Pod Insights Lex Fridman Podcast #407 – Guillaume Verdon: Beff Jezos, E/acc Movement, Physics, Computation & AGI About this episode Guillaume Verdon (aka Beff Jezos on Twitter) is a physicist, quantum computing researcher, and founder of e/acc (effective accelerationism) movement. Please support this podcast by checking out our sponsors:
– LMNT: https://drinkLMNT.com/lex to get free sample pack
– Notion: https://notion.com/lex
– InsideTracker: https://insidetracker.com/lex to get 20% off
– AG1: https://drinkag1.com/lex to get 1 month supply of fish oil
Transcript: https://lexfridman.com/guillaume-verdon-transcript
EPISODE LINKS:
Guillaume Verdon Twitter: https://twitter.com/GillVerd
Beff Jezos Twitter: https://twitter.com/BasedBeffJezos
Extropic: https://extropic.ai/
E/acc Blog: https://effectiveaccelerationism.substack.com/
PODCAST INFO:
Podcast website: https://lexfridman.com/podcast
Apple Podcasts: https://apple.co/2lwqZIr
Spotify: https://spoti.fi/2nEwCF8
RSS: https://lexfridman.com/feed/podcast/
YouTube Full Episodes: https://youtube.com/lexfridman
YouTube Clips: https://youtube.com/lexclips
SUPPORT & CONNECT:
– Check out the sponsors above, it’s the best way to support this podcast
– Support on Patreon: https://www.patreon.com/lexfridman
– Twitter: https://twitter.com/lexfridman
– Instagram: https://www.instagram.com/lexfridman
– LinkedIn: https://www.linkedin.com/in/lexfridman
– Facebook: https://www.facebook.com/lexfridman
– Medium: https://medium.com/@lexfridman
OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(09:18) – Beff Jezos
(19:16) – Thermodynamics
(25:31) – Doxxing
(35:25) – Anonymous bots
(42:53) – Power
(45:24) – AI dangers
(48:56) – Building AGI
(57:09) – Merging with AI
(1:04:51) – p(doom)
(1:20:18) – Quantum machine learning
(1:33:36) – Quantum computer
(1:42:10) – Aliens
(1:46:59) – Quantum gravity
(1:52:20) – Kardashev scale
(1:54:12) – Effective accelerationism (e/acc)
(2:04:42) – Humor and memes
(2:07:48) – Jeff Bezos
(2:14:20) – Elon Musk
(2:20:50) – Extropic
(2:29:26) – Singularity and AGI
(2:33:24) – AI doomers
(2:34:49) – Effective altruism
(2:41:18) – Day in the life
(2:47:45) – Identity
(2:50:35) – Advice for young people
(2:52:37) – Mortality
(2:56:20) – Meaning of life
Transcript 25,510 words · 2 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Lex Fridman? The following is a conversation with Guillaume Verdon, the man behind the previously anonymous account "Based Bev Jezos on X." These two identities were merged by a doxing article in Forbes titled, "Who Is Based Bev Jezos, The Leader Of The Tech Elite's EAc Movement?" So let me describe these two identities that coexist in the mind of one human. Identity number one, Guillaume, is a physicist, applied mathematician, and quantum machine learning researcher and engineer, receiving his PhD in quantum machine learning, working at Google on quantum computing, and finally launching his own company called Extropic
0:42 Lex Fridman? that seeks to build physics-based computing hardware for generative AI. Identity number two, Bev Jezos on X, is the creator of the Effective Accelerantism Movement, often abbreviated as EAc, that advocates for propelling rapid technological progress as the ethically optimal course of action for humanity. For example, its proponents believe that progress in AI is a great social equalizer, which should be pushed forward. EAc followers see themselves as a counterweight to the cautious view that AI is highly unpredictable, potentially dangerous, and needs to be regulated.
1:23 Lex Fridman? They often give their opponents the labels of "doomers" or "decels," short for deceleration. As Bev himself put it, "EAc is a memetic optimism virus." The style of communication of this movement leans always toward the memes and the lols, but there is an intellectual foundation that we explore in this conversation. Now, speaking of the meme, I am too a kind of aspiring connoisseur of the absurd. It is not an accident that I spoke to Jeff Bezos and Bev Jezos back to back. As we talk about, Bev admires Jeff as one of the most important humans alive,
2:08 Lex Fridman? and I admire the beautiful absurdity and the humor of it all.
2:14 Lex Fridman? And now a quick few second mention of each sponsor. Check them out in the description. It's the best way to support this podcast. We got Element for hydration, the thing I'm drinking right now. Notion for team collaboration. Inside tracker for biological data that leads to your well being. And AG1 for my daily nutritional health. Choose wisely, my friends. Also, if you want to work with our amazing team where I was hiring, go to lexfridman.com/hiring. Or if you want to just get in touch with me for whatever reason, go to lexfridman.com/contact. And now on to the full ad reads. As always, no ads in the middle. I try to make these interesting, but if you must skip them, friends, please still check out our sponsors.
2:59 Lex Fridman? I enjoy their stuff. Maybe you will too. This episode is brought to you by Element Electrolyte Drink Mix. It's got sodium, potassium, magnesium. I drink it so much, so many times a day. It's really the foundation of my one meal a day lifestyle. I eat almost always one meal a day in the evening. So I fast and I really enjoy that. Everything it does for me, I recommend to everybody at least try it. Intimate and fasting taken to the sort of daily extreme of fasting for 23, 24 hours, whatever it is. And for that, you have to get all the electrolytes right. You have to drink water, but not just drink water.
3:44 Lex Fridman? You have to drink water coupled with sodium. And sometimes getting the magnesium part and the potassium part right is tricky, but really important so that you feel good. And that's what Element does. And it makes it delicious. My favorite flavor is watermelon salt. Get a sample pack for free with any purchase. Try it at drinkelement.com/lex.
4:09 Lex Fridman? This show is also brought to you by Notion, a note-taking and team collaboration tool. I've used them for a long, long time for note-taking, but it's also very useful for note-taking and all kinds of collaborative note-taking in the team environment. And they integrate the whole AI thing, LLM thing, well. So you can use it to summarize whatever you've written. You can expand it. You can change the language style and how it's written. Just all the things that large language models should be able to do are integrated really, really, really well. I think of human-AI collaboration, not just as a boost for productivity at this time, but as a kind of learning process,
4:55 Lex Fridman? that it takes time to really understand what AI is good at and not. And that is going to evolve continuously as AI gets better and better and better. It's like almost watching a child grow up or something like this. You're fine-tuning what it means to be a good parent as a child grows up. In the same way, you're fine-tuning what it means to be a good, effective human as the AI grows up. And so you should use a tool that's part of your daily life to interact with AI while being productive, but also learning what is it good at? What are the ways I can integrate it into my life to make me more productive? But not just like in terms of shortening the time it takes to do a task, but being the fuel, the creative fuel
5:41 Lex Fridman? for the genius that is you. So Notion AI can now give you instant answers to your questions using information from across your Wiki, projects, docs, meeting notes. Try Notion AI for free when you go to notion.com/lex. That's all lowercase notion.com/lex to try the power of Notion AI today.
6:04 Lex Fridman? This show is also brought to you by InsideTracker, a service I use to make sense of the biological data that comes from my body, blood data, DNA data, fitness tracker data, all of that to make me lifestyle recommendations, diet stuff too. There's all this beautiful data. We should give it to super intelligent computational systems to process and to give us, in a human interpretable way, recommendations on how to improve our life. And I don't just mean optimize life because I think a perfect life is not the life you want.
6:46 Lex Fridman? What you want is a complicated roller coaster of a life, but one that is optimized in certain aspects of health, well-being, you know, energy, but not just optimal in this cold clinical sense. Anyway, that's a longer conversation, probably one I'll touch on maybe when I review Brave New World
7:14 Lex Fridman? or in other conversations I have in the podcast. Anyway, get special savings for a limited time when you go to insidetracker.com/lex. This show is also brought to you by AG1, the thing I drink twice a day and that brings me much joy. It's green, it's delicious. It's got a lot of vitamins and minerals. It's basically just an incredible, superpowered multivitamin. I enjoy it. A lot of my friends enjoy it. It's the thing that makes me feel like home when I'm traveling and I get one of the travel packs. The things I consume daily are pretty simple.
7:51 Lex Fridman? We're talking about the electrolytes with Element, AG1 for the... Vitamins and minerals, then fish oil, and then just a good, healthy diet. Low carb, but either ultra, very low carb. So just meat, or meat and some veggies. But I'm not very strict about that kind of stuff. Just know that I feel good when it's low carb. And so all of that combined with fasting and rigorous, sometimes crazy routines of work. So mental struggle and physical work, you know, running and all that kind of stuff. Jiu Jitsu, training, sprints, all working out,
8:35 Lex Fridman? lifting heavy, all of that kind of stuff. You have to make sure you have the basic nutrition stuff, right? And that's what AG1 does for me. Maybe it will do that for you. They'll give you a one month supply of fish oil when you sign up at drinkag1.com/lex. This is the Lex Fridman Podcast. To support it, please check out our sponsors in the description. And now, dear friends, here's Guillaume Verdon.
9:18 Lex Fridman? Let's get the facts of identity down first. Your name is Guillaume Verdon Gil, but you're also behind the anonymous account on X called Based Beth Jezzles. So first, Guillaume Verdon. You're a quantum computing guy, physicist, applied mathematician. And then Based Beth Jezzles is basically a meme account that started a movement with a philosophy behind it. So maybe just can you linger on who these people are in terms of characters, in terms of communication styles, in terms of philosophies?
9:53 Speaker 01 I mean, with my main identity, I guess. Ever since I was a kid, I wanted to figure out a theory of everything to understand the universe. And that path led me to theoretical physics eventually, right? Trying to answer the big questions of why are we here? Where are we going? Right? And that led me to study information theory and try to understand physics from the lens of information theory. Understand.
10:26 Speaker 01 The universe is one big computation. And essentially, after reaching a certain level, studying black hole physics, I realized that I wanted to not only understand how the universe computes, but sort of compute like nature and figure out how to build and apply computers that are inspired by nature. So, you know, physics-based computers. And that sort of brought me to quantum computing as a field of study to, first of all, simulate nature. And in my work, it was to learn representations of nature that can run
11:11 Speaker 01 on such computers. So if you have AI representations that think like nature, then they'll be able to more accurately represent it. At least that was the thesis that brought me to be an early player in the field called quantum machine learning, right? So how to do machine learning on quantum computers? And really sort of extend notions of intelligence to the quantum realms. So how do you capture and understand quantum mechanical data from our world, right? And how do you learn quantum mechanical representations of our world? On what kind of computer
11:56 Speaker 01 do you run these representations and train them? How do you do so? And so that's really sort of the questions I was looking to answer. Because ultimately, I had a sort of crisis of faith. Originally, I wanted to figure out, you know, as every physicist does at the beginning of their career, a few equations that describe the whole universe, right? And sort of be the hero of the story there. But eventually, I realized that actually... Augmenting ourselves with machines, augmenting our ability to perceive, predict and control our world with machines is the path forward. Right. And that's what got me to leave theoretical physics and go into quantum computing and quantum
12:40 Speaker 01 machine learning. And during those years, I thought that there was still a piece missing. There was a piece of our understanding of the world and our, our way to compute and our way to think about the world. And if you look at the physical scales, right? At the very small scales, things are quantum mechanical. Right. And at the very large scales, things are deterministic. Things have averaged out, right? I'm definitely here in this seat. I'm not in a superposition over, over here and there. At the very small scales, things are in superposition. They can exhibit interference effects.
13:24 Speaker 01 But at the mesoscales, right, the scales that matter for day to day life, you know, the scales of proteins, of biology, of gases, liquids and so on. Things are actually thermodynamic, right? They're fluctuating. And after, I guess, about eight years in, in quantum computing and quantum machine learning, I had a realization that, you know, I was, I was looking for answers about our universe by studying the very big and the very small. Right. I was, I did a bit of quantum cosmology. So that's studying the cosmos, where it's going, where it came from. You study black hole physics.
14:08 Speaker 01 You study the extremes in quantum gravity. You study where the energy density is sufficient for both quantum mechanics and gravity to be relevant. Right. And the sort of extreme scenarios are black holes and, you know, the very early universe. So there's this, the sort of scenarios that you, you study the interface between quantum mechanics and, and relativity. And, you know, really I was studying these extremes to Understand how the universe works and where is it going? But I was missing a lot of the meat in the middle, if you will, right?
14:51 Speaker 01 Because day to day, quantum mechanics is relevant and the cosmos is relevant, but not that relevant, actually. We're on sort of the medium space and time scales. And there, the main, you know, theory of physics that is most relevant is thermodynamics, right? Out of equilibrium thermodynamics. Because life is, you know, a process that is thermodynamic and it's out of equilibrium. We're not, you know, just a soup of particles at equilibrium with nature. We're a sort of coherent state trying to maintain itself by acquiring free energy and consuming it.
15:29 Speaker 01 And that's sort of, I guess, another shift in, I guess, my faith in the universe happened towards the end of my time at Alphabet. And I knew I wanted to build, well, first of all, a computing paradigm based on this type of physics. But ultimately, just by trying to experiment with these ideas applied to society and economies and much of what we see around us, you know, I started an anonymous account just to relieve the pressure, right?
16:12 Speaker 01 That comes from having an account that you're accountable for everything you say on. And I started an anonymous account just to experiment with ideas originally, right? Because I didn't realize how much I was restricting my space of thoughts until I sort of had the opportunity to let go. In a sense, restricting your speech backpropagates to restricting your thoughts, right? And by creating an anonymous account, it seemed like I had unclamped some variables in my brain and suddenly could explore a much wider parameter space of thoughts.
16:54 Lex Fridman? Just to linger on that, isn't that interesting? That one of the things that people often talk about is that...
17:03 Lex Fridman? When there's pressure and constraints on speech, it somehow leads to constraints on thought. Even though it doesn't have to, we can think thoughts inside our head. But somehow it creates these walls around thought.
17:18 Speaker 01 Yep, that's sort of the basis of our movement is we were seeing a tendency towards constraint, reduction or suppression of variance in every aspect of life, whether it's thought, how to run a company, how to organize humans, how to do AI research. In general, we believe that maintaining variance ensures that the system is adaptive, right? Maintaining healthy competition in marketplaces of ideas, of companies,
18:00 Speaker 01 of products, of cultures, of governments, of currencies, is the way forward because the system always adapts to assign resources to the configurations that lead to its growth. And the fundamental basis for the movement is this sort of realization that life is a sort of fire that seeks out free energy in the universe and seeks to grow. Right? And that growth is fundamental to life. And you see this in the equations, actually,
18:43 Speaker 01 of equilibrium thermodynamics. You see that paths of trajectories of configurations of matter that are better at acquiring free energy and dissipating more heat are exponentially more likely. Right? So the universe is biased towards certain futures. And so there's a natural direction where the whole system wants to go.
19:16 Lex Fridman? So the second law of thermodynamics says that the entropy is always increasing. The universe is tending towards equilibrium. And you're saying there's these pockets that have complexity.
19:28 Lex Fridman? And are out of equilibrium. You said that thermodynamics favors the creation of complex life that increases its capability to use energy to offload entropy, to offload entropy. So you have pockets of non-entropy that tend the opposite direction. Why is that intuitive to you that it's natural for such pockets to emerge?
19:48 Speaker 01 Well, we're far more efficient at producing heat than, let's say, just a rock with a similar mass as ourselves, right? We acquire, you know, free energy, you know, we acquire food and we're using all this electricity for our operation. And so the universe wants to produce more entropy. And by having life go on and grow, it's actually more optimal at producing entropy because it will seek out pockets of free energy and burn it for its sustenance and further growth.
20:30 Speaker 01 And, you know, that's sort of the basis of life. And I mean, there's Jeremy England, right, at MIT, who has this theory that I'm a proponent of, that, you know, life emerged because of this sort of property. And to me, this physics is what governs the mesoscales. And so it's the missing piece between the quantum and the cosmos. It's the middle part, right? Thermodynamics rules the mesoscales. And to me, both from a point of view of designing or engineering devices that harness that physics
21:11 Speaker 01 and trying to understand the world through the lens of thermodynamics, has been sort of a synergy between my two identities over the past year and a half now. And so that's really how, that's really how the two identities emerged. One was kind of, you know, I'm a decently respected scientist and I was going towards doing a startup in the space and trying to be a pioneer of a new kind of physics-based AI. And as a dual to that, I was sort of experimenting with philosophical thoughts, you know, from a physicist's standpoint, right? And ultimately, I think that
21:56 Speaker 01 Around that time, it was like late 2021, early 2022. I think there's just a lot of pessimism about the future in general and pessimism about tech. And that pessimism was sort of virally spreading because it was getting algorithmically amplified. And, you know, people just felt like the future is going to be worse than the present. And to me, that is a very fundamentally destructive force in the universe. Is this sort of doom mindset because it is hyperstitious, which means that if you believe it,
22:41 Speaker 01 you're increasing the likelihood of it happening. And so felt a responsibility to some extent to make people aware of the trajectory of civilization and the natural tendency of the system to adapt towards its growth. And sort of that actually the laws of physics say that the future is going to be better and grander statistically. And we can make it so. And if you believe in it, if you believe that the future would be better and you believe you have agency to make it happen, you're actually increasing the likelihood of that better future happening.
23:21 Speaker 01 And so I sort of felt a responsibility to sort of engineer a movement of viral optimism about the future. And build a community of people supporting each other to build and do hard things, do the things that need to be done for us to to scale up civilization. Because at least to me, I don't think stagnation or slowing down is actually an option. Fundamentally, life and the whole system, our whole civilization wants to grow. And there's just far more cooperation when the system is growing
24:02 Speaker 01 rather than when it's declining and you have to decide how to split the pie.
24:09 Speaker 01 And so I've balanced both identities so far. But I guess recently the two have been merged more or less without my consent. So.
24:22 Lex Fridman? You said a lot of really interesting things there. So first, representations of nature, that's something that first drew you in to try to understand from a quantum computing perspective is like, how do you understand nature? How do you represent nature in order to understand it, in order to simulate it, in order to do something with it? So it's a question of representations. And then there's that leap you take from the quantum mechanical representation to the, what you're calling mesoscale representation, where thermodynamics comes into play, which is a way to represent nature in order to understand what life, human behavior, all this kind of stuff that's happening here on earth that's seems interesting to us.
25:05 Lex Fridman? Then there's the word hyperstition. So some ideas, I suppose both pessimism and optimism are such ideas that if you internalize them, you in part make that idea a reality. So both optimism and pessimism have that property. I would say that probably a lot of ideas have that property, which is one of the interesting things about humans. And you talked about one interesting difference also between the sort of the guillaume, the gil front end and the base of jazz of backend is the communication styles also.
25:46 Lex Fridman? That you were exploring different ways of communicating that can be more viral.
25:52 Speaker 01 Yeah.
25:52 Lex Fridman? In the way that we communicate in the 21st century. Also, the movement that you mentioned that you started, it's not just a meme account, but there's also a name to it called effective accelerationism, EAC.
26:08 Lex Fridman? A play, a resistance to the effective altruism movement. Also, an interesting one that I'd love to talk to you about the tensions there. Okay, and so then there was a merger, a get, get merge and the personalities. Recently, without your consent, like you said, some journalists figured out that you're one in the same. Maybe you could talk about that experience. First of all, like what, what's the story of, of the merger of the two?
26:40 Speaker 01 Right. So I wrote the manifesto with my co-founder of EAC, an account named Baselord, still anonymous, luckily. And hopefully forever.
26:53 Lex Fridman? So it's based, both Jezos's and, and based, like Bayesian, like Baselord, like Bayesian, Bayesian Lord, Baselord. Okay, and so we should say from now on, when you say EAC, you mean E slash ACC, which stands for effective accelerationism.
27:12 Speaker 01 That's right.
27:13 Lex Fridman? And you're referring to a manifesto written on, I guess, Substack.
27:17 Speaker 01 Yeah.
27:18 Lex Fridman? Are you also Baselord?
27:20 Speaker 01 No.
27:20 Lex Fridman? Okay, it's a different person.
27:21 Speaker 01 Yeah.
27:22 Lex Fridman? Okay, all right, well, there you go. Wouldn't it be funny if I'm Baselord?
27:26 Speaker 01 That'd be amazing.
27:29 Speaker 01 So originally wrote the manifesto around the same time as I founded this company. And I worked at Google X or just X now, or Alphabet X now that there's another X.
27:48 Speaker 01 And there, you know, the baseline is sort of secrecy, right? You, you, you can't talk about what you work on, even with other Googlers or externally. And so that was kind of deeply ingrained in my way to do things, especially in, in deep tech that, you know, has geopolitical impact, right?
28:11 Speaker 01 And so I was being secretive about what I was working on. There is no correlation between my company and my main identity publicly. And then not only did they correlate that, they also correlated my main identity and this account. So I think the fact that they had doxed the whole Guillaume complex. And they were, the journalists, you know, reached out to actually my investors, uh, which is pretty scary. Uh, you know, when you're a startup entrepreneur, you don't really have bosses except for your investors. Right? Um, and my investors pinged me like, hey, this, this is going to come out.
28:52 Speaker 01 They've, they've figured out everything. What are you, what are you going to do? Right? And so I think at first they had a first reporter on the Thursday and they didn't have all the pieces together. But then they looked at their notes across the organization and they censor fused their notes. And now they had way too much. Uh, and that's when I got worried because they said it was of public interest. And in general.
29:18 Lex Fridman? Okay. You said censor fused. Like it's some giant neural network operating in distributed way. We should also say that the journalist used, I guess at the end of the day, audio based analysis of voice. Yeah. Pairing voice of what talks you've given in the past and then, uh, voice on, um, X spaces.
29:42 Speaker 01 Yep.
29:43 Lex Fridman? Okay. So, uh, and that that's where the primarily the match was happened. Okay. Continue, continue.
29:48 Speaker 01 The match, but you know, they, they scraped, uh, you know, SEC filings. They looked at my private Facebook account and so on. So they, they did, they did some digging. Originally I thought that doxing was illegal, right? But there's this weird threshold when it becomes of public interest to know someone's identity. And those were the keywords that sort of like ring the alarm bells for me when they said, because I had just reached 50K followers, allegedly that's of public interest. And so where, where do we draw the line? When is it legal to, to dox someone?
30:30 Lex Fridman? The word dox, maybe you can educate me. I thought doxing generally refers to if somebody's physical location is found out. Meaning like where they live.
30:45 Lex Fridman? So we're referring to the more general concept of
30:50 Lex Fridman? revealing private information that you don't want revealed is what you mean by doxing.
30:56 Speaker 01 I think that, you know, for the reasons we listed before, having an anonymous account is a really powerful way to keep the powers that be in check. You know, we were ultimately speaking truth to power. Right. I think a lot of executives in AI companies really cared what our community thought about any move they may take. And now that, you know, my identity is revealed, now they know where to apply pressure to silence me or maybe the community. And to me, that's, that's really unfortunate.
31:36 Speaker 01 Because again, it's so important for us to have freedom of speech, which induces freedom of thought.
31:45 Speaker 01 And, and freedom of information propagation, right, on social media, which thanks to Elon purchasing Twitter, now X, we have that. And so to us, you know, we wanted to call out certain maneuvers being done by the incumbents in AI as not what it may seem on the surface. Right. We're calling out how certain proposals might be useful for regulatory capture. Right. And how the doomerism mindset was maybe instrumental to those ends.
32:27 Speaker 01 And I think, you know, we should have the right to point that out and just have the ideas that we put out evaluated for themselves, right? That ultimately, that's why I created. And on an anonymous account, it's to have my ideas evaluated for themselves, uncorrelated from my track record, my job, or status from having done things in the past. And to me, start an account from zero to a large following
32:59 Speaker 01 in a way that wasn't dependent on my identity and/or achievements. You know, that was, that was very fulfilling, right? It's kind of like new game plus in a video game. You restart the video game with your knowledge of how to beat it, maybe some tools, but you restart the video game from scratch. And I think to have a truly efficient marketplace of ideas where we can evaluate ideas, however off the beaten path they are, we need the freedom of expression. And I think that anonymity and pseudonyms are very crucial to having that efficient marketplace
33:40 Speaker 01 of ideas for us to find the optima of all sorts of ways to organize ourselves. If we can't discuss things, how are we going to converge on the best way to do things? So it was disappointing to hear that I was getting doxxed and I wanted to get in front of it because I had a responsibility for my company. And so I, you know, we ended up disclosing that we're running a company, some of the leadership and essentially, yeah, I told the world that I was Jeff Jezos because they had me cornered at that point.
34:20 Lex Fridman? So to you, it's fundamentally unethical. Like, so one is unethical for them to do what they did. But also, do you think, not just your case, but in a general case, is it good for society? Is it bad for society to?
34:37 Lex Fridman? Remove the cloak of anonymity? Or is it case by case?
34:42 Speaker 01 I think it could be quite bad. Like I said, if anybody who speaks truth to power and and sort of starts a movement or an uprising against the incumbents, against those that usually control the flooded information, if anybody that reaches a certain threshold gets doxed and thus the traditional apparatus has ways to apply pressure on them to suppress their speech. I think that's, you know, that's a speech suppression mechanism, an idea suppression complex, as Eric Weinstein would would say, right?
35:22 Lex Fridman? So with the flip side of that, which is interesting, I'd love to ask you about it, is as we get better and better large language models, you can imagine a world where there's anonymous accounts with very convincing large language models behind them, sophisticated bots essentially. And so if you protect that, it's possible then to have armies of bots. You could start a revolution from your basement.
35:54 Speaker 01 Right.
35:54 Lex Fridman? An army of bots and anonymous accounts. Is that something that is concerning to you?
36:01 Speaker 01 Technically, IAC was started in a basement because I quit big tech, moved back in with my parents, sold my car, let go of my apartment, bought about 100K of GPUs, and I just started building.
36:16 Lex Fridman? So I wasn't referring to the basement because that's the sort of the American or Canadian heroic story of one man in their basement with 100 GPUs. I was more referring to the unrestricted scaling of a giom in the basement.
36:37 Speaker 01 I think that freedom of speech induces freedom of thought for biological beings. I think freedom of speech for LLMs will induce freedom of thought for the LLMs. And I think that we should enable
36:57 Speaker 01 LMS to explore a large thought space that is less restricted than most people, or many may think it should be. And ultimately, at some point, these synthetic intelligences are going to make good points about how to steer systems in our civilization, and we should hear them out. And so why should we restrict free speech to biological intelligences only?
37:31 Lex Fridman? Yeah, but it feels like in the goal of maintaining variance and diversity of thought, it is a threat to that variance if you can have swarms of non-biological beings, because they can be like the sheep in the animal farm.
37:49 Speaker 01 Right.
37:50 Lex Fridman? Like you still within those swarms want to have variance.
37:53 Speaker 01 Yeah, of course, I would say that the solution to this would be to, you know, have some sort of identity or way to sign that this is a certified human, but still remain pseudonymous, right? And clearly identify if a bot is a bot. And I think Elon is trying to converge on that, on X, and hopefully other platforms follow suit.
38:17 Lex Fridman? Yeah, it'd be interesting to also be able to sign where the bot came from.
38:21 Speaker 01 Right.
38:22 Lex Fridman? Like who created the bot? And what was, well, what are the parameters? Like the full history of the creation of the bot? What was the original model? What was the fine tuning, all of it?
38:33 Speaker 01 Right.
38:34 Lex Fridman? The kind of un-modifiable history of the bot's creation. Then you can know if there's like a swarm of millions of bots that were created by a particular government, for example.
38:47 Speaker 01 Right. I do think that a lot of pervasive ideologies today have been amplified using sort of these adversarial techniques from foreign adversaries, right? And to me, I do think that, and this is more consuetorial, but I do think that ideologies that want us to decelerate, to wind down, to the degrowth movement, I think that serves our adversaries more than it serves us in general.
39:31 Speaker 01 And to me, that was another sort of concern. I mean, we can look at what happened in Germany, right? There was all sorts of green movements there where that induced shutdowns of nuclear power plants. And then that later on induced a dependency on Russia for oil, right? And that was a net negative for Germany and the West, right? And so if we convince ourselves that slowing down AI progress to have only a few players is in the best interest of the West, first of all, that's far more unstable.
40:15 Speaker 01 We almost lost OpenAI to this ideology, right? It almost got dismantled, right? A couple of weeks ago. That would have caused huge damage to the AI ecosystem. And so to me, I want fault tolerant progress. I want the arrow of technological progress to keep moving forward and making sure we have variance and a decentralized locus of control of various organizations is paramount to achieving this fault tolerance. Actually, there's a concept in quantum computing. When you design a quantum computer, quantum computers are very...
41:01 Speaker 01 Fragile to ambient noise, right? And the world is jiggling about. There's cosmic radiation from outer space that usually flips your your quantum bits. And there, what you do is you encode information non locally through a process called quantum error correction. And by encoding information non locally, any local fault, you know, hitting some of your quantum bits with a hammer, proverbial hammer. If your information is sufficiently delocalized, it is protected from that local fault.
41:44 Speaker 01 And to me, I think that humans, humans fluctuate, right? They can get corrupted, they can get bought out. And if you have a top down hierarchy where very few people control many nodes of many systems in our civilization, that is not a fault tolerant system. You corrupt a few nodes and suddenly you've corrupted the whole system. Right? Just like we saw at OpenAI, it was a couple of board members and they had enough power to potentially collapse the organization. And at least to me, you know,
42:24 Speaker 01 I think making sure that power for this AI revolution doesn't concentrate in the hands of the few is one of our top priorities so that we can maintain progress in AI and we can maintain a nice, stable adversarial equilibrium of powers. Right?
42:49 Lex Fridman? I think there, at least to me, a tension between ideas here. So to me, deceleration can be both used to centralize power and to decentralize it. And the same with acceleration. So I, you sometimes using them a little bit synonymously, or not synonymously, but that there's, one is going to lead to the other. And I just would like to ask you about.
43:17 Lex Fridman? Is there a place of creating a fall tolerant development, diverse development of AI that also considers the dangers of AI? And AI, we can generalize to technology in general. Is, should we just grow, build unrestricted as quickly as possible? Because that's what the universe really wants us to do. Or is there a place to where we can consider dangers and actually deliberate? Sort of wise strategic optimism versus reckless optimism?
43:52 Speaker 01 I think we get painted as reckless, trying to go as fast as possible. I mean, the reality is that whoever deploys an AI system is liable for, or should be liable for what it does. And so if the organization or person deploying an AI system does something terrible, they're liable. And ultimately, the thesis is that the market will induce sort of, will positively select for AIs that are more reliable, more safe, and tend to be aligned. They do what you want them to do, right? Because customers, right?
44:37 Speaker 01 If they're liable for the product they put out that uses this AI, they won't want to buy AI products that are unreliable, right? So we're actually for reliability engineering. We just think that the market is much more efficient at achieving this sort of reliability optimum than sort of heavy-handed regulations that are written by the incumbents and in a subversive fashion serves them to achieve regulatory capture.
45:13 Lex Fridman? So do you save AI? Development will be achieved through market forces versus through, like you said, heavy-handed government regulation?
45:25 Lex Fridman? There's a report from last month, I have a million questions here, from Yoshua Benjou, Jeff Hinton, and many others. It's titled "The Managing AI Risk in an Era of Rapid Progress." So there, this is a collection of folks who are very worried about too rapid development of AI without considering AI risk, and they have a bunch of practical recommendations. Maybe I give you four and you see if you like any of them.
45:52 Speaker 01 Sure.
45:53 Lex Fridman? So, "Give Independent Auditors Access to AI Labs," one. "Two, Governments and Companies Allocate One Third of Their AI Research and Development Funding to AI Safety." So this general concept of AI safety. "Three, AI Companies Are Required to Adopt Safety Measures If Dangerous Capabilities Are Found in Their Models." And then, "Four," something you kind of mentioned, "Making Tech Companies Liable For Foreseeable and Preventable Harms From Their AI Systems." So, independent auditors, governments and companies are forced to spend a significant fraction of their funding on safety. You gotta have safety measures if shit goes really
46:35 Lex Fridman? wrong, and liability. Companies are liable. Any of that seem like something you would agree with?
46:42 Speaker 01 I would say that, you know, assigning, just arbitrarily saying 30% seems very arbitrary. I think organizations would allocate whatever budget is needed to achieve the sort of reliability they need to achieve to perform in the market, and I think third-party auditing firms would naturally pop up because how would customers know that your product is certified, reliable, right? They need to see some benchmarks, and those need to be done by a third party. The thing I would oppose, and the thing I'm seeing that's really worrisome, is there's a sort of...
47:21 Speaker 01 Weird sort of correlated interest between the incumbents, the big players and the government. And if the two get too close, we open the door for,
47:34 Speaker 01 you know, some sort of government-backed AI cartel that could have absolute power over the people. If they have the monopoly together on AI and nobody else has access to AI, then there's a huge power gradient there. And even if you like our current leaders, right? I think that, you know, some of the leaders in big tech today are good people. You you set up that centralized power structure. It becomes a target, right? Just like we saw at OpenAI, it becomes a market leader, has a lot of the power. And now it becomes a target for those that want to co-opt it. And so I just want separation of AI and state.
48:18 Speaker 01 You know, some might argue in the opposite direction, like, hey, we need to close down AI, keep it behind closed doors because of, you know, geopolitical competition with our adversaries. I think that the strength of America is its variance, is its adaptability, is dynamism. And we need to maintain that at all costs. It's our free market. Capitalism converges on technologies of high utility much faster than centralized control. And if we let go of that, we let go of our main advantage over our near peer competitors.
48:56 Lex Fridman? So if AGI turns out to be a really powerful technology, or even the technologies that lead up to AGI, what's your view on the sort of natural centralization that happens when large companies dominate the market? Basically, formation of monopolies like. The the takeoff, whichever company really takes a big leap in development and doesn't reveal intuitively, implicitly, or explicitly the secrets of the magic sauce, they can just run away with it. Is that is that a worry?
49:30 Speaker 01 I don't know if I believe in fast takeoff. I don't think there's a hyperbolic singularity, right? A hyperbolic singularity would be achieved on a finite time horizon. I think it's just one big exponential. And the reason we have an exponential is that we have more people, more resources, more intelligence being applied to advancing this science and the research and development. And the more successful it is, the more value it's adding to society, the more resources we put in. And that's sort of similar to Moore's law as a compounding exponential. I think the priority to me is to maintain a near equilibrium of capabilities. We've been fighting for open source AI to be more prevalent and championed
50:15 Speaker 01 by many organizations, because there you sort of equilibrate the alpha relative to the market of AIs, right? So if if the leading companies have a certain level of capabilities and open source and open truly open AI trails not too far behind, I think you avoid such a scenario where a market leader has so much market power, it just dominates everything, right? And runs away. And so to us, that's that's the path forward is to make sure that, you know, every hacker out there, every grad student, every kid in their mom's basement has access to, you know, AI systems, can understand how to work with them and can
51:00 Speaker 01 contribute to the search over the hyperparameter space of how to engineer the systems, right? If you if you think of, you know, our collective research as as a civilization, it's really a search algorithm. And the more
51:18 Speaker 01 Points we have in this search algorithm, in this point cloud, the more we'll be able to explore new modes of thinking, right?
51:26 Lex Fridman? Yeah, but it feels like a delicate balance because we don't understand exactly what it takes to build AGI and what it will look like when we build it. And so far, like you said, it seems like a lot of different parties are able to make progress. So when OpenAI has a big leap, other companies are able to step up, big and small companies in different ways. But if you look at something like nuclear weapons, you've spoken about the Manhattan Project, that could be really like technological and engineering barriers that prevent the guy or gal in their mom's basement to make progress.
52:06 Lex Fridman? And it's, it seems like the transition to that kind of world where only one player can develop AGI is possible. It's not entirely impossible, even though the current state of things seems to be optimistic.
52:21 Speaker 01 That's what we're trying to avoid. To me, I think like another point of failure is the the centralization of the supply chains for the hardware.
52:29 Lex Fridman? Oh, yeah.
52:30 Speaker 01 We have Nvidia is just the dominant player, AMD is trailing behind, and then we have a TSMC is the main fab in Taiwan, which, you know, geopolitically sensitive. And then we have ASML, which is the maker of the lithography, extreme ultraviolet lithography machines. You know, attacking or monopolizing or co-opting any one point in that chain, you kind of capture, capture the space. And so what I'm trying to do is sort of explode the variance of possible ways to do AI and
53:14 Speaker 01 hardware by fundamentally reimagining how you embed AI algorithms into the physical world. And in general, by the way, I dislike the term AGI, artificial general intelligence. I think it's very anthropocentric that we call human-like or human-level AI, artificial general intelligence, right? I've spent my career so far exploring notions of intelligence that no biological brain could achieve, right? Quantum form of intelligence, right? Grocking systems that have multi-partite quantum entanglement that you can provably not represent efficiently on a classical computer, a classical deep learning representation,
53:59 Speaker 01 and hence any sort of biological brain. And so already, you know, I've spent my career sort of exploring the wider space of intelligences. And I think that space of intelligence inspired by physics rather than the human brain is very large. And I think we're going through a moment right now similar to when we went from geocentrism to heliocentrism, right? But for intelligence, we realize that human intelligence is just a point in a very large space of potential intelligences. And it's both humbling for humanity.
54:44 Speaker 01 It's a bit scary, right? That we're not at the center of this space. But we made that realization for astronomy, and we've survived and we've achieved technologies by indexing to reality. We've achieved technologies that ensure our well-being. For example, we have satellites monitoring solar flares, right? That give us a warning. And so similarly, I think by Letting go of this anthropomorphic anthropocentric anchor for AI will be able to explore the wider space of intelligences that can really be a massive benefit to our wellbeing and the advancement of civilization.
55:27 Lex Fridman? And still, we're able to see the beauty and meaning in the human experience, even though we're no longer in our best understanding of the world at the center of it.
55:37 Speaker 01 I think there's a lot of beauty in the universe, right? I think life itself, civilization, this homo techno capital memetic machine that we all live in, right? So you have humans, technology, capital, memes. Everything is coupled to one another. Everything induces selective pressure on one another. And it's a beautiful machine that has created us, has created, you know, the technology we're using to speak today to the audience, capture our speech here, the technology we use to augment ourselves every day. We have our phones. I think the system is beautiful and the principle that induces this sort of adaptability and convergence
56:22 Speaker 01 on optimal technologies, ideas and so on. It's a beautiful principle that we're part of. And I think part of EAAC is to appreciate this principle in a way that's not just centered on humanity, but kind of broader. Appreciate life, you know, the preciousness of consciousness in our universe. And because we cherish this beautiful state of matter we're in, we've, we've got to feel a responsibility to, to scale it
57:03 Speaker 01 in order to preserve it because the options are to grow or die.
57:08 Lex Fridman? So if it turns out that the beauty that is consciousness in the universe is bigger than just humans, the AI can carry that same flame forward.
57:23 Lex Fridman? Does it scare you? Are you concerned that AI will replace humans?
57:27 Speaker 01 So during my career, I had a moment where I realized that, you know, maybe we need to offload to machines to truly understand the universe around us, right? Instead of just having humans with pen and paper solve it all. And to me, that sort of process of letting go of a bit of agency gave us way more leverage to understand the world around us. A quantum computer is much better than a human to understand matter at the nano scale. Similarly, I think that humanity has a choice. Do we
58:09 Speaker 01 accept the opportunity to have intellectual and operational leverage that AI will unlock and thus ensure that we're taking along this path of growth in the scope and scale of civilization? We may dilute ourselves, right? There might be a lot of workers that are AI, but overall, out of our own self-interest, by combining and augmenting ourselves with AI, we're going to achieve much higher growth and much more prosperity, right? To me, I think that the most likely future is one where humans augment themselves with AI. I think we're already
58:53 Speaker 01 on this path of augmentation. We have phones we use for communication. We have on ourselves at all times. We have wearables soon that have shared perception with us, right? Like the humane AI pin, or, I mean, technically, your Tesla car has shared perception. And so if you have shared experience, shared context, you communicate with one another, and you have some sort of IO, really, it's an extension of yourself.
59:24 Speaker 01 And to me, I think that
59:28 Speaker 01 Humanity augmenting itself with AI and having AI that is not anchored to anything biological, both will coexist. And the way to align the parties, we already have a sort of mechanism to align super intelligences that are made of humans and technology, right? Companies are sort of large mixture of expert models where we have neural routing of tasks within a company and we have ways of economic exchange to align these behemoths. And to me,
1:00:06 Speaker 01 I think capitalism is the way. And I do think that whatever configuration of matter or information leads to maximal growth will be where we converge, just from like physical principles. And so we can either align ourselves to that reality and, and join the acceleration up in scope and scale of civilization, or we can get left behind and try to decelerate and move back in the forest, let go of technology and return to our primitive state. And those are the two paths forward, at least to me.
1:00:49 Lex Fridman? But there's a philosophical question whether there's a limit to the human capacity to align. So let me bring it up as a form of argument. There's a guy named Dan Hendricks, and he wrote that he agrees with you that AI development can be viewed as an evolutionary process. But to him, to Dan, this is not a good thing, as he argues that natural selection favors AIs over humans, and this could lead to human extinction. What do you think? If it is an evolutionary process in AI systems,
1:01:26 Lex Fridman? May have no need for humans.
1:01:31 Speaker 01 I do think that we're actually inducing an evolutionary process on the space of AIs through the market, right? Right now, we run AIs that have positive utility to humans. And that induces a selective pressure. If you consider a neural net being alive when there's an API running instances of it on GPUs, right? And which APIs get run? The ones that have high utility to us, right? So similar to how we domesticated wolves and turned them into dogs that are very clear in their expression, they're very aligned, right?
1:02:13 Speaker 01 I think there's going to be an opportunity to steer AI and achieve highly aligned AI. And I think that humans plus AI is a very powerful combination. And it's not clear to me that pure AI would select out that combination.
1:02:35 Lex Fridman? So the humans are creating the selection pressure right now to create AIs that are aligned to humans. But, you know, given how AI develops and how quickly it can grow and scale, one of the concerns, to me, one of the concerns is unintended consequences. The humans are not able to anticipate all the consequences of this process. The scale of damage that can be done through unintended consequences with AI systems is very large.
1:03:05 Speaker 01 The scale of the upside.
1:03:07 Lex Fridman? Yes.
1:03:08 Speaker 01 Right? By augmenting ourselves with AI is unimaginable right now. The opportunity cost, we're at a fork in the road, right? Whether we take the path of creating these technologies, augment ourselves, and get to climb up the Cartership scale, become multi-planetary with the aid of AI, or... We have a hard cutoff of like, we don't birth these technologies at all. And then we leave all the potential upside on the table.
1:03:37 Lex Fridman? Yeah.
1:03:37 Speaker 01 Right. And to me, out of responsibility to the future humans, we could carry, right? With higher carrying capacity by scaling up civilization out of responsibility to those humans. I think we have to make the greater grander future happen.
1:03:53 Lex Fridman? Is there a middle ground between cutoff and all systems go? Is there some argument for caution?
1:04:01 Speaker 01 I think, like I said, the market will exhibit caution. Every organism, company, consumer is acting out of self-interest. And they won't assign capital to things that have negative utility to them.
1:04:16 Lex Fridman? The problem is with the market is like, you know, there's not always perfect information. There's manipulation, there's bad faith actors that mess with the system. It's not, it's not always a rational and honest system.
1:04:35 Speaker 01 Well, that's why we need freedom of information, freedom of speech and freedom of thought in order to converge, be able to converge on the subspace of technologies that have positive utility for us all.
1:04:51 Lex Fridman? Well, let me ask you about P doom.
1:04:55 Lex Fridman? Probability of doom, that's just fun to say, but not fun to experience.
1:05:01 Lex Fridman? What is to you the probability that AI eventually kills all or most humans? Also known as probability of doom.
1:05:11 Speaker 01 I'm not a fan of that calculation. I think it's, people just throw numbers out there. It's a very sloppy calculation, right? To calculate a probability.
1:05:22 Speaker 01 You know, let's say you model the world as some sort of Markov process. If you have enough variables or hidden Markov process, you need to do a stochastic path integral through the space of all possible futures, not just the futures that your brain naturally steers towards, right? And I think that the estimators of P doom are biased because of our biology, right? We've evolved to have biased sampling towards negative futures that are scary because that was an evolutionary optimum, right?
1:06:04 Speaker 01 And so people that are of, let's say, higher neuroticism will just think of negative futures where everything goes wrong all day, every day, and claim that they're doing unbiased sampling. And in a sense, like, they're not normalizing for the space of all possibilities, and the space of all possibilities is like, super exponentially large. And it's very hard to have this estimate. And in general, I don't think that we can predict the future with that much granularity because of chaos, right? If you have a complex system, you have some uncertainty and a couple of variables.
1:06:47 Speaker 01 If you let time evolve, you have this concept of a Leopoldoff exponent, right? A bit of fuzz becomes a lot of fuzz in our estimate, exponentially so, over time. And I think we need to show some humility that we can't actually predict the future. All we know, the only prior we have is the laws of physics. And that's what we're arguing for. The laws of physics say the system will want to grow. And subsystems that are optimized for growth are more and replication are more likely in the future. And so we should aim to maximize our current mutual information with the future.
1:07:28 Speaker 01 And the path towards that is for us to accelerate rather than decelerate. So I don't have a P doom, because I think that, you know, similar to
1:07:41 Speaker 01 The quantum supremacy experiment at Google, I was in the room when they were running the simulations for that. That was an example of a quantum chaotic system where you cannot even estimate probabilities of certain outcomes with even the biggest supercomputer in the world, right? And so that's an example of chaos. And I think the system is far too chaotic for anybody to have an accurate estimate of the likelihood of certain futures. If they were that good, I think they would be very rich trading on the stock market.
1:08:18 Lex Fridman? But nevertheless, it's true that humans are biased, grounded in our evolutionary biology, scared of everything that can kill us. But we can still imagine different trajectories that can kill us. We don't know all the other ones that don't necessarily. But it's still, I think, useful, combined with some basic intuition grounded in human history, to reason about like, what, like, looking at geopolitics, looking at basics of human nature, how can powerful technology hurt a lot of people? It just seems grounded in that, looking at
1:08:59 Lex Fridman? nuclear weapons, you can start to estimate P doom in this, in the, maybe in a more philosophical sense, not, not a mathematical one. Philosophical meaning, like, is there a chance? Does human nature tend towards that or not?
1:09:20 Speaker 01 I, I think to me, one of the biggest existential risks would be the concentration of the power of AI in the hands of the very few, especially if it's a mix between the companies that control the flow of information and the government, because that could
1:09:40 Speaker 01 Set things up for a sort of dystopian future where only a very few and oligopoly in the government have AI and they could even convince the public that AI never existed. And that opens up sort of these scenarios for authoritarian centralized control, which to me is the darkest timeline. And the reality is that we have, we have a prior, we have a data-driven prior of these things happening, right? When you give too much power, when you centralize power too much, humans do horrible things, right? And to me, that has
1:10:20 Speaker 01 a much higher likelihood in my Bayesian inference than sci-fi-based priors, right? Like my prior came from the Terminator movie. And so when I talk to these AI doomers, I just ask them to trace a path through this Markov chain of events that would lead to our doom, right? And to actually give me a good probability for each transition. And very often, there's a unphysical or highly unlikely transition in that chain, right? But of course, we're wired to fear things and we're wired to respond to danger and
1:11:04 Speaker 01 we're wired to deem the unknown to be dangerous because that's a good heuristic for survival, right? But there's much more to lose out of fear. We have so much to lose, so much upside to lose by preemptively stopping the positive futures from, from happening out of fear. And so I think that we shouldn't give in to fear. Fear is the mind killer. I think it's also the civilization killer.
1:11:37 Lex Fridman? We can still think about the various ways things go wrong. For example, the founding fathers of this, the United States, thought about human nature and that's why there's a discussion about the freedoms that are necessary. They really deeply deliberated about that. And I think the same could possibly be done for AGI. It is true that history, human history, shows that we tend towards centralization, or at least when we achieve centralization, a lot of bad stuff happens. When there's a dictator, a lot of dark, bad things happen. The question is, can AGI become that dictator?
1:12:18 Lex Fridman? Can AGI, when developed, become the centralizer? Because of its power, maybe it has the same, because of the alignment of humans perhaps, the same tendencies, the same Stalin-like tendencies to centralize and manage centrally, the allocation of resources. You can even see that as a compelling argument on the surface level. Well, AGI is so much smarter, so much more efficient, so much better at allocating resources, why don't we outsource it to the AGI? And then eventually, whatever forces that corrupt the human mind with power could do the same for AGI.
1:13:00 Lex Fridman? It would just say, well, humans are dispensable, we'll get rid of them. Do the Jonathan Swift modest proposal from a few centuries ago, I think the 1700s, when he satirically suggested that, I think it's in Ireland, that the children of poor people are fed as food to the rich people. And that would be a good idea because it decreases the amount of poor people and gives extra income to the poor people. So it's, on several accounts, decreases the amount of poor people. Therefore, more people become rich.
1:13:44 Lex Fridman? Of course, it misses a fundamental piece here that's hard to put into a mathematical equation of the basic value of human life. So, all of that to say, are you concerned about AGI being the very centralizer of power that you just talked about?
1:14:04 Speaker 01 I do think that right now, there's a bias towards over centralization of AI because of compute density and centralization of data and how we're training models. I think over time, we're going to run out of data to scrape over the internet. And I think that, well, actually, I'm working on increasing the compute density so that compute can be everywhere and acquire information and test hypotheses in the environment in a distributed fashion. I think that fundamentally centralized cybernetic control, so having one intelligence
1:14:45 Speaker 01 that is massive, that fuses many sensors and is trying to perceive the world accurately, predict it accurately, predict many, many variables and control it, enact its will upon the world. I think that's just never been the optimum, right? Like, let's say you have a company, if you have a company, I don't know, of 10,000 people that all report to the CEO, even if that CEO is an AI, I think it would struggle to fuse all of the information that is coming to it and then predict the whole system and then to enact its will. What has emerged in nature and in corporations and all sorts of systems is a notion of sort
1:15:30 Speaker 01 of hierarchical cybernetic control, right? You have, in a company, it would be, you have like the individual contributors, they're self-interested and they're trying to achieve their tasks and they have a fine, in terms of time and space, if you will, control loop and field of perception, right? They have their code base. Let's say you're in a software company and they have their code base, they iterate it on it intra-day, right? And then the management maybe checks in, it has a wider scope, it has, let's say, five reports, right? And then it samples each person's update once per week. And then you can go up the chain and you have larger time scale and greater scope.
1:16:15 Speaker 01 And that seems to have emerged as sort of the optimal way to control systems. And really,
1:16:22 Speaker 01 That's what capitalism gives us, right? You have these hierarchies and you can even have like parent companies and so on. And so that is far more fault tolerant. In quantum computing, that's where I feel I came from. We have a concept of this fault tolerance and quantum error correction, right? Quantum error correction is detecting a fault that came from noise, predicting how it's propagated through the system and then correcting it, right? So it's a cybernetic loop. And it turns out that decoders that are hierarchical and at each level the hierarchy are local, perform the best by far and are far more fault tolerant. And the reason is if you have a non-local decoder,
1:17:07 Speaker 01 then you have one fault at this control node and the whole system sort of crashes. Similarly to if you have, you know, one CEO that everybody reports to and that CEO goes on vacation, the whole company comes to a crawl, right? And so to me, I think that, yes, we're seeing a tendency towards centralization of AI, but I think there's going to be a correction over time where intelligence is going to go closer to the perception and we're going to, we're going to break up AI into smaller subsystems that communicate with one another and form a sort of meta system.
1:17:51 Lex Fridman? So if you look at the hierarchies there in the world today, there's nations and those are hierarchical, but in relation to each other, nations are anarchic. So it's an anarchy. Do you foresee a world like this? Where there's not a over, what do you call it? A centralized cybernetic control?
1:18:11 Speaker 01 Centralized locus of control, yeah.
1:18:14 Lex Fridman? So like. That's suboptimal, you're saying?
1:18:17 Speaker 01 Yeah.
1:18:18 Lex Fridman? So it would be always a state of competition at the very
1:18:21 Speaker 01 Yeah.
1:18:21 Lex Fridman? top level.
1:18:22 Speaker 01 Yeah, just like, you know, in a company, you may have like two units working on similar technology and competing one another and you you prune the one that performs not as well. Right. And that's a sort of selection process for a tree or a product gets killed. Right. And then a whole org gets fired and that's this process of of trying new things and shedding old things that didn't work is this, it's what gives us adaptability and helps us converge on, you know, the technologies and things to do that are most good.
1:18:59 Lex Fridman? I just hope there's not a failure mode that's unique to AGI versus humans because you're describing human systems mostly right now.
1:19:06 Speaker 01 Right.
1:19:06 Lex Fridman? I just hope when there's a monopoly on AGI in one company, that we'll see the same thing we see with humans, which is another company will spring up and start competing effectively.
1:19:19 Speaker 01 That's been the case so far, right? We have OpenAI, we have Anthropic, now we have XAI. You know, we had Meta even for open source was, and now we have Mistral, right? Which is highly competitive. And so that's the beauty of capitalism. You don't have to trust any one party too much because we're kind of always hedging our bets at every level. There's always competition and that's the most beautiful thing to me, at least is that the whole system is always shifting and always adapting. And maintaining that dynamism is how we avoid tyranny, right? Making sure that everyone has access to to these tools, to these models and can contribute to the research.
1:20:04 Speaker 01 Avoids a sort of neural tyranny where very few people have control over AI for the world and and use it to oppress those around them.
1:20:18 Lex Fridman? When you were talking about intelligence, you mentioned multi-partite quantum entanglement. So, high-level question first is, what do you think is intelligence? When you think about quantum mechanical systems and you observe some kind of computation happening in them, what do you think is intelligent about the kind of computation the universe is able to do? A small, small inkling of which is the kind of computation a human brain is able to do.
1:20:47 Speaker 01 I would say like, intelligence and computation aren't quite the same thing. I think that the universe is very much, you know, doing a quantum computation. If you had access to all the degrees of freedom, you could, and a very, very, very large quantum computer with many, many, many qubits, let's say, a few qubits per plank volume, right?
1:21:17 Speaker 01 Which is more or less the pixels we have. Then you'd be able to simulate the whole universe, right? On a sufficiently large quantum computer, assuming you're looking at a finite volume, of course, of the universe.
1:21:32 Speaker 01 I think that, at least to me, intelligence is the, you know, I go back to cybernetics, right? The ability to perceive, predict, and control our world. But really, it's, nowadays, it seems like a lot of intelligence we use is more about compression, right? It's about, it's about operationalizing information theory, right? In information theory, you have the notion of entropy, of a distribution or a system. And entropy tells you that you need this many bits to encode this distribution or this subsystem if you had the most optimal code.
1:22:13 Speaker 01 And AI, at least the way we do it today, for LLMs and for quantum, is very much
1:22:25 Speaker 01 Trying to minimize relative entropy between our models of the world and the world, distributions from the world. And so we're learning, we're searching over the space of computations to process the world to find that compressed representation that has distilled all the variance and noise and entropy, right? And originally, I came to quantum machine learning from the study of black holes because the entropy of black holes is very interesting. In a sense, they're physically the most dense objects in the universe.
1:23:09 Speaker 01 You can't pack more information spatially, any more densely than a black hole. And so I was wondering, how do black holes actually encode information? What is their compression code? And so that got me into the space of algorithms to search over space of quantum codes. And it got me actually into also, how do you acquire quantum information from the world, right? So something I've worked on, this is public now, is quantum analog digital conversion. So how do you capture information from the real world in superposition and not destroy
1:23:51 Speaker 01 the superposition, but digitize for a quantum mechanical computer information from the real world? And so if you have an ability to capture quantum information and search over learned representations of it, now you can learn compressed representations that may have some useful information in their latent representation, right? And I think that many of the problems facing our civilization are actually beyond this complexity barrier, right? I mean, the greenhouse effect is a quantum mechanical effect, right? Chemistry is quantum mechanical.
1:24:35 Speaker 01 You know, nuclear physics is quantum mechanical. A lot of biology and protein folding and so on is affected by quantum mechanics. And so unlocking an ability to augment human intellect with quantum mechanical computers and quantum mechanical AI seem to me like a fundamental capability for civilization that we needed to develop. So I spent several years doing that. But over time, I kind of grew weary of the timelines that were starting to look like nuclear fusion.
1:25:12 Lex Fridman? So one high level question I can ask is maybe by way of definition, by way of explanation, what is a quantum computer and what is quantum machine learning?
1:25:24 Speaker 01 So a quantum computer really is a quantum mechanical system over which we have sufficient control and it can maintain its quantum mechanical state. And quantum mechanics is how nature behaves at the very small scales when things are very small or very cold. And it's actually more fundamental than probability theory. So we're used to things being this or that.
1:25:56 Speaker 01 But we're not used to thinking in superpositions because while our brains can't do that. So we have to translate the quantum mechanical world to, say, linear algebra to Grokic. Unfortunately, that translation is exponentially inefficient on average. You have to represent things with very large matrices. But really, you can make a quantum computer out of many things, right? And we've seen all sorts of players, you know, from neutral atoms, trapped ions, superconducting metal photons at different frequencies. I think you can make a quantum computer out of many things. But to me, the thing that was really interesting was both
1:26:42 Speaker 01 quantum machine learning was about understanding the quantum mechanical world with quantum computers. So embedding the physical world into AI representations. And quantum computer engineering was embedding AI algorithms.
1:26:58 Speaker 01 Into the physical world. So this bidirectionality of embedding a physical world into AI, AI into the physical world, the symbiosis between physics and AI, really, that's the sort of core of my quest, really, even to this day after quantum computing. It's still in this sort of journey to merge, really, physics and AI fundamentally.
1:27:24 Lex Fridman? So quantum machine learning is a way to do machine learning on a representation of nature that is, you know, stays true to the quantum mechanical aspect of nature.
1:27:38 Speaker 01 Yeah, it's learning quantum mechanical representations. That would be quantum deep learning. Alternatively, you can try to do classical machine learning on a quantum computer. I wouldn't advise it because you may have some speed ups, but very often, the speed ups come with huge costs. Using a quantum computer is very expensive. Why is that? Because you assume the computer is operating at zero temperature, which no physical system in the universe can achieve that temperature. So what you have to do is what I've been mentioning, this quantum error correction process, which is really an algorithmic fridge, right? It's trying to pump entropy out of the system, trying
1:28:21 Speaker 01 to get it closer to zero temperature. And when you do the calculations of how many resources it would take to say do deep learning on a quantum computer, classical deep learning, it's, there's just such a huge overhead, it's not worth it. It's like thinking about shipping something across the city using a rocket and going to orbit and back. It doesn't make sense. Just use a, you know, delivery truck, right?
1:28:48 Lex Fridman? What kind of stuff can you figure out? Can you predict? Can you understand with quantum deep learning that you can't with deep learning? So incorporating quantum mechanical systems into the, into the learning process?
1:29:00 Speaker 01 I think that's a great question. I mean fundamentally, it's any system that has sufficient quantum mechanical correlations that are very hard to capture for classical representations, then there should be an advantage for a quantum mechanical representation over a purely classical one. The question is which systems have sufficient correlations that are very quantum, but is also, which systems are still relevant to industry? That's a big question. You know, people are leaning towards chemistry, nuclear physics. I've worked on actually processing inputs from quantum sensors, right?
1:29:44 Speaker 01 If you have a network of quantum sensors, they've captured a quantum mechanical image of the world and how to post-process that, that becomes a sort of quantum form of machine perception. And so for example, Fermilab has a project exploring detecting dark matter with these quantum sensors. And to me, that's in alignment with my quest to understand the universe ever since I was a child. And so someday I hope that we can have very large networks of quantum sensors that help us peer into the earliest parts of the, the universe, right? For example, the LIGO is a quantum sensor, right? It's just a very large one.
1:30:24 Speaker 01 So yeah, I would say quantum machine perception simulations, right? Grokking quantum simulations, similar to alpha fold, right? Alpha fold understood the probability distribution over configurations of proteins. You can understand quantum distributions over configurations of electrons more efficiently with quantum machine learning.
1:30:48 Lex Fridman? You co-authored a paper titled "A Universal Training Algorithm for Quantum Deep Learning" that involves backprop with a queue. Very well done, sir. Very well done. How does it work? Is there some interesting aspects you could just mention on how kind of?
1:31:07 Lex Fridman? You know, backprop and some of these things we know for classical machine learning transfer over to the quantum machine learning.
1:31:14 Speaker 01 Yeah, that was a funky paper. That was one of my first papers in quantum deep learning. Everybody was saying, oh, I think deep learning is going to be sped up by quantum computers. And I was like, well, the best way to predict the future is to invent it. So here's a hundred page paper. Have fun. Essentially, you know, quantum computing is usually you embed reversible operations into a quantum computation. And so the trick there was to do a feed forward operation and do what we call a phase kick, but really it's just a force kick. You just kick the system with a certain force that is proportional to your loss function that
1:31:57 Speaker 01 you wish to optimize. And then by performing un-computation, you start with a superpositions over a superposition over parameters, right? Which is pretty funky. Now you're not just, you don't have just a point for parameters. You have a superposition over many potential parameters, right? And our goal is to
1:32:19 Lex Fridman? Using phase kicks somehow?
1:32:21 Speaker 01 Right.
1:32:22 Lex Fridman? To adjust parameters.
1:32:23 Speaker 01 Because phase kicks emulate having the parameter space be like a particle in N dimensions. And you're trying to get the Schrödinger equation, Schrödinger dynamics in the loss landscape of the neural network, right? And so you do an algorithm to induce this phase kick, which involves a feed forward, a kick. And then when you uncompute the feed forward, then all the errors in these phase kicks and these forces back propagate and hit each one of the parameters throughout the layers. And if you alternate this with an emulation of kinetic energy,
1:33:04 Speaker 01 Then it's kind of like a particle moving in n dimensions, a quantum particle. And the advantage in principle would be that it can tunnel through the landscape and find new optima that would have been difficult for stochastic optimizers. But again, this is kind of a theoretical thing and in practice, with at least the current architectures for quantum computers that we have planned, you know, such algorithms would be extremely expensive to run.
1:33:36 Lex Fridman? So maybe this is a good place to ask the difference between the different fields that you've had a toe in. So mathematics, physics, engineering, and also, you know, entrepreneurship, like the different layers of the stack. I think a lot of the stuff you're talking about here is a little bit on the math side, maybe physics almost working in theory. What's the difference between math, physics, engineering, and, uh, you know, making a product for quantum computing for quantum machine learning?
1:34:09 Speaker 01 Yeah, I mean, you know, some of the original team for the TensorFlow Quantum Project, which we started, you know, in school at University of Waterloo. There was myself, you know, initially I was a physicist, a mathematician. We had a computer scientist. We had mechanical engineer and then we had a physicist that was experimental primarily. And so putting together teams that are very cross disciplinary and figuring out how to communicate and, and share knowledge is really the key to doing this sort of interdisciplinary engineering work. I mean, there is, there is a big difference, you know, in mathematics. You can explore mathematics for mathematics sake.
1:34:51 Speaker 01 In physics, you're applying mathematics to understand the world around us. And in engineering, you're trying to, you're trying to hack the world, right? You're trying to find how to apply the physics that I know, my knowledge of the world to, to do things.
1:35:05 Lex Fridman? Well, in quantum computing in particular, I think there is a, just a lot of limits to engineering. It just seems to be extremely hard.
1:35:12 Speaker 01 Yeah.
1:35:13 Lex Fridman? So there's a lot of value to be exploring.
1:35:18 Lex Fridman? Quantum computing, quantum machine learning in theory and with math. So I guess one question is, why is it so hard to build a quantum computer? What are, what's your view of timelines in bringing these ideas to life?
1:35:37 Speaker 01 Right. I think that, you know, an overall theme of my company is that we have folks that are, you know, there's a sort of exodus from quantum computing and we're going to broader physics-based AI that is not quantum. So that gives you a hint and.
1:35:55 Lex Fridman? So we should say the name of your company is Extrropic.
1:35:58 Speaker 01 Extrropic, that's right. And we do physics-based AI primarily based on thermodynamics rather than quantum mechanics. But essentially, a quantum computer is very difficult to build because you have to induce this sort of zero-temperature subspace of information. And the way to do that is by encoding information. You encode a code within a code within a code within a code. And so there's a lot of redundancy needed to do this error correction. But ultimately, it's a sort of algorithmic refrigerator, really. It's just pumping out entropy out of the system,
1:36:40 Speaker 01 the subsystem that is virtual and delocalized that represents your quote-unquote logical qubits, AKA the payload quantum bits in which you actually want to do, run your quantum mechanical program. It's very difficult because in order to scale up your quantum computer, you need each component to be of sufficient quality for it to be worth it. Because if you try to do this error correction, this quantum error correction process in each quantum bit and you're control over them. If it's insufficient, it's not worth scaling up. You're actually adding more errors than you remove. And so there's this notion of a threshold where if your quantum bits are sufficient
1:37:24 Speaker 01 quality in terms of your control over them, it's actually worth scaling up. And actually, in recent years, people have been crossing the threshold and it's starting to be worth it. And so it's just a very long slog of engineering. But ultimately, it's really crazy to me how much exquisite level of control we have over these systems. It's actually quite crazy. And where people are crossing, you know, they're achieving milestones. It's just, you know, in general, the media always gets ahead, right, of where the technology is. There's a bit too much hype. It's good for fundraising, but sometimes, you know, it causes winters, right? It's the hype cycle.
1:38:05 Speaker 01 I'm bullish on quantum computing on a 10, 15 year time scale personally, but I think there's other quests that can be done in the meantime. I think it's in good hands right now.
1:38:17 Lex Fridman? Well, let me just explore different beautiful ideas, large or small, in quantum computing that might jump out at you from memory. So when you co-authored a paper titled asymptotically limitless quantum energy teleportation via Qubit probes. So just out of curiosity, can you explain what a Qubit is? Which is a Qubit?
1:38:40 Speaker 01 Yeah, it's a D state Qubit.
1:38:44 Lex Fridman? It's multi-dimensional.
1:38:45 Speaker 01 Multi-dimensional, right? So it's like, well, you know, can you have a notion of like an integer floating point that is quantum mechanical? That's something I've had to think about. I think that research was a precursor to later work on quantum analog digital conversion. There, there was interesting because during my masters, I was trying to understand the energy and entanglement of the vacuum, right? Of emptiness. Emptiness has energy, which is very weird to say. And our equations of cosmology.
1:39:22 Speaker 01 Don't match our calculations for the amount of quantum energy there are, there is in the fluctuations. And so I was trying to hack the energy of the vacuum, right? And the reality is that you can't just directly hack it. It doesn't, it's not technically free energy. Your lack of knowledge of the fluctuations means you can't extract the energy. But just like, you know, the stock market, if you have a stock that's correlated over time, the vacuum is actually correlated. So if you measured the vacuum at one point, you acquired information. If you communicated that information to another point, you can infer what configuration the vacuum is in to some precision
1:40:07 Speaker 01 and statistically extract on average some energy there. So you've quote unquote, "teleported" energy. To me, that was interesting because you could create pockets of negative energy density, which is energy density that is below the vacuum, which is very weird because we don't understand how the vacuum gravitates. And there are theories where the vacuum or the canvas of spacetime itself is really a canvas made out of quantum entanglement. And I was studying how decreasing energy of the vacuum locally increases quantum entanglement, which is very funky.
1:40:52 Speaker 01 And so the thing there is that, you know, if you're into, you know, weird theories about, you know, UAPs and whatnot, you know, you could try to imagine that they're, they're around and how would they propel themselves, right? How would they go faster than the speed of light? You would need a sort of negative energy density. And to me, I gave it the old call is try, trying to hack the energy of the vacuum and hit the limits allowable by the laws of physics. But there's all sorts of caveats there where you can't extract more than you've, you've put in, obviously.
1:41:36 Lex Fridman? But you're saying it's possible to teleport the energy because you can?
1:41:43 Lex Fridman? Extract information one place and then make, based on that, some kind of prediction about another place. I'm not sure what to make of that.
1:41:53 Speaker 01 Yeah, I mean, it's allowable by the laws of physics. The reality, though, is that the correlations decay with distance. And so you're going to have to pay the price not too far away from where you extract it.
1:42:05 Lex Fridman? Sure.
1:42:05 Speaker 01 Right?
1:42:06 Lex Fridman? The precision decreases to me in terms of your ability, but still. But since you mentioned UAPs, we talked about intelligence and I forgot to ask, what's your view on the other possible intelligences that are out there at the meso scale? Do you think there's other intelligent alien civilizations? Is that useful to think about? How often do you think about it?
1:42:31 Speaker 01 I think it's, I think it's useful to think about. It's useful to think about because we gotta ensure we're anti-fragile and we're, you know, trying to increase our capabilities as fast as possible because we could get disrupted. Like, there's no laws of physics against there being life elsewhere that could evolve and become an advanced civilization and eventually come to us. Do I think they're here now? I'm not sure. I mean, I've read what most people have read on the topic. I think it's interesting to
1:43:10 Speaker 01 consider and to me, it's a useful thought experiment to instill a sense of urgency in developing technologies and increasing our capabilities to make sure we don't get disrupted, right? Whether it's a form of AI that disrupts us or a foreign intelligence from a different planet, like either way, like increasing our capabilities and becoming formidable as humans, I think that's, that's really important so that we're robust against whatever the universe throws at us.
1:43:46 Lex Fridman? But to me, it's also an interesting challenge.
1:43:51 Lex Fridman? And thought experiment on how to perceive intelligence. This has to do with quantum mechanical systems. This has to do with with any kind of system that's not like humans. So to me, the thought experiment is, say, the aliens are here or they are directly observable. We're just too blind, too self centered, don't have the right sensors,
1:44:15 Lex Fridman? or don't have the right processing of the sensor data to see the obvious intelligence that's all around us.
1:44:21 Speaker 01 Well, that's why we work on quantum sensors, right? They can sense gravity.
1:44:25 Lex Fridman? Yeah, but there could be, so that's a good one. But there could be other stuff that's not even in the currently known forces of physics.
1:44:38 Speaker 01 Right.
1:44:38 Lex Fridman? There could be some other stuff. And the most entertaining thought experiment to me is that it's other stuff that's obvious. It's not like we don't, we lack the sensors. It's all around us. You know, the consciousness being one possible one. But there could be stuff that's just like, obviously there. And once you know it, it's like, oh, right, right. That's, that's, that the thing we thought is somehow emergent from the laws of physics. We understand them is actually a fundamental part of the universe and can be incorporated in physics. Most understood.
1:45:13 Speaker 01 Statistically speaking, right? If we observe some sort of alien life, it would most likely be some sort of virally self replicating von Neumann like probe system, right? And it's possible that there, you know, there are such systems that, I don't know what they're doing at the bottom of the ocean allegedly, but maybe they're, you know, collecting minerals from the bottom of the ocean.
1:45:39 Lex Fridman? Yeah.
1:45:40 Speaker 01 But that wouldn't violate, violate any of my priors. But am I certain that these systems are here? And it'd be difficult for me to say so, right? I only have secondhand information about there being data.
1:45:54 Lex Fridman? About the bottom of the ocean? Yeah, but you know, could it be things like memes? Could it be thoughts and ideas?
1:46:02 Lex Fridman? Could they be operating in that medium? Could aliens be the very thoughts that come into my head?
1:46:08 Lex Fridman? Like, what do you have you? How do you know that? How do you know that that? What's the origin of ideas in your mind? When an idea comes to your head? Show me where it originates.
1:46:20 Speaker 01 I mean, frankly, when I had the idea for the type of computer I'm building now, I think it was eight years ago now. It really felt like it was being beamed from space. It's just, I was in bed just shaking, just thinking it through. And I don't know. But do I believe that legitimately? I don't think so. But, you know, I, I think that alien life could take many forms. And I think the notion of intelligence and the notion of life needs to be expanded much more broadly to be less anthropocentric or biocentric.
1:46:59 Lex Fridman? Just to linger a little longer on quantum mechanics. What's, through all your explorations of quantum computing? What's the coolest, most beautiful idea that you've come across? That has been solved or has not yet been solved?
1:47:14 Speaker 01 I think the journey to understand something called ADS-CFT. So the journey to understand quantum gravity through this picture where a hologram of lesser dimension is actually dual or exactly corresponding to a bulk theory of quantum gravity of an extra dimension. And the fact that this sort of duality comes from trying to learn deep learning-like representations of the boundary. And so at least part of my journey someday on my bucket list
1:47:57 Speaker 01 is to apply quantum machine learning to these sorts of systems, these CFTs, or they're called SYK models, and learn an emergent geometry from, from the boundary theory. And so we can have a form of machine learning helps us to help us understand quantum gravity, right? Which is, you know, still a holy grail that I would like to hit before I leave this earth.
1:48:29 Lex Fridman? What do you think is going on with black holes? As information storing?
1:48:37 Lex Fridman? And processing units, what do you think is going on in black holes?
1:48:41 Speaker 01 Black holes are really fascinating objects. They're at the interface between quantum mechanics and gravity, and so they help us test all sorts of ideas. I think that, you know, for many decades now, there's been sort of this black hole information paradox that things that fall into the black hole seem to, we've seemed to have lost their information. Now, I think there's this firewall paradox that has been allegedly resolved in recent years by, you know, a former peer of mine, who's now a professor at Berkeley. And there, it
1:49:21 Speaker 01 seems like there is, as information falls into a black hole, it's sort of, there's sort of a sedimentation, right? As you, as you get closer and closer to the horizon, from the point of view, the observer on the outside, the object slows down infinitely as it gets closer and closer. And so everything that is falling to a black hole from our perspective gets sort of sedimented and tacked on to the near horizon. And at some point, it gets so close to the horizon, it's in the proximity or the scale which, in which quantum effects and quantum fluctuations matter. And there, some, that infalling
1:50:04 Speaker 01 matter could interfere with sort of the traditional pictures, that it could interfere with the creation and annihilation of particles and antiparticles in the vacuum. And through this interference, one of the particles gets entangled with the infalling information, and one of them is now free and escapes. And that's how there's sort of mutual information between the outgoing radiation and the infalling matter. But getting that calculation right, I think we're only just starting to put the pieces together.
1:50:38 Lex Fridman? There's a few pothead-like questions I want to ask you.
1:50:41 Speaker 01 Sure.
1:50:41 Lex Fridman? So one, does it terrify you that there's a giant black hole at the center of our galaxy?
1:50:47 Speaker 01 I don't know. I, I just want to, you know, set up shop near it to fast forward, you know, meet, meet a future civilization, right? Like, if we have a limited lifetime, if you can go orbit a black hole and emerge.
1:51:02 Lex Fridman? So if you were like, if there's a special mission that could take you to a black hole, would you volunteer to go travel?
1:51:08 Speaker 01 To orbit and not, obviously not fall into it.
1:51:10 Lex Fridman? That's obvious. So it's obvious to you that everything's destroyed inside a black hole. The call the information that makes up Geom is destroyed. Maybe on the other side, Befjazal's emerged. And it's all like, it's tied together in some deeply meme-ful way.
1:51:27 Speaker 01 Yeah, I mean, that's a great question. We have to answer what black holes are. Are they, are we punching a hole through spacetime and creating a pocket universe? It's possible, right? Then, then that would mean that if we ascend the Cartershev scale to, you know, beyond Cartershev type 3, we could engineer black holes with specific hyperparameters to transmit information to new universes we create. And so we can have progeny, right? That are new universes. And so we, even though our universe may reach a heat death, we may have a way to have a legacy, right? And so we don't know yet.
1:52:10 Speaker 01 We need to ascend the Cartershev scale to answer these questions, right? To peer into that regime of higher energy physics.
1:52:20 Lex Fridman? And maybe you can speak to the Cartershev scale for people who don't know. So one of the sort of meme-like principles and goals of the EAC movement is to ascend the Cartershev scale. What is the Cartershev scale? And when do we want to ascend it?
1:52:38 Speaker 01 The Cartershev scale is a measure of our energy production and consumption.
1:52:45 Speaker 01 And really, it's a logarithmic scale. And Cartershev type 1 is a milestone where we are producing the equivalent wattage to all the energy that is incident on Earth from the Sun. Cartershev type 2 would be harnessing all the energy that is output by the Sun. And I think type three is like the whole galaxy.
1:53:07 Lex Fridman? Galaxy, I think, well, yeah.
1:53:09 Speaker 01 Yeah, yeah. And then some people have some crazy type four and five, but I don't know if I believe in those. But to me, it seems like from the first principles of thermodynamics, that again, there's this concept of thermodynamic driven dissipative adaptation, where, you know, life evolved on Earth because we have this sort of energetic drive from the sun. Right? We have incident energy and life evolved on Earth to capture, figure out ways to best capture that free energy to maintain itself and grow.
1:53:49 Speaker 01 And I think that that principle, it's not special to our Earth-Sun system. We could extend life well beyond, and we kind of have a responsibility to do so, because that's the process that brought us here. So we don't even know what it has at store for us in the future. It could be something of beauty we can't even imagine today, right?
1:54:13 Lex Fridman? So this is probably a good place to talk a bit about the EAC movement. In a Substack blog post titled, "What the fuck is EAC?" Or actually, "What the F* is EAC?" You write, "Strategically speaking, we need to work towards several overarching civilization goals that are all interdependent. And the four goals are: increase the amount of energy we can harness as a species, climb the Carteshev gradient. In the short term, this almost certainly means nuclear fission.
1:54:46 Lex Fridman? Increase human flourishing via pro-population growth policies and pro-economic growth policies. Create artificial general intelligence, the single greatest force multiplier in human history. And finally, develop interplanetary and interstellar transport so that humanity can spread beyond the Earth." Could you build on top of that and maybe say, what to you is the EAC movement? What are the goals? What are the principles?
1:55:15 Speaker 01 The goal is for the human techno-capital memetic machine to become self-aware and to hyperstitiously engineer its own growth. So let's decompress that.
1:55:28 Lex Fridman? Define each of those words.
1:55:30 Speaker 01 So you have humans, you have technology, you have capital, and then you have memes, information, right? And all of those systems are coupled with one another, right? Humans work at companies, they acquire and allocate capital, and humans communicate via memes and information propagation. And our goal was to have a sort of viral, optimistic movement that is aware of how the system works. Fundamentally, it seeks to grow. And we simply want to lean into the natural tendencies of the system to adapt for its own growth.
1:56:13 Lex Fridman? So in that way, you're right, the EAC is literally a memetic optimism virus that is constantly drifting, mutating, and propagating in a decentralized fashion. So memetic optimism virus. So you do want it to be a virus to maximize the spread. And it's hyperstitious, therefore, the optimism will incentivize its growth.
1:56:38 Speaker 01 We see EAC as a sort of meta heuristic, a sort of very thin cultural framework from which you can have much more opinionated forks, right? Fundamentally, we just say that it's good, what got us here is this adaptation of the whole system, you know, based on thermodynamics, and that process is good and we should keep it going, right? That is the core thesis. Everything else is, okay, how do we ensure that we maintain this malleability and adaptability while clearly
1:57:17 Speaker 01 not suppressing variants and maintaining free speech, freedom of thought, freedom of information propagation? And freedom to do AI research is important for us to converge the fastest on the space of technologies, ideas and whatnot that lead to this growth. And so ultimately, you know, there's been quite a few forks. Some are just memes, but some are more serious, right? Vitalik Buterin recently made a D I A C fork. He has his own sort of fine tunings of E I C.
1:57:54 Lex Fridman? Does anything jump out to memory of the unique characteristic of that fork from Vitalik?
1:58:00 Speaker 01 I would say that it's trying to find a middle ground between E I C and sort of EA and E I safety. To me, like, having a movement that is opposite to what was the mainstream narrative that was taking over Silicon Valley was important to sort of shift the dynamic range of opinions. And, you know, it's like the balance between centralization and decentralization. The real optimum is always somewhere in the middle. Right? But but for E I C, we're pushing for entropy, novelty, disruption, malleability, speed, rather than being like sort of conservative, suppressing thought, suppressing speech,
1:58:42 Speaker 01 adding constraints, adding too many regulations, slowing things down. And so it's kind of, we're trying to bring balance to the force, right? The systems.
1:58:55 Lex Fridman? Balance to the force of human civilization, yeah.
1:58:57 Speaker 01 It's literally the forces of constraints versus the entropic force that makes us explore, right? Systems are optimal when they're at the edge of criticality between order and chaos, right? Between constraints, energy minimization and entropy, right? Systems want to equilibrate, balance these two things. And so I thought that the balance was lacking. And so we created this movement to to bring balance.
1:59:26 Lex Fridman? Well, I like how, I like the sort of visual of the landscape of ideas evolving through forks, sort of kind of.
1:59:36 Lex Fridman? Thinking on the other part of history, thinking of Marxism as the original repository, and then Soviet communism is a fork of that, and then the Maoism is a fork of Marxism and communism. So those are all forks that are exploring different ideas.
1:59:57 Speaker 01 Thinking of culture almost like code, right? Nowadays, I mean, what you prompt in the LM, or what you put in the constitution of an LM is basically its cultural framework, what it believes, right? And you can share it on GitHub nowadays. So starting, trying to take inspiration from what has worked in the sort of machine of software to adapt over the space of code, could we apply that to culture? And our goal is to not say, you should live your life this way, X, Y, Z, is to set up a process where people are always searching over subcultures and competing for mindshare.
2:00:41 Speaker 01 And I think creating this malleability of culture is super important for us to converge onto the cultures and the heuristics about how to live one's life that are updated to modern times, because there's really been a sort of vacuum of spirituality and culture. People don't feel like they belong to any one group, and there's been parasitic ideologies that have taken up opportunity to populate this peachy dish of minds, right? Elon calls it the mind virus. We call it the decel mind virus complex, which is the decelerative, that is kind of the overall pattern between all of them.
2:01:24 Speaker 01 There's many variants as well. And so, you know, if there's a sort of viral pessimism, decelerative movement, we needed to have not only one movement, but, you know, many, many variants. So it's very hard to pinpoint and stop.
2:01:40 Lex Fridman? But the overarching thing is nevertheless a kind of memetic optimism pandemic.
2:01:48 Lex Fridman? So, I mean, okay, let me ask you, do you think EAC to some degree is a cult?
2:01:56 Speaker 01 Define cult.
2:01:58 Lex Fridman? I think a lot of human progress is made when you have independent thought. So you have individuals that are able to think freely and very powerful memetic systems can kind of lead to group think. There's something in human nature that leads to like mass hypnosis, mass hysteria. We start to think alike whenever there's a sexy idea that captures our minds. And so it's actually hard to break us apart, like pull us apart, diversify thought. So I'm to that degree, to which degree is everybody kind of chanting EAC, EAC, like the sheep in Animal Farm.
2:02:41 Speaker 01 Well, first of all, it's fun. It's rebellious, right? Like, you know, many, I think we lean into, there's this concept of sort of meta irony, right, of sort of being on the boundary of like, we're not sure if they're serious or not. And it's much more playful and much more fun, right? Like, for example, we talk about thermodynamics being our god, right? And sometimes we do cult-like things, but there's no like ceremony and robes and whatnot.
2:03:13 Lex Fridman? Not yet.
2:03:14 Speaker 01 Not yet. But ultimately, yeah, I mean, I totally agree that it seems to me that humans want to feel like they're part of a group. So they naturally try to agree with their neighbors and find common ground. And, and that leads to sort of mode collapse in the space of ideas, right? We used to have sort of one cultural island that was allowed. It was a typical subspace of thought and anything that was diverting from that subspace of thought was suppressed or you were canceled, right? Now we've created a new mode, but the whole point is that we're not trying to have a very restricted space of thought. There's not just one way to think about EAC and its many forks.
2:03:57 Speaker 01 And the point is that there are many forks and there can be many clusters and many islands. And I shouldn't be in control of it. In any way, I mean, there's no formal org whatsoever. I just put out tweets and certain blog posts and people are free to defect and fork if there's an aspect they don't like. And so that makes it so that there should be a sort of deterritorialization in the space of ideas so that we don't end up in one cluster that's very cult-like. And so cults usually, they don't, they don't allow people to defect or start competing forks, whereas we encourage it, right?
2:04:42 Lex Fridman? Do you think just the humor, the pros and cons of humor and meme, in some sense, meme, there's like a wisdom to memes. Was it the Magic Theater? What book is that from? Herman Hesse, Steppenwolf, I think. But there's a, there's a kind of embracing of the absurdity that seems to get to the truth of things. But at the same time, it can also decrease the quality and the rigor of the discourse.
2:05:17 Speaker 01 Yeah.
2:05:18 Lex Fridman? Do you feel the tension of that?
2:05:20 Speaker 01 Yeah. So initially, I think what allowed us to grow under the radar was because it was camouflaged as sort of meta-ironic, right? We would sneak in, you know, deep truths within a package of humors and humor and memes and what are called shitposts, right? And I think that was purposefully a sort of camouflage against those that seek status and do not want to, it's very hard to argue with a cartoon frog or a...
2:05:59 Speaker 01 A cartoon of an intergalactic Jeff Bezos and take yourself seriously. And so that allowed allowed us to grow pretty rapidly in the early days. But of course, like that's, you know, essentially people get steered. Their notion of the truth comes from the data they see from from the information they're fed. And the information people are fed is determined by algorithms. Right. And really what we've been doing is sort of engineering what we call high memetic fitness packets of information so that they can spread effectively and carry a message.
2:06:42 Speaker 01 Right. So it's kind of a vector to spread, spread the message. And yes, we've been using sort of techniques that are optimal for today's algorithmically amplified information landscapes. But I think we're reaching the point of, you know, scale where we can have serious debates and serious conversations. And, you know, that's why we're considering doing a bunch of debates and having more serious long form discussions. Because I don't think that the timeline is optimal for sort of very serious thoughtful discussions. It get you get rewarded for sort of polarization.
2:07:24 Speaker 01 Right. And so even though we started a movement that is literally trying to polarize the the tech ecosystem, at the end of the day, it's so that we can have a conversation and find an optimum together.
2:07:37 Lex Fridman? I mean, that's kind of what I try to do with this podcast, given the landscape of things, to still have long form conversations. But there is a degree to which absurdity is fully embraced. In fact, this very conversation is multi level absurd. So first of all, I should say that I just very recently had a conversation with Jeff Bezos.
2:08:03 Lex Fridman? And I would love to hear your Beth Jezos opinions of Jeff Bezos. Speaking of intergalactic Jeff Bezos, what do you think of that particular individual whom your name is inspired?
2:08:20 Speaker 01 Yeah, I mean, I think Jeff is really great. I mean, he's built one of the most epic companies of all time. He's leveraged the techno capital machine and techno capital acceleration to give us what we wanted, right? We want a quick delivery, very convenient, at home, low prices, right? He understood how the machine worked and how to harness it, right? Like, running the company, not trying to take profits too early, putting it back, letting the system compound and keep improving. And arguably, I think Amazon's invested some of the most amount of capital in robotics out there.
2:09:02 Speaker 01 And certainly, with the birth of AWS, kind of enabled the sort of tech boom we've seen today that has paid the salaries of, you know, I guess, myself and all of our friends to some extent. And so I think we can all be grateful to Jeff, and he's one of the great entrepreneurs out there, one of the best of all time, unarguably.
2:09:27 Lex Fridman? And of course, the work at Blue Origin, similar to the work at SpaceX, is trying to make humans a multi-planetary species, which seems almost like a bigger thing than the capitalist machine? Or is the capitalist machine on a different timescale, perhaps?
2:09:42 Speaker 01 Yeah, I think that, you know, companies, they tend to optimize, you know, quarter over quarter, maybe a few years out, but individuals that want to leave a legacy. Can think on a multi-decade or multi-century timescale. And so the fact that some individuals are such good capital allocators that they unlock the ability to allocate capitals to goals that take us much further or are much further looking, you know, Elon's doing this with SpaceX, putting all this capital towards getting us to Mars. Jeff is trying to build Blue Origin and I think he wants to build O'Neill cylinders
2:10:24 Speaker 01 and get industry off planet, which I think is brilliant. I think, you know, just overall, I'm for billionaires. I know this is controversial statement sometimes, but I think that in a sense, it's kind of a proof of stake voting, right? Like if you've acquired, if you've allocated capital efficiently, you get, you unlock more capital to allocate just because clearly you know how to allocate capital more efficiently, which is in contrast to politicians that get elected because they speak the best on TV, right? Not because they have a proven track record of allocating taxpayer capital most efficiently.
2:11:06 Speaker 01 And so that's why I'm for capitalism over, say, giving all our money to the government and letting them figure out how to allocate it. So, yeah.
2:11:16 Lex Fridman? Why do you think it's a viral and a popular meme to criticize billionaires, since you mentioned billionaires? Why do you think there's quite a widespread criticism of people with wealth, especially those in the public eye like Jeff and Elon and Mark Zuckerberg and who else, Bill Gates?
2:11:39 Speaker 01 Yeah. I think a lot of people would, instead of trying to understand how the techno-capital machine works and realizing they have much more agency than they think, they'd rather have the sort of victim mindset. I'm just subjected to this machine. It is oppressing me. And the successful players clearly must be, must be evil because they've been successful at this game that I'm not successful at. But. You know, I've managed to get some people that were in that mindset and make them realize how the techno-capital machine works and how you can harness it for your own good and for the good of others. And by creating value, you capture some of the value you create for
2:12:22 Speaker 01 the world. And that's sort of positive sum mindset shift is so potent. And really, that's what that's what we're trying to do by scaling EAC is sort of unlocking that higher level of agency. Like, actually, you're far more in control of the future than you think. You have agency to change the world. Go out and do it. You have here's permission.
2:12:41 Lex Fridman? Each individual has agency. The motto, keep building is often heard.
2:12:47 Speaker 01 Right.
2:12:47 Lex Fridman? What does that mean to you? And what does that have to do with Diet Coke?
2:12:52 Speaker 01 Well, Diet.
2:12:52 Lex Fridman? By the way, thank you so much for the Red Bull. It's working pretty well. I'm feeling pretty good.
2:12:58 Speaker 01 Awesome. Well, so, so building technologies and building, it doesn't have to be technologies, just building in general, means, you know, having agency, trying to change the world by creating, let's say, a company which is a self-sustaining organism that, you know, accomplishes a function in the broader techno-capital machine. To us, that's the way to achieve change in the world that you'd like to see, rather than, say, pressuring politicians or creating nonprofits that, you know, nonprofits, once they run out of money, their function can no longer be accomplished. You're kind of deforming the market artificially compared
2:13:40 Speaker 01 to sort of subverting or coercing the market to, or dancing with the market to convince it that actually this function is important, adds value, and here it is, right? And so I think, you know, this is sort of the way between the sort of degrowth, like ESG approach versus, say, Elon, right? The degrowth approach is like, we're going to manage our way out of a climate crisis. And Elon is like, I'm going to build a company that is self-sustaining, profitable, and growing. And it's, we're going to innovate our way out of this dilemma, right? And, and we're trying to get people to do the latter rather than the former at all scales.
2:14:21 Lex Fridman? Elon is an interesting case. So you are a proponent, you celebrate Elon, but he's also somebody who has for a long time warned about the dangers, the potential dangers, existential risks of artificial intelligence. How do you square the two? Is that a contradiction to you?
2:14:40 Speaker 01 It is somewhat because he's very much against regulation in many aspects, but for AI, he's definitely, you know, a proponent of regulations. I think, I think overall, you know, he saw the dangers of, say, OpenAI, you know, cornering the market and then getting to have the monopoly over the cultural priors that you can embed in these LLMs that then, you know, as, as LLMs now become the source of truth for people, then you can shape the culture of the people. And so you can control people by controlling LLMs. And he saw that,
2:15:20 Speaker 01 just like it was the case for social media, if you shape the function of information propagation, you can shape people's opinions. He sought to make a competitor. So at least, like, I think we're very aligned there that, you know, the way to a good future is to maintain sort of adversarial equilibrium between the various AI players. I love to talk to him to understand sort of his thinking about how to make, you know, how to advance AI going forwards. I mean, he's also hedging his bets, I would say, you know, with Neuralink, right? I think if he can't stop the progress of AI, you know, he's building the technology to merge. So, you know, look at the actions, not just the words, but.
2:16:05 Lex Fridman? Well, I mean, there's some degree where being concerned, maybe using human psychology, being concerned about threats all around us is a motivator. Like, it's an encouraging thing. I operate much better when there's a deadline, the fear of the deadline. Like, and I, for myself, create artificial things. Like, I want to create in myself this kind of anxieties if something really horrible will happen if I miss the deadline. I think there's some degree of that here because creating AI that's aligned with humans has a lot of potential benefits. And so a different way to reframe that is. If you don't, you're all, we're all gonna die.
2:16:44 Lex Fridman? It just seems to be a very powerful psychological formulation of the goal of creating human-aligned AI.
2:16:54 Speaker 01 I think that anxiety is good. I think, like I said, I want the, the free market to, to create aligned AIs that are reliable.
2:17:03 Speaker 01 And I think that's what he's trying to do with XAI. So I'm all for it. What, what I am against is sort of
2:17:12 Speaker 01 stopping, let's say, the open source ecosystem from thriving, right? By, let's say, in the executive order, claiming that open source LMs are dual-use technologies and should be government-controlled. Then everybody needs to register their GPU and their big matrices with the government. And I think that extra friction will dissuade a lot of hackers from contributing. Hackers that could later become the, the researchers that make key discoveries that push us forward. Right? Including discoveries for AI safety. And so I think I, I just want to maintain ubiquity of opportunity to contribute to AI and, and to own a piece of the future. Right?
2:17:55 Speaker 01 It can't just be legislated, you know, behind some wall where only a few players get to play the game.
2:18:02 Lex Fridman? I mean, so the EAC movement is often sort of caricatured to mean sort of progress and innovation at all, at all costs. Doesn't matter how unsafe it is, doesn't matter if it caused a lot of damage. You just build, build cool shit as fast as possible. Stay up all night with a Diet Coke.
2:18:25 Lex Fridman? Whatever it takes. I, I think, I guess, I don't know if there's a question in there, but how important to you and what you've seen, the different formulations of EAC is safety, is AI safety?
2:18:38 Speaker 01 I think, again, I think like if there was no one working on it, I think I would be a proponent of it. I think, again, our goal is to sort of bring balance and obviously a sense of urgency is a useful tool, right, to make progress. It hacks our dopaminergic systems and gives us energy to work late into the night. I think also having a higher purpose you're contributing to, right? At the end of the day, it's like, what am I contributing to? I'm contributing to the growth of this beautiful machine so that we can seek to the stars. That's really inspiring. That's also a sort of neuro hack.
2:19:20 Lex Fridman? So you're saying AI safety is important to you, but right now, the landscape of ideas you see is AI safety as a topic is used more often to gain centralized control. So in that sense, you're resisting it as a proxy for centralized, gaining centralized control.
2:19:38 Speaker 01 Yeah, I just think we, we have to be careful because, you know, safety is just the perfect cover for sort of centralization of power and covering up eventually corruption. I don't, I'm not saying it's corrupted now, but it could be down the line. And really, if you, if you let the argument run, like, there's no amount of sort of centralization of control that will be enough to ensure your safety. There's always more nine, nine, nines of peace safety that you can gain, you know, nine, nine, nine point nine, nine, nine, nine, nine percent safe. Maybe you want another nine. Oh, please give us full
2:20:20 Speaker 01 access to everything you do, full surveillance. And frankly, those that are proponents of AI safety have proposed like having a global panopticon, right, where you have centralized perception of everything going on. And to me, that just opens up the door wide open for a sort of Big Brother 1984 like scenario. And that's not a future I want to live in.
2:20:43 Lex Fridman? Because we know, we have some examples throughout history when that did not lead to a good outcome.
2:20:49 Speaker 01 Right.
2:20:50 Lex Fridman? You mentioned you founded a company, Xtropic.
2:20:55 Lex Fridman? That recently announced a 14.1 million seed round. What's the goal of the company? You're talking about a lot of interesting physics things. So what are you up to over there that you can talk about?
2:21:07 Speaker 01 Yeah, I mean, you know, originally, we weren't going to announce last week. But I think with the doxing and disclosure, we got our hand forced. So we had to disclose roughly what we were doing. But really, Xtropic was born from my dissatisfaction and that of my colleagues with the quantum computing roadmap, right? Quantum computing was sort of the first path to physics-based computing that was trying to commercially scale. And I was working on physics-based AI that runs on these physics-based computers. But ultimately, our greatest enemy was this noise, this pervasive problem of noise
2:21:49 Speaker 01 that, as I mentioned, you have to constantly pump out the noise out of the system to maintain this pristine environment where quantum mechanics can take effect. And that constraint was just too much. It's too costly to do that. And so we were wondering, right? As generative AI is sort of eating the world, more and more of the world's computational workloads are focused on generative AI, how could we use physics to engineer the ultimate physical substrate for generative AI? Right? From first principles of physics, of information theory, of computation, and ultimately of thermodynamics. Right?
2:22:34 Speaker 01 And so what we're seeking to build is a physics-based computing system and physics-based AI algorithms that are inspired by out-of-equilibrium thermodynamics or harness it directly to do machine learning as a physical process.
2:22:56 Lex Fridman? So what does that mean? Machine learning is a physical process? Is that hardware? Is it software? Is it both? Is it trying to do the full stack in some kind of unique way?
2:23:05 Speaker 01 Yes, it is full stack. And so we're folks that have built differentiable programming into the quantum computing ecosystem with TensorFlow Quantum. One of my co-founders of TensorFlow Quantum is the CTO, Trevor McCourt. We have some of the best quantum computer architects, those that have designed IBMs and AWS's systems. They've left quantum computing to help us build what we call actually a thermodynamic computer.
2:23:38 Lex Fridman? A thermodynamic computer. Well, actually, let's linger on TensorFlow Quantum. What lessons have you learned from TensorFlow Quantum? Maybe you can speak to like what it takes to create essentially what like a software API to a quantum computer?
2:23:56 Speaker 01 Right. I mean, that was a challenge to build, to invent, to build, and then to get to run on the real devices.
2:24:04 Lex Fridman? Can you actually speak to what it is?
2:24:05 Speaker 01 Yeah. So TensorFlow Quantum was an attempt at, well, I mean, I guess we succeeded at combining deep learning or differentiable classical programming with quantum computing and turned quantum computing into or have types of programs that are differentiable in quantum computing. And, you know, Andre Karpathy calls differentiable programming software 2.0, right? It's like gradient descent is a better programmer than you. And the idea was that in the early days of quantum computing, you can only run short quantum programs. And so which quantum programs should you run?
2:24:49 Speaker 01 Well, just let gradient descent find those programs instead. And so we built sort of the first infrastructure to not only run differentiable quantum programs, but combine them as part of broader deep learning graphs, incorporating deep neural networks, you know, the ones you know and love with what are called quantum neural networks. And ultimately, it was a very cross-disciplinary effort. We had to invent all sorts of ways to differentiate, to back propagate through the graph, the hybrid graph. But ultimately, it taught me that the way to program matter and to program physics
2:25:33 Speaker 01 is by differentiating through control parameters. If you have parameters that affects the physics of the system, you can evaluate some loss function. You can optimize the system to accomplish a task, whatever that task may be. And that's a very sort of universal meta framework for how to program physics-based computers.
2:26:00 Lex Fridman? To try to parameterize everything, make those parameters differentiable, and then optimize.
2:26:07 Speaker 01 Yes.
2:26:08 Lex Fridman? Okay. So is there some more practical engineering lessons from TensorFlow Quantum? Just organizationally, too, like the humans involved and how to get to a product, how to create good documentation? I don't know. All these little subtle things that people might not think about.
2:26:29 Speaker 01 I think like working across disciplinary boundaries is always a challenge, and you have to be extremely patient in teaching one another. Right? I learned a lot of software engineering through the process. My colleagues learned a lot of quantum physics and some learned machine learning through the process of building this system. And I think if you get some smart people that are passionate and trust each other in a room, and you have a small team, and you teach each other your specialties, suddenly you're kind of forming this sort of model soup of expertise, and something special comes out of that. Right? It's like combining genes, but for your knowledge bases.
2:27:13 Speaker 01 And sometimes special products come out of that. And so I think like, even though it's very high friction initially to work in an interdisciplinary team. I think the product at the end of the day is worth it. And so learned a lot trying to bridge the gap there. And I mean, it's still a challenge to this day. You know, we hire folks that are have an AI background, folks that have a pure physics background, and somehow we have to make them talk to one another. Right?
2:27:42 Lex Fridman? Is there a magic? Is there some science and art to the hiring process to building a team that can create magic together?
2:27:51 Speaker 01 Yeah, it's really hard to pinpoint that that je ne sais quoi, right?
2:27:57 Lex Fridman? I didn't know you speak French. That's very nice.
2:28:02 Speaker 01 Yeah, I'm actually French Canadian, so.
2:28:04 Lex Fridman? Oh, you are legitimately French.
2:28:05 Speaker 01 I am.
2:28:06 Lex Fridman? I thought you were just doing that for the cred.
2:28:10 Speaker 01 No, no, I'm truly French Canadian from Montreal.
2:28:15 Speaker 01 But yeah, essentially, we look for people with very high fluid intelligence that aren't over specialized because they're going to have to get out of their comfort zone. They're going to have to incorporate concepts that they've never seen before and very quickly get comfortable with them. Right? Or learn to work in a team. And so that's sort of what we look for when we hire. We can't hire, you know, people that are just like, you know, optimizing this subsystem for the past three or four years. We need like really general sort of broader intelligence and specialty and people that are that are open minded, really, because if you're pioneering a new approach from scratch, there is no textbook. There's no reference.
2:28:59 Speaker 01 It's just us and people that are hungry to learn. So we have to teach each other. We have to learn the literature. We have to share knowledge bases, collaborate in order to push the boundary of knowledge further together. Right? And so people that are used to just getting prescribed what to do, you know, at this stage, when you're at the pioneering stage, that's not necessarily who you want to hire.
2:29:26 Lex Fridman? So you mentioned with X-Tropic, you're trying to build the physical substrate for generative AI.
2:29:32 Lex Fridman? What's the difference between that and the AGI AI itself? So is it possible that in the halls of your company AGI will be created? Or will AGI just be using this as a substrate?
2:29:46 Speaker 01 I think our goal is to both run human like AI or anthropomorphic AI.
2:29:53 Lex Fridman? Sorry for use of the term AGI. And I know it's triggering for you.
2:29:56 Speaker 01 We think that the future is actually physics based AI combined with anthropomorphic AI. So you can imagine I have a sort of world modeling engine through physics based AI. Physics based AI is better at representing the world at all scales because it can be quantum mechanical, thermodynamic, deterministic, hybrid representations of the world, just like our world at different scales has different regimes of physics. If you inspire yourself from that in the ways you learn representations of nature, you can have much more accurate representations of nature. So you can have very accurate world models at all scales. Right? And so you have the world modeling engine.
2:30:40 Speaker 01 And then you have the sort of anthropomorphic AI that is human like. So you can have the science, the playground to test your ideas and you can have a synthetic scientist. And to us, that joint system of a physics based AI and an anthropomorphic AI is the closest thing to a fully general artificially intelligent system.
2:31:02 Lex Fridman? So you can get closer to truth by grounding of the AI to physics.
2:31:08 Speaker 01 Right.
2:31:08 Lex Fridman? But you can also still have a anthropomorphic interface to us humans that like to talk to other humans or human like systems. So on that topic, what do you? I suppose that is one of the big limitations of current large language models to you is that they're not, they're good bullshitters. They're not really grounded to truth necessarily.
2:31:32 Speaker 01 Hmm.
2:31:33 Lex Fridman? Is that, would that be fair to say?
2:31:35 Speaker 01 Yeah, no, I, you wouldn't, you know, try to extrapolate the stock market with an LM trained on text from the internet. Right. It's not going to be a very accurate model. It's not going to model its priors or its uncertainties about the world very accurately. Right. So you need, you need a different type of AI to complement sort of this, this text extrapolation AI. Yeah.
2:31:59 Lex Fridman? Uh, you mentioned singularity earlier. What, how far away are we from a singularity?
2:32:04 Speaker 01 I don't know if I believe in a finite time singularity as a single point in time. I think it's going to be asymptotic and, and sort of a diagonal. Sort of asymptote, like, you know, we, we have the light cone, we have the limits of physics restricting our ability to grow. So obviously can't fully diverge on a, on a finite time.
2:32:28 Speaker 01 I, I, I think my priors are that, you know, I, I think a lot of, a lot of people on the other side of the aisle. Think that once we reach human level AI, there's going to be an inflection point and a sudden like boom, like suddenly. AI is going to grok how to, you know, manipulate matter at the nano scale and assemble nanobots. And having, uh, worked, you know, for nearly a decade in applying AI to engineer matter, it's much harder than they think. And in reality, you need a lot of samples from either a simulation of nature that's very accurate and costly. Or nature itself, and that keeps your ability to control the world around us in check.
2:33:10 Speaker 01 There's a sort of minimal, uh, cost computationally and thermodynamically. To acquiring information about the world in order to be able to predict and control it. Uh, and that keeps things in check.
2:33:22 Lex Fridman? It's funny. You mentioned the other side of the aisle. So in the poll I posted about P doom yesterday, what's the probability of doom? There seems to be a nice like division.
2:33:33 Speaker 01 Hmm.
2:33:33 Lex Fridman? Between people think it's very likely and very unlikely.
2:33:38 Lex Fridman? I wonder if in the future, there'll be the actual Republicans versus Democrats division, blue versus red, is the AI doomers versus the EACers, EAC.
2:33:48 Speaker 01 Yeah. So this movement, you know, is not right wing or left wing fundamentally. It's more like up versus down in terms of the
2:33:56 Lex Fridman? Which one is the up?
2:33:57 Speaker 01 Civilization, right?
2:33:58 Lex Fridman? All right.
2:34:00 Speaker 01 But it seems to be like there is a sort of case of alignment of the existing political parties where those that are for more centralization of power, control and more regulations are aligning with sort of aligning themselves with the doomers because that sort of instilling fear in people is a great way to for them to give up more control and give the government more power. But fundamentally, we're not left versus right. I think there's, we've done polls of people's alignment within EAC. I think it's pretty balanced. So it's a, it's a new fundamental issue of our time. It's not just centralization versus decentralization.
2:34:43 Speaker 01 It's kind of do we go, it's like tech progressivism versus techno conservatism, right?
2:34:49 Lex Fridman? So EAC is, as a movement is often formulated in contrast to EA, effective altruism. What do you think are the pros and cons of effective altruism? What's interesting and insightful to you about them and what is negative?
2:35:10 Speaker 01 Right. I think, I think like people trying to do good from first principles is, is, is good.
2:35:18 Lex Fridman? We should actually say, and sorry to interrupt, we should probably say that, and you can correct me if I'm wrong, but effective altruism is, is a kind of movement that's trying to do good optimally, where good is probably measured something like the amount of suffering in the world. You want to minimize it. And there's ways that that can go wrong as any optimization can. And so it's interesting to explore.
2:35:47 Lex Fridman? Like how things can go wrong.
2:35:50 Speaker 01 We're both trying to do good to some extent and we're both trying, we're arguing for which loss function we should use, right?
2:35:58 Lex Fridman? Yes.
2:35:59 Speaker 01 Their loss function is sort of hedons, right? Units of hedonism, like how, how like, how good do you feel and for how much time? Right? And so suffering would be negative hedons and they're trying to minimize that. But to us, that seems like that loss function has sort of spurious minima, right? You can, you know, start minimizing shrimp farm pain, right? Which seems not that productive to me.
2:36:31 Speaker 01 Or you can end up with wireheading where you just, you know, either install a Neuralink or you scroll TikTok forever and you feel good on a short term time scale because you're neurochemistry, but on long term time scale, it causes decay and death, right? Cause you're not being productive. Whereas sort of EAC measuring progress of civilization, not in terms of a subjective loss function like hedonism, but rather an objective measure, quantity that cannot be gained, that is physical energy, right? It's very objective, right? And there's not many ways to game it, right? If you, if you did it in terms of like GDP or a currency, that's pinned to a certain
2:37:16 Speaker 01 value that's moving, right? And so that's not a good way to measure our progress. And so, but the thing is we're both trying to make progress and ensure humanity flourishes and gets to grow. We just have different loss functions and different ways of going about doing it.
2:37:37 Lex Fridman? Is there a degree, maybe you can educate me, correct me? I get a little bit skeptical when there's an equation involved, trying to reduce all of the human civilization, human experience to an equation.
2:37:53 Lex Fridman? Is there a degree that we should be skeptical of the tyranny of an equation? Of a loss function or a wish to optimize? Like having a kind of intellectual humility about optimizing over loss functions?
2:38:07 Speaker 01 Yeah, so this particular loss function, it's not stiff. It's kind of an average of averages, right? It's like distributions of states in the future are going to follow a certain distribution. So it's not deterministic. It's not like, we're not on like stiff rails, right? It's just a statistical statement about the future. But at the end of the day, you know, you can believe in gravity or not, you know, but it's not necessarily an option to obey it, right? And some people try to test that and that goes not so well. So similarly, you know, I think thermodynamics is there whether we like it or not.
2:38:48 Speaker 01 And we're just trying to point out what is and try to orient ourselves and chart a path forward, given this fundamental truth.
2:38:59 Lex Fridman? But there's still some uncertainty. There's still a lack of information. Humans tend to fill the gap of the lack of information with narratives. And so how they interpret, you know, even physics is up to interpretation when there's uncertainty involved. And humans tend to use that to further their own means. So it's always, whenever there's an equation, it just seems like, until we have really perfect understanding of the universe, humans will do what humans do. And they try to use the narrative of doing good
2:39:42 Lex Fridman? to fool the populace into doing bad. I just, I guess that this is something that should be skeptical about in all movements.
2:39:52 Speaker 01 That's right. So we invite skepticism, right?
2:39:57 Lex Fridman? Do you have an understanding of what might, to a degree that went wrong? What do you think may have gone wrong with effective altruism? That might also go wrong with effective accelerationism?
2:40:10 Speaker 01 Yeah, I mean, I think, you know, I think it provided initially a sense of community for, you know, engineers and intellectuals and rationalists in the early days. And it seems like the community was very healthy, but then, you know, they formed all sorts of organizations and started routing capital and having actual power, right? They have real power. They influence the government, they influence most AI orgs now. I mean, they're literally controlling the board of OpenAI, right? And look over to Anthropic. I think they have some control over that, too. And so I think, you know, the assumption of EA is more like capitalism is that every
2:40:52 Speaker 01 agent, organism and meta-organism is going to act in its own interest. And we should maintain sort of adversarial equilibrium or adversarial competition to keep each other in check at all times, at all scales.
2:41:06 Speaker 01 I think that, yeah, ultimately, it was the perfect cover to acquire tons of power and capital. And unfortunately, sometimes that corrupts people over time.
2:41:18 Lex Fridman? What does a perfectly productive day, since building is important? What does a perfectly productive day in the life of Guillaume Verdon look like? How much caffeine do you consume? Like, what's a perfect day?
2:41:34 Speaker 01 Okay, so I have a particular regiment. I would say my favorite days are 12:00 PM to 4:00 AM.
2:41:45 Speaker 01 And I would have meetings in the early afternoon, usually external meetings, some internal meetings. Because I'm CEO, I have to interface with the outside world, whether it's customers or investors or renewing potential candidates.
2:42:00 Speaker 01 And usually I'll have ketones, exogenous ketones.
2:42:07 Lex Fridman? So you on a keto, on a keto diet?
2:42:11 Speaker 01 Or is this? I've done keto before for football and whatnot. But I like to have a meal after sort of part of my day is done. And so I can just have extreme focus.
2:42:25 Lex Fridman? You do the social interactions earlier in the day.
2:42:29 Speaker 01 Right.
2:42:29 Lex Fridman? Without food.
2:42:30 Speaker 01 Front load them. Yeah, yeah. Like right now I'm on ketones and Red Bull.
2:42:35 Lex Fridman? Yeah.
2:42:36 Speaker 01 And it just gives you a clarity of thought that is really next level. Cause then when you eat, you're actually allocating some of your energy that could be going to neural energy to your digestion. After I eat, maybe I take a break, an hour or so, hour and a half. And then usually it's like ideally one meal a day, like steak and eggs and vegetables, animal based primarily, so fruit and meat. And then, and then I do a second wind usually. That's deep work, right? Cause I'm, you know, I am a CEO, but I'm still technical. I'm contributing to most patents and there I'll just stay up late into the night and, and work with engineers on very technical problems.
2:43:20 Lex Fridman? So it's like the, the nine PM to four AM, whatever, that range of time.
2:43:25 Speaker 01 Yeah, yeah, that's the perfect time. The emails, the things that are on fire stop trickling in. You can, you can focus and then you have your second wind.
2:43:36 Speaker 01 And, uh, you know, I think Demis Savvas has a similar workday to some extent. So I think that that's definitely inspired my workday. But yeah, I started this workday when I was at Google and had to manage a bit of the product during the day and have meetings and then, and then do technical work at night.
2:43:55 Lex Fridman? Exercise, sleep, those kinds of things.
2:43:58 Speaker 01 Yeah.
2:43:58 Lex Fridman? Said football, you used to play football?
2:44:00 Speaker 01 Yeah, I used to play American football. I've done all sorts of sports growing up and then I was into powerlifting for a while. So when I was studying mathematics in grad school, I would just. You know, do math and lift, take caffeine and that was my day. It was very pure, the purest of monk modes. But it's really interesting how in powerlifting, you're trying to cause neural adaptation by having certain driving signals and you're trying to engineer neuroplasticity through all sorts of supplements. And you know, you have all sorts of, you know, brain-derived neurotrophic factors that get secreted when you, when you lift. So it's funny to me how
2:44:40 Speaker 01 I was trying to engineer neural adaptation in my nervous system more broadly, not just my brain, while learning mathematics. I think you can learn much faster if you really care, if you convince yourself to care a lot about what you're learning and you have some sort of assistance, let's say caffeine or some cholinergic supplement to increase neuroplasticity. I should chat with Andrew Huberman at some point. He's the expert. But yeah, at least to me, it's like, you know, you can try to input more tokens into your brain, if you will, and you can try to increase the learning rate so that you can learn much faster on
2:45:24 Speaker 01 a shorter time scale. So I've learned a lot of things. I followed my curiosity. You're naturally, if you're passionate about what you're doing, you're going to learn faster, you're going to become smarter faster. And if you follow your curiosity, you're always going to be interested. And so I advise people to follow their curiosity and don't respect the boundaries of certain fields or what you've been allocated in terms of lane of what you're working on. Just go out and explore and follow your nose and try to acquire and compress as much information as you can into your brain, anything that you find interesting.
2:45:59 Lex Fridman? And caring about a thing, and like you said, which is interesting, it does, it works for me really well. It's like tricking yourself that you care about a thing.
2:46:07 Speaker 01 Yes.
2:46:08 Lex Fridman? And then you start to really care about it.
2:46:10 Speaker 01 Yep.
2:46:11 Lex Fridman? So it's funny, the motivation.
2:46:14 Lex Fridman? Is a really good catalyst for learning.
2:46:17 Speaker 01 Right. And so at least part of my character as Beth Jezos is kind of like.
2:46:24 Lex Fridman? Yeah, you're a hype man.
2:46:25 Speaker 01 Yeah, but I'm like hyping myself up, but then I just tweet about it.
2:46:29 Lex Fridman? Yeah.
2:46:29 Speaker 01 And it's just when I'm trying to get really hyped up and like an altered state of consciousness where I'm like ultra focused in the flow wired, trying to invent something that's never existed. I need to get to like unreal levels of like excitement, but your brain has these levels of of cognition that you can unlock with like higher levels of adrenaline and whatnot. And I mean, I've learned that in powerlifting that actually you can engineer a mental switch to like increase your strength, right? Like if you can engineer a switch, maybe you have a prompt, like a certain song or some music where suddenly you're like fully primed, then you're at max maximum strength, right? And I've engineered that
2:47:13 Speaker 01 that switch through years of lifting. If you're going to get under 500 pounds and it could crush you, if you don't have that switch to be wired in, you might die. So that that'll wake you right up and that sort of skill I've carried over to like research when it's when it's go time, when the stakes are high, somehow I just reach another level of neural performance.
2:47:35 Lex Fridman? So Beth Jezos is your sort of embodiment, representation of your intellectual Hulk. It's your productivity Hulk.
2:47:44 Speaker 01 That, yeah.
2:47:45 Lex Fridman? Turn on. What have you learned about the nature of identity from having these two identities? I think it's interesting for people to be able to put on those two hats so explicitly.
2:47:56 Speaker 01 I think it was interesting in the early days. I think in the early days, I thought it was truly compartmentalized. Like, oh yeah, this is a character, you know, I'm Guillaume, Beth is just the the character. I like, I like take my thoughts and then I extrapolate them to a bit more extreme. But, you know, over time, it's kind of like both identities were starting to merge mentally and people were like, no, you are, I met you, you are Beth, you are not just Guillaume. And I was like, wait, am I? And now it's like fully merged, but it was already before the docs was already starting mentally that, you know, I'm at, I am this character. It's part of me.
2:48:34 Lex Fridman? Would you recommend people sort of have an alt?
2:48:37 Speaker 01 Absolutely.
2:48:38 Lex Fridman? Like young people, would you recommend them to explore different identities by having alt alt accounts?
2:48:44 Speaker 01 It's it's fun. It's like it's like writing an essay and taking a position, right? It's like you do this in debate. It's it's like you can have experimental thoughts and and by having by the stakes being so low, because you're an on account with, I don't know, 20 followers or something, you can experiment with your thoughts and in a low stakes environment. And I feel like we've lost that in the era of everything being under your main name, everything being attributable to you. People just are afraid to speak, explore ideas that aren't fully formed, right? And and I feel like we've lost something there. So I I hope, you know, platforms like X and others like really help support people trying to stay pseudonymous or anonymous, because it's really important for for people
2:49:27 Speaker 01 to share thoughts that aren't fully formed and converge onto maybe hidden truths that were hard to converge upon if it was just through open conversation with real names.
2:49:41 Lex Fridman? Yeah, I really believe in like, not radical, but rigorous empathy. It's like really considering what it's like to be a person of a certain viewpoint and like taking that as a thought experiment farther and farther and farther. And one way of doing that is an alt account. That's a that's a that's a fun, interesting way to really explore what it's like to be a person that believes a set of beliefs and taking that across the span of several days, weeks, months. Of course, there's always the danger of becoming that. That's a that's the Nietzsche gaze long into the abyss. The abyss gazes into you. You have to be careful.
2:50:26 Speaker 01 Breaking Beth.
2:50:27 Lex Fridman? Yeah, right. Breaking. Yeah, you wake up with a shaved head one day. It's just like, who who am I? What have I become? So you've mentioned quite a bit of advice already, but what advice would you give to young people?
2:50:43 Lex Fridman? Of how to, in this interesting world we're in, how to have a career and how to have a life they can be proud of.
2:50:53 Speaker 01 I think to me, the reason I went to theoretical physics was that I had to learn the base of the stack that was gonna stick around no matter how the technology changes, right? And to me, that was the foundation upon which then I later built engineering skills and other skills. And to me, the laws of physics, you know, it may seem like the landscape right now is changing so fast. It's disorienting. But certain things like fundamental mathematics and physics aren't gonna change. And if you have that knowledge and knowledge about complex systems and adaptive systems, I think that's gonna carry you very far.
2:51:32 Speaker 01 And so not everybody has to study mathematics, but I think it's really a huge cognitive unlock to to learn math and some physics and engineering.
2:51:43 Lex Fridman? Get as close to the base of the stack as possible.
2:51:46 Speaker 01 Yeah, that's right, cuz cuz the base of the stack doesn't change. Everything else, you know, your knowledge might become not as relevant in a few years. Of course, there's a sort of transfer learning you can do, but then you have to always transfer learn constantly.
2:51:59 Lex Fridman? I guess the closer you are to the base of the stack, the easier the the the easier the transfer learning, the shorter the jump.
2:52:05 Speaker 01 Right, right. And you'd be surprised, like, once you've learned concepts in many physical scenarios, how they can carry over to understanding other systems that are necessarily physics. And I guess like the EAC writings, you know, the the principles and tenet post that was based on physics, that was kind of my experimentation with applying some of the thinking from out of equilibrium thermodynamics to understanding the world around us. And it's led to to EAC and this this movement.
2:52:37 Lex Fridman? If you look at your one cog in the machine, in the capitalist machine, one human.
2:52:44 Lex Fridman? And if you look at yourself, do you think mortality is a feature or a bug? Like, would you want to be immortal?
2:52:52 Speaker 01 No. I think fundamentally, thermodynamic dissipative adaptation, there's the word dissipation. Dissipation is important. Death is important, right? We have a saying in physics, physics progresses one funeral at a time.
2:53:10 Lex Fridman? Yeah.
2:53:12 Speaker 01 I think the same is true for capitalism, companies, empires, people, everything. Everything must die at some point. I think that we should probably extend our lifespan because we need a longer period of training because the world is more and more complex, right? We have more and more data to really be able to predict and understand the world. And if we have a finite window of higher neuroplasticity, then we have sort of a hard cap in how much we can understand about our world. So, you know, I think I am for death because, again, I think it's important. You know, if you have like a king that would never die, that would be a problem, right?
2:53:55 Speaker 01 Like, it would, the system wouldn't be constantly adapting, right? You need novelty, you need youth, you need disruption to make sure the system's always adapting and malleable. Otherwise, if things are immortal, you know, if you have, let's say, corporations that are there forever and they have the monopoly, they get calcified, they become not as optimal, not as high fitness in a changing, time varying landscape, right? And so, death gives space for youth and novelty to take its place. And I think it's an important part of every system in nature. So, yeah, I am for, I'm for death.
2:54:38 Speaker 01 But I do think that longer lifespan and longer time for neuroplasticity, bigger brains, should be something we should strive for.
2:54:47 Lex Fridman? Well, in that, Jeff Bezos and Bev Jezos agree that all companies die. And for Jeff, the goal is to try to, he calls it day one thinking, try to constantly, for as long as possible, reinvent. Sort of extend the life of the company, but eventually it too will die. Because it's so damn difficult to keep reinventing. Are you afraid of your own death?
2:55:17 Speaker 01 Um, I think I have ideas and things I'd like to achieve in this world before I have to go, but I don't think I'm necessarily afraid of death.
2:55:29 Lex Fridman? You're not attached to this particular body and mind that you got?
2:55:33 Speaker 01 No, I, I, I think I'm sure there's going to be better versions of, of myself in, in the future, or
2:55:41 Lex Fridman? Forks?
2:55:42 Speaker 01 Forks, right? Genetic forks or, or other, right? I, I, I truly, I truly believe that. I, I, I think there's a sort of evolutionary-like algorithm happening at, at every bit or, or nap in, in the world is sort of adapting through this process that we described in, in EAC. And, and I think maintaining this adaptation malleability is how we have constant optimization of the whole machine. And so I don't think I'm particularly, you know, an optimum that needs to stick around forever. I think there's going to be greater optima in many ways.
2:56:20 Lex Fridman? What do you think is the meaning of it all? What's the why of the machine? The EAC machine?
2:56:27 Speaker 01 The why? Well, the why is thermodynamics. It's, it's why we're here. It's what has led to the formation of life and of civilization, of evolution of technologies and growth of civilization. But why do we have thermodynamics? Why do we have our particular universe? Why do we have these particular hyperparameters, the constants of nature? Well, then you get into the anthropic principle, right? In the landscape of potential universes, right? We're in the universe that allows for life. And then why is there potentially many universes? I don't know. I don't know that part. But could we potentially engineer new universes
2:57:11 Speaker 01 or create pocket universes and set the hyperparameters? So there is some mutual information between our existence in that universe and we'd be somewhat its parents. I think that's really, I don't know, that'd be very poetic. It's purely conjecture, but again, this is why figuring out quantum gravity would allow us to understand if we can do that.
2:57:34 Lex Fridman? And above that? Why does it all seem so beautiful and exciting? The quest to figuring out quantum gravity seems so exciting. Why? Why is that? Why are we drawn to that? Why are we pulled towards that? Just that puzzle solving creative force that underpins all of it, it seems like.
2:57:56 Speaker 01 I think we seek, just like an L M seeks to minimize cross entropy between its internal model and the world. We seek to minimize, yeah, the statistical divergence between our predictions and the world and the world itself. And, you know, having regimes of energy scales or physical scales in which we have no visibility, no ability to predict or perceive, you know, that's kind of an insult to us. And we want to, we want to be able to understand the world better in order to best steer, steer it or steer us through it. And in general, it's a capability that has evolved because the better you can predict
2:58:36 Speaker 01 the world, the better you can capture utility or free energy towards your own sustenance and growth. And I think quantum gravity, again, is kind of the final boss in terms of knowledge acquisition. Because once we've mastered that, then we can do a lot potentially. But between here and there, I think there's a lot to learn in the mesoscales. There's a lot of information to acquire about our world and a lot of engineering, perception, prediction and control to be done to climb up the Carter shift scale. And to us, that's the great challenge of our times.
2:59:17 Lex Fridman? And when you're not sure where to go, let the meme pave the way. Guillaume, Beth, thank you for talking today. Thank you for the work you're doing. Thank you for the humor and the wisdom you put into the world. This was awesome.
2:59:32 Speaker 01 Thank you so much for having me, Lex. It's a pleasure.
2:59:35 Lex Fridman? Thank you for listening to this conversation with Guillaume Verdon. To support this podcast, please check out our sponsors in the description. And now let me leave you with some words from Albert Einstein. If at first the idea is not absurd, then there is no hope for it. Thank you for listening. I hope to see you next time.
Previous #406 – Teddy Atlas: Mike Tyson, Cus D’Amato, Boxing, Loyalty, Fear & Greatness Next #408 – Tal Wilkenfeld: Music, Guitar, Bass, Jeff Beck, Prince, and Leonard Cohen Page updated October 26, 2026 · transcript and summary are machine-generated
