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.
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 rises of the Cursors, the Anthropics, and the OpenAIs. Although capital is scarce and it's hard to get and all of these other things once you get it.
First off, thanks for both of you making time to be here. That's great. Jared Ser 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?
I'm going to let Steve go.
Oh, well, I'm no mathematician at all, but I mean I think it's just 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 understands them. 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, 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 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 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.
And I think that's just, I think that is itself shining a light on this moment that we're in right now.
You know, you're talking to two systems guys, two product guys. You're going to get like, we're going 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've got two comments. So one of them is like, 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 like, there's a lot of hours being spent trying to solve some math thing, right? But like 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 like, 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 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 that validation of this unlocking 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 an almost purely axiomatic domain that requires knowing a whole bunch of different things and putting the solutions together from very disparate spaces. Because often really when I read, so 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 borrowed from a bit of math that I didn't know. And so I think if there's a meta-learning here, the meta-learning is there is a set of problems that probably 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 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 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.
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 three hours less time than we could last week, right? But let's dig into it. So it's just not clear to me that the problems being solved are those that are roadblocks to like existing economically useful tasks, right? And if they were, it's not clear to me that they wouldn't have been solved. Like a 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 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.
I don't even know what 12-dimensional spaces are or what that means. And so like 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.
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 wasn't an 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, 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. 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.
Yeah. I do think what's interesting is that part of the reason that the mathematicians are very excited about it though is because they work a certain way. Like if you work 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 whatever. Whereas mathematicians and mathematics has this super long historic arc of layering on abstractions after abstractions. And we'll get, don't worry, we'll get to OSI in a minute. But like this idea that where, what, why they're so excited is like a bunch of math all of a sudden becomes a new level of abstraction.
Is it true that they're so... 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 solves 20% of my job, it's 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. This is amazing. But on the other hand of like, oh, we solved this math problem, oh, I'm so depressed I solved the math problem. Like maybe like literally the entire utility of that problem was keeping somebody employed to solve the problem 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. 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 me stop. No, I mean too cynical on this thing. I love math.
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 don't have to take it anymore. That was like discrete math basics. Yeah. Yeah. And then or algorithmic complexity theory which was a required class for a very long time and now... Do you remember Concrete Mathematics from Donald Knuth? Like... I didn't, I mean you're a Stanford guy, I'm not, but like my state school, my state school we didn't have that but that's a Cornell joke for us Cornellians. But you know, my class I got taught by one of the luminaries in the field of algorithms ironically a Stanford PhD, John Hopcroft of course, who invented for the people who are pragmatic, he invented like two-three trees and a bunch of stuff as his thesis at Stanford. That's a legend. 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 the, this is the four color, this is the four color... proven by computers. No, but that's where I'm going. You just buried the lead. Yeah, 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 algorithmic, 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. Yeah. Yeah. And so it turns out that one of the interesting things was they proved the four-color theorem. Yeah. But they did it, 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 such that no two adjacent areas have the same color. Right. Exactly. And you only need four colors. You'll never need five colors. 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. And so what sort of happened was no one ever arrived 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. Have you actually seen the proof? Yeah. Yeah. 200 pages of combinations, but they basically proved that 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. And to your point, that was actually very, very useful in the practical applications. Right. Right. And certainly as a topology person, like setting strong bounds and things like that. So actually I see... And I think that to me was just a really good lesson in when you have like a new level of abstraction that says this is a whole class of problems that can be solved. Yeah. 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.
So listen, 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 just can't, like you kind of do. So 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 representative of physical phenomenon, right? Like has anybody ever like taken a bunch of equations and actually predicted something 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 trying to compute physical phenomenon like the explosion of a star or like, you know, what would happen to like whatever, an airplane in an air simulator or a wind simulator. 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. Yeah. Like literally the equations of state for the... well, they were modeled, they were just, they were like we could measure temperature in these places. That's exactly right. So it was all based on empirical equations of state. And so I've always wondered 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 answers have any impact on that type of stuff, right? Right. So maybe there's some separate algorithmic domain to your point where they do, or you know, maybe like modeling or logistics. But when it comes to like, you know, will this star explode, will this building stand up, like the actual simulation, I think these things are pretty disjoint. And then I read a lot of these discourses on 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 obviously true. Yeah. Or there's any indication it's true at all.
So the way, one way that I think I might talk about that, you know, again, like this is so out of my league on the actual math and systems people. I'm good, but I'm compelled, I'm inherently a tools person, and so I kind of get this part of it, which is that what's happened is that AI might not be the next tool to solve math problems 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 props to show this off. So, of course, this is the original math tool. And so, before something like this, this is, you know, one of these real ones from like Beijing market.
Well, you know, it's the ones they sell to tourists in French. But I'm very proud of that because I negotiated it down to like seven cents. 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. So this, everybody knows what slide rules are. You know, nobody knows how to use them. This is called a Curta, which is an Austrian, basically it's a round slide rule.
And so it's like a coffee grinder or a pepper mill, and 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.
And this thing is, it's...
Wait, is that used for like multi-number arithmetic or is it used for stuff like logarithms?
No, it's only arithmetic. Well, I think, but of course it depends on how you use it. But it's from the mid-20th century, I think. And my uncle brought this back from the war. But what's incredible is this is like 600 pieces of machined metal inside this. It would cost like $50,000 to make one.
Now, do you know how to use it?
I actually did, but I'm not going to try to do it. I actually, for prepping for this, so I wouldn't be a complete... 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. But the interesting thing is, you know, then all of a sudden a whole new level of problems get solved.
Wait, so you're saying that the new model is the new calculator or the new graphing calculator? The new... I actually remember when, like, remember the TI-85?
Oh, of course. Yeah. Yeah.
I remember that came out. They were 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.
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 what it is, is there's this notion, people react to change more than they react to the baseline of where they all started. And so so much of the concerns in a... like I lived, I literally got like 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 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.
The whole... I was on that. My whole life I've been on the cusp of allowed and not allowed for everybody. I mean, I was there for the graphing calculator. Like, you literally have a blue book just to show all of your work, just to show that you weren't plugging it into the graph.
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 absolutely, absolutely. And but you just play that backwards and you realize that after, you know, after these guys, you went through this march 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...
Well, all of computers are basically from difference engines, which are just trying to calculate integrals. But, and of course, but to be really clear, to calculate integrals so that we could shoot missiles and cannons at each other.
That, okay, so that's what... yes. Which I'm not judging it, I'm just saying...
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 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 old, you know, and then those architectures got co-opted into, of course, the war effort for the logarithms. That's what came from it. Actually, it was very interesting. It was about 5,000 times faster than a human being when it came to, like, you know, doing this. And then of course...
And it didn't make mistakes, which was sort of the...
But it was very specifically math and very specific for economic utility. And the interesting question to me is, like, these models are clearly good at a type of math. Is it one that has somehow unlocked some sort of economic... And I don't know the answer to that.
Oh yeah. I, you know, but I think it's super interesting to keep going with that because to me, what's so cool is that doing that basic calculus for the war and making those missile tables 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, every parent was looking at their kids saying, 'Go learn that in school. Go win the Westinghouse competition. Go win the GE math competition.'
And was that because of the Cold War? Was it because...
Well, obviously the Cold War was a big cultural part of it for sure, but it was just a general, the future. I found this incredibly cool brochure from IBM. It's from 1953. So 1953.
Like, do you just have this stuff in your house?
I just stumbled across it. Like, 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.
But first you got to look. It's got like nuclear, like the whole thing. The future of computing is like a guy with 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 at that point, like, that's it. That's the computer at the time. This is pre-74, 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 the... And people were eating this 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. 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.
And so the idea, and I mean, like, imagine who was reading this in 1953, that it has to explain hexadecimal and binary and compare it to Roman.
And because like nobody, like nobody knew...
What is the name of that thing?
It's just called IBM Light on the Future with like a 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 is, you know, arithmetic unit, binary digit, bit, you know, like cathode ray tube, electrostatic storage tube. And you know, 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 gets to this point about abstraction for us and AI. These have been for 75 years how we thought computers are organized.
Input, storage, arithmetic, control, and output.
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.
Yeah. Everybody forgets networking, by the way.
Well, because most people stop worrying about networking...
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, because, like, but it was solved. Like, there was no, you know, for me, 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 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 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 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. Any CS department, which came out of the math department because of the missiles, ended up being like departments made up of those things, and then it all collapsed and produced us to the systems group.
Sure. Yeah. Yeah. So let me just push on one angle of this. Because I listen, 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.
Oh yeah. Yeah. Right. So for example, 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. 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 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...
Oh, yeah. No, but on the other side is we, I do think we've had that in the past. And so, you know, well, we did, we had the AlphaGo moment. It was the same thing. Like, we did a podcast, not in this room, but...
But even before AlphaGo, we had like, remember when chess got beaten in chess? Yes, we had the IBM chess thing when it was, and Frank Chen and I, we did this podcast at AlphaGo and we had to try to make people understand like why it was a good idea. And I think it's actually pretty reasonable for us to ask the question, which is there's things that these things solve 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 where I think people...
I think don't have like as positive a view, and I would submit that's because it's almost like solving the problem had become the end as opposed to the actual end. 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, right? And so this course, you know, and you're like doing the San Mandala and like your inner peace or something like, but that's not moving the economy forward, right? Well, we're both, look, we're systems people, but I'm actually an apps person. I know you're not, I don't system. Yeah, we're both people, but like system...
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. So, Leander Kahney wrote the book, but it's with Catmull, who at Pixar, and with Dan'l Lewin, who was Steve's super good friend and was also at Microsoft. This book is just fantastic because it explains all of it, encapsulates all of this notion of like building things that people actually need and solve problems. But it pointed out very clearly, you know, the NeXT was actually the machine that Tim Berners-Lee used to write the HTTP protocol, right? So he actually...
And he used the NeXT machine. That's an interesting...
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, and I think there was a great example of a company, a Seattle-based company that was called CyberPizza, and this was like a dot thing that didn't even make it to 2000, I think. But the idea was it was basically Uber or DoorDash 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 I should say for NeXTstep or OpenStep. But the idea was that that was showing what you could do with it. And literally the reaction was like, 'Wow, that's really cool, but have you heard of the telephone?'
Yeah. Yeah. Your point is not everything we've known how to use. So I'm a little focused on like there was a solution on the other side that people are going for. You're making a point that there's a lot of platforms that get built where that's not clear, but clearly they...
Well, the spreadsheet was like, I will show, here's probably one of my last visual aids for today. But like the word processor came out and this is in 1982 and people are using it on Apple II computers and this new kind of computer called CP/M, 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 so some people at law school...
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.
Weren't those called luggables?
Well, no. This was literally just called an Osborne, and it was the only one. So as a guess, Erik, you're a kid. How was the battery life in this?
There was no battery. This giant case that weighed 25 lbs, there's no battery in it. It just plugged in.
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 so it's literally the size of a sewing machine. It's bigger than a legal carry-on ever was. And that was my college computer. But in my senior year of high school, it got banned from Harvard Law School. So someone showed up to do their exams. So at Harvard, they used to bring your typewriter to exams because that way the professor could read it. And two kids brought computers in. One brought an Apple II and one brought the Osborne, and then the school banned them.
They just said this is, and for every reason you could read, and I have the Time magazine articles and the New York Times. Every article you could read reads like, don't use the graphing calculator, don't listen to rap music, or don't read, don't know jazz, or don't play Dungeons and Dragons, or the arguments that are going on now. Three years ago, I tried to get Cornell to use AI in freshman writing when the first, and they just stopped talking to me.
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. And this whole, but the thing is, you could also think of it as a level of abstraction because like no one's going to college now without a computer. Like, can I just put...
So I agree with you, but let me... Right. Right. 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 very much a social thing and not very much. And so it does feel like that's a little different than just...
Going up in abstractions because going up in abstractions, you still tend to have like a deterministic system that's a higher level of abstraction. That like you have a computer and like it's the human being that's kind of defining everything about the problem statement. It feels a little different.
Well, it definitely feels different. 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.
Maxima is, you know, machine aided, what was it? Machine aided computation, symbolic math I think was the, and that was the lab at MIT started in the late 60s, early 70s and that had started to sweep through. So my freshman engineering class we had a version of it that ran on IBM PC. It was called MUMath and like you could, like we got our calculus homework, you marched over to the engineering library, checked out a PC disc and then just typed in the answers to.
So you don't think that so.
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.
No, like 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 during the AI winter that was the 80s, Stanford the biggest, one of the AI winters, Stanford was, and we have a podcast on that from 15 years ago. One of the 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.
And so expert systems were the first time we got a taste of this debate.
I remember very well. I just.
Your classes were all this.
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.
Right. So, the big difference is that stuff was working, but now.
Does work and we're abdicating logic using these.
But so it's interesting because to compare and contrast.
And even in the case of Prolog, you're kind of like for these to, 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.
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 like 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.
Right but I just want to go back this layer and I don't mean to I just I just think so um your my entire career has been moving up layers of stack but like there's always a computer layer of stack.
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.
