Speaker A: 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.

Speaker B: Math is very much a leading edge indicator of what the market might be interested in. Why? 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. 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. And I think this is why we're seeing such meteoric roasts of the cursors, the anthropics, and the open AIs. Although capital is scarce and it's hard to get and all of these other things once you get it.

Host: First off, thanks for both of you making time to be here. That's great. Um Jared Ser tweeted a few days ago something along lines of uh how he told Claude to try to solve the reman hypothesis and to try harder and uh 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 uh accomplishments that are being made. H how do we make sense of this in terms of what is actually happening and what does it mean for for math?

Speaker A: I'm gonna let uh Steve go.

Steve: Oh, well, I'm no mathematician at all, but I am I mean I think it's just it's an important moment because it sort of divides the world into two groups like the groups that are just very very excited that like oh my god these things are being solved. It doesn't matter if you understand them. actually nobody unders 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 that 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 and they're the ones and so that actually confuses everybody because if you're of this school of the people who are like it's going to put people out of work and it's going to we're going to all get dumber and it's, you know, the dawn of idiocracy because computers are doing all of our work. You're confused that the people who are impacted most by what this level of AI did are the most excited.

Speaker A: Yeah. Yeah.

Steve: And I think that's just I I I think that that is itself shining a light on this moment that we're in right now.

Speaker A: You know, you're talking to two systems guys, two product guys. You're going to get like we're we're like to 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 I've got a two comments.

So one of them is like okay so I I view like economic utility to be a very important measure when you're talking about AI right so I was trying to think like there's a lot of hours been trying to solve some math thing right but like if you sum up the entire posttock salaries of all the people that have been working over the years on these problems is probably not very much and so part of me is saying like it's great that there's these capabilities I'm not sure that the fact have been longstanding as that much of an indication 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. It's just like I just don't think we have like that like that that validation of this unlock some deluge of like economic value.

And the second one is it's kind of not surprising to me that AI is very good at solving a almost purely axiomatic domain that you know requires knowing a whole bunch of different things and and putting you know putting the solutions together from very disperate spaces because often really when I read so I've I've been reading all these like everybody else has been obsessively they're like oh like I came up with the solution it's like Yeah, the solution was pretty straightforward. It just like it borrowed from a bit of math that I didn't know.

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

Steve: 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 mean, if you like I remember when I was in school, like there was some big thing that someone at AT&T invented a new algorithmic, a new program for doing linear algebra, like a new way to solve linear, 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 like 3 hours less time than we could last week, right?

But but let's dig into So, it's just not clear to me that the problems being solved are those that are roadblocks to like existingly economically useful tasks, right? And if they were, it's not clear to me that they wouldn't have been solved. Like posttock that's been ruminating on a problem getting paid 30k a year for 5 years. Like it's very different than like the market has decided that this is like the one thing to unlock.

And 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.

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

Speaker B: And and maybe I'm wrong here, but for me it's still in the domain of like it's really good at playing a game. like this is the best Starcraft player ever, which is cool and it's 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 was an economic need and b like how does that actually map? And so listen, I there's a huge range of these things. We get pitches all the time.

Some people will walk in and say, you know, 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, you know, fundamental mathematical principles. And once you understand that, you understand everything. And then, you know, there's other people candidly that walk in the door and they're just like, listen, that's great. Um, but like that doesn't tell you anything about reality. And so, you know, I think that there's more work to do and this isn't just about like getting better at math.

Steve: Yeah. I do think what's interesting is that the part of the reason that the mathematicians are very excited about it though is because they they work a certain way. Like if you work in in history, there's basically no abstraction in history. Like there's just a bunch of facts and then people develop like sort of these models that you can think of almost as force diagrams that explain war or famine or or whatever. Whereas mathematicians and mathematics has this super long historic arc of layering on abstractions after

Speaker A: ...and we'll get don't worry we'll get to OSI in a minute. But but but but like this idea that that where what why they're so excited is like a bunch of math all of a sudden becomes a new level of of abstraction.

Speaker B: Is it true that they're so I I've found that it's a mixed. I found that some are very excited and some are in like an existential crisis. The ones who are excited they basically say listen it it it solves 20% of my job is the 20% I didn't like anyways. So like this allows me to explore a new frontier that's very important or whatever. And what I've always wondered is like is that a function of the type of problem being solved? Like 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 existentially depressed. [laughter] This is amazing. But let me I where on the other hand of like oh we saw this math problem oh I'm so depressed I saw the math problem like maybe like literally the entire utility of that problem was keeping somebody employed to solve the problem

Speaker A: >> or or just writing articles in the back like another attempt at and here's where I went wrong. So let me offer it this way.

Speaker B: >> There's 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. Let let let me let me stop. No, I mean too cynical on this thing. I love math.

Speaker A: >> I think I think we caught you but like being a little cynical but not really but more that it's just [laughter] it's it's 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 cataloges for a bunch of schools. You don't have to take it anymore. That was like discrete math bas.

Speaker B: >> Yeah. Yeah.

Speaker A: and and like or and then or algorithmic complexity theory which was a required class for a very long time and now

Speaker B: >> do you remember concrete mathematics from Donald can like

Speaker A: >> I didn't I mean you're a Stanford guy I'm not but like my state a school my state a school we didn't have that but uh that's a Cornell joke for us Cornellians but but um you know my class I got taught by one of the luminaries in the field of algorithms ironically a Stanford PhD uh um John Hopcraftoft of course

Speaker B: >> who who invented for for the people who are pragmatic IC invented like two three trees and a bunch of stuff as his thesis at Stanford. That's a legend.

Speaker A: >> But but John was our our professor in all this crap and we had to learn like all this P equals NP stuff and I remember like the this is the four color this is the four color

Speaker B: >> proven by computers.

Speaker A: >> No ex but that's where I'm going. You just buried the lead.

Speaker B: >> 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 polomial 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 computebound. 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 the simulation to solve this.

Speaker A: >> Yeah. Yeah.

Speaker B: >> And and so it turns out that one of the interesting things was they proved the fourcolor theorem.

Speaker A: >> Yeah.

Speaker B: >> But they did it which by the way, I mean just the four color theorem says for any 2D planer map, you can color it. You can use only four colors. So such that no two adjacent areas have the same color. Right. Exactly.

Speaker A: >> And um uh and you only need four colors. You'll never need five colors.

Speaker B: >> And and we learned 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.

Speaker A: >> 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.

Speaker B: >> Have you actually seen the proof?

Speaker A: >> Yeah. Yeah. 200 pages of combinations,

Speaker B: >> 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's this sort of bankshot proof, but it was only possible because of compute.

Speaker A: >> And to your point, that was actually very very useful in the the the the practical applications.

Speaker B: >> Right. Right. and certainly as a topology person

Speaker A: >> like setting setting strong bounds and and things like that. So actually I see

Speaker B: >> and I think it 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.

Speaker A: >> Yeah.

Speaker B: >> 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.

Speaker A: >> So listen [clears throat] I listen it's hard not to get philosophical when you're talking about AI. So, I'm gonna 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 the math this math thing seems to me a little different because like it kind of begs the following question, which is will math ever be represented 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 an uh an air simulator or a wind simulator. Um but all of those and even though they're just calculating these like large you know differential equations they were all based on empirical results.

Speaker B: >> Yeah. like literally the equations of state for the

Speaker A: >> well they were model they were just they were like we could measure temperature in these places

Speaker B: >> that's exactly right so it was all it was all based on empirical equations of state and so I've always wondered like

Speaker A: >> 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 an these math answers have any impact on that type of stuff, right? 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 disjoint 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.

Speaker B: >> Yeah.

Speaker A: >> Or or there's any indication it's true at all. like it.

Speaker B: >> So the way one way to that I think I might >> um talk about that you know again like this is so out of my league on the actual math

Speaker A: >> two and two systems people

Speaker B: >> I'm good but but I'm >> compelled I'm 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 props to shows 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 market.

Speaker A: >> Well, I you know, it's

Speaker A: the ones they tell tourists in French. But, but I'm very proud of that because I negotiated it down to like seven cents. But, um, [laughter]

Speaker B: 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. It is. So this everybody knows what slide rules are. You know, nobody knows how to use them. Yeah. This is called a a kerta and which is a Austrian uh basically it's a round uh slide rule.

Speaker A: Yeah.

Speaker B: And so it it's like a coffee grinder or a pepper mill and you you you have the all these ways you set the numbers on the side and then you turn it one way to add another way to subtract.

Speaker A: Whoa.

Speaker B: And it's this thing is it's

Speaker A: Wait, is that used for like multi-umber arithmetic or is it used for stuff like like logarithm?

Speaker B: No, it's only arithmetic. Okay.

Speaker A: Well, I think but of course it depends on how you use it. But but um

Speaker B: 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.

Speaker A: Wow.

Speaker B: Inside this. It would cost like $50,000 to make one. Now,

Speaker A: do you know how to use it?

Speaker B: 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 idiot. Just go look what I have. I actually went through the trouble of learning how to use it, although it's been sitting on my shelf for years.

Speaker B: 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

Speaker A: wait, so you're saying that the the the new model is the new calculator or the new graphing calculator, the new I actually remember when like remember the TI85.

Speaker B: Oh, of course. Yeah. Yeah.

Speaker A: I remember that came out. They're like, "All of the math teachers had this crisis." I'm like, "You know, we used to give you a piece of paper. We plot the XY equation. Now they could do it on the calculator and they can solve equations and our field is dead."

Speaker B: 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 TI35 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 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

Speaker A: the whole I was on that I my whole life I've been on the cus of allowed and not allowed for everybody. I mean, I was I was there for the graphing calculator. Like, you literally have a blue book.

Speaker B: Yeah.

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

Speaker B: See, I missed the graph, which most of us were like actually writing video games in the back and could care less about its ability to write math. But

Speaker A: Absolutely. Absolutely. And 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 fier 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

Speaker B: well all of computer computers are basically from difference engines which are just trying to calculate integrals. But and and but and of course but to be really clear to calculate integrals so that we could shoot missiles and at and cannons at each other

Speaker A: that okay so that's what yes

Speaker B: which I'm not judging it I'm just saying

Speaker A: well 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 had 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 ward effort for the logorithms for that that's what I came from it actually it was very interesting uh was about 5,000 times faster than a human being when it came to like you know doing this and then of course

Speaker B: and it didn't make mistakes which was sort of the

Speaker A: but it was but it was very specifically math and very specific for economic utility. And 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

Speaker B: Yeah.

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

Speaker B: Oh yeah. I I you know, 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 win the Westinghouse competition. Go win the GE math competition."

Speaker A: And was that because of the Cold War? Was it because

Speaker B: Well, obviously the Cold War was a big cultural part of it for sure, but it was just a general the the future. I like I found this uh incredibly cool brochure from uh IBM from it's from 1953. So 193.

Speaker A: Like do you just have this stuff in your house?

Speaker B: 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 is computing.

Speaker A: Wait, I want to see it.

Speaker B: But but but like first you got to look. It's it's got like nuclear like the whole thing. The future of computing is like a guy with like Adams racing around his head. Oh wait, we're zooming in and doing the Carol Merrell thing. So but the the fascinating thing is it's from 1953. So you're Aniac and and that point like that's it. That's the computer at the time. This is pre74, 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 up. But here's the part that I want to get to. It talks about computers and it's the two families of computers.

Speaker A: Yeah.

Speaker B: And so of course you get the slide rule and that's explaining the history. And what this is really leading up to is we could do this for text too.

Speaker A: Yeah.

Speaker B: And so the idea and I mean like imagine who was reading this in 1953 that it has to explain hex and decimal and binary and compare it to Roman.

Speaker A: That's amazing.

Speaker B: And because like nobody like nobody knew

Speaker A: is the name of that thing.

Speaker B: It's just called IBM light on the future with like a rocket test tubeish 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 diction the dictionary isn't, 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 open this because there's one cool page that really matters. What is the organization of digital computers? And so this is the thing that that I gets to this point about abstraction for us and AI. This is these have been for for 75 years how we thought computers are organized.

Speaker A: Yeah.

Speaker B: Input, storage, arithmetic, control, and output.

Speaker A: Yeah.

Speaker B: And that's all that's what we learned in school. You took courses basically in each

Speaker A: 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.

Speaker B: Yeah. Everybody forgets networking by the way. Of course that was

Speaker A: well because most people stop worrying about networking

Speaker B: stop worrying about as soon as the packet leaves the computer I would say you know the late 90s was the end of basically a mandatory networking class

Speaker A: because because like but it was solved like like there was no you know 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 But but the interesting thing is those abstractions led to okay so now we have those abstractions they were basically fields that did each one of them like you spent 20 years of your career on storage 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 watch the invention of going from a teletype to a lineoriented 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. Any CS department which came out of the math department because of the missiles

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

Speaker A: Sure. Yeah. Yeah. Yeah. So let let me just push on one angle of this. Sure. Sure. Because I I listen I I clearly love the framing and we move up an abstraction and every abstraction there's still a set of problems. It's just a higher level of abstraction. But I still think this kind of notion of economic meat is very important.

Speaker B: Oh yeah. Yeah. Right. So for example um we we we you know Bletzley 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 um any act 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

Speaker A: Oh, yeah. No, but on the other side is we I I do think we've had that in the past. And so, you know,

Speaker B: well, we did we had the Alph Go moment. It was the same thing. Like we I we did a podcast not in this room, but

Speaker A: but even before Alpha Go, we had like remember when chess got bitten in chess? Yes, we had the IBM chess thing when it was and and Frank Chen and I we did this podcast at Alpha Go and we had to try to make people understand like why it was a good idea and

Speaker B: 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 and that and like that's going to move things forward and and when that tends to happen people tend to be excited and get behind it and there's these things you solve where I think people

Speaker A: I think don't have like as positive a view and I I would submit that's because it's almost like solving the problem had become the the end as opposed to the actual end. But 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,

Speaker B: right? And so this course, you know, and you're like doing the San Mandala and like your inner piece or something like but that's not moving the economy forward,

Speaker A: right? Well, we're both look, we're systems people, but I'm actually an apps person. I know you're not I don't system. Yeah, we're both people but like system

Speaker B: I of course absolutely think that that the wave that matters are apps and of course the internet

Speaker A: 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 um called Steve Jobs in Exile, which I absolutely think is required reading if you're listening to this podcast. Um so, uh Kane wrote the book, but it's with Catm who at Pixar and with Dan Leuen, who was at at Steve's super good friend and was also at Microsoft. They all they 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. 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,

Speaker B: right? So he actually

Speaker A: a next machine

Speaker B: and he used the next machine. That's an interesting

Speaker A: and it's super interesting 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 I'm like h like and and and I think there was a great example of a company a Seattle based company that was called uh cyber pizza and this was like a dot thing that didn't even make it to the 2000 I think but the idea was it was basically um uh uh in or Door Dash for pizza only pizza and they would basically you would order and then they would figure out a pizza place near 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. And and I should say for next step or open step. But um but the idea was 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?"

Speaker B: Yeah. 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 are going for. You're making a point that there's a lot of platforms that get built where that's not clear, but clearly they

Speaker A: well the the spreadsheet was like I 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 2 computers and this new kind of computer called CPM which is the origin of DOSs and people were like I don't understand why you just type and the people once you used a computer the idea of typing really really just didn't work anymore and and so some people at law school you have to show it now.

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

Speaker A: Werent those called Lugables?

Speaker B: 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 the battery life in this?

Eric: Um not long.

Speaker B: There was no battery. This giant case that weighed 25 lbs, there's no battery in it. It just plugged in.

Eric: But that was a trick question because every time I've ever plugged 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 2 and one brought the Osborne and then the school banned them.

Speaker B: Wow.

Eric: 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,

Speaker B: don't listen to rap music or don't read don't know

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

Speaker B: Wow.

Speaker A: And 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 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 that's exactly where we are now on all of this stuff. You could also think of it as a level of abstraction because like no one's going to college now without a computer. So I agree with you, but let me...

Speaker B: Right. Right.

Speaker A: Every once in a while I'm like well maybe it's a little different. So here'd be the argument. 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 like 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 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 like I'm not even really sure what the question is. Right. And again, like I think maybe you could say, well, Google was kind of like that, too. But it was still a very much a social thing. 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.

Speaker B: Well, it definitely feels different. Here'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 is black magic.

Speaker A: Maxima is you know uh 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 disc and then just typed in the answers.

Speaker B: So you don't think that so...

Speaker A: And that was cheating. Let me just push on just a little bit because I tend to agree with every once in a while I have like moments of doubt. So 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 abstraction. It feels a little different to me.

Speaker B: No, like that's the debate. I'm like, I'm all in on the debate. Like here's a Stanford example. So in the during the AI winter that was the 80s...

Speaker A: Stanford the biggest...

Speaker B: One of the AI winters, Stanford was... and we have a podcast on that from 15 years ago. 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. In fact, that's the whole era of the 80s in computers were the dawn of what they used to call expert systems.

Speaker A: I remember. And so expert systems were the first time we got a taste of this debate.

Speaker B: I remember very well. It just didn't work. Your classes were mostly about like you had a bunch of classes on this stuff. Tons of expert systems. I've had to build expert systems, right? I've written a lot of Prolog. Exactly. So, I very much understand it. I just thought like that never really worked and...

Speaker A: Right. So, the big difference is that stuff was working, but now...

Speaker B: does work and we're abdicating logic using these...

Speaker A: But so it's interesting because to compare and contrast, and even in the case of Prolog, 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 it what the end state should be. So it just feels a little different.

Speaker B: 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. It's actually because of the context we're in, and because you know think about like 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 is really hard labor and so the context really matters to these discussions. You can't separate them from...

Speaker A: Right but I just want to go back this layer and I don't mean to I just think so um my entire career has been moving up layers of stack but like there's always a computer layer of stack. Yeah. Yeah. You could always map it down to like the next layer in basically a deterministic way. 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 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 fundamentals because it feels a lot different to me than just like this is the next layer.

Speaker B: 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. Yeah. 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 arbitrary it's random and it's statistical.

Speaker A: 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...

Speaker A: Declarative programming is, you know, the end state.

Speaker B: Which is this Prolog-y kind of thing for people.

Speaker A: Or Datalog, you know, or SQL, you know the end state.

Speaker B: But then the computer does all the stuff to get to that end state and you can't really bound the computer time. So you're like, this is like a Makefile. It's like, "Here's what the end state looks like," and it does it.

Speaker A: 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 pray to the model God in the right words, and then it produces the answer that just ends up being useful.

Speaker B: Yeah. Yeah. And but I look, and it is stochastic.

Speaker A: 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.

Speaker B: Yeah. And it may be like computing a baby. Like this is where compute and natural phenomenon actually intersect pretty heavily, because the answer is produced from human output which is language, which is kind of different than...

Speaker A: If we do need to rethink some fundamental assumptions, what may that look like? Well, I just think that people like, you know, Stephen and myself have built these deep intuitions on how systems function and how they hit the industry based on 40, 50 years of 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 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 reshape those assumptions or not, and to what extent do we have to, because the laws of physics feel a little bit different.

Speaker A: 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?

Speaker B: You would end up spending a ton of money on building, buying your own computers and things if that's where you're going. If that's what... hire people, you buy computers, you blow up. Like you wouldn't know what to do with a billion dollars.

Speaker A: Oh, I see what you're saying. Yeah. Yeah. 10 years ago, I give you a billion. You hire engineers and you'd be... Right. Right. Like what do you do? Like, write code. You've got to, you know, 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 huge billion. It's a ton of money. If I give you a billion dollars two years ago...

Speaker B: Because 10 million, you'd buy a bunch of stuff from Hewlett-Packard and the money would be gone, for sure. For sure. This one is a billion dollars. I mean, like in software, you hire people and then it's all about the fucking scale. The mythical man-month is very real. Yep.

Speaker A: 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.

Speaker A: And so like this is like a law of physics where 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 be, especially people like us should be asking, which is like, to what extent do we have to re-evaluate 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, etc.

Speaker B: 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...

Speaker A: Such an important point. If you wanted 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 again, which is crazy. So it's almost like you have to hop back 40 years.

Speaker B: 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 they had the capital to acquire one, and it made them look like they knew what they were doing.

Speaker B: Five years ago, Patrick Collison 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 was involved with a few other aging things, 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.

Speaker A: Yeah. Yeah. You know, it's interesting. So prior to AI, there was always this battle between Eric Ries and Ben Horowitz, right?

Speaker B: Yeah. Yeah. See lean startup, and then, you know, Marc and Ben wrote like the art of the fat startup, [laughter] which basically argued: raise the money and go for it. But there's always been this natural limiter actually, which is engineering.

Speaker A: Yeah.

Speaker B: Complexity is, that's actually been the reality. And so Patrick Collison is right, is 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.

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

Speaker B: I'm telling you, yeah, my 10-year job, you know, was recruited and giving—literally giving these early teams money and then helping them recruit, and then watching, then waiting for two years while the engine... and I think engineering just doesn't scale.

Speaker A: It also has implications for 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? And they'll go up on, "Too much capital is chasing too few deals," and all this is like... you're a venture capitalist, don't you believe in positive sum stuff, right?

Speaker A: 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 couple 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... that's so funny, early stage venture investors who should think of like, you know, positive sum outcomes need to stop thinking about zero sum.

Speaker B: 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. And 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...

Speaker A: scheduling it to go 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 going to be able to automate lawyers with AI which is the weirdest thing in a world where everybody is against everything except having more lawyers 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 reality. 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. Oh wow.

Speaker B: And he wrote like a DOSS program to schedule a doctor's office. That's amazing.

Speaker A: Which you 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 5 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. It took him years to bang that out himself.

Speaker B: Yeah.

Speaker A: And and that's what you just

Speaker B: but that's the kind of thing that's gonna be able to h like now that problem can get solved by the person who knows

Speaker A: code no code is finally here.

Speaker B: 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 you 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 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

Speaker C: 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 innovators dilemma. Does it does that you know now that these startups are earth is incumbent or 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. The crazy thing if you would have told me six months ago you would have asked this question 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

Speaker D: distribution 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 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 marketing. You have no idea how much like to invest and where and like you didn't know what you're getting out of 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 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 um such meteoric growth of the cursors, the anthropics and the open AIs that results in capital access and that has put them on on uneven footing. So very

Speaker C: I and I think it's to your point about how hard 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

Speaker E: and I think

Speaker C: you have to have one cutler. Is that the

Speaker D: Well, it's but it's it it really it read read

Speaker C: that was mostly

Speaker D: Yeah, I know. No, I get it. No, but but he's brilliant.

Speaker C: But read the um read the exile Steve Jobs and exile book because it 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 mock at at Carnegie Melon 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

Speaker D: and 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.

Speaker C: That's nice. Great. 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 they never get crushed

Speaker D: like and that Ben always makes this point like they just and Mark does in his um

Speaker C: his movie it was AWS actually put out of business

Speaker D: right right exactly and because and the and because you know the startups don't aim aim straight at the incumbents and the incumbents just don't pay attention. They 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. who 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

Speaker C: you there's a bunch of stuff you just can't do like that you just you're just stuck

Speaker D: 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.

Speaker C: But the but the thing with cloud I actually think it's Yeah, you actually articulated it very well. And I actually use this because this is great. Like the thing with cloud is like nobody thought they could put AWS out of business.

Speaker D: Right. Right.

Speaker C: Like you just kind of accepted the oligopy and you built on top of it. And the question is is like will they kill us in our little kind of pipsqueak corner? The answer was

Speaker E: will they just add you for free or for some price

Speaker A: I actually think you know and that's always been the question. Will Microsoft [build] 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 coffee has been defined by these very complex large engineering efforts building a chip building a system. What's that the age of the new machine or

Speaker B: Yeah. Yeah. Yeah. The solar well

Speaker A: the solution

Speaker B: solar machine. Yeah.

Speaker C: Beautiful book right talked about how hard it was to build like you know these jack systems building an operating system even in the cloud like 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. Once you had 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 and 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 yeah by open AI and by anthropic and it just goes

Speaker A: because the cultural element

Speaker C: I I think that people people on the outside underestimate it until you've lived the cultural element of trying to to do I'll bet it's I'll bet it's cultural. It's not like a typical, you know, engineering problem like they're they got very excited and then they were like, "But what's in here?" I said, "Well, it's an ARM chip."

Speaker A: Oh, wow.

Speaker C: 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 going to that 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 lences 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 moors law at Intel and just like with Google they do hypers scale

Speaker A: so like if if AI moves on device.

Speaker C: Yeah. Yeah. Yeah. Of course.

Speaker A: Like that's not what they do.

Speaker C: Yeah. Sure.

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

Speaker C: for start

Speaker A: with this capital inversion kind of thing.

Speaker C: Go raise go raise capital and go after the

Speaker A: 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

Speaker C: and you just look at the raises that are happening right now and like you know these companies have been quite successful as a result. The um last thing when Vichel came in uh and we had him on the podcast, he was sort of um he thought elements 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 well we need more um 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 distribution stuff and they can move along that manifold. um in a perfectly Beijian 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. 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 but how do these things work? We know can it do out of distribution stuff? No. Um uh does you know is there transfer learning? Probably not. like if IRL one thing it doesn't tweet 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 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 what's it capable of now and if you if you consider this metaeconomic machinery which means the ability from anthropic to raise lots of money then 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 I honestly have decided that I cannot predict what an artifact worth that was, you know, that like you used $20 billion to create is capable of. I look, 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

Speaker B: 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, right? 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. It's [snorts] so many flops and so much data like I don't know what that's capable of.

Speaker C: I I and I think we are I think that that's really true and I and I think but I will say on on biio medicine in particular look the other half of my household is a research doctor who uses AI. We have a spark at home and she's loaded

Speaker B: like a a ton

Speaker C: like a sun spark. No, no. Uh uh Nvidia Spark. Oh yeah. No, no, no. Not not with a C or with a K. Oh yeah. Wow. We were in old times there for I was like not a Scott McNeely Spark. No. No. Um and like a relic

Speaker A: 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

Speaker A: 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 part of drugs has always been candidates not candidate. 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. Can I can I just 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 boastrum 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 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 But 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 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 $100 billion in the wrong way. So I think that's kind of where this conversation needs to evolve to. So less the fume, you know, and more the what does it mean to be able to concentrate resources,

Speaker B: which also was in all I mean this is you're you're basically talking about exponential growth and and this is just exponential in dollars and and we all know none of us can model exponential very well.

Speaker C: Yeah, we we've never been able to do that like like complex engineering project. You were not like tackling one problem with a lot of money or just kind of building this machine. Well, we we you know it's you're right. You're 100% right and I completely agree, but just I I remember just sitting in meeting after meeting Intel saying we have 5 gigahertz, we have this many gigahertz, this many transistors and literally nobody knows what we're going to do with them all.

Speaker A: No, no, you're building the machinery. I'm saying in this case, if you're like, I want to exhaustively explore every protein combination, right? We can just turn that into a money problem. Yes. Got a very strange,

Speaker D: which is a great way to say it that we can take previously um infinite problems and apply capital and it becomes finite.

Speaker C: Just makes it a capital problem and not an engineering problem. Yeah. Yeah. Which is just a very different laws of physics.

Speaker B: Yeah. Yeah. Let's wrap on that cuz it's 2:30. This has been a great episode. Thank you guys for

Speaker A: Thank you.