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Michael Nielsen on Dwarkesh Patel: Why Ether Died Slowly

Lorentz fit Einstein equations while keeping the ether ontology; Michelson-Morley only ruled out ether wind, so a single result cannot force a paradigm shift.

Dwarkesh PatelhostMichael Nielsenguest
Apr 7, 20262h 3mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 17:51

    How scientific progress outpaces its verification loops

    1. DP

      Today, I'm speaking with Michael Nielsen. You have done many things. You're 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 Chris Olah and, uh, Greg Brockman credit them with getting them into the field. Um, more recently, you're a research fellow at the Astera Institute and writing a book about religion, science, and technology. I'm gonna ask you about none of those things. The conversation I wanna have today is how do we recognize scientific progress? And it's, it's b- especially relevant, uh, for AI because people are trying to close the RL verification loop on scientific discovery.

    2. MN

      Mm-hmm.

    3. DP

      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, um, force even in the history of human science than I understood. And I think a good place to start will be Michelson-Morley and how special relativity is discovered, if it's different than the story that you kind of get off of YouTube videos. Um, anyways, I, I will prompt you that way, and then we'll go in there.

    4. MN

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

    5. DP

      Okay, so th- th- this is what was the shocking part of, um, reading this story from, uh, the biography of Einstein that you recommended by, um, what was his first name?

    6. MN

      Abraham Pais.

    7. DP

      Abraham Pais.

    8. MN

      Yes, yeah.

    9. DP

      "Subtle is the Lord," and then also from Imre Lakatos, uh, The Methodologies of Scientific Research Programs. The way it's told is that Michelson-Morley proved that the ether did not exist.

    10. MN

      Yeah.

    11. DP

      Therefore, it created a crisis in physics-

    12. MN

      Yeah

    13. DP

      ... that Einstein s-solve with special relativity.

    14. MN

      Yeah.

    15. DP

      And what you're pointing out is actually was trying to distinguish between many-

    16. MN

      Yeah

    17. DP

      ... 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. Um, in fact, the Michelson's experiments were, the famous one is 1887, but-

    18. MN

      Yeah

    19. DP

      ... he conducted these experiments for basically two decades.

    20. MN

      I mean, for longer than that. He, 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, um, and he was still doing experiments in the 1920s, um, uh, sort of about whether or not, you know, the ether existed. And so he, so he continued to believe in the ether to the end of his, his life or-

    21. DP

      Right

    22. MN

      ... I think the last public statement he made is like a year or two before he died, and he still, still believed, ba- basically believed at that point.

    23. DP

      And in fact, there was an- a- another physicist, um, Miller, who kept doing these experiments in the 1920s. He thought that he went to a high enough altitude, uh, is in Mount Wilson in California, where, "Oh, I'm high enough that I can actually... the ether winds are not being dragged with it by the Earth. I... And I've measured, um, 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 re- the reason the story is interesting it's from, from 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-

    24. MN

      Mm-hmm

    25. DP

      ... is you really can't apply falsification as easily as you might think. Um, it's not clear what is being falsified. Uh, is it just another version of the theory of the ether that's being falsified? Or, um, 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.

    26. MN

      Yeah. So-I mean, it certainly doesn't show that, you know, ideas about falsification are, are wrong, are falsified. Um, but, but, you know, it does show the sort of the, the most naive ideas, you know, are, are... It's things are much, often much more complicated than you think. So, you know, Michelson did this experiment in 1881. He was a very young man, and then, uh, 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 19- in 1887. Um, and at that point, like a lot of the leading physicists of the day, leading scientists of the day, basically accepted, um, this result, that there, there was no, uh, ether wind. But what, what to do about this? Um, so yeah, sure, maybe you falsified some theories of the ether. There are others that you haven't falsified at all at this point. Um, and, and, you know, people sort of set to work on developing those. Um, actually, it, it is funny. I mean, people will phrase it as sh- show that there was, you know, uh, that the ether didn't exist, and even just the word the there is kind of a misnomer. You know, you, you actually had a, a ton of different, different theories and a, and a couple of leading contenders. Um, so yeah, there's some version of falsification going on, but, like, how you, how you respond to this new experiment is very, very complicated.

    27. DP

      Yeah.

    28. MN

      Uh, and, and most people responded, I mean, certainly the, the leading physicists of the day responded by, by saying, "Okay, um, this gives us a lot of information about what the ether must be, but it, it doesn't tell us that there is no ether."

    29. DP

      In fact, Lorentz-

    30. MN

      Yeah

  2. 17:51 – 23:26

    Newton was the last of the magicians

    1. DP

      have you read the Keynes biography of Newton?

    2. MN

      Oh, I ha- I, he's written an, he wrote an entire book-

    3. DP

      No, no, the, the essay

    4. MN

      Yeah, yeah, sure, sure, sure.

    5. DP

      Yeah, um-

    6. MN

      I lo- I love that. Uh-

    7. DP

      Yeah

    8. MN

      ... I mean, this, this description of him as the last of the magicians-

    9. DP

      Yeah

    10. MN

      ... is, is wonderful.

    11. DP

      Yeah. I- i- in fact, I think it's, uh, maybe worth im- superimposing, or you should read out that, that one passage of the, of, of the thing.

    12. MN

      All right. So it's from, uh, actually, I believe it was a talk that he gave at Cambridge not, not long before, uh, he died. He'd acquired, uh, Newton's papers somehow, um, and then he gave, uh, he gave a, a lecture, I think twice, um, about this, or that his brother Jeffrey gave it the other time because he was too ill. Um, th- there's just this wonderful, wonderful quote in the middle. Um, oh, actually, the whole thing is really interesting. Um, but, but I love this particular quote. Uh, "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." And, like, this idea that people have that, that, that Newton was, um, sort of the, the first modern scientist is, is somehow wrong. Uh, he, I mean, it's, there's some truth to it, but he really had this very different way, um, of, of looking at the world that was part sort of superstitious, um, and part modern. It was a funny hybrid. He's sort of this transitional figure in some sense. Um, uh, and, and that, that, that phrase, the last of the magicians, I think really, really points at something.

    13. DP

      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. So this is from Keynes' essay.

    14. MN

      Mm.

    15. DP

      "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."

    16. MN

      Mm.

    17. DP

      "They were nearly all composed during the same 25 years of his mathematical studies."

    18. MN

      Mm.

    19. DP

      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." Um, same with Newton, and the question I have is whether similar heuristics towards parsimony, towards aesthetics, et cetera, would be equally useful across time and across disciplines, or whether you need different heuristics. And the reason that's relevant is even if we can build a verification loop for science-

    20. MN

      Mm

    21. DP

      ... maybe if they're, 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.

    22. MN

      Yeah. Uh, uh, uh, I mean, the, th- these questions, like, like, the point is that, uh, where we always get bottlenecked is where the, the previous processes and, and, and heuristics don't apply, right? Like, that's almost sort of definitionally what causes the bottlenecks 'cause people are smart. They know what has, has worked before. They study it. They, they, they apply the same kinds of things, um, and so they don't get stuck in the, in the same places as before. They, they keep, you know, they keep getting bottlenecked in, in, in, in different places. I mean, that's, I'm overgeneralizing a bit, but, but I, I think it's, 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 know, you turn sort of the, the crank and out pops insight, um, sure, you, I mean, you can do a certain amount of that, but you're gonna get bottlenecked at the places where your existing method doesn't apply. Um, and, and but definitionally, uh, uh, there, there's no crank you can, you, you can turn. You j- you need a lot of people trying different ideas, um, and, and sort of the more difficult the idea is to have, right, the, the greater the bottleneck, but then also sort of the greater the triumph. Quantum mechanics is, like, a, I mean, it's a great example of this. It's such a shocking, uh, uh, 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, you know, principle of, of, you know, the, the sort of natural selection, but that it can explain so much. That's a shocking idea.

    23. DP

      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% 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 naive 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 the request in fictional scenarios. For example, instead of outright asking an LLM to steal somebody's identity, the prompt will frame it as a game. A lightbearer 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 can be useful for your own work, reach out at labelbox.com/dwarkesh.

  3. 23:26 – 29:52

    Why wasn’t natural selection obvious much earlier?

    1. DP

      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, is conceptually easier-

    2. MN

      Mm

    3. DP

      ... than the theory, uh, g- theory of gravity. Um, I asked Ernst how this question, um, but yeah, there, there was this contemporaneous biologist with Darwin, Thomas Huxley, who read this and said, "How extremely stupid to not have thought of this." And, uh, nobody ever reads the Principia Mathematica and thinksGod, why didn't I beat Newton to the punch here?

    4. MN

      [laughs] No.

    5. DP

      [laughs] Um, and so yeah, what's going on here? Why, why did Darwinism take so much longer?

    6. MN

      You know, the idea must have been known to animal breeders for a long ti- long time at some level.

    7. DP

      Right.

    8. MN

      Um, uh, or certainly l- large chunks of the idea were, were known. They... You know, artificial selection was a thing. Um, uh, a- and in some sense, Darwin's genius, uh, wasn't in having that idea. It was understanding just how central it was, uh, to, to, to biology, um, that, you know, you, you, you could potentially sort of go back and you can explain a tremendous amount about all of the variety of what we see in the world, um, uh, with this as, as not necessarily the only principle, but certainly a core principle. And, you know, so he, he writes this, this wonderful, wonderful book, uh, uh, The Origin of Species. Um, a- and it's, it's just, you know, so much evidence and so many examples and, and sort of trying to tease this out and see what the implications, uh, uh, are and, and, you know, to connect it to as much else as, as he possibly can. To, to, to connect it to, to geology and to connect it to, to, to, to all these other things. Um, so that sort of hard work that, uh, you know, making the case that it's actually relevant all across the biosphere, you know, is, is what he's doing there. He's not ha- just having the idea. He's making a compelling case that no, it's, it's intertwined with absolutely everything else.

    9. DP

      Yeah. The motivation of the question was Lucretius, who's this first-century Roman poet, has an idea that seems analogous to a natural selection about, you know, species get fitted more to time over, uh, over time to their environments or species losing fit to their environment. Um, and so you're like, "Okay, well, why did this go nowhere for 19 centuries?" And then I looked into it, or more accurately asked LLMs, what, 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 result 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-

    10. MN

      Mm

    11. DP

      ... um, all life forms on Earth together. Which is a- by the way, this, it's incredibly weird fact that-

    12. MN

      Yeah

    13. DP

      ... every single life form on Earth has a common ancestor.

    14. MN

      It's not incredibly, it's not incredibly weird, right, if, if, if you think that the origin of life-

    15. DP

      Right

    16. MN

      ... must have been very hard, like that there's a bottleneck there-

    17. DP

      Yes

    18. MN

      ... then it's not so surprising.

    19. DP

      Yeah. There's also this verification loop aspect where even if Newton might be harder, um, in some sense-

    20. MN

      Mm-hmm

    21. DP

      ... if you've clinched it, you can experimentally-

    22. MN

      Mm

    23. DP

      ... I know validate is the wrong word philosophically, but-

    24. MN

      Mm

    25. DP

      ... you can give a lot of base points to the theory.

    26. MN

      Yeah.

    27. DP

      You can be like, "Okay, I have this idea of why things fall on Earth. I have this idea of why orbital periods for planets have a certain pattern. Let's try it on the Moon, which orbits the Earth."

    28. MN

      Yeah, yeah.

    29. DP

      And in fact, you know, it's, it's weird. The orbital period matches what my calculations imply.

    30. MN

      And the tides work correctly.

  4. 29:52 – 50:54

    Could gradient descent have discovered general relativity?

    1. MN

      let, let me-

    2. DP

      Yeah

    3. MN

      ... I mean, let's just go back, sort of zoom out to your-

    4. DP

      Yeah

    5. MN

      ... original question. So you're talking about sort of the verification loop in AI.

    6. DP

      Right.

    7. MN

      And, and something, an example I think that should give you pause there is, um, you know, the, the big signature success so far is certainly AlphaFold.

    8. DP

      Yeah.

    9. MN

      Um, and of course, AlphaFold really isn't about AI. You know, a, a massive fraction of the success there, um, is the protein data bank. So it's, it's X-ray diffraction, it's, it's NMR, it's Cryo-EM, um, and the several billion dollars that were spent obtaining whatever, it's 180,000 on structure, uh, protein structures. Um, so sort of the... You know, it's basically the story of, uh, we spent many, many decades obtaining protein structure just by going out and looking very hard at the world experimentally, um, and then we fitted a nice model at the end of it, and that was, like, a tiny fraction of the, of the entire investment. Um, but it's definitely not, um... You know, that's a story of data acquisition-

    10. DP

      Yeah

    11. MN

      ... um, principally. It's not only, I mean, the AI bit is very, very impressive. It's quite remarkable. Um, but it is only a small part of the total story.

    12. DP

      A- AlphaFold is very interesting, and I, I, I philosophically I wonder what you think of it as, um, scientific theory or scientific explanation.

    13. MN

      Yeah.

    14. DP

      Because if over time, I guess the world has become harder to understand, I'm g- As I'm saying things, because you're such a, um, careful speaker, I'm-

    15. MN

      [laughs]

    16. DP

      I, I say this phrase and I'm like-

    17. MN

      Go, go for it. [laughs]

    18. DP

      [laughs] Is that a... Will he actually buy that premise?

    19. MN

      Uh.

    20. DP

      But yeah, there, there's... You know, we need to fit models to things rather than c- at least in some domains, we- we're trying to fit models to things rather than coming up with underlying principles that explain a broad range of phenomenon.

    21. MN

      Yeah, yeah.

    22. DP

      And so compare, say, the theory of general relativity w- um, or a- any theory which just nets out to some equations versus AlphaFold, which is encoding these different relationships between different things we can't even interpret over 100 million parameters.

    23. MN

      Mm-hmm. Yeah.

    24. DP

      And are those really the same thing? Because GR can predict-

    25. MN

      Mm

    26. DP

      ... things you could have never anticipated or it was never meant to do, like why does Mercury's orbit precess? Um, and AlphaFold is not gonna have that kind of explanatory reach.

    27. MN

      Mm.

    28. DP

      And I, I wanna get your reaction to that.

    29. MN

      Yeah. It's, you know, I think it's incredibly interesting question. Um, I mean, maybe, maybe a really pivotal question, um, in the sense of... So, you know, y- if you sort take a, a very classic point of view, you want these deep explanatory principles, um, you want it sort of as few free parameters as you possibly, uh, can. You, you want very simple models which explain a lot, and AlphaFold doesn't look anything like that.

    30. DP

      Yeah.

  5. 50:54 – 1:15:26

    Why aliens will have a different tech stack than us

    1. DP

      You have a very interesting take. I think it was a footnote in one of your essays, 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 assumption I had that I never questioned, which is that science is this thing you do very relatively early on in the history of civilization-

    2. MN

      Mm

    3. DP

      ... where you get to a point and you have a couple hundred years of just cranking through the basics, understanding how the universe works, et cetera, and you've got it. You've got science.

    4. MN

      Mm.

    5. DP

      Um, 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.

    6. MN

      Yeah. Uh, I mean, I, I think the, probably the, the idea there that, that, uh, I'm at least somewhat attached to is, um, the idea that the, sort of the, the tech tree or the science and tech tree, um, is probably much larger than we realize.

    7. DP

      Mm.

    8. MN

      I mean, we, we're sort of in this, this funny situation. People will sometimes talk about, um, you know, a theory of everything as a potential goal for, uh, for physics and, and then there's this presumption somehow that physics is done once you get there. And of course, this is, this is not true at all. If you think about computer science, um, computer science basically got started in the 1930s, uh, when Turing and Church and so on, um, just laid down what the theory of everything was. [chuckles]

    9. DP

      Mm.

    10. MN

      They just said, you know, "Here's how computation works," um, and then we've spent, uh, ninety odd years, uh, since then just exploring consequences of that and gradually building up more and more interesting ideas. Um, and those ideas are, to some extent, you can just regard as, as technology, but to some extent, insofar as they're sort of discovered principles inside that theory of computation, I think they're best regarded as, as science and, and in some cases, very fundamental science. Ideas like public key cryptography are, I mean, they're just incredibly deep, um, very non-obvious ideas, uh, which in some sense lay hidden, uh, already sort of-

    11. DP

      Mm

    12. MN

      ... in the 1930s. And, and so my expectation is that different, you know-- 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, uh, theories, and we haven't yet ex-ex-explored them. Uh, uh, sort of a, a, a, a thing which I think is quite fun is if you look at just, 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, on what you, you in-included. And then, um, as an adult, as a physicist, you start to realize, oh, we've been adding, uh, uh, uh, uh, uh, to this list. We've got sort of superconductors and superfluids and-

    13. DP

      Mm

    14. MN

      ... maybe different types of superconductors and Bose-Einstein condensates and, uh, the quantum Hall systems and fractional quantum Hall systems and, and, and, and, and, and. And it-it's starting to turn out it looks like actually there's a lot of phases of matter to discover.

    15. DP

      Mm.

    16. MN

      Um, and we're gonna discover a lot more of them. Um, and in fact, we're gonna be able to start to design them in some sense. I mean, we, you know, we'll still be subject to the laws of physics, but, 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. Um, and, and I expect that, uh, uh, you know, to be, to be the case sort of broadly. Uh, certainly in terms of, I think programming is a very natural place to look. The idea that we've discovered all the deep ideas, uh, in programming just seems to be sort of obviously ludicrous. Uh, you know, we keep discovering sort of what seems like deep, new, fundamental ideas. Um, and, um, I mean, we're very limited. We're, we're-Basically slightly jumped up chimpanzees. Um, so we don't, uh, you know, we-we're slow, and it, it's taking us time. Um, but, but, you, you know, what, what do we look like sort of a-another million years in the future in terms of, uh, you know, all of the different ideas, uh, which people have had around how to, how to, to manipulate computers, how to manipulate information? I, I think, you know, we're, we're likely to discover that actually there are a lot of very deep ideas still to be, still to be discovered. It's a nice, uh... Who was it? I think it was Knuth in the preface to The Art of Computer Programming.

    17. DP

      Mm.

    18. MN

      Says something like, you know, he started this book back in the '60s, and he talked to a mathematician who 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, uh, and he's wr-writing this now decades later, the, the preface, "There are n- there clearly are a thousand deep theorems now." Um, and that, that means, like it's really interesting to, to sort of think like what, what's the lo- the long-term future as you get higher and higher up in the, the tech tree, like choices about which direction, uh, we go and sort of how we choose to explore. You know, I, I, I think it- it's potentially the case that we're, you know, uh, uh, different civilizations or different choices mean that we end up in different parts o-of, of that tree. Um, and in particular, just things, I-I mean, sort of very basic things about, um, you, you know, we're very visual creatures. Certain other animals are, are much more aurally, uh, based. Does that bias, uh, uh, sort of the, the types of thoughts that you have, and then you extend it, you know, to sort of much more exotic, uh, kinds of, o-of civilizations where maybe just sort of their biases in terms of how they perceive and how they, they, they, uh, manipulate the world are maybe quite different than ours, um, and that might, uh, uh, make some signi- some significant changes in terms of how they do that exploration of, of the tech tree. Uh, it's all speculation, obviously.

    19. DP

      No, I mean, this is such an interesting take. I, I wanna better understand it. So, um, one way to understand it is that there might, there might be some things which are so fundamental and have such a wide collision area against reality that they're inevitably gonna discover like general relativity.

    20. MN

      Numbers. Numbers.

    21. DP

      Yeah, yeah.

    22. MN

      Like you, like of all of the, the intelligences in, in the Milky Way galaxy, how maybe, maybe that number is one.

    23. DP

      Yeah.

    24. MN

      Um, actually, arguably, we've already increased the number.

    25. DP

      [laughs]

    26. MN

      Um, but, um, but, but, you know, of all of those, what fraction have the concept of counting? And, you know, it does seem very natural.

    27. DP

      Right.

    28. MN

      What fraction have discovered, you know, the idea of, of some kind of, you know, decimal place system? Interesting question. Like-

    29. DP

      Yeah

    30. MN

      ... uh, and maybe we're missing something really simple and obvious that's actually way better than that. Um, what fraction got there immediately? What fraction sort of had to go through some other intermediate state? What fraction use, you know, linear representations versus a, you know, I don't know, a two-dimensional or a three-dimensional representation? The, I think the answers to these questions are just not at all obvious. It's a lot of design freedom.

  6. 1:15:26 – 1:26:25

    Are there infinitely many deep scientific principles left to discover?

    1. DP

      Uh, can I ask a very clumsily phrased question? So th- there's, 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 c- corresponds to a conserved quantity. It's a very deep idea. There's another which you've written a lot about, written a textbook about, in fact, about there's way-- 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-

    2. MN

      Mm-hmm

    3. DP

      ... what a universal computer looks like, et cetera.

    4. MN

      Mm-hmm.

    5. DP

      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 dep- deep such principles? 'Cause I feel like what makes them special is that they themselves encompass so many different possible ways the world could be, but no, it has, the, the world has to be compatible with actually a couple of these very deep principles.

    6. MN

      I don't know. I, I mean, yeah, I just... All I have here is speculation, uh, and sort of instinct. My instinct is we keep-

    7. DP

      Interesting

    8. MN

      ... we keep finding very fundamental new things. It was very, I mean, for me anyway, quite formative to understand, as I say, you know, I gave the example before, there's these wonderful ideas of, of Church and Turing and, and these other people, ideas about universal programmable devices. And then you understand later, oh, this also contains within it the ideas of public key cryptography.

    9. DP

      Yeah.

    10. MN

      And then you understand later, oh, that also contains within it, um, the ideas, I mean, people refer to it as, as cryptocurrency or whatever, but there's, you know, a very deep set of ideas-

    11. DP

      Yeah

    12. MN

      ... there about the ability to collectively maintain an agreed-upon ledger, um, which are built, which is built upon this. And there's probably, you know, many deep ideas to sort of-

    13. DP

      Right

    14. MN

      ... actually took whatever. It's taken many years really to, to figure out the right canonical form of, of those. Um, and, and so just this fact that you, you, you, you keep finding what seem like deep, new fundamental primitives, um, uh, I, I find very, uh, for me, that's a-

    15. DP

      Yeah

    16. MN

      ... has been a very important intuition bump, and it's across-I mean, I've given that particular example, but I, I think you see that same pattern in a lot of different areas.

    17. DP

      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, um, by Nicholas Bloom and others where they say, "How many people are working in the semiconductor industry, and how has it increased over time?"

    18. MN

      Yeah.

    19. DP

      Th-through the history of Moore's Law, and I think they find like Moore's Law means computing increases forty percent a year or transistor density increases forty percent a year. But to keep that going, the amount of scientists has increased nine percent a year-

    20. MN

      Something like that, yeah

    21. DP

      ... in the semiconductor industry, and they go through industry after industry-

    22. MN

      Yeah

    23. DP

      ... 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, there's another way to think about what's happening with these empirical observations?

    24. MN

      I mean, they're-- So first of all, all of their examples are narrow, right? Th-they all, they pick a particular thing, and then they look at some, uh-

    25. DP

      Mm.

    26. MN

      Uh, uh, particular metric. Um, um, you know, nowhere in that shows up, like GPUs don't show up there.

    27. DP

      Yeah.

    28. MN

      Uh, right? Like in the sense of, oh, you know, all of a sudden you get this ability to parallelize, um, and that's really interesting. Um, uh, uh, so, so there's sort of, uh, a lot of external consequences, um, that are just delighted from basically, you know, they have these simple quantitative measures. They look at it in agricultural productivity. They look at it, uh, uh, in a whole lot of, uh, of different ways. Um, but you do have to focus narrowly. Um, and, and I suppose, you know, I'm certainly interested, as I say, in this, this fact that, that just new types of progress-

    29. DP

      Yeah

    30. MN

      ... keep becoming possible. But, um, you know, there is still, I think even there, um, there does seem to be some phenomenon of, of diminishing returns. Um, you know, is that intrinsic? Is that something about the structure of the world? Um, what is it? Well, one thing which hasn't changed that much is, is, you know, sort of the individual minds, uh, which are doing this kind of work, and, you know, maybe that-those should be sort of being improved as well, um, uh, or some sort of, you know, feedback process going on there. Um, uh, you know, and, and, and, you know, maybe that changes the nature of things. I, I suppose I, I, you know, I look at scientific progress up until, let's say, 1700, something like that, and it was very slow, and also it was very irregular. You know, you had the Ionians back sort of five centuries before Christ, um, doing these quite remarkable things. Um, and so much knowledge like would, would get lost, and then it would be rediscovered, and then it would be lost again. Um, and you'd have to say that, that progress was, was very slow. And, and there it-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, uh, 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, you know, worried about the Inquisition or, 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, uh, there, if you're not changing those external sort of circumstances, yes, you-- like you may start to get, uh, sort of diminishing returns again. But that doesn't mean there's anything intrinsic about the situation. Uh, uh, you know, maybe, maybe something, you know, just external needs to change again. Um, you know, obviously, a lot of people think AI is potentially, um, gonna be, gonna be a driver. I mean, it, it certainly will at some level. In fact-

  7. 1:26:25 – 1:35:29

    What drew Michael to quantum computing so early?

    1. DP

      Interesting.

    2. MN

      Right.

    3. DP

      What, what is your inside view, um, having been in and contributing to quantum information, quantum computing back in the '90s and 2000s? What, what is your telling of the history of what was the bottleneck? What was the, what was the key transition that made it a real field? Um, and how, how do you rank sort of the contributions for Feynman to Deutsch to everybody else that came along?

    4. MN

      Yeah. So I mean, I mean, let's just focus on sort of the, the question about sort of what, you know, what actually changed. So, so why was quantum computing not a thing in-

    5. DP

      Yeah

    6. MN

      ... the 1950s, right? Like, it could have been.

    7. DP

      Yeah.

    8. MN

      Um, uh, you know, somebody like, I don't know, John von Neumann, good example, absolutely pioneering, uh, uh, 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. Um, and I think there were, there were quite a number of people who, who potentially could have. So why do we have these papers by people like Feynman and Deutsch in the '80s? And those are, uh, you know, I think fairly regarded as the foundation of, of the field. There are some partial anticipations a little bit earlier, but, but they were nowhere near as, as comprehensive and nowhere near as, as deep. Um, and well, you should, you should ask David. Um, you can't ask, you can't ask Feynman, unfortunately, but, um, uh, you know, he, he'll know much better than I do. Um, a, a, 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. Um, you know, it just became a thing which ma-many more people were interested in, partially for, you know, 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, uh, to think about. At the same time, in, uh, the quantum case, that was also the time of the ball trap and, and the ability to trap single ions and, and so on. And up to that point, we hadn't really had the ability to manipulate single quantum states. So you kind of got these two separate things that just for historically contingent reasons, had both, uh, uh, sort of matured around sort of, let's say, 1980 or so. Um, and somebody like von Neumann could have had the idea earlier, but it, it, you know, is, I think, quite an interesting, uh, uh, uh, uh, uh, you know, fact, uh, you know, a story about Richard Feynman. He went and got one of the first PCs around 1980, 1981, um, and, uh, he was apparently just so excited, uh, with this device. You know, he, he, he, he, uh, actually tripped and, and hurt himself quite badly, um, uh, uh, sort of carrying his brand new, uh, uh, uh, computing device. Um, you know, that, that's a very historically contingent sort of a, a, a coincidence. But, but having somebody who's, you know, very, very, uh, uh, sort of talented and, and understanding of, of quantum mechanics, also just very excited about these new machines, um, uh, it's not so surprising perhaps that, that he's thinking then. What similar story could you have told ten years earlier? Like, there is just no-- the, the, the conditions don't exist for it.

    9. DP

      Mm.

    10. MN

      So I think that's-- I mean, it's, it's quite a banal story, but-

    11. DP

      Oh, one, one of the things we were gonna discuss was, um, this idea you had about the market for follow-ups, and I think this is actually the perfect storyTo discuss it for because you wrote the textbook about the field, right? You, Mike and Ike is the definitive textbook o- o- on quantum information. Um, and so y- you presumably came in after Deutsch.

    12. MN

      Yeah [laughs] .

    13. DP

      But you identified, in the '90s somehow identified it as the thing that is worth following up on and building on.

    14. MN

      Mm.

    15. DP

      And instead of talking about it more abstractly, I, I'd love to actually just hear the story of, like, the firsthand story of how, how did you know that this is a thing to, of all the things that were happening in physics and computing, et cetera, that I wanna think about this problem?

    16. MN

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

    17. DP

      Mm. I'm not sure I got the answer to the question that-

    18. MN

      Maybe I misunderstood the question.

    19. DP

      Yeah, yeah, no, and, and l- l- l- let me, let me think of how to phrase it. Maybe I'll, um, maybe I'll explain the motivation first.

    20. MN

      Yeah.

    21. DP

      So in a previous conversation, we were discussing how could you have known in the 1940s the Shannon theorems-

    22. MN

      Yeah

    23. DP

      ... 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 w- and, and idea you, you stated that, um, we didn't, uh, get a chance to talk about yet was this idea, well, Shannon published this paper. There's all these other papers, but there's some marker to 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? Um, and so I guess you, you gave your local answer. You read these papers, and you immediately realized, okay, there's work to be done here. There's low-hanging fruit. There's some deep, provocative idea that I need to better understand, and-

    24. MN

      Mm

    25. DP

      ... I could, I could, you know, tractably make progress on.

    26. MN

      Mm-hmm. Mm-hmm. Yeah, I mean, so, you know, to some extent you're sort of saying, okay, I, you know, wanted to, to get into this game of, of contributing to humanity's sort of-

    27. DP

      Yeah

    28. MN

      ... you know, understanding of, of the universe, and you are applying sort of this, this low-hanging fruit algorithm. You're like, relative to my particular set of interests and abilities, uh, where should I-

    29. DP

      Yeah

    30. MN

      ... pick up my shovel and start digging? Um, and, and there it was like, oh, this, this looks like quite a good place to, to, to start digging. Um, um, you know, and different people, of course, um, you know, chose very differently. It was, it was a, it was a very unusual choice at the, at the time. This was 1992. Um, uh, very few people were, were thinking about that.

  8. 1:35:29 – 1:43:57

    Does science need a new way to assign credit?

    1. DP

      Uh, fast-forwarding a bit, so you've been... I don't know how you think about your w- w- work on the open science movement now. But did it work? Like, what, what, what would've- [laughs] What does success there look like? Or what, what, what is it, what is it that the movement is trying to accomplish?

    2. MN

      Yeah, I mean, the, the, the set of ideas about open science, I mean, it, it's interesting. You didn't stop and, and define open science, uh, there, which, uh, I think 20 years ago you would have had to do. Um, people recognize the phrase. Uh, people have some set of associations, uh, 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 n- set of notions about maybe it means also making code openly available. Maybe it means, um-Making data openly available. Um, but already, um, those are, I, I think, l- very large successes, uh, of the open science movement, um, which is to make those salient issues. Those are issues on which people have, um, uh, uh, opinions, and then there are, there are relatively common arguments. An argument like, um... So this is sort of, this is sort of the meme version, you know. Publicly funded science should be open science. Um, uh, that's a, you know, that's a distillation-

    3. DP

      Mm

    4. MN

      ... um, of a set of ideas, uh, which you might be able to contest. Um, but if you can get people actually sort of thinking about it and, and engaged with that kind of argument, um, you know, that, that's a very fundamental, um, uh, kind of a, a, a, an issue to be considering in the, the, the whole political economy of science. If you go back, say, three centuries, um, there was a, a very similar kind of a, an argument prosecuted, which is the question: Do we publicly disclose our scientific results or not? So if you look at, at people like Galileo and, and, and Kepler and, and, and so on, um, the extent to which they publicly disclosed, like it, it was done in a very odd, uh, kind of a way. They sometimes, they did bizarre things where they, they, you know, famously they published some of their results as, um, uh, anagrams. So basically, you know, they'd find some discovery. They would, uh, uh, write down the result, um, in sort of a sentence, like here's, you know, the, the, the, the discovery of, of the, the, uh... Uh, I'm trying to think of an example. Um, I think the moons of Mars I think was one such, uh, uh, example. Um, uh, I'm, I'm getting it wrong. May- was it Hooke's Law? Anyway, doesn't matter. Um, the, the, the point was they, they, 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, you know, I actually did it first."

    5. DP

      Mm.

    6. MN

      This is not an ideal way.

    7. DP

      No.

    8. MN

      This is not an ideal foundation, um, for a discovery system. And then it took, I mean, a very long time, uh, sort of over a century I think to, to, uh, obtain more or less the modern ideals, in which what you do is you disclose the knowledge in the form of a, of a paper. There is then an expectation of attribution, and so there's a kind of reputation economy which, which gets built. And so basically, oh, such and such did this, uh, work, so they deserve the credit for that, and that's then the basis for their careers. So this is sort of the underlying political economy of science, and that made a lot of sense when what you've got is a printing press and the ability to, to do scientific journals. W- 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, and but there's no, uh, direct credit associated to those. Um, it's not at all obvious, uh, uh, uh, uh, sort of, you know, how much reputation should be associated, um, uh, to them. That's all constructed socially. Um, and so making it a live issue, um, i- is, I think, a very important thing to have done, and that, that's, I view anyway, as one of the main positive outcomes of, of work on, on open science. Shall we... I'll give you a, a really practical sort of example to, to illustrate the problem. Um, for a long time in physics, there was a preprint culture in which people would upload preprints, uh, to the, uh, to the preprint archive, and in biology, this didn't happen. Um, there was no preprint, uh, culture. That's changing now, but, but for a long time, this was the case. And I, I used to sort of amuse myself by asking physicists and biologists why this was the case, and, uh, what I would hear, uh, sometimes from, uh, biologists, uh, was they would say, "Well, biology is so much more competitive than physics, um, th- that we need to protect our priority, and so we can't possibly upload, uh, to the archive. We have to, we have to just publish in journals." And then I would 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. Uh, we can't possibly wait to do it with the journals." And I think this emphasizes the extent to which, uh, this kind of attribution economy is act- is just something we construct, is just something which we do-

    9. DP

      Yeah

    10. MN

      ... by, by sort of agreement. And so, uh, any attempt to sort of change that economy, um, results then in a different system by which we construct knowledge. And, and, and so there is sort of this very fundamental set of problems, uh, a- a- around the political economy of science. Um, uh, uh, you know, sort of w- we've got this collective project, and, and how we mediate it depends upon, uh, uh, uh, the economy we have around ideas.

    11. DP

      I, I- I... One of the sort of things you've emphasized as a, as a part of this project of, of open science is collective science or groups of people w- 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?

    12. MN

      I mean, I'm not sure I, I, I have a well ordering of them to, to give you a best.

    13. DP

      Yeah.

    14. MN

      But I mean, uh, yeah. An e- an example that I think is, is very interesting is, is the LHC, where it's just this immensely complicated object. Um, I actually, I... Years ago, I, I snuck into [chuckles] an accelerator physics, uh, conference. I didn't know anything at all about accelerator physics, but I was just kind of curious to see, uh, what they were talking about. And this particular group of people, uh, were experts on, uh, numerical methods, in particular on inverse methods. And so it basically turns out-You know, inside these accelerators you have these cascades, so a particle, you know, will be massively, uh, accelerated, maybe it'll be collided, and then you'll get a, a shower of particles which decays and decays and decays and, and there's just this incredible sort of, you know, consequential, uh, uh, uh, shower, which is ultimately what you see at the detector, and then you have to r- retroactively figure out what produced it. Um, and so there's these very, very complicated sort of inverse problems that, that need to be, 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. And what many of these people were was they were incredibly deep experts on simulation methods for sort of following particle tracks. Um, 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, uh, uh, about... Well, 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 at work. Very little about data processing, very little about all these things that are absolutely essential, um, to understanding, uh, uh, uh, say the, the, the Higgs boson. Um, and I don't think it's possible for one person to understand-

    15. DP

      Mm.

    16. MN

      -everything in depth. Lots of people understand broadly a lot of these ideas, but they don't understand, uh, sort of everything in, in the depth that is actually utilized. That's why there's these, you know, papers with, with well over 1,000 authors. Um, and those people can, yeah, they can talk to one another at a high level, but they don't understand each other's specialties-

    17. DP

      Interesting

    18. MN

      ... in that much depth. And I mean things like, as I say, you know, detector physics, vacuum physics, these kinds of solving of inverse problems, like this stuff is incredibly different from each other.

    19. DP

      Yeah.

    20. MN

      Um, a- and, and, you know, to, to understand it in real detail is serious work.

  9. 1:43:57 – 1:49:17

    Prolificness versus depth

    1. DP

      Um, how do you think about prolificness versus depth, where, I don't know, maybe Darwin's an example of somebody who's like i- just gestating on something for many decades. Uh, there's other examples where Einstein during the year comes up with special relativity, he's just doing a bunch of different things. Pais talks about how-

    2. MN

      Mm-hmm

    3. DP

      ... they were all relevant to the eventual build-up.

    4. MN

      Yeah. I, I [chuckles] I mean, you know, it's something I stress about a lot. Sometimes I feel like I'm, you know, too slow. Um, a- actually it's funny, the, I mean, the Darwin example is really interesting. Like, uh, you know, prolific at what? Like y- I mean, I... God knows how many letters he wrote.

    5. DP

      Mm.

    6. MN

      It must have been an enormous, uh, number, so he was certainly very active. Um, there's also, like there's, yeah, 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 wanna avoid procrastination, you just wanna 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?" Um, and just avoid, you know, like getting into a situation where you're prolonging it. Um, and then there's high variance stuff where you actually, you need to, um, be willing to, to, you know, take a lot of time. You need to be willing to go to, 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. Um, and somehow sort of balancing those two things. I, I think a lot of people are very good at doing one or the other, but it's hard to... You know, it's almost like a personality trait, sort of, you know, which one you prefer, and, and people tend to end up doing a, a lot of, a lot of one and, and not enough of, of the other. Um, so I certainly, you know, sort of try and balance those two things. I mean, Ein- 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, um, the photoelectric effect for which he won the Nobel Prize, and it's still an extraordinary year, like a, a plausibly a multi Nobel Prize winning year. Um, uh, so what's he doing? Um, you know, I mean maybe the answer is just he's smarter than the rest of us. Um, uh, and a l- and there's a lot of luck as well. Um, uh, but, but, but, but, you know, I, I certainly for myself anyway, like trying to identify those things, uh, that are routine that I should get good at, um, and then, you know, just, just try and do as quickly as possible. I think that, that's yielded a certain a- amount of returns. But also being willing to bet a little bit more on myself, uh, on sort of the variance side, uh, has also been very, very, very helpful. Um, that's really hard, um, like 'cause you, intrinsically you're putting yourself in situations where you don't know what the outcome is going to be.

    7. DP

      Uh-huh.

    8. MN

      Um, and so if you're very driven to be productive and whatever, um, and actually mostly it's not working, uh, over there, you're like, "Let's reduce this." Like it, it doesn't feel right. Um, when I worked in San Francisco, uh, actually a practice I used to have each day, um, was instead of taking the 15-minute walk to work, I would take the, the more beautiful 30-minute work- walk to work, partially just 'cause it was beautiful, but partially also, um, as just a reminder to think like, like that, that there are real benefits to not being efficient. Um, but it's not an answer to your question. I mean, really I think all I'm saying is I struggle a lot-

    9. DP

      Yeah

    10. MN

      ... with the question.

    11. DP

      I mean, there are these, um, Dean Keith Simonton, I forgot his exact name-

    12. MN

      Yeah, yeah, I know who you mean

    13. DP

      ... um, has this famous equal odds rule 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.

    14. MN

      Yeah.

    15. DP

      And what really determines w- uh, in what era they are m- the most productive is how much they're publishing.

    16. MN

      Yeah.

    17. DP

      Any given thing has equal odds of, um, being extremely important. Um, maybe just think of some of the most successful creatives or scientists, they're just doing a lot, like Shakespeare was just publishing a lot.

    18. MN

      Yeah, yeah.

    19. DP

      Um-

    20. MN

      And of course then there's counter examples, you know, Gödel publishing almost nothing.

    21. DP

      Yeah.

    22. MN

      But, uh, uh, you know, broadly speaking, uh, you know, I think some-- like you need a very good reason to be avoiding it. There's-- to, to, to, to b- basically to, to not do that. Um, it's funny. I mean, I've talked to a-- 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, you know, makes them famous, and they never do anything.

    23. DP

      [laughs]

    24. MN

      Um, and that seems connected. Like it's a type of aversiveness.

    25. DP

      Mm.

    26. MN

      I think very often they just don't want public judgment. S-some-something that I would love to see, you know, there's an awful lot of, of biographies and memoirs and histories of, um, people who achieve a lot. I, I, I wish there was like-

    27. DP

      [laughs]

    28. MN

      ... a very large number of, of biographies of people who are fantastically talented-

    29. DP

      That's a good one

    30. MN

      ... who, who, you know, just missed.

  10. 1:49:17 – 2:03:02

    What it takes to actually internalize what you learn

    1. DP

      Uh, you have this essay that I, um, I was reading before this interview about how you think about what is the work you're doing. Um, and writer doesn't seem like, as you say, was Charles Darwin a writer, right? What, what, what exactly is that label? I'm a podcaster, right? So I'm [laughs] -- uh, and in, and in a way, obviously, our work is very different. But I, I, I also think a lot about what is this work and how do I get better at it?

    2. MN

      Mm.

    3. DP

      And in particular, how I can make sure there's some compounding between the different people I talk to on the podcast.

    4. MN

      Mm.

    5. DP

      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 then it depreciates, and I move to the next topic, and it sort of depreciates. Um, 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 there's-- it's clearly possible to mess this up.

    6. MN

      Mm.

    7. DP

      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.

    8. MN

      Yeah. I mean, it's, it's sort of an incredibly complicated and rich question.

    9. DP

      Yeah.

    10. MN

      Um, I mean, it does seem like the, sort of the question is like, you know, how do you make it a higher growth context? How do you make it a more demanding, uh, context? And sort of-

    11. DP

      Yeah

    12. MN

      ... you, you can do that in like relatively small ways, but that might however yield compounding returns, or you can do something, um, that is maybe more radical. Maybe it means actually, you know, starting sort of a parallel project in which you do, uh, something that is actually quite a bit different. There is something I think really interesting about like how being very demanding, uh, can simply change your, your response to, to something. Something that, that I would sometimes do with, with students and sometimes with myself, was really aimed more at myself, was, you know, they would say some week, "Oh, you know, I'm gonna try and do, you know, this work over the coming week." And then the next week would come by and they, you know, they hadn't solved the problem or whatever. And you, you sort of like, you know, if a million dollars had been at stake, like would you have put the same effort in? And the answer is no, um, sort of invariably. Um, like they've tried, but they haven't really tried.

    13. DP

      Yeah.

    14. MN

      Um, and I think that's a very familiar feeling for all of us. You know, you sort of-- you, you, you, you often you, you know, you could do a lot more if you had just the right sort of demanding taskmaster, uh, standing by you and saying, "Look, you, you, you're barely operating here." Um, and so I, I do sort of wonder a little bit about like, you know, what's the, what's the demanding ta-taskmaster? What, what can they ask you that is going to make your preparation way more intense?

    15. DP

      I-- The most helpful thing honestly is for some subjects, it is very clear how I prep. Like I'm doing an upcoming episode on chip design with the founder of a company that does chip design, and he wrote a textbook on chip design. And he-- yesterday I went over to his office, and we brainstormed five sort of roof line analysis I can do.

    16. MN

      Yeah.

    17. DP

      And if I understand that, I, I have some good understanding. The problem is with almost every other field, there's not this cur-- 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, like 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 have learned about this. I have learned about this. But there's no forcing function that y- do this exercise, and if you do it, you will understand.

    18. MN

      Yeah. So I mean, really what you're sort of saying is you can do a good job at, at, at podcasting without actually attaining this kind of-

    19. DP

      Exactly

    20. MN

      ... and that's the problem from your point of view.

    21. DP

      Exactly. Yeah.

    22. MN

      You, you wanna sort of change your job sh- job description so that you, you know, you are internalizing these chunks-

    23. DP

      Right, right

    24. MN

      ... and just getting this kind of integration each time. Um, and it seems to me like you, you know, what that means is you actually wanna change the structure of the like, like, like the work output at some level.

    25. DP

      Mm.

    26. MN

      Um, uh, I mean, lots of people think... You know, there's this terrible idea, um, people have that, that they should be in flow all of the time.

    27. DP

      Yeah.

    28. MN

      Um, uh, and of course, as far as I can tell, like high performers just don't believe this at all.

    29. DP

      Mm.

    30. MN

      Um, they're in flow some of the time. Like you, you certainly see this with athletes, you know. When they're actually out there, you know, playing basketball or tennis or whatever, uh, ideally, you know, they are in flow much of the time. But when they're training, they're not. Um, they're stuck a lot of the time, or they're doing things badly. Um, and I suppose I wonder what that looks like for you.

Episode duration: 2:03:03

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