The New Economics of AI | Martin Casado & Steven Sinofsky

a16z General Partners Martin Casado and Erik Torenberg are joined by Board Partner Steven Sinofsky to explore what recent breakthroughs in AI and mathematics tell us about where the technology is headed, and whether some of the basic assumptions that have governed computing for decades are starting to break. Martin and Steven debate whether AI's progress in mathematics represents a genuine leap in reasoning or simply a new tool for solving problems at a higher level of abstraction. From the four-color theorem and early computers to graphing calculators and today's models, they trace how new technologies have repeatedly changed which problems humans need to solve themselves, and ask what makes this moment different. The conversation then turns to one of the biggest shifts in AI: problems that were once constrained by engineering talent can increasingly be attacked with capital and compute. They discuss what that means for startups versus incumbents, venture capital, the coming wave of AI applications, and why pouring billions into increasingly capable models may force us to rethink what these systems can ultimately accomplish.

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

• 0:00Martin Casado

  Right now, if I give 20 people a billion dollars, they can actually use it usefully. We've kind of moved the industry from like this engineering bound problem to a capital problem that's fundamentally very different.

• 0:11Steven Sinofsky?

  Math is very much a leading edge indicator of what the market might be interested in, why.

• 0:17Martin Casado

  Some people will walk in and say, the foundations to AGI and to reasoning is going to be math, but like that doesn't tell you anything about reality. For me, it's still in the domain of like, it's really good at playing a game.

• 0:28Steven Sinofsky?

  The startups don't aim straight at the incumbents and the incumbents just don't pay attention. Microsoft is worried way more about what Amazon and Google are doing than anyone in a startup space. Everybody who's from a big company in Silicon Valley, you always think, oh my God, we're just going to crush all of these little companies. And then you realize they never get crushed.

• 0:46Martin Casado

  And I think this is why we're seeing such meteoric growth of the cursors, the anthropics and the open AIs.

• 0:53Steven Sinofsky?

  Although capital is scarce and it's hard to get and all of these other things, once you get it.

• 0:58Erik Torenberg?

  For most of modern computing, the bottleneck was engineering. AI may be turning it back into a capital problem. In this episode, I sit down with Martine Casado and Stephen Sinofsky to ask whether AI is changing some of the fundamental assumptions we've built up over decades of computing. They start with AI's recent progress in mathematics, what these breakthroughs actually tell us about reasoning, why mathematicians are paying attention, and whether math is a leading indicator for where AI creates economic value. From there, these amount to a much bigger shift. For decades, giving a small engineering team vastly more money couldn't make them build vastly faster. Today, a team of 20 can productively deploy enormous amounts of capital into compute.

• 1:41Erik Torenberg?

  Martine and Stephen explore what that inversion means for startups, incumbents, venture capital, and what happens when previously intractable problems can increasingly be turned into capital problems.

• 1:56Erik Torenberg?

  Well, Martine, when you're not making major acquisitions or having big news, you're also very curious about what's going on on the frontier of AI. And we're actually going to start getting into math to personal. Thanks for both of you making time to come on the podcast.

• 2:08Martin Casado

  Happy to be here. It's great.

• 2:09Erik Torenberg?

  Jared Sumner tweeted a few days ago, something along the lines of how he told Claude to try to solve the Riemann hypothesis and to try harder. And I don't know if there was actually any progress made, but it's part of the larger conversation around, hey, it seems like there's some accomplishments that are being made. How do we make sense of this in terms of what is actually happening and what does it mean for math?

• 2:30Steven Sinofsky?

  I'm no mathematician at all, but I mean, I think it's an important moment because it sort of divides the world into two groups. The groups that are just very, very excited that, oh my God, these things are being solved. It doesn't matter if you understand, actually, nobody understands. The number, the universe of people who understand what these things are is very small. And then there are the people who are just like, oh, it's fake. It's going to put people out of jobs. Then no one's going to know the future of where these fields go. And the most interesting thing about it is the group that's most excited are mostly the mathematicians. And so that actually confuses everybody because if you're of the school of the people who are like, it's going to put people out of work and we're going to all get dumber and it's the dawn of idiocracy because computers are doing all of our work.

• 3:13Steven Sinofsky?

  You're confused that the people who are impacted most by what this level of AI did are the most excited. I think that is itself shining a light on this moment that we're in right now.

• 3:25Martin Casado

  You're talking to two systems, guys. Two product guys. Is it going to get like, we're like, I have the same caveat. I feel there's some things like we're actually both very expert on, this is not one of them. So I'm going to kind of, from the peanut gallery, I've got two comments. So one of them is, okay, so I view economic utility to be a very important measure when you're talking about AI, right? So I was trying to think, there's a lot of hours been trying to solve some math thing, right? But if you sum up the entire postdoc salaries of all the people that have been working over the years on these problems, it's probably not very much. And so part of me is saying it's great that there's these capabilities. I'm not sure that the fact they've been longstanding is that much of an indication

• 4:08Martin Casado

  because there hasn't been a huge economic incentive in order to solve. Now, that doesn't mean that it's not hard or whatever. I just don't think we have that validation of this unlocks some deluge of economic value. And the second one is, it's kind of not surprising to me that AI is very good at solving almost purely axiomatic domain that, you know, requires knowing a whole bunch of different things and putting the solutions together from very disparate spaces. Cause often really when I read, so I've been reading all these, like everybody else has been obsessively, they're like, oh, like it came up, the solution is, yeah, the solution was pretty straightforward. It's just like, bar from a bit of math that I didn't know.

• 4:50Martin Casado

  And so I think if there's like a meta learning here, the meta learning is there is a set of problems that probably require you to be too broad for most humans or most education and it's going to solve those. It's clearly very good at solving axiomatic systems, but I don't think it provides a strong indication of, is this solving things that the market hasn't been able to solve? Cause there really hasn't been a market around these. And so I think that's the next question for us to answer. So very exciting. Seems kind of reasonable, understandable, not sure what the longer term implications are.

• 5:21Steven Sinofsky?

  I do think that there's something interesting that math is very much a leading edge indicator of what the market might be interested in, why. I remember when I was in school, like there was some big thing that someone at AT&T invented a new algorithm, like a new program for doing linear algebra, like a new way to solve linear algebra, which is super important right now in the AI world. But his big thing was, well, now we can just calculate like the United Airlines flight map in three hours less time than we could last week.

• 5:51Martin Casado

  Right, but let's take it as, so it's just not clear to me that the problems being solved are those that are roadblocks to like existingly economically useful tasks.

• 5:59Steven Sinofsky?

  Right.

• 6:00Martin Casado

  And if they were, it's not clear to me that they wouldn't have been solved. Like postdoc that's been ruminating on a problem, getting paid 30K a year for five years, like it's very different than like the market has decided that this is like the one thing to unlock. And maybe they're there, maybe these problems that are being solved are like the key problems to unlocking some big economically productive use case. I just haven't seen that yet. So that for me is like the next thing I'm kind of looking for.

• 6:26Steven Sinofsky?

  I don't even know what 12 dimensional space is or what that means. And so like, I'm completely with you. Like, I don't even know what problems are in 12 dimensional space. Are you very skinny or are you very tiny? I'm really confused by that.

• 6:37Martin Casado

  And maybe I'm wrong here, but for me, it's still in the domain of, it's really good at playing a game.

• 6:41Steven Sinofsky?

  Yeah.

• 6:42Martin Casado

  Like, this is the best Starcraft player ever, which is cool and is very powerful. But like, I have a hard time connecting that with, A, like, maybe the reason we didn't have them before is because there just wasn't economic need and B, like, how does that actually map? And so, listen, there's a huge range of these things. We get pitches all the time. Some people will walk in and say, the foundations to AGI and to reasoning is going to be math. And once you do that, you'll be able to answer every question because the universe is based on some fundamental mathematical principles. And once you understand that, you understand everything. And then there's other people candidly that walk in the door and they're just like, listen, that's great, but that doesn't tell you anything about reality. And so I think that there's more work to do and this isn't just about getting better in math.

• 7:24Steven Sinofsky?

  I do think what's interesting is that part of the reason that mathematicians are very excited about it though, is because they work a certain way. If you work in history, there's basically no abstraction in history. There's just a bunch of facts. And then people develop sort of these models that you can think of almost as force diagrams. They explain war or famine or whatever. Whereas mathematicians and mathematics has this super long historic arc of layering on abstractions after abstraction. Don't worry, we'll get to OSI in a minute. But this idea that why they're so excited is a bunch of math, all of a sudden becomes a new level of abstraction.

• 8:00Martin Casado

  Is it true that they're, so I've found that it's a mix. I've found that some are very excited and some are in an existential crisis. The ones that are excited, they basically say, listen, it solves 20% of my job. Is it 20% I didn't like anyways? So this allows me to explore a new frontier that's very important or whatever. And what I've always wondered is, is that a function of the type of problem being solved? I just can't imagine if AI came and solved cancer, like whatever, someone that works on cancer would be like, oh, I'm so existential depraved. This is amazing.

• 8:33Steven Sinofsky?

  It is true.

• 8:33Martin Casado

  But let me, I, we're on the other end of always have this math problem. I'm so depressed, I saw the math problem. Maybe like literally the entire utility of that problem was keeping somebody employed to solve the problem.

• 8:44Steven Sinofsky?

  Or just writing articles in the back, like another attempt at, and here's why I went wrong. So let me offer it this way.

• 8:51Martin Casado

  There's nothing on the other side of the solution. And so like, we're depressed because like now, like whatever, like this useless activity is gone.

• 8:57Steven Sinofsky?

  Let me, let me stop.

• 9:00Martin Casado

  No, I mean, too cynical on this thing. I love math, it's great.

• 9:03Steven Sinofsky?

  I think, I think we caught you. But like, being a little cynical, but not really, but more that it's just, let me take a side of it this way. Looking at the history of computer science. Because I had to take this class, which I looked at all the course catalogs for a bunch of schools, you know, to take it anymore. That was like discrete math.

• 9:18Martin Casado

  Yeah, yeah.

• 9:19Steven Sinofsky?

  Or algorithmic complexity theory, which was a required class for a very long time. And now.

• 9:23Martin Casado

  Do you remember Concrete Mathematics?

• 9:24Steven Sinofsky?

  Oh, yeah, yeah.

• 9:25Martin Casado

  From Donald Cano's, like from.

• 9:26Steven Sinofsky?

  I mean, you're a Stanford guy, I'm not, but like, I stayed at Ag school, we didn't have that. But that's a Cornell joke for us Cornelians. But my class, I got taught by one of the luminaries in the field of algorithms, ironically, a Stanford PhD. John Hopcroft.

• 9:39Martin Casado

  Of course.

• 9:40Steven Sinofsky?

  Who invented, for the people who are pragmatic, invented like two, three trees and a bunch of stuff. As his thesis at Stanford.

• 9:45Martin Casado

  Absolute legend.

• 9:46Steven Sinofsky?

  But John was our professor in all this crap. And we had to learn like all this P equals NP stuff. And I remember like, this is the four.

• 9:53Martin Casado

  Is that the four color theorem?

• 9:54Steven Sinofsky?

  This is the four color.

• 9:55Martin Casado

  Proven by computers.

• 9:56Steven Sinofsky?

  No, but that's where I'm going.

• 9:57Martin Casado

  You just buried the lead.

• 9:59Steven Sinofsky?

  Yeah, but, but like, so those of you that don't know, we had to take a whole course in college on, that basically boiled down to this problem. And the interesting thing is why? And it was because the theoreticians had postulated that if you can solve this problem in, in algorithmic, in, in exponential, in non exponential time, in polynomial time, then you could solve all these other problems, like the traveling salesperson problem and all these other problems. Much, much faster, which mattered because all of our computers were just so compute bound. So if you were the AT&T people that gave, you know, like, here's our node of like 6,000 switches. Like, how do you route optimally? You'd be like, well, we don't have enough, that's like two years of running the simulation to solve this.

• 10:43Steven Sinofsky?

  And, and so it turns out that one of the interesting things was, they proved the four color theorem. But they did it.

• 10:50Martin Casado

  Which, by the way, I mean, just, the four color theorem says for any 2D planar map, you can color it, you can use only four colors. So such that no two adjacent areas have the same color, right?

• 11:01Steven Sinofsky?

  Exactly.

• 11:01Martin Casado

  And, um, uh, and you only need four colors. You'll never need five colors.

• 11:06Steven Sinofsky?

  And, and we learn it just so people, like you kids know, that's literally how we learned it. And we could all repeat it like that. It's this very weird imprint over this problem. And so what, what sort of happened was, no one ever arrived at a, at a, basically what you could think of as like a proof that looked like calculus. Instead, what they did is they actually proved that the number of potential solutions was finite.

• 11:28Martin Casado

  Have you actually seen the proof?

• 11:30Steven Sinofsky?

  Yeah, yeah, yeah.

• 11:30Martin Casado

  200 pages of combinations.

• 11:32Steven Sinofsky?

  But they basically proved that you, there's a finite number of them. And then they just computed all of them and said, look, it's only four colors. And so it was this sort of bank shot. Proof, but it was only possible because of compute.

• 11:44Martin Casado

  And to your point, that was actually very, very useful in the, the, the, the practical application.

• 11:48Steven Sinofsky?

  Right, right. And certainly as a topology person.

• 11:50Martin Casado

  Like setting, setting strong bounds and, and things like that. So that's actually.

• 11:53Steven Sinofsky?

  And I think, I think that that, to me, was just a really good lesson in, in, when you have like a new level of abstraction that says this is a whole class of problems that can be solved. You can then build tools working at that level of abstraction. And everybody doesn't have to start from like, okay, what's the two, three tree representation of what we're doing?

• 12:14Martin Casado

  So I, it's hard not to get philosophical when you're talking about AI. So I'm going to get philosophical and you can tell me to shut up, but I, I just can't, like you, you kind of do. So, so the math, this math thing seems to me a, a little different because like, it kind of begs the following question, which is, will math ever be representative of physical phenomenon, right? Like, has anybody ever like taken a bunch of equations and actually predicted something like physical? And I don't know the answer to that. Like, so I worked in these large simulation codes and these large simulation codes are actually, um, trying to compute physical phenomenon, like the explosion of a star or like, you know, what would happen to like, whatever, an airplane in like, uh, an, uh, an air simulator or a wind, uh, wind simulator.

• 12:59Martin Casado

  But all of the, and even though they're just calculating, these calculating these like large, you know, differential equations, they were all based on empirical results.

• 13:07Steven Sinofsky?

  Yeah.

• 13:08Martin Casado

  Like you're literally the equations of state for the.

• 13:10Steven Sinofsky?

  Well, they're a model, they were just, they were like, we can measure temperature in these places.

• 13:13Martin Casado

  That's exactly right. So it was all, it was all based on empirical equations of state. And so I've always wondered like, like, is simulation computationally irreducible? And so you actually have to actually run the simulation. In that case, it's not clear to me to what extent AI helps. Like, I know people are trying to solve this problem with AI, but like, I don't know if these math, these math answers have any impact on that type of stuff, right? So maybe there's some separate algorithmics domain to, to your point where they do, or, you know, maybe like, like modeling or logistics. But when it comes to like, you know, will, you know, will this star explode? Will this building stand up? Like the actual simulation, I think these things, things are pretty disjointed.

• 13:57Martin Casado

  And, and then I read a lot of these discourses on the, the math solutions. And there's kind of these claims where if it can solve all math, you can predict anything. And I just think that that's a huge, huge logical leap, which is not clear to me that is, is, is, is obviously true.

• 14:13Steven Sinofsky?

  Yeah.

• 14:13Martin Casado

  Or, or there's any indication it's true at all, like.

• 14:16Steven Sinofsky?

  So the way, one way that I think I might, um, talk about that, you know, again, like this is so out of my league on the actual math.

• 14:23Martin Casado

  This is one of the two systems people talk about.

• 14:27Steven Sinofsky?

  I'm inherently a tool, I'm inherently a tools person. And so I kind of get this part of it, which is that what's happened is, is that, that AI might not be the next tool to solve math problems at, at some scale that matters. But it might lead to the development of a new kind, a new level of model. And so I brought like Prost to show this off. So of course, this is the original math tool. And so before something like this, this is a, you know, one of these real ones from like Beijing, Marko.

• 14:57Martin Casado

  Oh, it's like actual.

• 14:59Steven Sinofsky?

  You know, it's the ones they tell tourists, friend, but, but I'm very proud of that because I negotiated it down to like seven cents. But, um, but, but you know, that became a level of abstraction. And all of a sudden, like you just had this basic math thing. And then you just fast forward a whole bunch. I brought this because it's just so freaking cool.

• 15:16Martin Casado

  What is?

• 15:17Steven Sinofsky?

  So this, everybody knows what slide rules are, you know, nobody knows how to use them. This is called a Kurtup, and which is an Austrian, uh, basically it's a round slide rule. And so it's like a coffee grinder or a pepper mill. And you, you, you have all these ways, you set the numbers on the side and then you turn it one way to add, another way to subtract.

• 15:37Martin Casado

  Whoa.

• 15:37Steven Sinofsky?

  And it's, this thing is, it's like.

• 15:39Martin Casado

  Is that used for like multi-number arithmetic? Or is it used for stuff like, like logarithms?

• 15:45Steven Sinofsky?

  No, it's only arithmetic. I think, but of course, it depends on how you use it. But, but, um, it, it, it's from the, the 20th, mid 20th century, I think. And, uh, my uncle brought this back from the war. And, uh, but it's, what's incredible is this is like 600 pieces of machined metal. Inside this, it would cost like $50,000 to make one now.

• 16:07Martin Casado

  Do you try to use it?

• 16:08Steven Sinofsky?

  I, I actually did, but I'm not going to try to do it. I, I actually, for prepping for this, so I wouldn't be a complete moron, just go, who could I have? I actually went through the trouble of learning how to use it, although it's been sitting on my shelf for years. But it's, um, but the interesting thing is, you know, then all of a sudden, a whole new level of problems get solved. And.

• 16:24Martin Casado

  Wait, so you're saying that, that the new model is the new calculator or the new graphing calculator?

• 16:28Steven Sinofsky?

  Right.

• 16:28Martin Casado

  Or the new, I, I actually remember when like, remember the TI-85?

• 16:32Steven Sinofsky?

  Oh, of course, yeah, yeah.

• 16:33Martin Casado

  I remember when that came out, they're like, all of the math teachers had this crisis. And like, you know, we used to give you a piece of paper where you plot the X, Y equation. Now they could do it on the calculator and they can solve equations and our field is dead.

• 16:46Steven Sinofsky?

  But, but, but what's interesting is, this is why it's so important to AI today. Those people didn't complain about when calculus came out, because calculus was a baseline to them. And, and what it is, is there's this notion, this, this people react to change more than they react to the baseline of where they all started. And so, so much of like the concerns in a, in a, in a, like, I lived, I literally got like a, uh, the TI-35 were the first calculators in schools. The only advanced math they did, they had a percent key and factorial, which we didn't even know what it was. And you, you could do like 59 factorial and that was the max that you could display. And, you know, I went to college and the classes were no calculators allowed.

• 17:27Steven Sinofsky?

  The whole, I was on that, I, my whole life I've been on the cusp of allowed and not allowed for everyone.

• 17:32Martin Casado

  I was, I was there for the graphing calculator.

• 17:34Steven Sinofsky?

  Oh, the graph.

• 17:34Martin Casado

  They literally have a blue book.

• 17:36Steven Sinofsky?

  Yeah.

• 17:36Martin Casado

  Just to show all of your work, just to show that you weren't plugging it into the.

• 17:40Steven Sinofsky?

  See, I missed the graph.

• 17:41Martin Casado

  Which most of us were like actually writing video games in the back and could care less about its ability to write math, but.

• 17:46Steven Sinofsky?

  Absolutely, absolutely. But, but you just play that backwards and you realize that after, you know, after these guys, you went through this march of, of algebra and then linear algebra and then calculus and, you know, all of, and then, you know, and then with calculus, then you ended up with Fourier transforms and fluid dynamics. And all of that was first to your earlier point, were all based on need. I mean, so much of this math.

• 18:09Martin Casado

  Well, all of computer, computers are basically from difference engines, which are just trying to calculate integrals.

• 18:14Steven Sinofsky?

  What, and, and but, and of course, but to be really clear, to calculate integrals so that we could shoot missiles and, and cannons at each other.

• 18:22Martin Casado

  That's, okay, so that's what, yeah, yeah, yes.

• 18:25Steven Sinofsky?

  Which I'm not judging it, I'm just saying.

• 18:27Martin Casado

  Well, yes, I don't mean to be pedantic about this, but like, it's one of my favorite parts of history. It actually started with, with tides, which also got massive economic value, which you're trying to calculate the tides. And this is where you kind of had like the, the old, you know, um, and then that, those architectures got co-opted into, of course, the war effort for the logarithms for that. This is what Anthei came from. It's just great. Actually, it was very interesting. Anthei was about 5,000 times faster than a human being when it came to like, you know, doing this. And then of course.

• 18:58Steven Sinofsky?

  And it didn't make mistakes, which was sort of the.

• 19:00Martin Casado

  But it was, but it was very specifically math and it was very specific for economic utility. And the, the interesting question to me is, is like, these models are clearly good at a type of math. It is, is it one that has somehow blocked some sort of economic?

• 19:12Steven Sinofsky?

  Yeah.

• 19:12Martin Casado

  And I don't know of the answer to that.

• 19:14Steven Sinofsky?

  Oh, yeah, I, I don't, but I think it's super interesting to keep going with that because to me that it's so, what's so cool is that, that, that, doing that, that basic calculus for the war and making those missile tables and, and things like that. Then it unlocked the space race, basically, and jet engines and factory automation and all of these things. And, you know, people were cheering that on. Like that, to me culturally is the most interesting thing. Like there was just this, not only were they cheering it on, they were, every parent was looking at their kids saying, go learn that in school. Go, go win the Westinghouse competition, go win the GE math competition.

• 19:51Martin Casado

  Is that because of the Cold War? Was it because?

• 19:54Steven Sinofsky?

  Well, obviously, the Cold War was a big cultural part of it for sure, but it was just a general, the future. I like, I found this incredibly cool brochure from IBM. It's from 1953. So 1953.

• 20:08Martin Casado

  Do you just have this stuff in your house?

• 20:09Steven Sinofsky?

  I just stumbled across it. Like this, this one I just got. I can't even believe this exists. But this is like, this is a brochure about the future of computing.

• 20:18Martin Casado

  Yeah, I want to see it.

• 20:19Steven Sinofsky?

  But, but, but like, first, you gotta like, it's, it's got like nuclear, like the whole thing. The future of computing is like a guy with like atoms racing around his head. Oh, wait, we're zooming in and doing the Carol Merrill thing. So, but the fascinating thing is it's from 1953. So you're ENIAC and that point, like, that's it. That's the computer at the time. This is pre 704, pre 370. And so it's a brochure from IBM explaining what a computer might be, not even is. And it's like, it took millions of years to invent and recognize the usefulness of the wheel. That's the opening sentence of, of, of the, and, and, but like you, people were eating the stuff of it. But here's the part that I want to get to. It talks about computers and it's the two families of computers.

• 21:03Martin Casado

  Yeah.

• 21:04Steven Sinofsky?

  And so of course you get the slide rule.

• 21:05Martin Casado

  Yeah.

• 21:06Steven Sinofsky?

  And that's explaining the history. And what this is really leading up to is we could do this for text too.

• 21:12Martin Casado

  Yeah.

• 21:13Steven Sinofsky?

  And so the idea, I mean, like, imagine who's reading this in 1953 that it has to explain hex and decimal and binary? And compare it to Roman.

• 21:20Martin Casado

  That's amazing.

• 21:21Steven Sinofsky?

  And because like, nobody, like, nobody knew.

• 21:23Martin Casado

  Who's the name of that thing?

• 21:24Steven Sinofsky?

  It's just called IBM Light on the Future.

• 21:26Martin Casado

  I love this.

• 21:27Steven Sinofsky?

  Rocket test tube-ish spotlight looking thing. And it's incredible. There's like oscilloscope waves in the back. It is the most incredible thing. It has this dictionary in the back. Imagine the first time someone explains a computer and the dictionary, the dictionary is, you know, arithmetic unit, binary digit bit, you know, like cathode ray tube, electrostatic storage tube. And, you know, but, but the thing is, is the reason I opened this, because there's one cool page that really matters. What is the organization of digital computers? And so this is the thing that I gets to this point about abstraction for us and AI.

• 22:03Martin Casado

  Yeah.

• 22:03Steven Sinofsky?

  This, these have been for, for 75 years, how we thought computers were organized.

• 22:09Martin Casado

  Yeah.

• 22:09Steven Sinofsky?

  Input, storage, arithmetic, control and output.

• 22:13Martin Casado

  Yeah.

• 22:13Steven Sinofsky?

  And that's all, that's what we learned in school. You took courses basically in each one of those. Last night we were going back and forth on the abstractions that will remain in computer science. And you tossed in networking, which is sort of control.

• 22:25Martin Casado

  Yeah. Everybody forgets networking, by the way. Of course that was.

• 22:28Steven Sinofsky?

  Well, because most people stopped worrying about networking.

• 22:32Martin Casado

  Stop worrying about it as soon as the packet leaves the computer.

• 22:35Steven Sinofsky?

  Well, and probably, I would say, you know, the late 90s was the end of basically a mandatory networking class. Because, because like, but it was solved.

• 22:44Martin Casado

  Yeah.

• 22:44Steven Sinofsky?

  Like, like there was no, you know, it was, for me, it was, it was the transistor. I was like the last time that computer science majors had to know what a transistor was. And trust me, I actually don't get what one is now. It's like a triangle symbol.

• 22:56Martin Casado

  Oh.

• 22:56Steven Sinofsky?

  But, but the interesting thing is those abstractions led to, okay, so now we have those abstractions. There were basically fields that did each one of them. Like you spent 20 years of your career on storage.

• 23:06Martin Casado

  Yep.

• 23:07Steven Sinofsky?

  And you watched the march from, from tubes to drums to spinning discs to tapes and so on. And, you know, if you did output, you watched the invention of going from a teletype to a line oriented teletype, to a terminal, black and white, to color, to vector, and the whole deal. And all of those were the fields and they all rose in parallel. NECS department, which came out of the MAP department because of the missiles,

• 23:31Martin Casado

  Yeah.

• 23:32Steven Sinofsky?

  ended up being like departments made up of those things. And then it all collapsed and produced us to the systems group.

• 23:39Martin Casado

  Sure. Yeah. Yeah. Yeah. So let me just push on one angle of this.

• 23:42Steven Sinofsky?

  Sure, sure.

• 23:43Martin Casado

  Cause I, I, listen, I loved, I clearly love the framing and we move up in abstraction. And every abstraction, there's still a set of problems, which is a higher level of abstraction. But I still think this kind of notion of economic need is very.

• 23:53Steven Sinofsky?

  Oh, yeah, yeah. Right. So for example, um, we, we, you know, Bletchley 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.

• 24:08Martin Casado

  Um, 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, so at that point, these things were lauded as like saving humanity. Everybody was super excited. All the physicists loved computers and used computers. And for me, the thing about the current solving math is I don't know what that thing on the other.

• 24:38Steven Sinofsky?

  Oh, yeah.

• 24:38Martin Casado

  No, but the other side is, and I do think we've had that in the past. And so, you know.

• 24:43Steven Sinofsky?

  Well, we did. We had the AlphaGo moment. It was the same thing.

• 24:46Martin Casado

  Yeah.

• 24:46Steven Sinofsky?

  Like we, I, we did a podcast, not in this room, but.

• 24:49Martin Casado

  But even before AlphaGo, we had like, remember when chess, we had Bitten and chess. Yeah.

• 24:53Steven Sinofsky?

  Right. We had the IBM chess thing, whatever it was. And Frank Chen and I, we did this podcast, the AlphaGo. And we had to try to make people understand like why it was a good idea. And.

• 25:01Martin Casado

  And I think it's actually pretty reasonable for us to ask the question, which is there's things that these things solve. And, 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, which I think people.

• 25:16Steven Sinofsky?

  Well, I think, I think.

• 25:18Martin Casado

  Don't have like as positive view. And I would submit that's because it's almost like solving the problem had become the, the end.

• 25:26Steven Sinofsky?

  Yeah.

• 25:26Martin Casado

  As opposed to the actual end.

• 25:28Steven Sinofsky?

  But.

• 25:28Martin Casado

  As we should maybe all step back and be like, if you're really like sad about something being solved, maybe it wasn't worth working on to begin with.

• 25:37Steven Sinofsky?

  Right. And so, so look, of course.

• 25:39Martin Casado

  You know, and you're like doing the Sandman dollar and like you take inner peace or something like, but that's not moving the economy forward.

• 25:44Steven Sinofsky?

  Right. Well, we're both, look, we're systems people, but I'm actually an apps person.

• 25:47Martin Casado

  I know, I know. Yeah. We're both systems people.

• 25:50Steven Sinofsky?

  But, but like, I.

• 25:51Martin Casado

  Core system.

• 25:52Steven Sinofsky?

  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. Um, so Kane wrote the book, but it's with Cat Moll, who at Pixar and with Dana Lewin, who was at Steve's super good friend and was also at Microsoft. They all, they, 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.

• 26:37Steven Sinofsky?

  But it pointed out very clearly, you know, the, the next was actually the machine that Tim Berners-Lee used to write the HTTP protocol. Right. So he actually.

• 26:46Martin Casado

  A next machine?

• 26:46Steven Sinofsky?

  And he used the next machine.

• 26:48Martin Casado

  That's interesting.

• 26:49Steven Sinofsky?

  And it's super interesting because, because nobody knew what this machine was for or what it did. But 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 other researchers and to share papers and, and like, eh. Like, and, 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, uh, or 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. They did that. And they had actually pizzas in the back in case it didn't work.

• 27:31Steven Sinofsky?

  And, and I should say for next step or open step. But, um, but the idea was that, that, that, 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?

• 27:47Martin Casado

  Yeah. Your point is not everything we've known how to use. So I, I'm a little focused on like, there was a solution on the other side that people were going for. You're making a point that there's a lot of platforms that get built where that's not clear, but clearly.

• 27:59Steven Sinofsky?

  Well, the, the, she was like, I will, I will show, here's my, probably one of my last visual aids for today. But like, the word processor came out and, and, 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're just type. And the people, once you use a computer, the idea of typing really, really does, didn't work anymore. And, and so some people at law.

• 28:26Martin Casado

  Well, you have to show it now.

• 28:27Steven Sinofsky?

  Yeah, I will. I'm just, I like building up. But these people at Harvard Law School, they brought in the first laptop. So that's the first laptop.

• 28:35Martin Casado

  Weren't those called lugables?

• 28:37Steven Sinofsky?

  Well, no, they, this was just called, this was literally just called an Osborne. And it was the only one. So, so as a guess, how, Eric, you're, you're a kid. How, how much, how was long was the battery life in this?

• 28:48Martin Casado

  Um, not long.

• 28:52Steven Sinofsky?

  There was no battery. This giant case that weighed 25 pounds, there's no battery in it. It just plugged in. But that was the trick question. Cause every time I've ever logged mine out, I have mine from college. Like people are like, well, how long does the battery last? And, 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, in my senior year of high school, it got banned from Harvard Law School. So some, someone showed up to do their exams. So at Harvard, they used to bring your typewriter to exams because that way the professor could read it. And it, and two kids brought, brought computers in. One brought an Apple II and one brought the Osborne. And then the school banned them.

• 29:30Martin Casado

  Wow.

• 29:30Steven Sinofsky?

  They just said, this is, and for all, the, 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.

• 29:41Martin Casado

  Yeah.

• 29:42Steven Sinofsky?

  Don't listen to rap music or don't read, don't, no jazz.

• 29:46Martin Casado

  Yeah.

• 29:47Steven Sinofsky?

  Or.

• 29:47Martin Casado

  Don't play Dungeons and Dragons.

• 29:49Steven Sinofsky?

  The, the arguments that are going on now. Three years ago, I, I tried to get Cornell to use AI in, in freshman writing. When the first, and, and they just stopped talking to me.

• 30:00Martin Casado

  Wow.

• 30:01Steven Sinofsky?

  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 could also think of it as a level of abstraction. Cause like, no one's going to college now without a computer. Like.

• 30:30Martin Casado

  Can I, can I, can I, can I just put?

• 30:31Steven Sinofsky?

  Yeah, no.

• 30:31Martin Casado

  So, so I, I agree with you, but let me.

• 30:34Steven Sinofsky?

  Right, right.

• 30:35Martin Casado

  Every once in a while, I'm like, well, maybe it's a little different. So here'd be the argument. Um. I don't think in 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 the compute network and 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. Where in, 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, like, I'm not even really sure what the question is.

• 31:20Steven Sinofsky?

  Right, right.

• 31:20Martin Casado

  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 abstraction that like you have a computer and like it's a human being. That's kind of defining everything about the problem statement. It feels a little different.

• 31:40Steven Sinofsky?

  Well, it definitely feels different. Here's, I also think for me, graphing calculators felt different. To me, graphing calculators felt like cheating. And, 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 where 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.

• 32:18Martin Casado

  I remember the first time I saw Mathematica, I'm like, this stuff has blacks.

• 32:21Steven Sinofsky?

  So Maxima is, it was, 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 Mu Math. 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.

• 32:52Martin Casado

  So you don't think that.

• 32:54Steven Sinofsky?

  And that was cheating.

• 32:54Martin Casado

  Let me, let me just push on just a little bit. Because I tend to agree with you, but every once in a while I have like moments of doubt. And so, so I don't remember writing programs where you actually abdicate logic. Like, 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 the higher level abstraction. It feels a little different to me.

• 33:37Steven Sinofsky?

  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 in the, in the, during the AI winter, that was the eighties. Stanford, the biggest.

• 33:48Martin Casado

  One of the, right, one of the AI winters.

• 33:50Steven Sinofsky?

  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, 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. In fact, that's the whole era of, of the eighties and computers were the dawn of what they used to call expert system.

• 34:23Martin Casado

  I remember that.

• 34:24Steven Sinofsky?

  And so expert systems were the first time we got a taste of this debate.

• 34:27Martin Casado

  I, I remember very well, I just.

• 34:28Steven Sinofsky?

  Your classes were all this.

• 34:29Martin Casado

  It just didn't work.

• 34:31Steven Sinofsky?

  Well, your, your classes were mostly about, like, you had a bunch of classes on this stuff.

• 34:36Martin Casado

  Hundreds of guests on expert systems. I've had to build expert systems.

• 34:38Steven Sinofsky?

  Right, right.

• 34:39Martin Casado

  I've, I've, I've written a lot of prolog.

• 34:42Steven Sinofsky?

  Exactly.

• 34:43Martin Casado

  So I very much understand it. I just thought like that never really worked and.

• 34:47Steven Sinofsky?

  Right, so the big difference is that stuff was working.

• 34:50Martin Casado

  But now it's working and we're abdicating logic.

• 34:52Steven Sinofsky?

  Right, and so it's, but so it's interesting because to compare and contrast.

• 34:56Martin Casado

  And even in the case of prolog, you're kind of like, for these.

• 34:58Steven Sinofsky?

  You're coding it, it's algorithmic.

• 35:00Martin Casado

  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 it what the end state should be. So it just feels a little different.

• 35:09Steven Sinofsky?

  And it's especially, so I agree. Like, I, I love having this debate because I, I think so much of it boils down to the concern and the willies that you get thinking about it. It's actually because of, of the context we're in. And, and like, because, you know, think about like, we, we have all this stuff going on where people don't want to build data centers. 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, and is really hard labor and all. And so the context really matters to, to these discussions. You can't separate them from.

• 35:48Martin Casado

  Right, but I saw, I just want to go back this layer. And I don't mean to, I just, I just think, so your, my entire career has been moving up layers of stack. But like, it's always a computer layer of stack.

• 35:58Steven Sinofsky?

  Yeah, yeah.

• 35:59Martin Casado

  You could always map it down to like the next layer in basically a deterministic way.

• 36:04Steven Sinofsky?

  Yeah, yeah.

• 36:04Martin Casado

  There's high 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 it's actually different. So what 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. Like, 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.

• 36:37Steven Sinofsky?

  Oh, yeah.

• 36:38Martin Casado

  And the metals.

• 36:39Steven Sinofsky?

  We do, because it feels a lot different to me than just like, this is the next layer. The, 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 which, where everybody is that 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. And now we're at this where, where it's, it's arbitrary, it's random and it's statistical.

• 37:12Martin Casado

  Right, so, 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.

• 37:25Steven Sinofsky?

  Which is this prologgy kind of thing.

• 37:26Martin Casado

  Or data log, or SQL. You know the end state, but then the computer does all the 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. It's like, here'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. 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.

• 37:58Steven Sinofsky?

  Yeah, yeah.

• 37:58Martin Casado

  In a way and.

• 37:59Steven Sinofsky?

  But I, I, look.

• 38:00Martin Casado

  And it is still.

• 38:01Steven Sinofsky?

  That's an actual 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?

• 38:12Martin Casado

  Yeah, and it may be like computing, maybe like this is where 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.

• 38:25Erik Torenberg?

  If we do need to rethink some fundamental assumptions, what may that look like?

• 38:32Martin Casado

  Well, I just think that like, 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? What guarantees that you can you provide? How does this impact productivity? There's a lot of things that we've got intuitions on. And 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 differently. I'll just give you one example.

• 39:17Martin Casado

  I mean, I've said this many times, I just think it's so important. 20 years ago, and if you're a startup of 10 people and I gave you a billion dollars, what would you do with it?

• 39:24Steven Sinofsky?

  You would have ended up spending a, a ton of money on building, on buying your own computers and things, if that's where you're going, if that's.

• 39:29Martin Casado

  Well, hire people, you buy computers, blow up. Like, you wouldn't know what to do with a billion dollars.

• 39:34Steven Sinofsky?

  Oh, oh, I see what you're saying. Yeah, yeah, yeah.

• 39:35Martin Casado

  10 years ago, I give you a billion, you hire, you hire engineers and you'd be fine.

• 39:39Steven Sinofsky?

  Right, right, right.

• 39:40Martin Casado

  Like, what do you do?

• 39:40Steven Sinofsky?

  Right, right.

• 39:41Martin Casado

  Write code, you've got to, you know, you've got, you know.

• 39:44Steven Sinofsky?

  The billion, the important part of that is, it's a billion. It's not that you got money, it's that it's a, it's a huge, it's a ton of money.

• 39:51Martin Casado

  If I give you a billion dollars two years ago.

• 39:53Steven Sinofsky?

  Because 10 million, you'd buy a bunch of stuff from Hewlett Packard and the money would be gone.

• 39:56Martin Casado

  Yeah, for sure, for sure. This one is a billion dollar, yeah. I mean, like in software, you hire people and then.

• 40:02Steven Sinofsky?

  There's nothing you could do.

• 40:03Martin Casado

  Yeah, the mythical manhunt is very real.

• 40:05Steven Sinofsky?

  Yep.

• 40:06Martin Casado

  And right now, if I give 20 people a billion dollars, they can actually use it usefully. It's very, so it's like, it's like, we've kind of moved the industry from like this engineering bound problem to a capital problem that's fundamentally very different. We've never been like that before. And so like, this is like a law of physics where like our early intuition, which is like, all problems are 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? And it's not just one level of abstraction. It actually changes the nature of capital versus innovation versus competition versus defensibility, et cetera.

• 40:42Steven Sinofsky?

  I, that's a great, a great way to, to think about it. Because it forces you to think about a, a new model. It's also interesting that computing was capital bound for the, the first 30 or 40 years. Like, if you wanted to do something.

• 40:56Martin Casado

  This is such an important point.

• 40:57Steven Sinofsky?

  If you wanted to do something with a computer, like, your first step was, we have to get one. And then you couldn't.

• 41:01Martin Casado

  You were capital bound and then you were engineering bound and now we're capital bound.

• 41:05Steven Sinofsky?

  Again, yeah.

• 41:05Martin Casado

  Which is crazy. So it's almost like you have to like hop back 40 years.

• 41:08Steven Sinofsky?

  Yeah, Mad Men goes through the scenario where the computer shows up at the advertising agency. And, 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, but it was interesting because they, they, they couldn't figure out what to do, but they were excited that they 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 Carlson interviewed Sam Altman in, 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, being a project who's 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.

• 41:49Martin Casado

  Yeah, yeah, you know, it's interesting. So, so prior to AI, there was always this battle between Eric Ries and Ben Horowitz, right? So lean startup and then, and then, you know, Mark and Ben wrote like the art of the fat startup. Which basically argue raise the money and go for it. But there's always been this natural limiter, actually, which is engineering.

• 42:07Steven Sinofsky?

  Yeah.

• 42:07Martin Casado

  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 it.

• 42:22Steven Sinofsky?

  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, you, as we know, the building that's based on people was also hard, like just scaling that and doing more. And then nine people can't do anything faster and.

• 42:42Martin Casado

  I'm telling you, yeah, my, my, my, my 10 year job, you know,

• 42:47Steven Sinofsky?

  Was recruited.

• 42:47Martin Casado

  And giving, yeah, it's like literally giving these early teams money and then helping them recruit and then watching, then waiting for two years.

• 42:56Steven Sinofsky?

  Well, yeah. And I think.

• 42:57Martin Casado

  Engineering just doesn't scale.

• 42:58Erik Torenberg?

  It also has implications for venture capital because for the last decade, people have been saying, hey, there's way too much capital.

• 43:02Steven Sinofsky?

  Yeah.

• 43:02Erik Torenberg?

  There's way too much capital.

• 43:03Steven Sinofsky?

  I just think this is such a crazy view.

• 43:05Martin Casado

  So there's been this view in venture, this zero sum thinking, which is funny for the people that shouldn't be zero sum thinking. You know, like, and they'll go up on.

• 43:11Steven Sinofsky?

  Too much capital is chasing too few deals and all.

• 43:14Martin Casado

  Yeah, this is like, this is me, like, you're a venture capitalist, don't you believe in positive sum stuff, right? And, and, but if you look at the numbers, the more capital that flows into private markets, the larger the market gets. And there's a, there's a, there's a, you know, the couple of reasons. One of them is the one we've talked about, like tactical 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.

• 43:51Steven Sinofsky?

  Well, one way to think about that is, is I'll bring it back to what I, I think that your foundation enables what I think is the most exciting thing, which is we're really on the cusp of a wave of, of apps. And, and like the fact that now you can apply capital without also being a recruiter for 10 years and, 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, to, to go to a, a, 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.

• 44:34Steven Sinofsky?

  And, but, but like all of, 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. Wouldn'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.

• 45:16Martin Casado

  Oh, wow.

• 45:17Steven Sinofsky?

  And he wrote like a DOS program to schedule a doctor's office.

• 45:21Martin Casado

  That's amazing.

• 45:21Steven Sinofsky?

  Which you'd think it's just scheduling, it's a calendar with hours. But it turns out, this was me, 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 or 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. It took him years to bang that out himself.

• 45:53Martin Casado

  Yeah.

• 45:54Steven Sinofsky?

  And, and that's, but that's the kind of thing that's going to be able to hap, like now that problem can get solved by the person who knows.

• 46:02Martin Casado

  No code, no code is finally here.

• 46:04Steven Sinofsky?

  Well, it could really be. And, and, and it, and it might actually be that, that you're not just building this throwaway code that's hard to do, but also everybody else's abstraction layer is rising. So the, you know, you don't need to design that piece of code. Like, you know, 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. And so there's, there's a lot to how important this is in terms of what you're able to do.

• 46:36Martin Casado

  Yep.

• 46:37Erik Torenberg?

  I want to talk about any other fundamental assumptions that might be interesting to revisit. Is, is it sort of, how about incumbents versus startups? You know, the, we've talked a lot about innovator's dilemma. Does it, does that, you know, now that these startups are, or these incumbents, you know, have the capital advantage, are they able to, to do more? But at the same time, we're seeing startups that you would think incumbents would just destroy.

• 46:55Martin Casado

  This is the craziest thing. If you would have told me six months ago, you would have asked us questions, say like, what, like, what advantages do incumbents have? They have the same advantage incumbents always have, they have the capital. And they have the cash flow and they have like whatever.

• 47:07Steven Sinofsky?

  Distribution and, you know.

• 47:08Martin Casado

  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 incumbents, specifically for these two reasons. You know, I think that the, the, the, um, the distribution point is, is often misunderstood how impactful it is. In the past, if you had a company and you wanted to get people to use your stuff, it was hard. You'd hire a 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.

• 47:51Martin Casado

  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 growth. And so the things 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. That results in capital access and that has put them on, on, on even footing. So very.

• 48:17Steven Sinofsky?

  Yeah, I, and I think it's to your point about how hard it, like, 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.

• 48:31Martin Casado

  Yeah.

• 48:31Steven Sinofsky?

  And, and I think.

• 48:33Martin Casado

  You have to have one cutler, is that the?

• 48:34Steven Sinofsky?

  Well, it's, but it's, it, it really, it read, read.

• 48:38Martin Casado

  That was mostly you.

• 48:39Steven Sinofsky?

  Yeah, I know, no, I get it. No, but, but.

• 48:41Martin Casado

  He's brilliant.

• 48:41Steven Sinofsky?

  But read the, um, read the exile, Steve Jobs in exile book, because you can, you really get a sense for like building up. In fact, you know, of course, Next was famously just, it took the code from Mac at, at Carnegie Mellon and started from there. We couldn't have done it from, from scratch completely. But this, this whole idea of, of just, um, how, how important it is to, to think through the, the domain specific and the, and how you disrupt people. Because there was an old joke at Harvard Business School when Clay was, 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.

• 49:21Martin Casado

  Oh, I like it.

• 49:21Steven Sinofsky?

  And, and I love that. I was there in 98 when he was writing the book and the paper and everything. And that's when I was teaching.

• 49:27Martin Casado

  That's I see.

• 49:27Steven Sinofsky?

  And, and, 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. You always think that when you're at the big company. And then you realize that they never get crushed. Like, and that Ben always makes this point, like, they just, and Mark does in his, um, his movie.

• 49:51Martin Casado

  It was AWS I actually put out of business.

• 49:52Steven Sinofsky?

  Right, right, exactly. And because, and the, and because, you know, the startups don't aim straight at the incumbents.

• 49:59Martin Casado

  Yeah.

• 49:59Steven Sinofsky?

  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 that, that, this, what the elements of disruption that matter are the, the cultural ones of being a big company. And those are constant. Those are the laws of physics. And so you can't, 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, and legacy and customers. Because, you know, the way you behave, if you have, you know, 500,000 customers you're serving.

• 50:37Martin Casado

  Yeah.

• 50:37Steven Sinofsky?

  You, there's a bunch of stuff you just can't do. Like, you're just, you're just stuck.

• 50:42Martin Casado

  Yeah.

• 50:42Steven Sinofsky?

  Like you, and, and that is really the essence of, of disruption. And that's why we're at a magic moment where it's not just that, that the culture is there like it always is, but the startup ecosystem, it is very, it has, it has, it's a reflection of what happened during cloud, which was a whole bunch of stuff that you needed. Again, 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. You, now you're up and running in the first hours of your first dinner.

• 51:17Martin Casado

  But the, 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.

• 51:27Steven Sinofsky?

  Right, right.

• 51:28Martin Casado

  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?

• 51:36Steven Sinofsky?

  Right.

• 51:36Martin Casado

  The answer was.

• 51:36Steven Sinofsky?

  Will they just add you for free or for some price that couldn't match or whatever?

• 51:40Martin Casado

  I actually think, you know, and that's always been the question, will Microsoft?

• 51:43Steven Sinofsky?

  Right.

• 51:43Martin Casado

  The app, right? But, 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, company's been defined by these very complex, large engineering efforts, building a chip, building a system. What's that, the, the age of the new machine or?

• 51:59Steven Sinofsky?

  Yeah, yeah, yeah, the solid, well.

• 52:00Martin Casado

  The solid.

• 52:01Steven Sinofsky?

  Solid machine, yeah.

• 52:01Martin Casado

  Beautiful book, right? It talks about how hard it is to build like, you know, the.

• 52:04Steven Sinofsky?

  Exactly.

• 52:05Martin Casado

  Tech, all systems, building an operating system, even the cloud, like, I mean, like Jeff Dean and 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, that 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 a 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.

• 52:35Steven Sinofsky?

  Yeah.

• 52:35Martin Casado

  By OpenAI and by Anthropic. It just goes.

• 52:37Steven Sinofsky?

  Because of the cultural element. That I, I think that people, people on the outside underestimated until you've lived the cultural element of trying to, to do something.

• 52:47Martin Casado

  I'll bet it's, I'll bet it's cultural. It's not like a typical, you know, engineering problem like they're very good at. They've out executed like, like GCP is fantastic. That's an ass of engineering. And then also I bet it's probably hard to free up that much capital for one of these companies, honestly.

• 53:01Steven Sinofsky?

  Well, all the, all the big, 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.

• 53:13Martin Casado

  Right.

• 53:13Steven Sinofsky?

  You know, you had the rumors of, of, I don't remember which company, you know, the rumors of like, well, they're rationing the tokens so that they, they go to the enterprise customers and not to the, the internal products. And so the internal products are, are AI starved.

• 53:28Martin Casado

  Yeah.

• 53:28Steven Sinofsky?

  And of course, none of their competitors to those products are, are starved.

• 53:32Martin Casado

  Yeah.

• 53:32Steven Sinofsky?

  And I, I, I just, I, I've learned to really appreciate the, the, the call. I mean, look, I, I fought and fought and fought to not be disrupted by, by the mobile platforms, like by ARM, basically. And, and Intel just didn't care.

• 53:50Martin Casado

  Yeah.

• 53:50Steven Sinofsky?

  You know, I, 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. And they got very excited and then they were like, but what's in here? I said, well, it's ARM chip.

• 54:03Martin Casado

  Oh, wow.

• 54:04Steven Sinofsky?

  And, and you know, like the fact I even brought one into the building, you know, and it was very, very tough. And, and, and they just never felt that that was gonna, that, that was like a chip used in a, in a printer. And also, and they looked at me like, we're in, we're, we're ARM licensees. We, we knew all, and I'm like, but it's the power, it's the graphics, it's the, you know, always connected, all of this stuff. And the culture was they, they do Moore's law at Intel. And just like with Google, they do hyperscale.

• 54:37Martin Casado

  Yeah.

• 54:37Steven Sinofsky?

  So like if, if AI moves on device.

• 54:41Martin Casado

  Yeah, yeah, yeah, of course.

• 54:41Steven Sinofsky?

  Like, that's not what they do.

• 54:43Martin Casado

  Yeah, sure.

• 54:43Steven Sinofsky?

  And, and, you know, and if it with, with Microsoft, they were squeezed. They're squeezed now, you know? And I, I think you've raised super interesting points about the opportunity though for,

• 54:54Martin Casado

  For start.

• 54:55Steven Sinofsky?

  With this capital inversion.

• 54:56Martin Casado

  Yeah, go raise, go raise capital and go after the.

• 54:58Steven Sinofsky?

  Well, and it, 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.

• 55:07Martin Casado

  And you just look at the raises that are happening, right? You know, and like, you know, these companies have been quite successful as a result.

• 55:14Erik Torenberg?

  The, um, last thing, when Vishal came in and we had him on the podcast, he was sort of, um, 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, the sort of math progress, um, is consistent with that or, or, or what is your latest thinking on sort of the limitations of the, of the current sort of, uh, you know, model architecture versus like, will we need more?

• 55:46Martin Casado

  So here's my, 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. They can only do in distribution stuff and they can move along that manifold, um, in a perfectly Bayesian way. So we, okay, so we can say these words. And then, then the question is, is okay, but what are the implications of that? Like what problems can it solve, right? I think it's just so hard for a human being to reason, to reason about a digital artifact, in this case, the model that was built with $5 billion. 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.

• 56:31Martin Casado

  On the other hand, that is so much data and that is so much compute, maybe 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 stuff? No.

• 56:50Martin Casado

  Does, you know, is there transfer learning? Probably not. Like if I are all one thing, it doesn't teach something else. Like are these, 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 the, we all agree about that. But, but I don't think anybody knows is, okay, but you're still putting $10 billion in that thing. What's it capable of now? And if you, 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?

• 57:34Martin Casado

  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, I honestly have decided that I cannot predict what an artifact was, that was, you know, that like you use $20 billion to create is capable of.

• 57:50Steven Sinofsky?

  I, I, like, I, 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, I do think on, on the, and, and I.

• 58:11Martin Casado

  But, but even this one is a little different. This is like, I, I, I take $20 billion and I put it into a model.

• 58:17Steven Sinofsky?

  Right.

• 58:17Martin Casado

  And, and then you and I look at that model and we can do whatever we want. I don't think we can comprehend the, like what that even means.

• 58:24Steven Sinofsky?

  Yeah.

• 58:24Martin Casado

  So many flops and so much data, like, I don't know what that's capable of.

• 58:28Steven Sinofsky?

  I, and I think we are, I think that that's really true. And I, and I think, but I will say on, on bio medicine 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.

• 58:41Martin Casado

  A spark?

• 58:42Steven Sinofsky?

  Like a, a ton.

• 58:44Martin Casado

  A sun spark?

• 58:45Steven Sinofsky?

  No, no, a, a, a, an India spark.

• 58:48Martin Casado

  Oh.

• 58:48Steven Sinofsky?

  Yeah. No, no, no, not, not with a C or the K.

• 58:51Martin Casado

  Oh, yeah.

• 58:51Steven Sinofsky?

  Wow. We were in old times there. I was like, not a Scott McNeely spark. No, no.

• 58:56Martin Casado

  Not anything, a relic.

• 58:57Steven Sinofsky?

  And, no, and, and, um, and like, it's all AI. Like she does brain stuff and, and surgical brain stuff, all AI. And it's so interesting to see, because what it, what it really can do is it, it just, it, it sees the patterns that you can't, that only experience could tell somebody. But 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

• 59:42Steven Sinofsky?

  part of drugs has always been candidates.

• 59:45Martin Casado

  Yeah, sure.

• 59:46Steven Sinofsky?

  Not candidate, it's always been efficacy and safety. The candidates have, since the eighties have been able to develop more than we could test. It's human patients and it's very, very, very hard.

• 59:56Martin Casado

  Can I, can I please tell you something that I got wrong on this? So, um, I, 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, um, I was responding to this Bostrum, Bostrum 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 poo-pooed that because that's clearly not what's happening. And I think the most, 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. Like the scaling laws are holding and I, I don't, you know, I don't know what it means to just, let's say we do a hundred billion dollar training run to like have this thing

• 1:00:41Martin Casado

  that you're putting a hundred billion dollars in. And then that, that money comes from this meta economic machinery that may be able, 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 a hundred billion dollars in the wrong way. So I think that's kind of where this conversation needs to evolve to, so less the boom, you know, and more the, what does it mean to be able to concentrate resources?

• 1:01:20Steven Sinofsky?

  Which also was, it, I mean, this, you're, you're basically talking about exponential growth. And, and this is just exponential in dollars.

• 1:01:28Martin Casado

  And.

• 1:01:28Steven Sinofsky?

  Yeah. And, and we all know, none of us can model exponential very well.

• 1:01:32Martin Casado

  Yeah, but we've never been able to do that. Like, like complex engineering project, you were not like tackling one problem.

• 1:01:38Steven Sinofsky?

  Yeah.

• 1:01:38Martin Casado

  With a lot of money, you're just kind of building this machine.

• 1:01:41Steven Sinofsky?

  Well, we, we, you know, I, it's, you're right. You're a hundred percent right and I completely agree. But just, I, 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.

• 1:01:56Martin Casado

  No, no, you're building the machinery. I'm saying this, yes. If you're like, I want to exhaustively explore every protein combination.

• 1:02:03Steven Sinofsky?

  Right.

• 1:02:03Martin Casado

  We can just turn that into a money problem.

• 1:02:05Steven Sinofsky?

  Yeah, yeah.

• 1:02:05Martin Casado

  Kind of very strange.

• 1:02:06Steven Sinofsky?

  Which is a great way to say it, that we can take previously, um, infinite problems and apply capital and it becomes finite.

• 1:02:15Martin Casado

  Just making a capital problem and not an engineering.

• 1:02:17Steven Sinofsky?

  Yeah, yeah.

• 1:02:17Martin Casado

  Which is just a very different laws of physics.

• 1:02:19Erik Torenberg?

  Yeah, yeah. Let's wrap on that because it's 3:30. This has been a great episode. Thank you guys for taking us.

• 1:02:23Steven Sinofsky?

  Thank you.

• 1:02:23Martin Casado

  Thank you.

• 1:02:26Erik Torenberg?

  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 Substack at A16Z.substack.com. Thanks again for listening and I'll see you in the next episode.

• 1:02:52Erik Torenberg?

  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. 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/disclosures.