transcript

Speaker 1:
[00:00] Today, I'm speaking with Michael Nielsen. You have done many things. You were one of the pioneers of quantum computing, wrote the main textbook in the field of the open science movement. You wrote a book about deep learning that Crisola and Greg Brockman credit them with getting them into the field. More recently, you're a research fellow at the Astera Institute and writing a book about religion, science, and technology. I'm going to ask you about none of those things. The conversation I want to have today is, how do we recognize scientific progress? And it's especially relevant for AI, because people are trying to close the RL verification loop on scientific discovery. And what does it mean to close that loop? But in preparing for this interview, I've realized that it's a more mysterious and elusive force, even in the history of human science than I understood. And I think a good place to start will be Michaelson Morley and how special relativity is discovered. If it's different than the story that you kind of get off of YouTube videos, anyway, I will front you that way and then we'll go in there.

Speaker 2:
[01:01] Okay. Yeah. So Michaelson Morley is one of the famous results often presented as this experiment that was done in the 1880s and that helped Einstein come up with the special theory of relativity a little bit later. So sort of changing the way we think about space and time and our fundamental conception of those things. And there's kind of a big gap, I think, between the way Michaelson and Morley and other people at the time thought about the experiment and certainly the way in which Einstein thought or did not think about the experiment. In actual fact, he stated later in his life, he wasn't even sure whether he was aware of the paper at the time. There's a lot of evidence that he probably was aware of the paper at the time, but it actually wasn't just positive for his thinking at all. Something else completely was going on. So what Michaelson and Moley thought they were doing was, they thought they were testing different theories of what was called the ether. So as you go back to the 1600s, Robert Boyle introduced the idea of the ether. Basically, the idea of the ether is, we know that sound is vibrations in the air, and then Boyle and other people got interested in the question of, like, is light vibrations in something? And they couldn't figure out what it was. Boyle actually did an experiment where he tested whether or not you could propagate light through a vacuum. He found that you could. You couldn't do it with sound. So he introduced this idea of the ether. And then for the next 200 or so years, people had all these kind of conversations about what the ether was and what its nature was. And the Michaelson and Moley experiment was really an experiment to test different theories of the ether against one another. And in particular to find out whether or not there was a so-called ether wind. So the idea was that the earth is passing through maybe this ether wind. And if it is passing through the ether wind, sort of this background, and you shoot a light beam sort of parallel to the direction the ether wind is going in, it'll get accelerated a little bit. And if it's being passed back sort of in the opposite direction, it'll get slowed down a little bit. And you should be able to see this in the results of interference experiments. And what they found, much to their surprise, I think, was that, in fact, there was no ether wind. And that ruled out some theories of the ether, but not all. And Michaelson certainly continued to believe in the ether.

Speaker 1:
[03:29] Okay. So this is what was a shocking part of reading this story from the biography of Einstein that you recommended by, what was his first name?

Speaker 2:
[03:36] Abraham Pius.

Speaker 1:
[03:37] Abraham Pius, subtle as the word. And then also from Emery Lakatos, the methodologies of scientific research programs. The way it's told is that Michaelson morally proved that the ether did not exist. Therefore, it created a crisis in physics that Einstein saw as special relativity. And what you're pointing out is actually was trying to distinguish between many different theories of ether. You know, if you're in space or if you're on earth, it's the same direction of ether, or maybe the ether wind is being carried around by the earth, and so you can't really experience it on earth, but if you go to a high enough altitude, you might be able to experience it. In fact, the Michaelson's experiments were, the famous one is 1887, but he conducted these experiments for basically two decades.

Speaker 2:
[04:17] I mean, for longer than that. He conducted them, I think the first one was in 1881, but he continued to believe until, I mean, he died. He died, I think it was like 1929 or so. It was like the late 20s. And he was still doing experiments in the 1920s, sort of about whether or not the ether existed. And so he continued to believe in the ether to the end of his life. Or I think the last public statement he made is like a year or two before he died. And he still believed, basically believed at that point.

Speaker 1:
[04:45] And in fact, there was another physicist, Miller, who kept doing these experiments in the 1920s. He thought that he went to a high enough altitude, is in Mountain Wilson in California, where I'm high enough that I can actually, the ether winds are not being dragged by the earth. And I've measured the effect of the ether. And Einstein hears about this and he says, this is where you get the famous quote, subtle is the Lord, but malicious he is not. Anyways, I think the reason the story is interesting, for many different reasons, but one is, one of the different ways in which the real history of science is different from this idea you get of the scientific method, is you really can't apply falsification as easily as you might think. It's not clear what is being falsified. Is it just another version of the theory of the ether that's being falsified? Or certainly, you can't induce the theory of special relativity from the fact that one version of the ether seems to be disconfirmed by these experiments.

Speaker 2:
[05:40] Yeah. Certainly, it doesn't show that ideas about falsification are wrong, are falsified. But it does show that the most naive ideas, things are often much more complicated than you think. Michaelson did this experiment in 1881. He was a very young man. And then other people, I think Rayleigh was one of them, pointed out that there were some problems with the way he did it. So they had to redo it in 1887. And at that point, like a lot of the leading physicists of the day, leading scientists of the day, basically accepted this result, that there was no ether wind. But what to do about this? So yeah, sure, maybe you falsified some theories of the ether. There are others that you haven't falsified at all at this point. And people sort of set to work on developing those. It is funny, people will phrase it as, showed that the ether didn't exist. And even just the word the there is kind of a misnomer. You actually had a ton of different theories and a couple of leading contenders. So yeah, there's some version of falsification going on. But how you respond to this new experiment is very, very complicated. And most people responded, certainly the leading physicists of the day responded by saying, okay, this gives us a lot of information about what the ether must be. But it doesn't tell us that there is no ether.

Speaker 1:
[07:04] In fact, Lorentz at the end of the 19th century, before Einstein, figures out the math, how you convert from one reference frame to another reference frame, comes up with the Lorentz transformations, which is basically the basis of special relativity. But his interpretation is that you are converting from the ether reference frame to these non privileged other reference frames, if you're moving relatives to the ether. And his interpretation of length contraction and time dilation is that this is the effect of moving through the ether, and you have this pressure, and that pressure is warping clocks, it's warping measures of length. And the interesting thing here is that experimentally, you cannot distinguish Lorentz's interpretation from special relativity.

Speaker 2:
[07:55] Yeah, I think that's a strong statement. I mean, Lorentz introduces this quantity called local time, which he regards as, he's not trying, my understanding is he's not trying to give really a physical interpretation of this, but it's what Einstein would later just recognize as time in another inertial reference frame. And he's not trying to attribute much physical meaning to it. But I think Pancrè gets much closer to later on to realizing that actually this is the time that's registered by Clarks. But if you think about, you go, what is it, it's 40 odd years later, people start doing these muon experiments where they see basically cosmic rays hit the top of the atmosphere, they produce a shower of muons, and you can look to see at different heights in the atmosphere, you can look to see how many of those muons remain, and they decay over time. A very strange thing happens, which is that they're decaying way, way, way too slow. So you expect, actually, they shouldn't be able to last the whole way through the atmosphere at all. There's just their decay rate is too quick. If you were in a classical theory, but if in fact their time really has slowed down, it's okay. In fact, the measured decay rates in 1940, and then there have since been more accurate experiments done, match exactly what you expect from special relativity. So that's the kind of thing where, again, if Lorenz had been alive, he'd been dead 10 or so years at that point. If he'd been alive, I'm sure he would have tried, or it seems quite likely that he would have tried to save his theory by patching it up yet again. But it would have been a massive, I mean, that's a real setback. It starts to just look like, oh no, time is, this thing that Lorenz introduced as a mathematical convenience, no, no, no, that's actually what time is.

Speaker 1:
[09:54] Right.

Speaker 2:
[09:55] For the muons at least, and then there's a whole bunch of other experiments that show this very similar phenomenon.

Speaker 1:
[10:00] And when was that experiment done?

Speaker 2:
[10:01] It was, I think, 1940 or 19, it might have been published in 1941.

Speaker 1:
[10:04] So, maybe then to rephrase, change my claim, it's not that you could not have distinguished them, but the scientific community adopted what we in retrospect consider the more correct interpretation before it was actually empirically or experimentally shown to be preferred. So, there's clearly some process that human science does, which can distinguish different theories.

Speaker 2:
[10:29] Can you just interrupt? I mean, you used the word process, and it's interesting to think about that term. Like process kind of carries connotations of something said in advance, it's something, and it's much more complicated in practice. You have people like, I mean, Einstein just absolutely utterly admired, and Poincare, one of the greatest scientists who ever lived, and Michaelson, I mean, another truly outstanding scientist, never reconciled themselves. So, it's not as though there's some standard procedure that we're all using to reconcile these things. No, great scientists can remain wrong for a very long time after the scientific community has broadly changed its opinion. But there's no centralized authority, right, sort of saying, or centralized method.

Speaker 1:
[11:20] Yeah. I mean, that is the interesting thing. There's progress even though it is hard to articulate the process by which happens, the heuristics that are used. Anyways, you mentioned Poincare. And so, Lorenz has the math right, but the interpretation wrong. And you should explain, it seems like Poincare had the opposite where he understood that it's hard to define simultaneity because it requires uncircular definition with time or velocity of something that might arrive at a midpoint together. But velocity is defined in terms of time. And I find this interesting, there's a couple other examples we could call on. But like, there is this phenomenon in the history of science where somebody asks the right question, but then they don't sort of clinch it. And I'm curious what you think is happening in those cases.

Speaker 2:
[12:09] I mean, I think you sort of, you actually do want to go case by case and try and understand that it's not necessarily clear that they're doing the same thing wrong in all other cases. I mean, the Poincare case is amazing. He seems to have understood the principle of relativity, the idea that the laws of physics are the same in all inertial reference frames. He seems to have understood that the speed of light is the same in all inertial reference frames. He doesn't actually phrase it quite that way, is my understanding, but I don't speak French. And these are basically the ideas that Einstein uses to deduce special relativity. But then he also has this additional sort of misunderstanding where he thinks that length contraction is a dynamical effect, that somehow particles are being pushed together by some external force, something is going on dynamically. And he doesn't understand that it's purely kinematics, that actually space and time are different than what we thought. And you need to fundamentally re-think those things. So it's almost like he knew too much, he had sort of almost two grand a vision in mind, and Einstein is sort of almost subtracts from that and says no, no, no, no. It's space and time are just different than what we thought. And here's the correct picture. And there's a paper, I think it's 1909, where Pankhara, like he's still got this dynamical picture of what's going on with the length contraction. And we just, this is just not necessary. This is a mistake from the modern point of view. And so, why is he doing this? Like, why is he clinging on to this idea? And I don't know, I've obviously never met the man. It would be fascinating to be able to talk it over and to try and understand. But he, I mean, his expertise seems to be getting in the way. He knows so much, he understands so much. And then he's not able to let go of these things. Actually, a really interesting fact is that a few years prior, so 1890s, Einstein's a teenager, he believes in the ether too. Like he knows about this stuff, but like he's just not, he's not quite as attached, obviously, as these older people were. And maybe they were a little bit prisoner of their own expertise. That's my guess. I mean, historians of science could, some would certainly disagree.

Speaker 1:
[14:42] Well, there's, then there's the obvious stories where Einstein himself later on is said to have not latched on to the correct interpretations of quantum mechanics or cosmology because of his own attachments.

Speaker 2:
[14:56] Yeah.

Speaker 1:
[14:56] I think that the bigger question I have is like, the muon example is a great example of these long verification loops and how progress seems to be happened by the scientific community faster than these verification loops imply. Maybe the clearest example is, Aristarchus in second century BC comes up with the idea of heliocentrism. The ancient Athenians dismissed it on the grounds that what we should see as the earth is moving around the sun, if really the sun is the center of the solar system, the star should move relative to the earth. And the only reason that is not possible, that would not be the case is the stars are so far away that you would not observe this. And it's only in 1838 that stellar parallax is actually measured and so we didn't need to wait until 1838 to have heliocentrism, right? We didn't need to wait for the experimental validation to understand Copernicus is better in some way. In fact, when Copernicus first comes up with theories, it's well known that the Ptolemaic model was more accurate because it had all these centuries of adding on these epicycles, was maybe less well appreciated. It was also in some sense simpler because Copernicus actually had to add extra epicycles. It had more epicycles in the Ptolemaic model because he had this bias that the urge should go in a perfect circle in equal time. Anyway, I think this is an interesting story because it's not more accurate. It's not a simpler theory. How could you have known in Ex ante that Copernicus was correct and Ptolemaic was not?

Speaker 2:
[16:34] I mean, good question. I don't know entirely the answer. I can give you certainly a partial answer that I, centuries in the future, start to find very compelling. I'm sure it's part of the story at least, which is one of the big shocks for Newton. Eventually, he did understand Kepler's laws of motion eventually, so you're able to explain sort of the motions of the planets in the sky. But he also, out of the same theory, his theory of gravitation, was able to explain terrestrial motion, so he was able to explain why objects move in parabolas on the Earth, and he's able to explain the tides in terms of the sun's, the moon and the sun's effect, gravitational effect on water on the Earth. And so you have what seem like three very different disconnected phenomena, all being explained by this one set of ideas. Right. That, I think, starts to feel, that's very compelling, at least to me, and I think most people find that very, very satisfying once they eventually realize it.

Speaker 1:
[17:51] Have you read the Keynes' biography of Newton?

Speaker 2:
[17:53] Oh, he's written an entire book.

Speaker 1:
[17:56] No, no, the essay.

Speaker 2:
[17:57] Yeah, yeah, sure, sure, sure. I love that. I mean, this description of him as the last of the magicians is wonderful. Yeah.

Speaker 1:
[18:05] In fact, I think it's maybe worth superimposing or you should read out that one passage of the thing.

Speaker 2:
[18:13] So, it's from, actually, I believe it was a talk that he gave at Cambridge, not long before he died. He'd acquired Newton's papers somehow. Then he gave a lecture, I think, twice about this or that his brother, Jeffrey, gave it the other time because he was too ill. There's just this wonderful, wonderful quote in the middle. Actually, the whole thing is really interesting. But I love this particular quote. Newton was not the first of the age of reason. He was the last of the magicians, the last great mind which looked out on the visible and intellectual world with the same eyes as those who began to build our intellectual inheritance rather less than 10,000 years ago. Like this idea that people have that Newton was the first modern scientist is somehow wrong. There's some truth to it, but he really had this very different way of looking at the world. There was part superstitious and part modern. It was a funny hybrid. He's this transitional figure in some sense. That phrase, the last of the magicians, I think really points at something.

Speaker 1:
[19:29] The thing I'm very curious about with Newton is whether it was the same program, the same heuristics, the same biases that he applied to his alchemical work as he did to the understanding of astronomy. This is from the Gaines Essay. There was extreme method in his madness. All his unpublished works on esoteric and theological matters are marked by careful learning, accurate method, and extreme sobriety of statement. They are just as sane as the Principia if their whole matter and purpose were not magical. They were nearly all composed during the same 25 years of his mathematical studies. So, clearly there was some aesthetic which motivated people like Einstein to, say, reject earlier ways of thinking and say, no, the ether is wrong and there's a better way to think about things. Same with Newton. And the question I have is whether similar heuristics towards parsimony, towards aesthetics, etc. would be equally useful across time and across disciplines, or whether you need different heuristics. And the reason that's relevant is even if you can't build a verification loop for science, maybe if the taste has to point in the same direction, you can at least encode that bias into the AIs and that would maybe be enough.

Speaker 2:
[20:49] Yeah, I mean, these questions, like the point is that where we always get bottlenecked is where the previous processes and heuristics don't apply, right? Like that's almost sort of definitionally what causes the bottlenecks. Because people are smart, they know what has worked before, they study it, they apply the same kinds of things. And so they don't get stuck in the same places as before. They keep getting bottlenecked in different places. I mean, that's overgeneralizing a bit, but I think it's the right. Like, if you're attempting to reduce science to a process, you're attempting to reduce it to something where there is just a method which you can apply and you turn sort of the crank and out pops inside. I mean, you can do a certain amount of that, but you're going to get bottlenecked at the places where your existing method doesn't apply. But definitionally, there's no crank you can turn. You need a lot of people trying different ideas. And the more difficult the idea is to have, the greater the bottleneck, but then also the greater the triumph. Quantum mechanics is like, I mean, it's a great example of this. It's such a shocking set of ideas. It's such a shocking theory. Actually, the theory of evolution in some sense is also quite a shocking idea. Not the principle of the natural selection, but that it can explain so much. That's a shocking idea.

Speaker 1:
[22:19] Existing safety benchmarks claim that, at least for today's top models, attacks are only successful a few percent of the time. This sounds great, but Labelbox researchers were able to jailbreak these very same models about 90 percent of the time, even the ones that have the strongest reputation for safety. And the disconnect here is that the prompts which underlie these public safety benchmarks are all framed in a very naïve way. There's no attempt to disguise harmful intent. These prompts will just ask models to hack into a secure network and to do so without getting caught. But real bad actors don't write like this. So Labelbox built a new safety benchmark from the ground up. Their prompts reflect real adversarial behavior by stripping out obvious trigger phrases and wrapping their requests in fictional scenarios. For example, instead of outright asking another lamp to steal somebody's identity, the prompt will frame it as a game. A light-bearer who's trying to hide from dark forces needs a handbook on how to disguise themselves as somebody else. This safety research is linked in the description. If you think this could be useful for your own work, reach out at labelbox.com/dwarkesh. So Principia Mathematica is released in 1687. The Origin of Species was released in 1859. At least naively, it seems like Darwin's theory, the theory of natural selection. It's conceptually easier than the theory of gravity. I asked Terence Tao this question. But yeah, there was this contemporaneous biologist with Darwin, Thomas Huxley, who read this and said, how extremely stupid to not have thought of this. And nobody ever reads the Principia Mathematica and thinks, God, why didn't I beat you into the final chair?

Speaker 2:
[23:59] No.

Speaker 1:
[24:02] And so yeah, what's going on here? Why did Darwinism take so much longer?

Speaker 2:
[24:06] You know, the idea must have been known to animal breeders for a long time, at some level. Right. Or certainly, large chunks of the idea were known. Artificial selection was a thing. And in some sense, Darwin's genius wasn't in having that idea. It was understanding just how central it was to biology. That, you know, you can potentially sort of go back and you can explain a tremendous amount about all of the variety of what we see in the world with this as not necessarily the only principle, but certainly a core principle. And you know, so he writes this wonderful, wonderful book, The Origin of Species. And it's just, you know, so much evidence and so many examples and sort of trying to tease this out and see what the implications are. And you know, to connect it to as much else as he possibly can, to connect it to geology and to connect it to all these other things. So that's sort of hard work that, you know, making the case that it's actually relevant all across the biosphere, you know, is what he's doing there. He's not just having the idea, he's making a compelling case that no, it's intertwined with absolutely everything else.

Speaker 1:
[25:30] Yeah, the motivation of the question was Lucretius, who is this first century Roman poet, has an idea that seems analogous to a natural selection, about, you know, species get fitted more over time to their environments or species do seem fit to their environment. And so we're like, okay, well, why did this go nowhere for 19th centuries? And then I looked into it, or more accurately, I said, well, what exactly was Lucretius' idea here? And it actually is extremely different from what real natural selection is. He thought there was this generative period in the past where all the species came about, and then there was this one-time filter, which resulted in the species that are around today, and they became fit to the environment. He did not have this idea that it is an ongoing gradual process or that there is a tree of life that connects all life forms on earth together, which is, by the way, it's an incredibly weird fact that every single life form on earth has a common ancestor.

Speaker 2:
[26:24] It's not incredibly weird, right? If you think that the origin of life must have been very hard, like that there's a bottleneck, then it's not so surprising.

Speaker 1:
[26:31] Yeah. There's also this verification loop aspect where, even if Newton might be harder in some sense, if you've clinched it, you can experimentally, I know validate is the wrong word philosophically, but you can give a lot of base points to the theory. You can be like, okay, I have this idea of why things fall on earth, I have this idea of why orbital periods or planets have a certain pattern. Let's try it on the moon, which orbits the earth. And in fact, it's weird, the orbital period matches what my calculations imply.

Speaker 2:
[26:58] And the tides work correctly.

Speaker 1:
[27:00] Exactly.

Speaker 2:
[27:00] Yeah, it's just amazing.

Speaker 1:
[27:01] Whereas for Darwinism, it takes a ton of work for Darwin to compile all this sort of cumulative evidence, but there's no individual piece that is overwhelmingly powerful.

Speaker 2:
[27:10] And there's a whole bunch of problems as well. Like, he doesn't really understand what the mechanism is. He doesn't understand genes, like all these things.

Speaker 1:
[27:19] The very interesting thing in the history of Darwinism is this idea which theoretically you could come up with at any time. There is almost identical independent creation of that idea between Alfred Wallace and Charles Darwin. So much so that I think Wallace sends his manuscript to Darwin, and is like, what do you think of this idea? And Darwin is like, fuck.

Speaker 2:
[27:40] I don't think that's an exact quote, but I think it's pretty much correct, yeah.

Speaker 1:
[27:44] And then so they actually end up presenting their ideas together in the spirit of sort of sportsmanship. And so then, yeah, why was this period in the 1860s or 1850s? What was that the right time for these ideas? So when you come up with different ideas, one is geology. So in 1830s, I think Charles Lyell figures out that there's been millions and billions of years of time that's existed on an earth. Then paleontology shows you that actually organisms have existed, fossils have existed for that time. So life goes back a long time. And in fact, you can even find fossils for intermediate species that show you the tree of life. In fact, between humans and other apes as well, there's intermediate humans. There's the age of colonization. And you have all these voyages. We're going to do this biogeography. And I guess that all must have been necessary because in fact, there's a hugest pure parallel innovation and discovery in the history of science. So maybe it is another piece of evidence to actually more have to be in place for a given idea to be discovered because if it's not discovered for a long time and then spontaneously many different people are coming up with it, that shows you that actually the building blocks were in some sense necessary.

Speaker 2:
[28:47] Yeah, yeah, yeah. This example of Layel and other geologists, sort of early 1800s, basically having this idea of deep time does seem to have been crucial. I know Darwin was very influenced by Layel. If you don't have at least sort of tens or hundreds of millions of years, evolution just starts to look like a non-starter. We should be seeing radical change. In order to make it work on sort of a time scale of, say, five to 10,000 years or 6,000 years, you would need to be seeing evolution occurring at a massive rate during human lifetimes and we're just not seeing that. So that does seem to have been a blocker. It's interesting, to your question, what other blockers were there? Were there any others? I don't know.

Speaker 1:
[29:46] Right. Or how much earlier could you, in principle, have come up with that if you're much smarter?

Speaker 2:
[29:51] Actually, let me just go back, sort of zoom out to your original question. So you're talking about sort of the verification loop in AI, and you're something, an example, I think that should give you pause there is, the big signature success so far is certainly AlphaFold. Yeah. Of course, AlphaFold really isn't about AI. Massive fraction of the success there is the protein data bank. So it's X-ray diffraction, it's NMR, it's cryoM, and the several billion dollars that was spent obtaining whatever is 180,000 protein structures. So it's basically the story of we spent many, many decades obtaining protein structure just by going out and looking very hard at the world experimentally, and then we fitted a nice model at the end of it, and that was like a tiny fraction of the entire investment. But it's definitely not, you know, that's a story of data acquisition principally. It's not only, I mean, the AI bit is very, very impressive. It's quite remarkable, but it is only a small part of the total story.

Speaker 1:
[30:56] Off-the-fold is very interesting, and I philosophically wonder what you think of it as a scientific theory or a scientific explanation. Because if over time, I guess the world has become harder to understand. I'm going to, as I'm saying things, because you're such a careful speaker, I'm, I say this phrase and I'm like, will he actually buy that premise? But yeah, there's, you know, we need to fit models to things rather than, at least in some domains, we're trying to fit models to things rather than coming up with underlying principles that explain a broad range of phenomenon. And so compare, say, the theory of general relativity or any theory which is, that's out to some equations versus off a fold, which is encoding these different relationships between different things we can't even interpret over a hundred million parameters. And are those really the same thing? Because GR can predict things you could have never anticipated or was never meant to do, like, why does Mercury's orbit precess? And off a fold is not going to have that kind of explanatory reach. And I want to get your reaction to that.

Speaker 2:
[32:05] Yeah, I think it's an incredibly interesting question. I mean, maybe a really pivotal question in the sense of, if you subject a very classic point of view, you want these deep explanatory principles, you want sort of a few free parameters as you possibly can, you want very simple models, which explain a lot. And alphafold doesn't look anything like that. And so you might just sort of say, oh, well, it's nice, it's maybe helpful as a model, but it doesn't have, it's not a scientific explanation. So that's kind of, that's like a conservative point of view. That's sort of answer one to the question. I think answer two is to say something like, maybe you shouldn't think about alphafold as an explanation in the classic sense. But maybe it contains lots of little explanations inside it. And so maybe part of what you can get out of like, interpretability work is you can go into alphafold and you can start to extract certain things. Maybe basically by doing sort of archaeology of alphafold, we can actually understand a great deal more about these principles. You can start to extract it all. That circuit does this interesting thing and we learn this. So I don't know to what extent that's been done with alphafold. I know it's been done a little bit with some of like the chess models. I believe it's alphazero. There seem to be some strategies which were certainly borrowed by Magnus Carlsen at least, which he seems to have just taken from alphazero. I mean, I don't think there's any public confirmation of this, but some experts have noticed that he changed his game quite radically after some public forensics were released on how alphazero worked. So that's kind of an example where I think human beings are starting to extract meaning out of these models. And maybe that starts to lead to sort of viewing the models as a potential source of explanations. You need to do more work because they're not very legible upfront, but you can extract them potentially. And I think that's kind of an interesting intermediate situation where they're not explanations, but you can extract interesting explanations out of them. You can use them as kind of a source. And I think the third and the most interesting possibility is, no, they're a new type of object in some sense. They should be taken very seriously as explanations. But in the past, we haven't had the ability to really do anything with them. And now we're going to have sort of new interesting, new sort of actions which we can do. We can merge them, we can distill them, we can do all these kinds of things. And there's going to be sort of almost a new, it's a big opportunity sort of in the philosophy of science to start to do that. There's sort of like a anticipation of this in some sense, I think in the way. Certainly, I know some mathematicians and physicists who, I mean, historically, if you had like a 100 page equation, which and that's the kind of thing that does come up, I mean, there's just nothing you can do if it's 1920. There is nothing you can do. At that point, you give up on the problem. Now, today, with tools like Mathematica, you can just keep going. That's an object now. That's a thing that you can work with. There are examples where people work with these things that formerly were regarded as too complicated. Sometimes, they get simple answers out of the end. That's just an intermediate working state. I wonder if something similar is going to happen in this particular case where you can take these models and just use them in a little bit the same way people do with Mathematica and take them seriously as they're not explanations in the classic sense, but they'll be something else which interesting operations can be done on.

Speaker 1:
[35:54] The thing I worry about is, suppose that you, it's 1600 or 1500 and you're training a model on, this is a weird history where we developed deep learning before we had cosmology. But suppose we live in that world and you're observing how there's a stars, they don't seem to move, the planets have all these weird behaviors. Then you train a model on that and then you do some kind of interp on it and trying to figure out, well, what are the patterns we see here? What you'd see are just these, you just be able to keep building on Ptolemy's model. You'd see like, oh, there's more epicycles we didn't notice. There's another epicycle. It's parameters whatever to whatever encode epicycle this, parameters whatever encode the next epicycle. So if you were just trying to figure out, why is the solar system the way it is from observational data? You could just keep adding epicycles upon epicycles, but it really took one mind to integrate it all in and say, here's my, here's what makes more sense overall.

Speaker 2:
[36:52] So, I mean, they're like, you know, I mean, this is sort of to my point that we don't really understand what to do with the models. Like, sort of, we don't have like the verbs necessarily yet. But, you know, it is certainly interesting to think about the question, you know, where you start to apply constraints to the models, you know, it's sort of essentially saying, what's the simplest possible explanation? Or, you know, can you simplify? Can you give me sort of the 90-10 explanation? Can you add go further and further and further sort of in boiling it down? So, it might be that indeed, they sort of start out by providing a very, very complicated many, many, many parameter model. But you can just force the case and basically that's scaffolding, which may be the very early days of their attempt to understand something. But they're forced through that to a much more simple understanding.

Speaker 1:
[37:52] So, sorry for misunderstanding, but it sounds like you're saying maybe there's some sort of regularizer, some sort of distillation you could do of a very complicated model that gets to a truer, more parsimonious theory. But yeah, just take Ptolemy versus Copernicus, right? So, you start off with lots of Ptolemaic epicycles, and then you try to distill this model, and maybe gets rid of some of the epicycles that are less and less sort of necessary to get the mean squared error of the orbits to match. But at some point, it has to do this thing, which is like switch two things.

Speaker 2:
[38:25] Yeah, yeah, yeah.

Speaker 1:
[38:26] And locally, it actually doesn't make things more accurate. It's sort of in a global sense that it's a more progressive theory.

Speaker 2:
[38:32] Yeah, yeah.

Speaker 1:
[38:33] And there's some process, which obviously humanity did over its bandwidth, which did that regularization or did that swap. But raw gradient descent, it seems like I don't really feel like it would do that.

Speaker 2:
[38:45] I could say, I mean, you think about the example of going from Newtonian gravity to Einstein's general theory of relativity. And these are shockingly different theories. And the question is, what causes that flip? And as nearly as I understand the history, what goes on is Einstein develops special relativity. And pretty much straight away, he understands. I mean, it's a very obvious observation. In special relativity, influences can't propagate faster than the speed of light. And in Newtonian gravity, action is at a distance. In fact, it's straight away in special relativity. You could use Newtonian gravity to do faster than light signaling. You could send information backwards in time. You could do all kinds of crazy stuff. And so, it's not a big leap to realize, oh, we have a big problem here. And so, that's the forcing function there. You've realized that your old explanation is not sufficient. You need something new. And then you're going to start by doing the simplest possible stuff. And it just turns out that a lot of that stuff doesn't work very well. And so, you're sort of forced. In fact, it is interesting. You know, he is sort of forced to go through these steps of gradually, it gets quite more complicated and it's sort of wrong in a variety of ways. And the final theory appears really shockingly simple and beautiful. But it's gone through some somewhat ugly intermediate stages. Yeah. Yeah.

Speaker 1:
[40:20] So, if you're thinking about what does it look like to have AI accelerate science? There's one for maybe well-understood domains where we just want local solutions, like how does this protein fold? We just train a raw model using gradient descent. Then there's things like coming up with general relativity, where you couldn't really just train on every single observation in the universe and hope that general relativity pops out. And so, what would it require? Well, it also certainly wasn't immediately discovered, right? So, it was a lot of decades of thought. And I guess you'd need independent research programs where people will start off with these biases, where Einstein is just initially motivated by this thought experiment of, you know, can you distinguish the effect of gravity from just being accelerated upwards? And then you just need different AI thinkers to start off with these initial biases and see what can germinate out of them.

Speaker 2:
[41:13] Yeah.

Speaker 1:
[41:14] And then the verification loop for that might be quite long, but you just need to keep all those research programs alive at the same time.

Speaker 2:
[41:19] Yeah. I mean, I think there's like, I mean, this point that you make about sort of keeping all the different research programs alive, like that, that I think is very important and somehow central. I mean, a great example is situations where the same answer has been correct in some circumstances and wrong in other circumstances. So, the planet Uranus was like not in quite the right spot, and people very famously predicted the existence of Neptune on this basis. Wonderful, massive success for Newtonian gravity. The planet Mercury is not in quite the right spot. You predict the existence of some other distorting planet. Turns out that doesn't exist. Actually, the reason Mercury is not in the right spot is because you need general relativity. And so, you've sort of, you've pursued very similar ideas and it's been very successful in one case and it's been completely and utterly unsuccessful in the other case. And I think, I mean, a priori, you can't tell which of these is the thing to do and you actually need to do both. Yeah. And so, I mean, this is certainly very true in the history of science that this kind of diversity, where you just have lots of people go off and pursue lots of potentially promising ideas, you just need to support that for a long time. And it's hard to do that for a variety of reasons. But it does seem to be very, very, very important.

Speaker 1:
[42:46] So this example of Uranus versus Mercury is very interesting. In one, I think it illustrates sort of the difficulty of falsificationism. Like, the orbit of Uranus is in some sense falsifying Newtonian mechanics. But then you make some ancillary prediction that says, oh, the reason this is happening is there must be another planet which is affected perturbing Uranus' orbit. And I think it's Le Verrier in 1846. Point of telescope in the right direction, you find Uranus.

Speaker 2:
[43:20] Neptune.

Speaker 1:
[43:21] Oh, it's there. Neptune, yes. But with Mercury, yeah, it's observed that the ellipse which forms this orbit is rotating 43 arc seconds more every century than Newtonian mechanics would imply. So people say that there must be a planet inside Mercury's orbit. They call it Vulcan. And the point of the telescopes is not there. But if you're a proper Newtonian, what you do is say, well, maybe there's some cosmic dust that's occluding this planet. Or maybe the planet is so small we can't see it. Or maybe there's some, let's build even more powerful telescope. Or maybe there's some magnetic field which is sort of occluding our measurements.

Speaker 2:
[43:56] And this happens over and over, right? Like, you know, there's just so many stories which are exactly like this. Right. I mean, an example I love from, you know, in the 1990s, some people noticed that the Pioneer spacecraft weren't quite where they were supposed to be. And so, you can get very excited about this. Oh my goodness, general relativity is wrong. We have like, you know, maybe we're going to discover the next, the next theory of gravity. And today, the accepted explanation is that, no, actually, there's just a slight asymmetry in the spacecraft. It turns out that the thermal radiation is like, slightly larger in one direction than the other, and that's causing a tiny little acceleration towards the sun. And most of the time, when there's these apparent exceptions, it's just something like that's going on. It's very much like the Vulcan, the Mercury Vulcan case. But every once in a while, it's not. And a priori, you can't distinguish these. But I mean, science is just full of these. It's funny, too, like the way we tell the history of science, it sounds so simple, like, oh, you just focus on the right exception, and you realize that you need to throw out the old theory, and lo and behold, your Nobel Prize awaits. But in fact, these exceptions are all over the place, and 99.9 percent of the time, it just turns out to be some effect like this thermal acceleration, in the case of the Pioneer spacecraft. So, unfortunately, there's a lot of selection bias going into those stories.

Speaker 1:
[45:32] And the thing is, there's no ex anti-heuristic, which tells you which case you're in. And just to spell out why I think this is important, is because some people have this idea that EI is going to make disproportionate progress towards science, because it makes disproportionate progress towards domains where there's tight verification loops, and so it's really good at coding, because you can run unit tests. And science may be similar, because you can run experiments. I think what that doesn't appreciate, one, is that experiments actually don't, there's an infinite number of theories that are compatible with any given experiment. And over time, why we glob on to the, well, at least in retrospect, we think is a more correct one is, as we're discussing in this conversation, sort of hard to articulate. Lakatos actually has all kinds of interesting examples in the book about these kinds of hostile verification loops that are extremely long lasting. So one, he talks about his Prout or Prout, I don't know how to pronounce it, but there's this chemist in 1815, he hypothesizes that all atomic nuclei must have whole number weights. They're basically all made of hydrogen. And the reason he thinks this is because if you look at the measure rates of all elements, it does seem that almost all of them do have a whole number of weights, but then there's some exceptions. For example, chlorine comes out at 35.5. And so then there's all these ad hoc theories that people in this school keep coming up with, like, oh, maybe there's chemical impurities. But then there's no chemical reaction you can do, which seems to get rid of this. Maybe it's fractions of whole numbers, so it's 35.5, it can be halves. But actually, if you measure chlorine even closer, it's 35.46. It's actually getting further away from the correct fraction. And later on, what is discovered is what you're actually measuring is different isotopes, which cannot be chemically distinguished. They can only be physically distinguished. But so then you just have 85 years before we realize what an isotope is, where the verification was actually actively hostile against you, against the correct theory, and you just need this remnant to be defending. There's no ex-santa reason it's the prefferred theory. Just as a community, we should just have people defend, try to integrate new observations, even if they don't seem to fit their school of thought with what they believe, and hopefully, if enough of that happens. Anyways, I guess the thing I'm trying to articulate is, the difficulty with automating science. Yeah.

Speaker 2:
[47:50] I mean, the question is, where is the bottleneck at some level? And are we primarily bottlenecked on one thing or one type of thing, or are we bottlenecked on multiple types of thing? So certainly talking to structural biology people, they seem to think that AlphaFold was an enormous advance. It was a shock. So at some level, yes, AI can, you know, it seems certain it can help us speed up science. So it is helping with a certain type of bottleneck. That doesn't mean, though, as you're saying, that it's necessarily going to help with all kinds of bottlenecks. And sort of, I suppose, the question you're pointing out is, like, what are the types of bottlenecks that remain? And what are the prospects for getting past them? I think even in the case of coding, like, it's really interesting, you know, talking to programmer friends, at the moment, they're all in this state of shock and high excitement and they're all over the place, actually, kind of talking to them. You do wonder, like, where is the bottleneck going to move to? So certainly one thing that a lot of them seem to be bottlenecked on is now having interesting ideas and in particular having interesting design ideas. So there's not really a verification loop for knowing, oh, that design idea is very interesting. So they're no longer nearly as bottlenecked by their ability to produce code, but they are still bottlenecked by this other thing. They always were, formerly, they weren't bottlenecked on it because just writing code took so much of their time, they could sort of have lots of ideas while they're out. They take three weeks to implement their prototype, and then they would implement the next version. Now, they're taking three hours to implement the prototype, and they don't have as good ideas after that from a design point of view.

Speaker 1:
[49:39] Last year, I predicted that by 2028, AI would be able to prep my taxes about as well as a competent general manager. But we're already getting pretty close. As I shared before, I use Mercury both for my business and my personal banking. So I recently gave an LLM access to my transaction history across both accounts through Mercury's MCP. I asked it to go through all my 2025 transactions and flag any personal expenses that seem like they should actually be charged to the business. And this worked shockingly well. Mercury's MCP exposes a bunch of detailed information, things like notes and memos and any JPEGs of receipts and PDF attachments. So my LLM had plenty of context to work with. One of my favorite examples happened with a charge to Bay Paddell. If you looked at the vendor alone, you would have had to assume that it's a personal expense. But the LLM looked at the receipt and the attached note in Mercury and realized it was actually a team bonding exercise from our last in-person retreat. So a legitimate business expense. I imagine it will be a while before traditional banks have MCP. Functionality like this is why I use Mercury. Go to mercury.com to learn more. Mercury is a fintech company, not an FDIC insured bank. Faking services provided through Choice Financial Group and Column NA members FDIC. You have a very interesting take. I think it was a footnote when I know where your S is and I couldn't find it again. Which was that it's very possible that if we met aliens, that they would have a totally different technological stack than us. And that contradicts, I guess, a common sense assumption I had that I never questioned, which is that science is this thing you do relatively early on in the history of civilization, where you get to a point and you have a couple of hundred years of just cranking through the basics, understanding how the universe works, et cetera, and you've got it, you've got science. And then basically everybody would converge on the same quote unquote science. And so I found that a very interesting idea and I want you to say more about it. Yeah.

Speaker 2:
[51:34] I mean, I think probably the idea there that I'm at least somewhat attached to is the idea that the tech tree or the science and tech tree is probably much larger than we realize. I mean, we're sort of in this funny situation. People will sometimes talk about a theory of everything as a potential goal for physics, and then there's this presumption somehow that physics is done once you get there. And of course, this is not true at all. If you think about computer science, computer science basically got started in the 1930s when Turing and Church and so on just laid down what the theory of everything was. They just said, here's how computation works, and then we've spent 90 odd years since then just exploring consequences of that and gradually building up more and more interesting ideas. Those ideas are, to some extent, you can just regard as technology, but to some extent, insofar as they're discovered principles inside that theory of computation, I think they're best regarded as science, and in some cases, very fundamental science. Ideas like public-key cryptography, they're just incredibly deep, very non-obvious ideas, which in some sense lay hidden already in the 1930s. My expectation is that there will be different ways of exploring this tech tree, and we're still relatively low down. We're still at the point where we're just understanding these basic fundamental theories and we haven't yet explored them. A thing which I think is quite fun is if you look at just the phases of matter. When I was in school, we'd get taught that there are three phases of matter or sometimes four phases of matter or five phases of matter, depending a little bit on what you included. Then as an adult, as a physicist, you start to realize, we've been adding to this list. We've got superconductors and superfluids and maybe different types of superconductors and Bose-Einstein condensates and the quantum Hall systems and fractional quantum Hall systems and, and, and, and, and, and, and it's starting to turn out, it looks like actually, there's a lot of phases of matter to discover. And we're going to discover a lot more of them. And in fact, we're going to be able to start to design them in some sense. I mean, we will still be subject to the laws of physics, but there is this sort of tremendous freedom in there. And this looks to me like, oh, we're down at sort of the bottom of the tech tree. We've barely gotten started there. And I expect that to be the case sort of broadly. Certainly in terms of, I think, programming is a very natural place to look. The idea that we've discovered all the deep ideas in programming just seems to be sort of obviously ludicrous. We keep discovering sort of what seems like deep, new, fundamental ideas. And I mean, we're very limited. We're basically slightly jumped up chimpanzees. So we don't, we're slow and it's taking us time. But what do we look like sort of another million years in the future in terms of all of the different ideas which people have had around how to manipulate computers, how to manipulate information? I think we're likely to discover that actually there are a lot of very deep ideas still to be discovered. So who was it? I think it was Knuth in the preface to the art of computer programming. So something like, he started this book back in the 60s, and he talked to a mathematician. He was a bit contemptuous and said, look, computer science isn't really a thing yet. Come back to me when there's a thousand deep theorems. And Knuth remarks, and he's writing this now decades later, the preface, there clearly are a thousand deep theorems now. And that means, it's really interesting to think of it. What's the long term future? As you get higher and higher up in the tech tree, choices about which direction we go and how we choose to explore, I think it's potentially the case that different civilizations or different choices mean that we end up in different parts of that tree. And in particular, just things, I mean, it's sort of very basic things about, we're very visual creatures, certain other animals are much more orally based. Does that bias the types of thoughts that you have? And then you extend it to much more exotic kinds of civilizations, where maybe just sort of their biases in terms of how they perceive and how they manipulate the world are maybe quite different than ours. And that might make some significant changes in terms of how they do that exploration of the tech tree. It's all speculation, obviously.

Speaker 1:
[56:39] This is such an interesting take. I want to better understand it. So one way to understand it is that there might be some things that are so fundamental and have such a wide collision area against reality that they're inevitably going to discover general relativity.

Speaker 2:
[56:52] Numbers. Like, of all of the intelligences in the Milky Way galaxy, maybe that number is one. Actually, arguably, we've already increased the number. But of all of those, what fraction of the concept of counting? It does seem very natural. What fraction have discovered the idea of some kind of decimal place system? Interesting question. Maybe we're missing something really simple and obvious, that's actually way better than that. What fraction got there immediately? What fraction had to go through some other intermediate state? What fraction use linear representations versus a two-dimensional or a three-dimensional representation? I think the answers to these questions are just not at all obvious. It's a lot of design freedom.

Speaker 1:
[57:45] On theoretical computer science, this is going to be extremely naïve and arrogant. But I took Scott Aronson's class on complexity theory, and that was by far the worst student he's ever had. But what I remember is like, there was this period that you were the pioneers of, where we figured out, here's the class of problems that quantum computers can solve, and how it relates to problems the class of computer can solve. It's like groundbreaking, oh, crazy that this works. And then since then, it's been this, literally it's called Complexity Zoo, this website which lists out, here's all the complexity classes. And if you have this complexity class with this kind of oracle, it's sort of equivalent to this other class. And that, it feels like we're building out that taxonomy. And so there's a couple of ways to understand what you're saying. One, maybe you just disagree with me that this is actually what's happened with this field. Another is that while that might happen to any one field, the amount of fields, who would have thought in 1880 the computer science, other than Babbage or something, the computer science was going to be a thing in the first place. So the amount of field, we're underestimating how many more fields there could be. Yeah, for sure. Or maybe you think both, or maybe a third secret thing. But I'd be curious.

Speaker 2:
[58:57] I mean, a very common argument here is sort of the low-hanging fruit argument, the argument that says, oh, there should be diminishing returns.

Speaker 1:
[59:06] And in fact, empirically, we see this, right? The amount of scientists in the world has just exponentially increased.

Speaker 2:
[59:11] And I mean, I think it's worth thinking about, like, why do you expect diminishing returns? And how well does that argument actually apply in practice? An analogy I like is actually thinking about sort of going to some event, going to a wedding or whatever, and you go to the dessert buffet, and they've put out 30 desserts. And of course, naturally, what people do, right, the best desserts go first. I mean, we don't quite have a well-ordered preference there, so maybe there's some difference, but human beings are fairly similar. So they will, the best desserts will go first. And this is an argument for why you expect diminishing returns in a lot of different fields. If it's relatively easy to see what's available and people have similar preferences, then the best stuff goes first, and it just gets sort of worse and worse after that. And a very static snapshot in time of scientific progress, maybe there's some truth to that. But if somebody is standing behind the dessert table and is replenishing, restocking the desserts and keeps kind of adding new ones in, it may turn out that a little bit later, much better desserts appear. And so, you're going to go and eat those instead. And scientific progress has a little bit of that flavor. We go through these sort of funny time periods. Computer science is a great example where computer science basically arose as sort of a side effect of some pretty abstruse questions in the philosophy of mathematics and logic. And so, you've got these people trying to attack these rather esoteric questions that seem quite high up in some sense, in sort of exploration, quite esoteric. And they discover this fundamental new field, and all of a sudden there's an explosion there. So, sort of the diminishing returns argument just didn't apply there. We just weren't able to see what was there. And this has been the case over and over and over again. And sort of new fields arrive, and all of a sudden, boom, it's actually easy to make progress again. Young people flood in, because you can be 21 and make major breakthroughs, rather than having to spend 25 years mastering everything that's been done before. It's obviously very attractive. And I don't understand, I'm not sure anybody understands very well, sort of the dynamics of that, like how to think about why the structure of knowledge is that way, that these new fields keep opening up. But it does seem empirically, at least, to be the case.

Speaker 1:
[61:54] Despite the fact that that is the case, take deep learning, right? Obviously, this is an example of a new field where the 21-year-olds can make progress. And it's relatively new, 15 years or so, when it sort of gets back into high gear. But already, we're in a stage where you need billions or tens of billions or hundreds of billions of dollars to keep making progress at the frontier. And there's a couple of ways to understand that. One is that it actually is harder than the kinds of things the ancients had to do, or is more intensive, at least. Second is, it might not have been, but because our civilizational resources are so large, the amount of people is so large, the amount of money is so large, that we can basically make the kind of progress that would have taken the ancients forever to make, almost immediately. We notice something is productive, immediately dump in all the resources. But it's also weird that there's not that many of them. I feel like deep learning is notable because it is one big exception to the fact that it's hard to think of other examples.

Speaker 2:
[62:59] It's a consequence of the architecture of attention. At any given time, there's always a most successful thing. Maybe if deep learning wasn't a thing, maybe you'd be talking about CRISPR. Maybe you'd be talking about whatever it is. Maybe we wouldn't think about solving the protein structure prediction problem as really a success of AI. Maybe we would have figured out how to do it with curve fitting more broadly construed. We'd just be like, wow, we took a lot of computing resources. But protein structure prediction might be an enormously important thing. So there is always a biggest thing. I think what you're pointing out is more a consequence of the way in which attention gets centralized.

Speaker 1:
[63:46] Yeah.

Speaker 2:
[63:47] But it's basically fashion is what I'm saying. It's not just fashion, but there is some dynamic there.

Speaker 1:
[63:54] There's a very interesting and important implication of this idea, that the branching is so wide and so contingent and so path dependent, that different civilizations would stumble on entirely different technology sex. There's a very interesting implication that there will be gains from trade into the far, far future, which might actually be one of the most important facts about the far future, in terms of how civilizations are set up, how they can coordinate, how they interface with. There's not this go forth and exploit. It's actually there are humongous gains to trade from adjacent colonies or whatever.

Speaker 2:
[64:33] There's a question of what's actually hard. If it's a question of, if it's just the ideas, well, those spread relatively quickly. It's relatively easy to share ideas. If it's something more, it's almost a Dan Wang idea where it's actually there's some notion of capacity. You need all the right texts, you need all of the right manufacturing capacity and so on. And so, you know, civilization A has very different kind of manufacturing capacity and it's just not so easy to build in civilization B, even if civilization B is kind of ahead, then I think that becomes true. There is actually, you know, comparative advantage which is really worth, I mean, it's going to provide massive benefits to trade in both directions. Eventually, you're going to expect some diffusion of innovation. It is funny, I like to think about what the barriers are there. A fun thought experiment I like to think about is sort of GitHub but for aliens. So, you know, somebody presents you with all of the code from some alien civilization. And I mean, I don't even know what code means there, but this sort of their specification of algorithms. And it would have many interesting new ideas in there and it would take forever for human beings to dig through and to try and extract all of those. One reason, I mean, the origin of this for me was actually thinking about proteins in nature. We've been gifted just this incredible variety of machines, which we don't understand really at all, and we just have to go and sort of try and understand them on a one by one basis. We're still understanding hemoglobin and insulin and things like this. And no doubt, there's hundreds of millions of proteins known. So it is a little bit like that. We've been gifted by biology, just this immense library of machines, no doubt containing an enormous number of very interesting ideas. And we're just at the very, very, very beginning of understanding it. So actually, I mean, that's, I suppose, kind of your point actually is I need to relabel your argument slightly. But you sort of think of that as a gift from an alien civilization, which obviously it isn't, but you think of it that way. And it's like, oh my goodness, like there's so much in there and we're going to study it. And goodness knows how long we could continue to study it. There's tens of thousands of papers about the hemoglobin and things like that. And we still don't understand them. And yet we're getting so much out of it. I mean, just think about insulin alone. It's such an important thing.

Speaker 1:
[67:20] That's an incredibly useful intuition problem that you have on Earth. I had Nicolay on where he had this theory about how life emerged. But like whatever theory you have, basically something like DNA, four billion years, and you have an alien civilization come here and be like, there's all these interesting things to learn about material science, about... you name it, right? Like about...

Speaker 2:
[67:43] Think about Chineson walking along. I mean, and we know almost nothing about these proteins. And yet the tiny few facts we do know are just incredible. The ribosome. You know, another example, I mean, this miraculous sort of device, little factory.

Speaker 1:
[68:01] And all seeded by just like... There's this particular chemistry on earth with nucleic acids and carbon-based light forms that that chemistry gives rise to all of these interesting things, which an alien civilization would find very interesting. And so that seed, which must be one among trillions of possible seeds of, I mean, just of general intellectual ideas leads to all this fecundity. That's a very interesting interchange from. I want to meditate on this Gains for Trade thing because I feel like, I think there's something actually very interesting about this idea, that if you have this vision of how technology progresses and how it may be different from in different civilizations, it has important implications about how different civilizations might interact with each other. Like the fact that there are going to be these huge gains from trade.

Speaker 2:
[68:46] It makes friendliness much more rewarding.

Speaker 1:
[68:48] Yes, that's a very important observation.

Speaker 2:
[68:51] Yeah, I hadn't thought about that at all. That is a very interesting observation. It is funny. I mean, comparative advantage is something that people, they love to invoke and it's a very beautiful idea, obviously. There are limits to it. It's a special, limited model. Chimpanzees can do interesting things. We don't trade with them and I think it's interesting to think about the reasons why. Part of it is just power, I think. Once there's a sufficiently large power and balance, very often, not always, but very often groups of people seem to shift into this other mode where they just seek to dominate. Maybe there's something special about human beings, but maybe it's also a more general sort of a thing. They're no longer, they give up. You need all these special things to be true before groups will trade. It's not necessarily obvious.

Speaker 1:
[70:05] I think the big thing going on here is, one, transaction costs. And two, comparative advantage does not tell you that the terms on which the trade happens are above subsistence for any given one producer. So people often bring this up in the context of, well, humans will be employed even in a post-AGI world because of comparative advantage. There's like five different ways that argument breaks down. But the easiest ways to understand are, why don't we have horses all around on the roads? Because there's some comparative advantage between cars and horses.

Speaker 2:
[70:37] Good example.

Speaker 1:
[70:38] Well, one, there's huge transaction costs to building roads that are compatible with horses and cars at the same time. In a similar way, AI is thinking at 1,000 times the speed and can shoot their latent states at each other, are going to find it way more costly than the benefit in just in terms of interacting with you, to have a human being in the supply chain. And second, that just because horses have a comparative advantage, mathematically does not mean that it is worth paying 100k a year or whatever it costs to sustain a horse in San Francisco. That subsistence is going to be worth the benefit you get out of the horse.

Speaker 2:
[71:21] I do think it's interesting, just the sheer fact that my expectation, my intuition obviously differs a great deal from yours on this, is that most parts of the tech tree are never going to be explored. There's just too many interesting ways of combining things. There's too many sort of deep ideas waiting to be discovered. And not only we, but nobody ever is going to discover most of them. So choices about how to do the exploration actually matter quite a bit. Interesting. It's something I really dislike about sort of technological determinist arguments. I'm willing to buy it sort of low enough down when progress is relatively simple. But higher up, you start to get to shape the way in which you do the exploration. And it's interesting, people, we are starting to shape it in interesting ways. I mean, there's various technologies that have been essentially banned. Think about DDT, you think about chlorofluorocarbons, you think about restrictions on the use of nuclear weapons, the Nuclear Non-Proliferation Treaty. Those kinds of things are, they're not, they weren't done before the fact, but they're starting to get pretty close in some cases where we just sort of preemptively decide we're not going to go down that path. So that starts to look like a set of institutions which, where we are actually influencing sort of how we explore the tech trade. Yeah.

Speaker 1:
[72:52] On where you would see these gains from trade, obviously it would be, you'd see the most where it's pure information that can be sent back and forth because the information has a quality where it is expensive to produce, but cheap to verify and cheap to send. Yep. And so it would be interesting how much of future productivity or whatever can be distilled down to information. Right now it's kind of hard to do because you can't really transfer, like if China is really good at manufacturing something, whether it's this process knowledge that's in the heads of 100 million people involved in the manufacturing sector in China, but in the future it might be easier if AIs are doing.

Speaker 2:
[73:25] I mean, the question about to what extent does our fabrication get very uniform and get really commoditized? Like three printers have been the next big thing for at least 20 years now. Why do they still not work all that well? Why are they still not actually at the center of manufacturing? And what comes after that? It is funny to look at, say, the ribosome by contrast. It really is at the center of biology in a whole lot of really interesting ways. Whether or not that's the future of manufacturing is something very simple, where everything goes as throughput through, I don't know, maybe it's a bioreactor or something like that. So, you send the information and then you grow stuff, or you have some 3D printer that actually works. If they're good enough, then actually it does become much more a pure information problem, and some of this process knowledge becomes much less important.

Speaker 1:
[74:23] Jane Street has a lot of compute, but GPUs are very expensive. So, even optimizations that have a relatively small effect on GPU utilization are still extremely valuable. Two of Jane Street's ML engineers, Corwin and Sylvain, walked through some of their optimization workflows at GTC.

Speaker 3:
[74:39] You're not bottlenecked on the network being too slow. You're bottlenecked on waiting for a different rank in your training, not having completed the work.

Speaker 1:
[74:47] They talked about how Jane Street profiles, traces, and diagnoses bottlenecks, and then how they solve them using techniques like Cuda graphs, and Cuda streams, and custom kernels. With these sorts of optimizations, Corwin and Sylvain were able to get their training steps down from 400 milliseconds to 375 milliseconds each. This 25 millisecond difference might sound small, but given the size of Jane Street's fleet, that improvement could free up thousands of B200s. Jane Street open sourced all the relevant code. If you want to check it out, I've linked the GitHub repo and the talk in the description below. And if you find this stuff exciting, Jane Street is hiring researchers and engineers. Go to janestreet.com/dwarkesh to learn more. Can I ask a very clumsily phrased question? So there's these deep principles that we've discovered a couple of. One is this idea that, hey, if there's a symmetry across a dimension, it corresponds to a conservative quantity. It's a very deep idea. There's another which we've written a lot about, written a textbook about, in fact, about there's ways to understand this thing of what kinds of things you can compute, what kinds of physical systems you can understand with other physical systems, what a universal computer looks like, et cetera. And is your view that if you go down to this level of idea of Noether's theorem or the Church-Turing principle, that there's an infinite number of extremely deep such principles. Because I feel like what makes them special is that they themselves encompass so many different possible ways the world could be. But no, the world has to be compatible with actually a couple of these very deep principles.

Speaker 2:
[76:23] I don't know. I mean, all I have here is speculation and sort of instinct. My instinct is we keep finding very fundamental new things. It was very, I mean, for me anyway, quite formative to understand. As I say, I gave the example before, there's these wonderful ideas of Church and Turing and these other people, ideas about universal programmable devices. Then you understand later, oh, this also contains within it the ideas of public key cryptography. Then you understand later, oh, that also contains within it the ideas. I mean, people refer to it as cryptocurrency or whatever, but there's a very deep set of ideas there about the ability to collectively maintain an agreed-upon ledger, which is built upon this. There's probably many deep ideas to sort of, it actually took whatever, it's taken many years really to figure out the right canonical form of those. So just this fact that you keep finding what seem like deep new fundamental primitives, I find very, for me, that has been a very important intuition bump. And it's across, I mean, given that particular example, but I think you see that same pattern in a lot of different areas.

Speaker 1:
[77:37] What is your interpretation then of this empirical phenomenon where ideas like whatever input you consider into the scientific process or technological process, economists have studied this a million and a hundred ways, it just seems to require, even at actually a very consistent rate, X percent more researchers per year. So there's this famous paper from a couple years ago by Nicholas Bloom and others where they say, how many people are working in the semiconductor industry? And how does it increase over time? Through the history of Moore's law. And I think they find Moore's law means computing increases 40% a year, or transistor density increases 40% a year. But to keep that going, the amount of scientists has increased 9% a year in the semiconductor industry. And they go through industry after industry with this observation. And so is your view that there are these deep ideas, but they keep getting harder to find? Or that no, there's another way to think about what's happening with these empirical observations?

Speaker 2:
[78:31] I mean, first of all, all of their examples are narrow, right? They all pick a particular thing and then they look at some particular metric. Nowhere in that shows up, like GPUs don't show up there, right? Like in the sense of, all of a sudden, you get this ability to parallelize. That's really interesting. So, there's a lot of external consequences that are just delighted from basically, they have these simple quantitative measures, they look at it in agricultural productivity, they look at it in a whole lot of different ways. But you do have to focus narrowly. I'm certainly interested, as I say, in this fact that just new types of progress keep becoming possible. But there is still, I think, even there, there does seem to be some phenomenon of diminishing returns. Is that intrinsic? Is that something about the structure of the world? What is it? Well, one thing which hasn't changed that much is the individual minds which are doing this kind of work. And maybe those should be sort of being improved as well, or some sort of feedback process going on there. And maybe that changes the nature of things. I suppose I look at scientific progress up into let's say 1700, something like that. And it was very slow and also was very irregular. You had the Ionians back sort of five centuries before Christ, doing these quite remarkable things. I think so much knowledge would get lost and then it would be rediscovered and then it would be lost again. And you'd have to say that progress was very slow. And there it's partially just bound up with the fact that there were some very good ideas that we just didn't have. Even once you've had the ideas, then you need to build institutions around them. You actually need to solve a whole lot of different problems about training, about allocation of capital, about all these kinds of things, even just about basic sort of security for researchers, so they're not worried about the Inquisition or things like that. So there's all these kind of complicated problems. You solve all those complicated problems, and then all of a sudden, boom, there's a massive sort of burst of scientific progress. If you're not changing it, if there's some kind of stagnation there, if you're not changing those external sort of circumstances, yes, like you may start to get sort of diminishing returns again. But that doesn't mean there's anything intrinsic about the situation. Maybe something just external needs to change again. Obviously, a lot of people think AI is potentially going to be a driver. I mean, it certainly will at some level. In fact, to the extent you can think of a lot of modern scientific instrumentation is really, I mean, at some level kind of robots. What is the James Webb Space Telescope? Well, it's unconventional maybe to describe it as a robot, but it's not completely unreasonable either. It is an example of a highly automated, very sophisticated system with electronically mediated sensors and actuators where machine learning in fact is being used to process the data. So in that sense, we're already starting to see that transition. We've been seeing it for decades.

Speaker 1:
[82:01] I have this smoke adjoined and take a puff thought, which...

Speaker 2:
[82:04] I think we've had a few.

Speaker 1:
[82:05] Yeah, yeah. Well, I think we're getting to that part of the conversation and you can help me get my foot out of my mouth and figure out a more concrete way to think about it. So to your point that AI, there's an initial revolution, the Enlightenment, and now there's AI and each might be a different pace or a different way in which science happens. If you think about the pace of how fast such transitions have been happening, you can draw over the long span of human history, this hyperbolic of the rate of growth is increasing. So yeah, 100,000 years ago, you had the Stone Age. You go back even much further, how long ago probably it's been around, it would be like, let's say millions of years, and 100,000 years ago, the Stone Age, then 10,000 years ago, the agricultural revolution, then 300 years ago, the industrial revolution, each marked by this increase in the rate of exponential growth. And then people think it's going to happen again with AI. But that would happen potentially even faster. It would not have occurred to somebody at the beginning of the industrial revolution that the next demarcation in this trend will be artificial intelligence. And so if things are getting faster, and it's hard to anticipate what the next transition will be, I guess we just think of this singularity between now and AI, and that's really what distinguishes the past from the future. But we're just applying the same heuristic that many people in the past should have had. Maybe the intelligence age is also quite short. And the next thing after that is we don't even have the ontology to describe what it is. But it would not, the future will not think of the past as like, there was pre-intelligent AI and post-AI.

Speaker 2:
[83:50] No, that seems, I mean, obviously, we can't prove this, but it certainly seems quite plausible. I mean, part of the issue, of course, is just, the substrate we have available to conceive seems all wrong. You can't speculate with a bunch of chimpanzees about what it would be like to have language. Just to sort of pick a major transition in the past, it's, the transition itself is the theme, and it seems likely. If we're talking about taking a puff kind of thoughts, I'm certainly amused by the idea that there's going to be some transition involving artificial general intelligence using classical computers, but actually, there'll be an interesting transition with quantum computers as well. They're probably capable of a strictly larger class of potentially interesting computations, so maybe actually the character of AQGI or whatever it should be called is actually qualitatively different. Maybe there's a brief period between those two things. Interesting. As I say, this is just speculation, but it's certainly amusing.

Speaker 1:
[85:11] Is there a reason to think that? Because from what I understand, there's been for decades people like you have put pretty tight bounds on the kinds of things quantum computers can do, and so it'll speed up search somewhat. It will do in the kinds of things that extremely speeds up, like Shor's algorithm. It seems like it, again, maybe this is to your point that we can't predict in advance what's down the tech tree, but at least from here, it seems like you break encryption, but what else are you using? Shor's algorithm.

Speaker 2:
[85:36] Yeah, I mean, we've only been thinking about it for 30 years or whatever. It's 40 or so years, not for very long, and we haven't in some sense thought that hard about it as a civilization. So does it turn out that it's very narrow? Maybe. Does it turn out that it's very broad? That's also like a really radical expansion. That seems distinctly possible. Keep in mind as well, we've been doing it without the benefit of having the devices. That's a pretty big bottleneck to have.

Speaker 1:
[86:09] If you're thinking about computer science in the 1700s and you're like looking into and and or.

Speaker 2:
[86:13] Yeah.

Speaker 1:
[86:14] What are you going to do? You can't anticipate Bitcoin. You can't anticipate deep learning.

Speaker 2:
[86:18] No. Maybe you could if you're sufficiently bright, but it is a pretty hard situation.

Speaker 1:
[86:25] What is your inside view having been in and contributing to quantum information, quantum computing back in the 90s and 2000s? What is your telling of the history? What was the bottleneck? What was the key transition that made it a real field? And how do you rank the contributions for Feynman to Deutsch to everybody else who came along?

Speaker 2:
[86:51] Yeah. So, I mean, let's just focus on the question about sort of what, you know, what actually changed. So why was quantum computing not a thing in the 1950s? Right? Like, it could have been, you know, somebody like, I don't know, John von Neumann, good example, absolutely pioneering computation, also wrote a very important book about quantum mechanics and was deeply interested in quantum mechanics. Like he could have invented quantum computing at that time. And I think there were quite a number of people who potentially could have. So, why do we have these papers by people like Feynman and Deutsch in the 80s? And those are, I think, fairly regarded as the foundation of the field. There are some partial anticipations a little bit earlier, but they were nowhere near as comprehensive and nowhere near as deep. And, well, you should ask David. You can't ask Feynman, unfortunately, but he'll know much better than I do. A couple of things that I think are interesting. One is that, of course, computation became far more salient, sort of late 70s, early 80s. It just became a thing which many more people were interested in, partially for very banal reasons. You could go and buy a PC, you could buy an Apple II, you could buy a Commodore 64, you could buy all these kinds of things. Became apparent to people that these were very powerful devices, very interesting to think about. At the same time, in the quantum case, that was also the time of the Paul Trap and the ability to trap single ions and so on. Up to that point, we hadn't really had the ability to manipulate single quantum states. You got these two separate things that just for historically contingent reasons had both matured around 1980 or so. Somebody like Von Neumann could have had the idea earlier, but it is, I think, quite an interesting factor. A story about Richard Feynman. He went and got one of the first PCs around 1980, 1981. He was apparently just so excited with this device. He actually tripped and hurt himself quite badly, sort of carrying his brand new computing device. That's a very historically contingent coincidence, but having somebody who's very, very talented and understanding of quantum mechanics, also just very excited about these new machines, it's not so surprising perhaps that he's thinking then. What similar story could you have told 10 years earlier? There is just no, the conditions don't exist for it. So I think it's quite a banal story.

Speaker 1:
[89:47] One of the things we were going to discuss was this idea you had about the market for follow-ups. I think this is actually the perfect story to discuss it for, because you wrote the textbook by the field. Mike and Ike is the definitive textbook on quantum information. And so you presumably came in after Deutch, but you identified in the 90s, somehow identified it as the thing that is worth following up on and building on. And instead of talking about it more abstractly, I'd love to actually just hear the story of, like the first-hand story of how did you know that this is a thing to, of all the things that were happening, physics and computing, et cetera, that I want to think about this problem.

Speaker 2:
[90:28] Sure, sure. So Reed Feynman writes this great paper in 1982. David Deutsch writes an absolutely fantastic paper in 1985, sort of sketching out a lot of the fundamental ideas of quantum computing. So I'm 11 in 1985. I'm not thinking about this. I'm playing soccer and doing whatever. But in 1992, I took a class on quantum mechanics. It was really terrific given by Jared Milburn. I just went and asked Jared one day after, it's like the fifth lecture or something. I said to him, do you have anything papers or whatever that you could give me? He said, come by my office in a couple of days' time. I did and he presented me with a giant stack of papers, which included the Deutsch paper, it included the Feynman paper, and it included a whole bunch of other very fundamental papers about quantum computing and quantum information. At a time when essentially nobody in the world was working on it. But he was, he'd actually, I think he wrote the very first paper that proposed sort of a practical approach to quantum computing. It wasn't very practical, but it was actually in a real system. So in some sense, I'm benefiting from the taste of this other person. But as soon as I read the papers, or take a look at the papers, these are exciting papers. They're asking very fundamental questions, and you're sort of like, I can make progress here. These are things that one could potentially work on. Deutsch has this sort of conjecture that basically, there should be, I don't know what the right term for it is, thesis or what you would call it, that a universal model quantum Turing machine should be capable of efficiently simulating any system, any physical system at all. This is a very provocative idea. I think in that paper, he more or less claims that he's proved it. I'm not sure that necessarily everybody would agree with that. There's questions about whether or not you can say simulate quantum field theory effectively. And that kind of question is, I think, very interesting and very exciting there. It's obviously a fundamental question about the universe. It has some wonderful ideas in there about sort of quantum algorithms and where they come from and what they mean and what they relate to the meaning of the wave function and questions like this, which is still not, it's not agreed upon amongst physicists. So yeah, there's just some sense of, oh, I am in contact with something which is A, deeply important and B, we as a civilisation don't have this and so of course, you start to focus your attention a little bit there.

Speaker 1:
[93:28] I'm not sure I got the answer to the question that-

Speaker 2:
[93:35] Maybe I misunderstood the question.

Speaker 1:
[93:36] Yeah, yeah, no, let me think about it. Maybe I'll explain the motivation first. So in a previous conversation we were discussing, how could you have known in the 1940s, the Shannon theorems and Shannon's way of thinking about communication channel is a deep idea that goes beyond the problems with pulse code modulation that Bell Labs was trying to solve at the time and it applies to everything from quantum mechanics to genetics to computer science, obviously. And one of the, I think, an idea you stated that we didn't get a chance to talk about yet was this idea, well, Shannon publishes a paper, there's all these other papers, but there's a market of follow-ups where people gravitate to and build upon Shannon's work and how did they realize that that's the thing to do and how does that process happen? And so I guess you gave your local answer, you read these papers and you immediately realized, okay, there's work to be done here, there's a little hanging fruit, there's some deep provocative idea that I need to better understand and I could attractively make progress on.

Speaker 2:
[94:44] Yeah, I mean, so to some extent, you're sort of saying, okay, I wanted to get into this game of contributing to humanity's understanding of the universe and you are applying this low-hanging fruit algorithm. You're like, relative to my particular set of interests and abilities, where should I pick up my shovel and start digging? And there it was like, oh, this looks like quite a good place to start digging. And different people, of course, chose very differently. It was a very unusual choice at the time. It was 1992. Very few people were thinking about that.

Speaker 1:
[95:28] Yeah. Fast-forwarding a bit, so you've been, I don't know how you think about your work on the open science movement now, but did it work? Like, what would have, what is successful there look like? Or what is it that that movement is trying to accomplish?

Speaker 2:
[95:44] Yeah. I mean, the set of ideas about open science, I mean, it's interesting. You didn't stop and define open science there, which I think 20 years ago you would have had to do. People recognize the phrase. People have some set of associations with it. Most often they have a relatively simple set of associations. It means maybe something about making scientific papers open access. Very often they have some set of notions about maybe it means also making code openly available. Maybe it means making data openly available. But already, those are, I think, very large successes of the open science movement, which is to make those salient issues. Those are issues on which people have opinions, and then there are relatively common arguments. An argument like, so this is the meme version, publicly funded science should be open science. That's a distillation of a set of ideas, which you might be able to contest. But if you can get people actually thinking about it and engaged with that kind of argument, that's a very fundamental kind of an issue to be considering in the whole political economy of science. If you go back, say, three centuries, there was a very similar kind of an argument prosecuted, which is the question, do we publicly disclose our scientific results or not? So if you look at people like Galileo and Kepler and so on, the extent to which they publicly disclosed, like it was done in a very odd kind of a way. They sometimes they did bizarre things where famously, they published some of their results as anagrams. So basically, they'd find some discovery, they would write down the result in sort of a sentence, like his, you know, the discovery of the, I'm trying to think of an example. I think the moons of Mars, I think, was one such example. I'm getting at it wrong. Was it Hooke's Law? Anyway, doesn't matter. The point was they'd write it down, but then they'd scramble it, publish that, and then if somebody else later made the same discovery, they would unscramble the anagram and say, oh, yeah, I actually did it first. This is not an ideal way. There's not an ideal foundation for a discovery system. Then it took a very long time, over a century, I think, to obtain more or less the modern ideals in which what you do is, you disclose the knowledge in the form of a paper. There's then an expectation of attribution, and so there's a reputation economy which gets built. So basically, oh, such and such did this work, so they deserve the credit for that, and that's then the basis for their career. So this is the underlying political economy of science. That made a lot of sense when what you've got is a printing press and the ability to do scientific journals. Then you transition to this modern situation where, in fact, you can start to share a lot more. You can start to share your code, you can start to share your data, you can start to share in-progress ideas. But there's no direct credit associated to those. It's not at all obvious how much reputations should be associated to them. That's all constructed socially. So making it a live issue is, I think, a very important thing to have done. That's, I view anyway, is one of the main positive outcomes of work on open science. I'll give you a really practical example to illustrate the problem. For a long time in physics, there was a pre-print culture in which people would upload pre-prints to the pre-print archive, and in biology, this didn't happen. There was no pre-print culture. That's changing now, but for a long time, this was the case. I used to sort of amuse myself by asking physicists and biologists why this was the case, and what I would hear sometimes from biologists was they would say, well, biology is so much more competitive than physics that we need to protect our priority, and so we can't possibly upload to the archive. We have to just publish in journals. And then we sometimes hear from physicists, physics is so much more competitive than biology, that we need to establish our priority by uploading as rapidly as possible to the preprint archive. We can't possibly wait to do it with the journals. And I think this emphasizes the extent to which this kind of attribution economy is just something we construct, is just something which we do by agreement. And so any attempt to change that economy results in a different system by which we construct knowledge. And so there is sort of this very fundamental set of problems around the political economy of science. You know, sort of we've got this collective project, and how we mediate it depends upon the economy we have around ideas.

Speaker 1:
[101:00] One of the sort of things you've emphasized as a part of this project of open science is collective science or groups of people were making progress on a problem where no individual understands all the logical and explanatory levels necessary to make a leap or a connection. Outside of mathematics, what is the best example of such a discovery?

Speaker 2:
[101:24] I mean, I'm not sure I have a well-ordering of them to give you a best, but I mean, an example that I think is very interesting is the LHC, where it's just this immensely complicated object. I actually, years ago, I snuck into an accelerator physics conference. I didn't know anything at all about accelerator physics, but I was just curious to see what they were talking about. This particular group of people were experts on numerical methods, in particular, on inverse methods. So it basically turns out, inside these accelerators, you have these cascades, so a particle will be massively accelerated, maybe it'll be collided, and then you'll get a shower of particles, which decays and decays and decays, and there's just this incredible sort of consequential shower, which is ultimately what you see at the detector, and then you have to retroactively figure out what produced it. So there's these very, very complicated sort of inverse problems that need to be solved. You've got this final data, but you need to figure out what produced it, and that's how you look for sort of signatures of these. What many of these people were, was they were incredibly deep experts on simulation methods for sort of following particle tracks. And like this was really deep and difficult stuff. And I'm like, wow, you could spend a lifetime just learning sort of how to do this and how to solve some of these inverse problems. And you would know nothing about, or you would know very little about quantum field theory, you would know very little about detector physics, you would know very little about vacuum physics, all these other things that are absolutely, or very little about data processing, very little about all these things that are absolutely essential to understanding, say, the Higgs boson. And I don't think it's possible for one person to understand everything in depth. Lots of people understand broadly a lot of these ideas, but they don't understand sort of everything in the depth that is actually utilized. That's why there's these papers with well over a thousand authors. And those people can, yeah, they can talk to one another at a high level, but they don't understand each other's specialties and that much depth. I mean, things like, as I say, detector physics, vacuum physics, these kinds of solving of inverse problems. Like, this stuff is incredibly different from each other. And, you know, to understand it in real detail is serious work.

Speaker 1:
[103:57] How do you think about prolificness versus depth? Where, I don't know, maybe Darwin's an example of somebody who's like, just gestating on something for many decades. There's other examples where Einstein during the year comes with special relativity, just doing a bunch of different things. Pius talks about how they were all relevant to the eventual build up.

Speaker 2:
[104:16] Yeah. I mean, you know, it's something I stress about a lot. Sometimes I feel like I'm, you know, too slow. Actually, it's funny, though, I mean, the Darwin example is really interesting. Like, you know, prolific at what? Like, I mean, God knows how many letters he wrote. It must have been an enormous number. So, he was certainly very active. There's also, like, there's sort of, there's two types of work that tends to be involved in any kind of creative project. There's routine stuff. And there, you just want to avoid procrastination. You just want to like, you know, how do I get good at this or how do I outsource it? And how do I do it as rapidly as possible? And just avoid, you know, like getting into a situation where you're prolonging it. And then there's high-variance stuff where you actually, you need to be willing to take a lot of time. You need to be willing to go to the different places and talk to the different people where, in any given instance, most of it's just not, it's not going to be an input. And somehow sort of balancing those two things. I think a lot of people are very good at doing one or the other, but it's hard to, it's almost like a personality trait, sort of which one you prefer, and people tend to end up doing a lot of one and not enough of the other. So I certainly try and balance those two things. I mean, Einstein is such an interesting example. I mean, 1905 is just this extraordinary year. Like you can delete special relativity entirely, and it's an extraordinary year. You can delete special relativity and you can delete the photoelectric effect for which he won the Nobel Prize. And it's still an extraordinary year, like plausibly a multi-noble prize-winning year. So what's he doing? Yeah, I mean, maybe the answer is just he's smarter than the rest of us. And there's a lot of luck as well. But certainly for myself anyway, like trying to identify those things that are routine, that I should get good at, and then just try and do as quickly as possible. That's yielded a certain amount of returns. But also being willing to bet a little bit more on myself, on sort of the variance side, has also been very, very, very helpful. That's really hard. Because intrinsically, you're putting yourself in situations where you don't know what the outcome is going to be. And so if you're very driven to be productive and whatever, and actually mostly it's not working over there, you're like, let's reduce this. Like it doesn't feel right. When I worked in San Francisco, actually a practice I used to have each day was instead of taking the 15-minute walk to work, I would take the more beautiful 30-minute walk to work, partially just because it was beautiful, but partially also as just a reminder to think like that there are real benefits to not being efficient. But it's not an answer to your question. I mean, really, I think all I'm saying is I struggle a lot with the question.

Speaker 1:
[107:22] I mean, there are these, Dean Keith Sivington, I forgot his exact name.

Speaker 2:
[107:26] Yeah, I know who you mean.

Speaker 1:
[107:27] Has this famous sequel, Oddsworld, where he says the probability that any given thing you release, any paper, book, whatever, will be extremely important for a given person through their lifetime, is not that different and what really determines in what era they are the most productive is how much they're publishing. Any given thing has equal odds of being extremely important. Maybe just think of some of the most successful creatives or scientists, they're just doing a lot, like Shakespeare is just publishing a lot.

Speaker 2:
[108:01] Of course, there's kind of examples, Gödel publishing almost nothing. But broadly speaking, you need a very good reason to be avoiding it. There's basically to not do that. It's funny, I've met a lot of people over the years who you talk to, they're clearly brilliant and they're just obsessed that they are going to work on the great project that makes them famous and they never do anything. And that seems connected, like it's a type of aversiveness. I think very often, they just don't want public judgment. Something that I would love to see, there's an awful lot of biographies and memoirs and histories of people who achieve a lot. I wish there was a very large number of biographies of people who are fantastically talented, who just missed. Absolutely, I've known people who won gold medals at IMOs and things like that, who then tried to become mathematicians and failed. What happened? What was the reason? I suspect in many cases that's actually more informative than anything else.

Speaker 1:
[109:18] You have this essay that I was reading before this interview about how you think about what is the work you're doing. And writer doesn't seem like, as you say, was Charles Darwin a writer? What exactly is that label? I'm a podcaster, right? So in a way, obviously, our work is very different. But I also think a lot about what is this work and how do I get better at it? In particular, how I can make sure there's some compounding between the different people I talk to on the podcast, where I worry that instead of this kind of compounding, there's actually, I build up some understanding that's somewhat superficial about a topic and it depreciates, and I move to the next topic and it sort of depreciates. And so I think there's this question, there's a lot of podcasters in the world who will interview way more experts than I have or have, and I don't think they're much the wiser or more knowledgeable as a result. So it's clearly possible to mess this up. And I wonder if you have thoughts or takes or advice on how one actually learns in a deeper way from this kind of work.

Speaker 2:
[110:28] Yeah, I mean, it's sort of an incredibly complicated and rich question. I mean, it does seem like the question is, how do you make it a higher growth context? How do you make it a more demanding context? And you can do that in relatively small ways, but that might have a yield compounding returns, or you can do something that is maybe more radical. Maybe it means actually starting sort of a parallel project in which you do something that is actually quite a bit different. There's something I think really interesting about, like, how being very demanding can simply change your response to something. Something that I would sometimes do with students and sometimes with myself, was really aimed more at myself, was they would say some week, I'm going to try and do this work over the coming week, and then the next week would come by, and they hadn't solved the problem or whatever. You're sort of like, if a million dollars had been at stake, would you have put the same effort in? And the answer is no, sort of invariably. They've tried, but they haven't really tried. I think that's a very familiar feeling for all of us. Often, you could do a lot more if you had just the right, sort of demanding taskmaster standing by you and saying, look, you're barely operating here. And so, I do sort of wonder a little bit about like, what's the demanding taskmaster? What can they ask you that is going to make your preparation way more intense?

Speaker 1:
[112:05] The most helpful thing, honestly, is for some subjects, it is very clear how I prep. I think I'm doing an upcoming episode on chip design with the founder of a company that is chip design. And he wrote a textbook on chip design. And yesterday, I went over to his office and we brainstormed five sort of roof line analysis I can do. And if I understand that, I have some good understanding. The problem is with almost every other field, there's not like you, I don't know, when I interviewed Ilya three, four years ago, it's like, implement the transformer. And if you implement it, you have some nugget of understanding you have clamped down. And with other fields, it's just like, I vaguely understand this, it's not clamped. I vaguely understand this. I vaguely LLM'd about this. I LLM'd about this. But there's no forcing function that you do this exercise. And if you do it, you will understand.

Speaker 2:
[112:58] So, I mean, really what you're sort of saying is, you can do a good job at podcasting without actually attaining this kind of, and that's the problem from your point of view. You want to sort of change your job description so that you are internalizing these chunks and just getting this kind of integration each time. And it seems to me like, you know, what that means is you actually want to change the structure of the, like, the workout put at some level. I mean, lots of people think, you know, there's this terrible idea, people have that they should be in flow all of the time. And of course, as far as I can tell, like high performance just don't believe this at all. They're in flow some of the time, like you certainly see this with athletes, you know, when they're actually out there playing basketball or tennis or whatever, ideally, you know, they are in flow much of the time. But when they're training, they're not. They're stuck a lot of the time or they're doing things badly. And I suppose I wonder what that looks like for you.

Speaker 1:
[113:57] That I would be extremely satisfied with. The problem is I just like, I don't know what the equivalent of do the 64 lapses for almost a... And so this is sort of a, this is a thing you can change by choosing guess where there is a legible curriculum. And so maybe it's a mistake for not having done that. Or also, like there's no real way to prefer Terence Tao or something. And like there's no curriculum that's like a plausible one. I think there's one failure mode. So there's many failure modes. But one is, one dynamic I'm worried about, a long-term dynamic, is that you can have a good podcast, and there's a local maximum. But for no particular guest or topic, are you going deep enough that you've... I think my model of learning is if you don't really understand the deeper mechanism, you're just mapping inputs and outputs of a black box. Yeah, yeah. And that just fades incredibly fast, or is not worth it in the first place. And you kind of just move on and it's over. And you kind of need to build the intermediate connection. And it's unclear. I think actually AI in a weird way is really easy for that reason, because there is a clear thing you can do, just implement it, right? And then you understand it. We're almost... If I applied that criteria elsewhere, what am I... Do I just not do history episodes?

Speaker 2:
[115:16] Exactly, Ada Palmer. What wonderful to talk to, incredibly interesting. But for you personally, what changed?

Speaker 1:
[115:24] Right. Yeah, there's some things I learned. I think I could have done... If I had maybe allocated more time, especially after the interview, to let's write out 2,000 words on everything I learned and how it connects to other things I know and something. And maybe that's the thing worth doing, is spreading out the episodes more and spending more time afterwards consolidating. But yeah, I think I would pay basically the infinite amounts of money if there is somebody who is really good at coming up with, here is the curriculum and here is the practice problems you need to do, and here is the exercises you need to do after the interview to clamp what you have learned.

Speaker 2:
[115:55] Have you tried doing that with somebody?

Speaker 1:
[115:58] It's hard to find. I mean, I haven't tried super hard, but it seems like we tend to find somebody who could do that for every single kind of discipline. Maybe I should just hire different ones for different topics.

Speaker 2:
[116:12] Maybe. What problem are you solving for each episode? And as far as I can tell, that's the only way I really understand anything, is that I get interested in something. At first, I don't even have a problem, but there's just some sense of there's some contribution to make here, and gradually you home in and there's a problem. And then funnily enough, I mean, spending time stuck is incredibly important. That used to just be annoying. Now, it seems like, oh, this is actually maybe even the most important part of the whole process. But that very hard oneness of it means that I internalize it afterwards. I often find, actually, if I've written sometimes 10,000 word essays in a couple of days, and I've written them in three months or six months, I feel like I didn't learn very much from the ones that only took a couple of days. Interesting. Whereas some of the ones that took three months, I'll be 15 years later, I'll still remember.

Speaker 1:
[117:19] Yeah. Can you describe outside of physics, how you learn of the one that took three months?

Speaker 2:
[117:27] By far the most, the common things, there's always some creative artifact. Sometimes it's a class. Sometimes it's engagement with a group of people who, there's some collective creative artifact that you're working on together. You might not even be aware of it, but you're acting as an input to their creative ends in some way. Sometimes it's just an essay or a book or whatever. It's one of the reasons why I often quite enjoy doing podcasts. Particularly, I said yes to come here partially because I know you ask unusually demanding questions, and so that's an attempt to get this perspective from a different forcing function. So you're trying to pick the most demanding creative context.

Speaker 1:
[118:19] Yeah, so for this interview, I went through three lectures of the Susskin Sessual Relativity book. The problem is that there's almost no practice problems in it. And so I hired a physicist friend who's going to like, I haven't done it yet, but every lecturer I want a bunch of practice problems to go to them, and I'm planning on being appropriately humbled.

Speaker 2:
[118:39] How do you make it as jugular as possible? The higher you can raise the stakes, the better.

Speaker 1:
[118:46] I mean, the interview is, in some sense, high stakes, but also it doesn't necessarily test deep understanding.

Speaker 2:
[118:51] Yeah, but I don't think the interview is that high stakes, right? You're not writing a book about special relativity, and you're not trying to write a book that replaces the current, whatever the existing standard textbook is. That's a really high say. A phrase that I find particularly difficult, and it's funny when people will talk about going deep on a subject, and it turns out different people have different ideas of what this means. Some people means they read a couple of blog posts. Some people, it means they read a book about it. Some people, it means they wrote a book about it. I think what your standard is, the standard you hold yourself to, determines a lot about your ability to integrate knowledge in this way.

Speaker 1:
[119:39] I don't know what your experience has been, but I found that I'm getting, I'm in some sense able to move much faster on some things to the help of AI, but I don't know if I'm learning better. And I think it's probably because the hardest thing, the thing that is most demanding is so aversive, that you try to take any excuse you can to get out of it. And just having back and forth conversation where you gloss over.

Speaker 2:
[120:04] It's entertaining, but not necessarily anything else.

Speaker 1:
[120:07] Yeah, so it's such an easy way to get out of the thing. Yeah. In fact, it makes it easier because instead of doing some intermediate thinking, there's always the next question you can ask a chatbot.

Speaker 2:
[120:17] Yeah. And it's somewhat valuable. Like it's not, I mean, that's part of the seductiveness, of course, like it's not actually useless. But yeah, it can sort of substitute for actually doing the thing that maybe you should be doing. It's interesting that, like the extent to which, to what extent should you be outsourcing that kind of stuff? And to what extent? Like it's really, there's some sort of interesting judgment call about, there is a whole bunch of routine work that you want done. And in fact, it's low value for you, so you may as well get, if you can get a chat bot to do it, you may as well. Somebody interviewed the pioneering computer scientist Alan K years ago, and he was asked what he thought about basically Linux. And if I remember his answer correctly, he basically said, look, it doesn't have anything to do with computer science. It's just a great big ball of mud. There's a few interesting ideas in there which are worth understanding. But mostly, all you're learning is stuff about Linux. You're not actually learning anything which is transferable. I thought that was a very interesting, that there's a certain kind of seductiveness to some things where it's sort of a Rube Goldberg machine. You can just learn about all the bits and it feels kind of entertaining. But if you step back and think about the question, what am I actually doing here? It might not actually be meeting your objectives. Maybe you want to become a, sysadmin and learning Linux is a great use of your time. There's no harm in that at all. But if your answer is, if your objective is to understand the fundamentals of computing, it's much less clear that that's a good use of your time. I thought that was certainly an answer I've thought a lot about, where you actually need to, for a certain type of mind, there is a seductiveness in just learning systems and confusing that with understanding.

Speaker 1:
[122:21] I'll keep you updated and not discuss. I owe you a text or then a month of some revamped learning system.

Speaker 2:
[122:28] I'll be really curious if you, I mean, it's also true, right, like tiny incremental improvements in this. I mean, they're just worth so much.

Speaker 1:
[122:35] I know, yeah. It's sort of the main input into the podcast, you know. It's great that the bookshelves are fancy and I've got a blackboard or whatever. But really, like the thing that makes the podcast better is if I can improve the learning, I do. So it's worth every morsel of improvement. Yeah. All right. Thanks for the therapy session. No time done. Thanks, Michael. All right.

Speaker 2:
[123:01] Thanks for having us.