. You know the end state, but then the computer does all this stuff to get to that end state. And you can't really bound the compute time. So you're like, this is like a make files.

37:37Here's what the end state looks like, and it does it. And this is like this new thing where it's almost like you don't really know what the end state is specifically. Specifically, and you just kind of like, you know, you kind of like, you know, pray to the model God in like the right words. And then it produces the answer that just ends up being useful. Yeah, yeah. In a way. But I look at it. That's a factual statement like that. But it's also interesting to think about it going forward in terms of is that itself the next layer of abstraction in how we think of computing? Yeah. And it may be like computing.

38:13and maybe like this is where I compute and like, or like natural like phenomenon actually intersects pretty heavily because the answer is produced from like human output, which is language, which is kind of different than like.

38:24Erik Torenberg:If we do need to rethink some fundamental assumptions, what may that look like? Well, I just think that like, um, people like, you know, Steve and myself have built these deep intuitions on how systems function and how they hit the industry based on 40 50 years of like watching this stuff and i just don't know like things like will value go to the model or to the app how much capital can you apply to this stuff what classes of problems can you solve versus not solve um what guarantees that can you provide uh how does this impact productivity there's a lot of things that we've got intuitions on.

39:08And for me, the big question is, do we have to, like, reshape those assumptions or not? And to what extent do we have to? Because a lot of physics feel a little bit different. I'll just give you one example. I mean, I've said this many times. I think it's so important. 20 years ago, if you're a startup of 10 people and I gave you a billion dollars, what would you do with it? You would end up spending a ton of money on building, buying your own computer or something, if that's where you're going. Well, hire people, you buy computers, you blow up. Like, you wouldn't know what to do with a billion dollars.

39:34Oh, I see what you're saying. Yeah, yeah. Yeah, 10 years ago, I give you a billion. You hire engineers and you'd be fucked. Right, right, right. Like, what do you do? Right, right. Write code. You've got two, you know, you've got, you know. The billion, the important part of that is it's a billion. It's not that you've got money. It's that it's a huge. It's a billion. It's a ton of money. If I give you a billion dollars two years ago. Because 10 million, you'd buy a bunch of stuff from Hewlett-Packard and the money would be gone. For sure. Right, right. This one is a billion dollar. Yeah, I mean, like, in software, you hire people.

40:02There's nothing you could do. The mythical man month is very real. And right now, if I give 20 people a billion dollars, they can actually use it usefully. So it's like we've kind of moved the industry from this engineering-bound problem to a capital problem that's fundamentally very different. We've never been like that before. And so this is like a law of physics where our early intuition, which is like all problems, your engineering problems, starts to change. So I think there's this very open question we should, especially people like us, should be asking, which is like, to what extent do we have to reevaluate our priors on this stuff?

40:34And it's not just one level of abstraction. It actually changes the nature of capital versus innovation versus competition versus defensibility, etc. That's a great way to think about it because it forces you to think about a new model. It's also interesting that computing was capital bound for the first 30 or 40 years. Like if you wanted to do something. This is such an important point. If you want to do something with a computer, like your first step was we have to get one. And then you couldn't. You were capital bound and then you were engineering bound and now we're capital bound. Capital bound again.

41:05Yeah, which is crazy. So it's almost like you have to like hop back 40 years. Yeah, Mad Men goes through the scenario where the computer shows up at the advertising agency. And they run around trying to figure out, explain what it does for people, which they also got a copy machine, the same thing they did. But it was interesting because they couldn't figure out what to do, but they were excited that it had the capital to acquire one. and it made them look like they knew what they were doing.

41:28Erik Torenberg:Five years ago, Patrick Olson interviewed Sam Altman in a podcast, and Patrick was saying, hey, you know, we've been in this era of lean startup, but for your projects, you know, OpenAI, this sort of energy project you was involved with, a few other aging thing, you've raised colossal amounts of money right out the gate. Is that underrated? And it's sort of just speaking to what you're saying. Yeah, yeah, yeah. You know, it's interesting. So prior to AI, there was always this battle between Eric Rize and Ben Horowitz, right? Yeah, yeah. So you lean startup, and then Mark and Ben wrote, like, the art of the fat startup.

42:00Which basically, you raise the money and go for it. But there's always been this natural limiter, actually, which is the engineering. Yeah. Complexity is, oh, that's actually been the reality. And so Patrick Collison is right. It's like we now have a discipline for taking a lot of money with small teams and using it productively. That's a very, very big change. I don't think we've internalized this. Which also, it's incredibly, that is why there can be so much optimism now. Because although capital is scarce and it's hard to get and all of these other things, once you get it, as we know, the building based on people was also hard.

42:38Like just scaling that and doing more. And then nine people can't do anything faster. I'm telling you, my 10-year job, you know. Was recruiting. and giving, yeah, it's like literally giving these early teams money and then helping them accrue and then watching and waiting for two years. Well, yeah. And I think engineering just doesn't scale.

42:58Erik Torenberg:It also has implications of venture capital because for the last decade, people have been saying, hey, there's way too much capital. Way too much capital. I just think this is such a crazy view. So there's been this view in venture, this zero-sum thinking, which is funny from the people that shouldn't be zero-sum thinking. You know, like, and they'll go up on. Too much capital is chasing too few gears and all. This is like, you're a venture capitalist. don't you believe in positive some stuff, right? And, but if you look at the numbers, the more capital that flows into private markets, the larger the market gets.

43:25And there's a couple of reasons. One of them is the one we've talked about, like technical waves that actually are able to consume capital like AI. But there's another one is if there's more capital available on the private markets, companies will stay private longer. So more value accrues on the private side. And so I think capital going to private markets grows the TAM. It's not a limited TAM. And I think the people that should be, There's some funny early stage venture investors who should think of like, you know, positive sum outcomes need to stop thinking about zero sum. Well, one way to think about that is I'll bring it back to what I think your foundation enables what I think is the most exciting thing, which is we're really on the cusp of a wave of apps.

44:03And like the fact that now you can apply capital without also being a recruiter for 10 years and have output, now all of the world that's unserved by software, which is literally all of it, like everybody who complains about whether it's medical records or scheduling to go to a doctor or my favorite are lawyers. Like nobody has cheered more that, oh my God, we're finally getting able to automate lawyers with AI, which is the weirdest thing in a world where everybody is against everything except having more lawyers. But like all of this, this means that the person who has the domain experience, like we used to love like venture capital thing, like, oh, you know, it turns out it's like really, really hard to like build commercial real estate.

44:48Wouldn't it be great if somebody who understands commercial real estate built a software company, but then they don't know how to build software. Well, they should get a co-founder who knows how to build software and teach them about commercial, 20 years of commercial real estate. It's really hard. But now the path from that kind of idea is a capital problem. And that's a new level of abstraction. And I mean, I remember my very, very first customer visit as a professional product developer was to visit a doctor who happened to have gone to medical school after majoring in the earliest computer science.

45:16And he wrote like a DOS program to schedule a doctor's office. That's amazing. Which you'd think it's just scheduling. It's a calendar with hours. But it turns out this was me, a 20-year-old me, hearing this guy explain, no, you don't understand. You call the doctor and, you know, you're talking to a scheduler. So they're listening for keywords to decide is this five minutes, 20 minutes? Do they need the x-ray machines? Do they need the EKG? And so they're actually scheduling like a blood draw and all of this stuff in parallel, not just the 10 minutes you need with the doctor. And so that's what his software did.

45:50It took him years to bang that out himself. Yeah. And that's the kind of thing that's going to be able to happen. Like now that problem can get solved by the person who knows. No code is finally here. Well, it could really be. And it might actually be that you're not just building this throwaway code that's hard to do, but also everybody else's abstraction layer is rising. So you don't need to design that piece of code. Like if you're doing it for a phone, well, the phone's abstraction level has risen. So you're not building a text control. You're not building UI controls. Whereas 20 years ago, step one of building a company was building all of those things.

46:30And so there's a lot to how important this is in terms of what you're able to do.

46:36Erik Torenberg:I want to talk about any other fundamental assumptions that might be interesting to revisit. Is it sort of, how about incumbents versus startups? You know, we've talked a lot about the innovator's dilemma. Does that, you know, now that these startups are, are these incumbents, you know, have the capital advantage? Are they able to do more? By the same time, we're seeing startups that you would think incumbents would just destroy. This is the crazy thing. If you would have told me, six months ago, you would have asked this question, say, like, what advantages do incumbents have? They have the same advantage incumbents always have.

47:04They have the capital. And they have the cash flow. And they have, like, whatever. Distribution. Yeah, distribution. And, like, what's crazy is AI, A, solves the distribution problem. It just solves the demand problem. And B, these companies are able to raise so much money that they're actually on competitive footing with like the Microsofts and the medicine and the Microsofts. And so I think we're in a very new territory when it comes to these new challenges versus the income specifically for these two reasons. You know, I think that the distribution point is often misunderstood how impactful it is.

47:40In the past, if you had a company and you wanted to get people to use your stuff, it was hard. You'd hire marketing. You have no idea how much like to invest and where and like you didn't know what you're getting on return on investment. But the demand is so unlimited for tokens and for GPUs, like literally you can just decide how much money you're putting into it in order to drive top of funnel and growth. And so the things that have typically been very, very hard for startups, you know, are much easier now. And I think this is why we're seeing such meteoric growth of the cursors, the anthropics, and the open AIs.

48:11That results in capital access, and that has put them on uneven footing. So very high space. And I think it's to your point about how hard it—look, my whole life was managing thousands of people of engineers to build things that couldn't be built anywhere else. Like, it was—the moat to build an operating system, it was infinite. And I think— You have to have one Cutler? Is that the— Well, it's—but it's—it really—read— That was mostly it. Yeah, I know. No, I get it. He's brilliant. But read the Steve Jobs in Exile book because you really get a sense for like building up. In fact, you know, of course, Next was famously just it took the code from Mock at Carnegie Mellon and started from there.

48:55We couldn't have done it from scratch completely. But this whole idea of just how important it is to think through the domain specific and how you disrupt people. because there was an old joke at Harvard Business School when Clay was still with us, which was they really, it's weird that they teach disruption as a theory in the business school when really it should just be a fact in the physics department. And I love that. I was there in 98 when he was writing the book and the paper and everything. That's when I was teaching. That's how I see. And I really, I used to be, of course, there's a lore with everybody who's from a big company in Silicon Valley or when you arrive like I did, the theory is always like, you always think, oh my God, we're just going to crush all of these little companies.

49:40You always think that when you're at the big company. And then you realize they never get crushed. And Ben always makes this point. Like, they just, and Mark does in his movie. It was AWS actually put out of business. Right, right, exactly. And because, you know, the startups don't aim straight at the incumbents. And the incumbents just don't pay attention. The incumbents are only interested in what the other incumbents are doing. Microsoft is worried way more about what Amazon and Google are doing than anyone in a startup space. But the elements of disruption that matter are the cultural ones of being a big company, and those are constant.

50:18Those are the laws of physics. And so you just can't change those. You can't change scorecards. You can't change field sales and go-to-market and compensation and org structures and legacy and customers. Because, you know, the way you behave, if you have, you know, 500 ,000 customers you're serving, there's a bunch of stuff you just can't do. Like you're just stuck. And that is really the essence of disruption. And that's why we're at a magic moment where it's not just that the culture is there like it always is, but the startup ecosystem. It's a reflection of what happened during cloud, which was a whole bunch of stuff that you needed.

51:02Again, it's this abstraction layer. You don't, you know, if you're a startup like you were, you don't have to go build a data center and build your own egress and call AT &T and do all of that stuff. Now you're up and running in the first hours of your first dinner. But the thing with cloud, I actually think it's, you actually articulated it very well. I'm going to actually use this because this is great. Like the thing with cloud is like, nobody thought they could put AWS out of business. Right, right. Like you just kind of accepted the oligopoly and you built on top of it. And the question is, is like, will they kill us in our little kind of pipsqueak corner.

51:36Will they just add you for free or for some price you couldn't add or whatever? I actually think, you know, and that's always been the question, will Microsoft have the app, right? But now these companies are actually taking on the incumbents. And you make this great point, which I actually had thought about it this way, but like, coffee's been defined by these very complex, large engineering efforts, building a chip, building a system. What's that, the age of the new machine? Yeah, yeah, yeah. The soul of the new machine, yeah. Beautiful book, right? It's talking about how hard it is to build like, you know, these systems, building an operating system, even in the cloud.

52:09Like, I mean, like Jeff Dean in that era of people were building these distributed clusters and they're the first time that people could figure out how to do that. And once you had that, this was a massive advantage. So these were these massive engineering efforts that no startup could do. And now for these models, it really is just capital access. So it's some very different laws of physics where like if you can amass the capital, you can do something like, I mean, you know, I mean, Google is Google. They have all the data. They have all the intelligence. and like their models are getting trounced by open AI and by Anthropik.

52:37It just goes... Because the cultural element. I think that people on the outside underestimate it until you've lived the cultural element of trying to do something. I'll bet it's cultural. It's not like a typical, you know, engineering problem. Like they're very good at it. Because they've out-executed, like GCP is fantastic. That's a massive engineering effort. And then also, I bet it's probably hard to free up that much capital for one of these companies, honestly. Well, all the big companies you can tell from their earnings calls how consternated they've been over the capital. You know, you have Google doing their bond deal to move it off balance sheet basically in some weird way.

53:13You know, you had the rumors of, I don't remember which company, you know, the rumors of like, well, they're rationing the tokens so that they go to the enterprise customers and not to the internal products. And so the internal products are AI starved. And of course, none of their competitors to those products are starved. And I just, I've learned to really appreciate the, I mean, look, I fought and fought and fought to not be disrupted by the mobile platforms, like by ARM, basically. And Intel just didn't care. You know, I came down here, I sat across the table from all the Intel leadership, and I pulled out the first Surface, and I said, here's our new computer.

53:57And they got very excited, and then they were like, but what's in here? I said well it's an ARM chip oh wow and you know like the fact that I even brought one into the building you know and it was very very tough and they just never felt that that was gonna that was like a chip used in a printer and also and they looked at me like we're ARM licensees we knew all and I'm like but it's the power it's the graphics it's you know always connected all of this stuff And the culture was they do Moore's Law at Intel. And just like with Google, they do hyperscale. So like if AI moves on device. Yeah, of course.

54:42Like that's not what they do. Yeah, sure. And, you know, with Microsoft, they were squeezed. They're squeezed now, you know. And I think you raise super interesting points about the opportunity, though, for. For sure. With this capital inversion. Yeah, go raise capital and go after the. Well, and it's not just, it's like, you're also saying, like, we're actually not going to question you if you're trying to raise that capital. Like, we're not going to look at you like you're crazy. And you just look at the raises that are happening, right? You know, these companies have been quite successful as a result.

55:15Erik Torenberg:The last thing, when Vishal came in and we had him on the podcast, he was sort of, he thought all of them were a great achievement, but he was bearish on their ability to invent new discoveries, particularly like scientific breakthroughs or things like that. And I'm curious if you think the sort of math progress is consistent with that or what is your latest thinking on sort of the limitations of the current sort of model architecture versus like, well, we need more. So here's kind of my new view, which is I think we know exactly how these things work. You put a bunch of data in them, they're stuck to that data.

55:54They can only do in distribution stuff, and they can move along that manifold in a perfectly Bayesian way. So we can say these words. And then the question is, okay, but what are the implications of that? What problems can it solve, right? I think it's just so hard for a human being to reason about a digital artifact, in this case the model, that was built with$5 billion. dollars. So like in the history of humanity, we've never created a single digital artifact that had that many flops and that much data in it. So on one hand, we know exactly how it works from a mechanic standpoint. On the other hand, that is so much data and it is so much compute.

56:35Maybe all of that stuff's already in there and it can solve anything that you want. And so, you know, the conversation has moved from the how do these things work? We know. Can it do out of distribution and stuff? No. Does, you know, is there transfer learning? Probably not. Like if IRL, one thing, it doesn't teach something else. Like is the singularity here? Probably not. I think everybody kind of, most, many people kind of agree on, like we're not in fast takeoff, you know, we're stuck to it being in distribution. We haven't closed it. We all agree about that. But what I don't think anybody knows is, okay, but you're still putting 10 billions of dollars in that thing.

57:15What's it capable of now? And if you consider this meta-economic machinery, which means the ability from Anthropic to raise lots of money and then pour all of that money into this thing to create this super powerful thing, I don't think any of us can predict what that means and where that goes. And so it's a different conversation, but the question is the same. It's like, will that be able to cure cancer? Maybe. But if you put$20 billion into something, maybe it can cure cancer effectively. And that's where I think the discourse has evolved and where it is now. And I honestly have decided that I cannot predict but an artifact that was, you know, that like you used$20 billion to create is capable of.

57:51Look, I think it's just so important. It's important for people who are deep in watching everything that's new to admit that they can't predict. And I think that that's great because it turns out, like, I wrote 58 memos on what the internet was going to be. And I was wrong a lot of them by far. But I do think on the, and I think - But even this one is a little different. This is like, I take$20 billion and I put it into a model. And then you and I look at that model and we can do whatever we want. I don't think we can comprehend what that even means. There's so many flops and so much data. Like, I don't know what that's capable of.

58:28And I think we are. I think that that's really true. And I think, but I will say on biomedicine in particular, like the other half of my household is a research doctor who uses AI. We have a Spark at home and she's loaded. A Spark? Like a sun spark? No, no, an NVIDIA spark. Oh, yeah. No, no, no, not with a C or the K. Oh, yeah, wow. We were in old times there. I was like, not a Scott McNeely spark, no, no. Not a Scott McNeely spark. No, and like it's all AI. Like she does brain stuff and surgical brain stuff, all AI. And it's so interesting to see, Because what it really can do is it just, it sees the patterns that you can't, that only experience could tell somebody.

59:19But if there's 10 ,000 papers on a topic that's part of her model, then, like, it's just finding the patterns that you just, that no one has. And that's a pretty basic AI capability at this point. But it's actually opening up solutions or problems or research directions and things like that. I will say just for the, like, this is not a magic to discover drugs because the hard part of drugs has always been candidates. Not candidates. It's always been efficacy and safety. The candidates have, since the 80s, have been able to develop more than we could test. It's human patients and it's very, very, very hard.

59:56Can I please tell you something that I got wrong on this? So I love the question that you asked, which is how's our thinking evolved on, like, you know, whether these things, you know, like their capabilities in generality, which is I was responding to this Bostrom notion of recursive self-improvement, fast takeoff. You create one of these things, you step back, and it takes over the world, right? And so I kind of pooh-poohed that because that's clearly not what's happening. And I think a lot of people agree that that's the case, right? But here's what I got wrong. What I got wrong is I did not know that we could effectively just continue to pour money in this.

1:00:32Like the scaling laws are holding. And I don't know what it means to just, let's say we do a$100 billion training run, to like have this thing that you're putting$100 billion in. And then that money comes from this meta-economic machinery that may be able to want to solve whatever. They may want to solve cancer, but they may also want to create a weapon. Like, who knows? And so this concentration of this many resources in a useful way, I think, is very new. I don't think we understand the implications. I think you could reasonably argue that that's very dangerous if you kind of apply that$100 billion in the wrong way.

1:01:07So I think that's kind of where this conversation needs to evolve to. So less the foom, you know, and more the what does it mean to be able to concentrate resources. Which also was, I mean, you're basically talking about exponential growth. And this is just exponential in dollars. Yeah. And we all know none of us can model exponential very well. Yeah, but we've never been able to do that. Like complex engineering projects, you were not like tackling one problem with a lot of money. You're just kind of building this machine. Well, we, you know, you're right. You're 100 % right. And I completely agree.

1:01:46But just, I remember just sitting in meeting after meeting, Intel saying we have five gigahertz, we have this many gigahertz, this many transistors. And literally, nobody knows what we're going to do with them all. No, you're building the machinery. I'm saying in this case, if you're like, I want to exhaustively explore every protein combination, we can just turn that into a money problem. It's kind of very strange. Which is a great way to say it, that we can take previously infinite problems and apply capital, and it becomes finite. It just makes it a capital problem and not an engineering problem, which is just a very different loss of physics.

1:02:19Erik Torenberg:Yeah, yeah. Let's wrap on that because it's 2.30. It's been a great episode. Thank you guys for today, bro. Thank you. Thank you. Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z and subscribe to our sub stack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund.

1:03:05Erik Torenberg:Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16Z.com forward slash disclosures.

1:03:25Thank you.