Guillaume Verdon: Beff Jezos, E/acc Movement, Physics, Computation & AGI | Lex Fridman Podcast #407
- The following is a conversation
with Guillaume Verdon, the man behind the
previously anonymous account BasedBeffJezos on X. These two identities were merged by a doxxing article in "Forbes" titled, "Who is BasedBeffJezos, the leader of the tech
elites e/acc 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 in quantum computing, and finally launching his
own company called Extropic that seeks to build
physics-based computing hardware for generative AI. Identity number two, Beff Jezos on X is the creator of the effective
accelerationism movement often abbreviated as e/acc, that advocates for propelling
rapid technological progress as the ethically optimal
course of action for humanity. For example, as proponents
believe that progress in AI is a great social equalizer,
which should be pushed forward, e/acc followers see
themselves as a counterweight to the cautious view that
AI is highly unpredictable, potentially dangerous,
and needs to be regulated. They often give their opponents
the labels of quote doomers or decels, short for deceleration. As Beff himself put it, "e/acc is a mimetic 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 Beff Jezos back to back. As we talk about, Beff admires Jeff as one of the most important humans alive, and I admire the beautiful absurdity and the humor of it all. 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. Let's get the facts of
identity down first. Your name is Guillaume Verdon, Gill, but you're also behind
the anonymous account on X called based BasedBeffJezos. So first, Guillaume Verdon,
you're a quantum computing guy, physicist, applied mathematician, and then BasedBeffJezos 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. - 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 the universe
as 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 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 realm. 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 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 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 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 here and there. At the very small scales,
things are in superposition. They can exhibit interference effects. 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
thermodynamical, right? They're fluctuating. And after, I guess about eight
years in quantum computing and quantum machine learning,
I had a realization that, you know, I was looking for
answers about our universe by studying the very big
and the very small, right? 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, 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. And so there's the sort of scenarios that you study the interface between quantum mechanics 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? 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. 'Cause life is, you know, a
process that is thermodynamical 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. And that 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, 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 back propagates 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. - Just to linger on that,
isn't that interesting that one of the things that
people don't often talk about is that 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. - Yep, that's sort of
the basis of our movement is we were seeing a
tendency towards constraint, reduction or suppression of variants 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, 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 of out 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. - So the second law of thermodynamics says that the entropy is always increasing in the universe that's
tending towards equilibrium. And you're saying there's these pockets that have complexity 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? - 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. 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 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 this 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
standpoint, right? And ultimately, I think
that around that time, you know, it was like
late 2021, early 2022, I think there was 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 gonna 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, 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 gonna
be better and grander statistically, and we can make it so. And if you believe in it, if you believe 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. And so I sort of felt a responsibility to sort of engineer a movement, a 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 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
rather than when it's declining, and you have to decide
how to split the pie. 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. - 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 what you're calling
mesoscale representation, where the 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. Then there's the word hyperstition. So some ideas, and I suppose both pessimism
and optimism of such ideas that if you internalize them, you in part make that idea reality. So both optimism, 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 Gill front end and the BasedBeffJezos backend is the communication styles also, that you are exploring
different ways of communicating that can be more viral 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, e/acc, 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 merged on the personalities recently without your consent, like you said. Some journalists figured out
that you're one and the same. Maybe you could talk about
that experience first of all, like what's the story of
the merger of the two? - Right. So I wrote the manifesto
with my co-founder of e/acc, an account named bayeslord,
still anonymous, luckily, and hopefully forever. - So it was BasedBeffJezos
and Based, Bayesian? Like bayeslord, like Bayesian,
BayesianLord, bayeslord. Okay, and so we should say from now on, when you say e/acc, you mean E slash A-C-C which stands for
effective accelerationism. - [Guillaume] That's right. - And you're referring
to a manifesto written, on I guess Upstack. Are you also bayeslord? - No. - [Lex] Okay, it's a different person. - Yeah. - Okay. All right, well there you go. Wouldn't it be funny if I'm bayeslord. - That'd be amazing. 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, and there, you know, the baseline
is sort of secrecy, right? 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 deep tech that, you know, has
geopolitical impact, right? And so I was being secretive
about what I was working on. There was 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 doxxed the whole Guillaume
complex, and they were, the journalists, you know, reached out to actually my
investors, which is pretty scary. You know, when you're
a startup entrepreneur, you don't really have bosses except for your investors, right? And my investors ping me like,
hey, this is gonna come out. They've figured out everything. What are you gonna 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 sensor fused their notes and now they had way too much. And that's when I got worried, 'cause they said it was of
public interest and in general. - Like how you said sensor fused, like it's some giant neural network operating in a distributed way. We should also say that
the journalists used, I guess at the end of the day, audio-based analysis of voice. Comparing voice of what talks
you've given in the past and then voice on X Spaces. - Yep. - Okay, so and then
that's where the primarily the match happened, okay, continue. - The match, but you know,
they scraped, you know, SEC filings, they looked at my private
Facebook account and so on. So they did some digging. Originally I thought that
doxxing 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 do we draw the line? When is it legal to doxx someone. - The word doxx, maybe you can educate me. I thought doxxing generally refers to if somebody's physical
location is found out, meaning like where they lived. So we're referring to
the more general concept of revealing private information that you don't want revealed
is what you mean by doxxing. - 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 and AI companies really cared what our community thought about any move they may take. And now that, you know,
my identity's revealed, now they know where to apply pressure to silence me or maybe the community. And to me, that's really
unfortunate, because again, it's so important for us
to have freedom of speech, which induces freedom of thought 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. 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? Ultimately that's why I
created 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 in a way that wasn't dependent on my identity and/or achievements, you know, 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, right? 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 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 gonna 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, 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 Beff Jezos because they had me
cornered at that point. - So to you it's fundamentally unethical. Like so one, it's 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
remove the cloak of anonymity, or is it case by case? - I think it could be quite bad. Like I said, if anybody
who speaks truth to power, and sort of starts a
movement or an uprising against the incumbents, against those that usually
control the flow of information, if anybody that reaches a
certain threshold gets doxxed 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 say, right? - So, but the flip side of
that, which is interesting, I'd love to ask you about it, is as we get better and better
at 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, with an army of bots
and anonymous accounts. Is that something that
is concerning to you? - Technically, e/acc was
started in a basement. 'Cause 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. - So I wasn't referring to the basement, 'cause that's the sort of
the American or Canadian heroic story of one man in their basement with a hundred GPUs. I was more referring to
the unrestricted scaling of a Guillaume in the basement. - 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 LLMs 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 gonna 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? - 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. 'cause they can be like
the sheep in "Animal Farm." Like you still within those
swarms want to have variants. - 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 synonymous, 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. - Yeah, it'd be interesting
to also be able to sign where the bot came from,
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. Like the kind of unmodifiable
history of the bot's creation. So then you can know if there's like a swarm
of millions of bots that were created by a particular
government, for example. - 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 conspiratorial, but I do think that ideologies
that want us to decelerate, to wind down, to you know,
the degrowth movement, I think that serves our adversaries more than it serves us in general. 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 the dependency on Russia for oil, right? And that was a net negative for
Germany and the West, right? And so if we convinced 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. We almost lost OpenAI
to this ideology, right? It almost got dismantled,
right, a couple weeks ago, that would've 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
fragile to ambient noise, right? And the world is jiggling about, there's cosmic radiation from outer space that usually flips 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. And to me, I think that
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 board members
and they had enough power to potentially collapse the organization. And at least to me, you know, 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? - 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 like you sometimes using
them a little bit synonymously or not synonymously, but that one is going
to lead to the other. And I just would like to ask you about, is there a place of creating
a fault-tolerant development, diverse development of AI that also considers the dangers of AI? And AI, we can generalize
to technology in general. Should we just grow, build unrestricted as quickly as possible, because that's what the universe
just 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? - I think we get painted
as, you know, 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, if they're liable for the product they put
out that uses this AI, they won't wanna 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. - So to you, safe AI
development will be achieved through market forces versus
through, like you said, heavy-handed government regulation. There's a report from last month, I have a million questions
here, from Yoshua Bengio, Jeff Hinton, and many others. It's titled "The Managing AI Risk in an Era of Rapid Progress." So there's 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. So give independent auditors
access to AI labs, one. Two, governments and companies allocate 1/3 of their AI research
and development funding to AI safety. So there's 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 wrong. And liability, companies are liable. And, you know, that seemed like something you would agree with. - I would say that, you know,
assigning just, you know, 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 this sort of 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, 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 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 wanna co-opt it. And so I just want separation
of AI and state, 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, its 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 near peer competitors. - 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 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 a worry? - 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 sort of similar to Moore's law as a compounding exponential, I think the priority to me is to maintain near
equilibrium of capabilities. We've been fighting for open
source AI to be more prevalent and championed by many organizations, because there you sort
of equilibrate the alpha relative to the market of AIs, right? So 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 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 contribute to the search over the hyperparameter space of how to engineer the systems, right? If you think of, you know,
our collective research as a civilization, it's
really a search algorithm, and the more points we have
in the search algorithm in this point cloud, the
more we'll be able to explore new modes of thinking, right? - 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, there could be really like technological and engineering barriers that prevent the guy or
gal in her mom's basement to make progress. And it seems like the
transition to that kind of world where only one player can
develop AGI is possible. It's just not entirely impossible, even though the current state of things seems to be optimistic. - That's what we're trying to avoid. To me, I think like
another point of failure is the centralization of the supply chains for the hardware, right? We have Nvidia is just
the dominant player, AMD's trailing behind, and then we have 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 the space. And so what I'm trying to do
is sort of explode the variance of possible ways to do AI in hardware by fundamentally re-imagining 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? Grokking systems that have multipartite
quantum entanglement that you can provably
not represent efficiently on a classical computer, a classical deep learning representation, 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 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 realized that human
intelligence is just a point in a very large space of
potential intelligences. And it's both humbling for humanity. 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 wellbeing. 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, we'll be able to explore the
wider space of intelligences that can really be a massive
benefit to our wellbeing and the advancement of civilization. - 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. - I think there's a lot of
beauty in the universe, right? I think life itself, civilization, this homo, techno,
capital, mimetic machine that we all live in, right? So you have humans,
technology, capital, memes, everything is coupled to one another. Everything induces the 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 on optimal
technologies, ideas, and so on. It's a beautiful principle
that we're part of. And I think part of e/acc 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 gotta feel a responsibility to scale it in order to preserve it, because the options are to grow or die. - 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. Does it scare you, or are you concerned that
AI will replace humans? - 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 nanoscale. Similarly, I think that
humanity has a choice. Do we 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 and 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 gonna 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 on
this path to 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. And to me, I think that 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 superintelligences
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, 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 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. - 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 Hendrycks, and he wrote that he agrees with you that AI development could 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, and AI systems may have
no need for humans? - 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? I think there's gonna 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. - 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 that humans are not able to anticipate all the consequences of this process. The scale of damage that could be done through unintended consequences with AI systems is very large. - The scale of the upside, 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 Kardashev 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. 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. - Is there a middle ground between cutoff and all systems go? Is there some argument for caution? - 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. - The problem is with the
market is like, you know, there's not always perfect information. There's manipulation,
there's a bad faith actors that mess with the system. It's not always a rational
and honest system. - 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, right. - Well, let me ask you about
Pdoom, probability of doom. That's just fun to say,
but not fun to experience. What is to you the probability that AI eventually kills all or most humans, also known as probability of doom? - I'm not a fan of that calculation. I think people just
throw numbers out there. It's a very sloppy calculation, right? To calculate a probability, 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? I think that the estimators of Pdoom are biased because of our biology, right? We're evolved to have bias sampling towards negative futures that are scary, because that was an
evolutionary optimum, right? 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 variables. If you let time evolve, you have this concept of a
Lyapunov 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 and replication are more
likely in the future. And so we should aim to maximize our current mutual
information with the future. And the path towards that
is for us to accelerate rather than decelerate. So I don't have a Pdoom,
'cause I think that, you know, similar to 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. - 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 nuclear weapons. You can start to estimate Pdoom in maybe in a more philosophical sense, not a mathematical one. Philosophical meaning
like, is there a chance, does human nature tend
towards that or not? - 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 set things up for a sort of dystopian
future where only a very few and an 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 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 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 talked to these AI doomers, I just asked 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 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 happening out of fear. And so I think that we
shouldn't give into fear. Fear is the mind killer. I think it's also the civilization killer. - We can still think about the
various ways things go wrong. For example, the founding
fathers of 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? An AGI one develop, become the centralizer because of its power? Maybe 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. And 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. 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. Of course it misses a
fundamental piece here that's hard to put into
a mathematical equation on 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? - I do think that right now there's a bias towards
overcentralization of AI because of compute density
and centralization of data and how we're training models. I think over time we're
gonna 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
that is massive, that, you know, fuses many sensors and is trying to perceive
the world accurately, predict it accurately,
predict many, many variables and control it, right, enact
its will upon the world. I think that's just never
been the optimum, right? Like let's say you have
a company, you know, 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 of hierarchical
cybernetic control, right? You have, you know, 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 in field
of perception, right? They have their code base. Let's say you're in a software company, they have their code base, they iterate it on it intraday, 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
timescale and greater scope. And that seems to have emerged as sort of the optimal
way to control systems. And really 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 my field 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, 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 gonna
be a correction over time where intelligence is gonna
go closer to the perception and we're gonna break up
AI into smaller subsystems that communicate with one another and form a sort of meta system. - So if you look at the hierarchies that are 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'd you call it? A centralized cybernetic control? - [Guillaume] Centralized
locus of control, yeah. - So like that's suboptimal you're saying? So it would be always
a state of competition at the very top level. - Yeah, just like, you know, in a company you may have like two units
working on similar technology and competing with one another, and 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 trying new things, and shedding old things that didn't work it's what gives us adaptability and helps us converge on, you know, the technologies and things
to do that are most good. - I just hope there's not a failure mode that's unique to AGI versus humans. 'Cause you're describing human
systems mostly right now. 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. - I mean 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, 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 'cause 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 these tools, to these models, and can
contribute to the research, avoids a sort of neural tyranny where very few people have
control over AI for the world and use it to oppress those around them. - When you were talking
about intelligence, you mentioned multipartite
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
the human brain is able to do? - 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 Planck volume, right? Which was 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. 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 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. And AI, at least the way we do it today for LLMs and for quantum, is very much 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, define
that compressed representation that has distilled all
the variants 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. You can't pack more information spatially any more densely than in 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 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 there, 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, 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 need 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. - 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? - Hmm, 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, but we're not used to
thinking in superpositions 'cause, well, our brains can't do that. So we have to translate the
quantum mechanical world to say linear algebra to grok it. 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, and at different frequencies. I think you could make a quantum computer out of many things. But to me, the thing that
was really interesting was both 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
into the physical world. So this bidirectionality of embedding 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. - 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. - 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 speedups, but very often the speedups
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 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, there's just such a huge
overhead, it's not worth it. It's like thinking about
shipping something across a city using a rocket and
going to orbit and back. It doesn't make sense. Just use an, you know,
delivery truck, right? - 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 learning process? - 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? 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 universe, right? For example, the LIGO is
a quantum sensor, right? It's just a very large one. So yeah, I would say quantum machine perception
simulations, right? Grokking quantum simulations
similar to AlphaFold, right? AlphaFold understood the
probability distribution over configurations of proteins. You can understand quantum distributions over configurations of
electrons more efficiently with quantum machine learning. - You co-authored a paper titled "A Universal Training Algorithm
for Quantum Deep Learning" that involves baqprop with a Q. Very well done, sir, very
well done. How does it work? Is there some interesting
aspects you can just mention on how kinda, you know, baqprop and some of these things we know from classical machine learning transfer over to the
quantum machine learning? - 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 gonna 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 feedforward 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, you know, proportional
to your loss function that you wish to optimize. And then by performing uncomputation, you start with the superpositions, 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. - Is using phase kicks somehow
to adjust the parameters? - 'Cause phase kicks emulate having the parameter space be like a particle in n dimensions, and you're trying to get
the Schrodinger equation, Schrodinger dynamics in the lost landscape of the neural network, right? And so you do an algorithm
to induce this phase kick, which you know, involves
a feed forward, a kick, and then when you
un-compute 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, 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've 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. - 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 to
you between math, physics, engineering, and you know, making a product for quantum computing, for quantum machine learning? - 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 polymathematician. 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 share knowledge is really the key to doing this sort of interdisciplinary
engineering work. I mean, there is a big
difference, you know, in mathematics you can explore mathematics for mathematics' sake, 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 do things. - Well, in quantum
computing in particular, I think there's just a lot
of limits to engineering. It just seems to be extremely hard. So there's a lot of value to be exploring 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's your view of timelines in bringing these ideas to life? - 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. - So we should say the name
of your company is Extropic. - Extropic, 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 subsystem that
is virtual and delocalized that represents your quote
unquote logical qubits. AKA, the payload quantum bits in which you actually want to 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, and each quantum bit and
your 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 of sufficient 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 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. I'm bullish on quantum computing on a 10, 15 year timescale personally, but I think there's other quests that can be done in the meantime. I think it's in good hands right now. - 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 Qudit Probes." So just out of curiosity, can
you explain what a qudit is versus a qubit? - Yeah, it's a D state qubit. - [Lex] It's multidimensional - Multidimensional, 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 it was interesting,
because during my master's, 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 don't match our calculations for the amount of quantum energy 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'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's actually correlated. So if you measured the
vacuum at one point, you acquired information. And if you communicated that
information to another point, you can infer what
configuration the vacuum is in to some precision, 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 space time 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. 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 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 college 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 put in obviously, - But you're saying it's
possible to teleport the energy because you can extract
information one place and then make based on that, some kind of prediction
about another place. I'm not sure what I make of that. - 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 gonna have to pay the price not too far away from where
you extract it to, right. - The precision decreases. I mean, in terms of your
ability to, 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 mesoscale? Do you think there's other
intelligent alien civilizations? Is that useful to think about? How often do you think about it? - 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 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 really important. So that we're robust against whatever the
universe throws at us. - But to me it's also
an interesting challenge and thought experiment on
how to perceive intelligence. This has to do with
quantum mechanical systems. This has to do 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, or don't have the right
processing of the sensor data to see the obvious intelligence
that's all around us. - Well, that's why we work
on quantum sensors, right? They can sense gravity. - 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. 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 lack the
sensors, it's all around us. You know, 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. The thing we thought is somehow emergent from
the laws of physics. We understand them, it's actually a fundamental
part of the universe and can be incorporated in
physics once understood. - Statistically speaking, right, if we observed 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. But that wouldn't
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. - About the bottom of the ocean. Yeah, but, you know, could
it be things like memes? Could it be thoughts and ideas? Could they be operating at that medium? Could aliens be the very
thoughts that come into my head? Like, what do you, have you. How do you know that,
how do you know that, what's the origin of ideas, in your mind, when an idea comes to your head? Show me where it originates. - 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. 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 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. - Just to linger a little
longer on quantum mechanics, 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? - 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 is to apply
quantum machine learning to these sorts of systems, these CFDs or they're called SYK models, and learn an emergent geometry
from the boundary theory. And so we can have a
form of machine learning 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. - What do you think is
going on with black holes as the information storing
and processing units, what do you think is
going on with black holes? - 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 we've seen 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 seems like there is, as information falls into
a black hole, it's sort of, there's sort of a sedimentation right? As you get closer and
closer to the horizon from the point of view of
the observer on the outside, the object slows down infinitely as it gets closer and closer. And so everything that is
falling into 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, in which quantum effects and
quantum fluctuations matter. And there that and falling
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. - There's a few pothead-like
questions I want to ask you. So one, does it terrify you that there's a giant black hole
at the center of our galaxy? - I don't know, I just want to, you know, set up shop near it to
fast forward, you know, meet a future civilization, right? Like if we have a limited lifetime, if you could go orbit a
black hole and emerge. - 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? - To orbit and not
obviously not fall into it? - That's obvious. So it's obvious to you that everything's destroyed
inside a black hole. Like all the information that makes up Guillaume is destroyed. Maybe on the other side,
Beff Jezos emerges. And it's all like, it's tied together in some deeply meme-ful way. - Yeah, I mean, that's a great question. We have to answer what black holes are. Are we punching a hole through space-time and creating a pocket universe? It's possible, right? Then that would mean that if
we ascend the Kardashev scale to, you know, beyond Kardashev Type III, 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 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, we need to ascend the Kardashev scale to answer these questions, right, to peer into that regime
of higher energy physics. - And maybe you can speak
to the Kardashev scale for people who don't know. So one of the sort of meme-like principles and goals of the e/acc movement is to ascend the Kardashev scale. What is the Kardashev scale
and when do we wanna ascend it? - The Kardashev scale is a measure of our energy production and consumption. And really it's a logarithmic scale. And Kardashev Type I is a milestone where we are producing
the equivalent wattage to all the energy that is
incident on Earth from the Sun. Kardashev Type II would be
harnessing all the energy that is output by the Sun. And I think Type III is like
the whole galaxy equivalent. - [Lex] Galaxy, I think level. Yeah. - Yeah, yeah. And then some people have
some crazy Type IV and V, 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. And I think 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 in store for us in the future. It could be something of beauty we can't even imagine today, right. - So this is probably a good place to talk a bit about the e/acc movement. In a Substack blog post titled, what the fuck is e/acc? Or actually what the F* is e/acc, 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 Kardashev gradient. In the short term, this almost certainly
means nuclear fission. 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 to maybe say what to you
is the e/acc movement? What are the goals?
What are the principles? - The goal is for the human
technocapital mimetic machine to become self-aware, and to hyperstitiously
engineer its own growth. So let's decompress that.
- Define each of those words. - Yeah, so you have humans,
you have technology. You have capital, and then you have memes
and 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. - So in that way, you're right, the e/acc is literally
a mimetic optimism virus that is constantly drifting, mutating, and propagating in a
decentralized fashion. So mimetic 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. - We see e/acc as a sort of metaheuristic, 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. That 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 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/acc fork. He has his own sort of
fine tunings of e/acc. - Does anything jump out to memory of the unique characteristic
of that fork from Vitalik? - I would say that it's
trying to find a middle ground between e/acc and sort
of EA and AI 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's always
somewhere in the middle, right? But for e/acc we're pushing
for entropy, novelty, disruption, malleability, speed, rather than being like
sort of conservative, suppressing thought, suppressing
speech, 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. - Balance to the force of
human civilization, yeah. - 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 bring balance. - Well, I like how, I
like the sort of visual of the landscape of ideas
evolving through forks. So kinda 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. And so those are all forks. They're exploring different ideas. - Thinking of culture,
almost like code, right? Nowadays, I mean what you prompt the LLM or what you put in the
constitution of an LLM is basically its cultural framework, what it believes, right? And you can share it on GitHub nowadays. So trying to take inspiration
from what has worked in this 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, it's to set up a process where people are always
searching over subcultures and competing for mindshare. 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 Petri
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. 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. - But the overarching
thing is nevertheless a kind of mimetic optimism pandemic. So I mean, okay, let me ask you, do you think e/acc to
some degree is a cult? - Define cult? - 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 mimetic systems can kind of lead to groupthink. 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 like break us apart, like pull us apart, diversify a thought. So to that degree, to which degree is everybody
kinda chanting e/acc, e/acc, like the sheep in "Animal Farm"? - 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 metairony, 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. - Not yet. - Not yet, but ultimately,
yeah, I mean I totally agree that it seems to me that humans wanna feel like
they're part of a group, so they naturally try to
agree with their neighbors and find common ground, 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 e/acc and its many forks. 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 de-territorialization
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 allow people to
defect or start competing forks, whereas we encourage it, right. - Do you think just the humor, the pros and cons of humor and
meme, so in some sense meme, there's like a wisdom to memes, what is it, the magic theater,
what book is that from? Hermann Hesse, "Steppenwolf," I think. But 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. Do you feel the tension of that? - Yeah, so initially I think what allowed us to grow under the radar was because it was camouflaged as sort of metaironic, right? We would sneak in, you know, deep truths within a package of humor,
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, or a cartoon of an
intergalactic Jeff Bezos and take yourself seriously. And so that 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 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 mimetic fitness
packets of information so that they can spread effectively and carry a message, right? So it's kind of a vector
to 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. 'Cause I don't think that
the timeline is optimal for sort of very serious,
thoughtful discussions. You get rewarded for sort
of polarization, right? And so even though we started a movement that is literally trying to
polarize the tech ecosystem, at the end of the day, it's so that we can have a conversation and find an optimum together. - 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, and I would love to hear your Beff Jezos opinions of Jeff Bezos. Speaking of intergalactic Jeff Bezos. What do you think of that
particular individual whom your name is inspired? - 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 technocapital machine and technocapital acceleration to give us what we wanted, right? We want 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 drink, trying to take profits too
early, putting it back, letting the system compound
and keep improving. And you know, arguably I
think Amazon's invested some of the most amount of
capital in robotics out there. 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, you know, Jeff, and he's one of the great
entrepreneurs out there, one of the best of all time unarguably. - And of course the work at Blue Origin similar to the work at SpaceX is trying to make humans
a multiplanetary species, which it seems almost like a bigger thing than the capitalist machine, or it's a capitalist machine at a different timescale, perhaps. - Yeah, I think that, you know, companies they tend to optimize, you know, quarter over quarter,
maybe a few years out. But individuals that wanna leave a legacy can think on a multi-decadal
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 and get industry off planet,
which I think is brilliant. I think, you know, just
overall, I'm for billionaires. I know this is a controversial
statement sometimes, but I think that in a sense it's kind of a proof
of stake voting, right? Like if you've allocated
capital efficiently, 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. 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. - Why do you think it's a viral and it's 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. - Yeah, I think a lot of people would, instead of trying to understand how the technocapital machine works and realizing they have much
more agency than they think, they'd rather have this
sort of victim mindset. I'm just subjected to this
machine, it is oppressing me. And the successful players
clearly 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 technocapital 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 the world. And that's sort of positive
sum mindset shift is so potent. And really that's what we're
trying to do by scaling e/acc 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. Here's permission. - Each individual has agency. The motto keep building is often heard. What does that mean to you, and what does that have
to do with Diet Coke? - Well, Diet Coke. - By the way, thank you
so much for the Red Bulls. It's working pretty well.
I'm feeling pretty good. - Awesome. Well, 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 technocapital 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 to sort of subverting
or coursing 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 this sort of
degrowth like ESG approach versus say, Elon, right? The degrowth approach is like, we're gonna manage our way
out of a climate crisis. And Elon is like, I'm
gonna build a company that is self-sustaining,
profitable, and growing. And we're gonna innovate our
way out of this dilemma, right? And we're trying to get
people to do the latter rather than the former, at all scales. - 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? - 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 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 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, just like it
was the case for social media, if you shape the function
of information propagation, you can shape people's opinions. He's 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 equilibria between the various AI players. I'd 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. - 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 anxiety as 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, we're all gonna die. It just seems to be a very powerful psychological formulation of the goal of creating human-aligned AI. - I think that anxiety is good. I think, like I said,
I want the free market to create aligned AIs that are reliable and I think that's what
he's trying to do with xAI. So I'm all for it. What I am against is sort
of stopping, let's say, the open source ecosystem
from thriving right? By, let's say, in the executive order, claiming that open source
LLMs or dual use technologies then 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 researchers that make key discoveries
that push us forward, right? Including discoveries for AI safety. And so I think I just wanna maintain ubiquity of opportunity
to contribute to AI, and to own a piece of the future, right? It can't just be legislated,
you know, behind some wall where only a few players
get to play the game. - I mean, so the e/acc movement
is often sort of caricatured to mean sort of progress
and innovation at all costs, doesn't matter how unsafe it is, doesn't matter if it
cause a lot of damage. You just build cool shit
as fast as possible, stay up all night with a
Diet Coke, whatever it takes. 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 e/acc is safety, is AI safety? - 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. - 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. - Yeah, I just think 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'm not saying it's corrupted now, but it could be down the line. And really, 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 999s of pSafety that you can gain, you
know, 999.99999% safe. Maybe you want another nine? Oh, please give us full
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 wanna live in. - 'Cause we know we have some
examples throughout history when that did not lead to a good outcome. You mentioned you founded
a company Extropic 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? - Yeah, I mean, you know, originally we weren't
gonna announce last week, but I think with the
doxxing and disclosure, we got our hand forced. So we had to disclose
roughly what we were doing, but really Extropic 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, you know, 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 that, you know, 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? 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. - So what does that mean? Machine learning as 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? - Yes, it is full stack. And so we're folks that
have built, you know, 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
IBM's and AWS's systems, they've left quantum
computing to help us build what we call actually a
thermodynamic computer. - 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, - Right, I mean that was
a challenge to build, to invent, to build, and then to get to run
on the real devices. - [Lex] Can you actually
speak to what it is? - 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 turn quantum computing
into, or have types of programs that are differentiable
in quantum computing. And you know, Andrej 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? 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 is by differentiating
through control parameters. If you have parameters that affects the physics of the system, and 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 metaframework for how to program
physics-based computers. - So try to parameterize everything, make those parameters
differential, and then optimize. - [Guillaume] Yes. - 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, how do you have, I don't know, all these
little subtle things that people might not think about. - 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. 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 have an AI background, folks that have a pure physics background, and somehow we have to make
them talk to one another, right. - Is there a magic, is
there some science and art to the hiring process, to building a team that can create magic together? - Yeah, it's really hard to pinpoint that je ne sais quoi, right, the. - [Lex] I didn't know you
speak French. That's very nice. - Yeah, I'm actually French Canadian, so. - Oh, you are legitimately
French Canadian. - [Guillaume] I am a legit. - I thought you were just
doing that for the cred. - No, no, I'm truly French
Canadian from Montreal. But yeah, essentially we look for people with very high fluid intelligence that aren't overspecialized, because they're gonna have to
get out of their comfort zone. They're gonna 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 open-minded, really. 'Cause if you're pioneering
a new approach from scratch, there is no textbook,
there's no reference. 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, you know. - So you mentioned with Extropic you're trying to build
the physical substrate for generative AI. 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? - I think our goal is to
both run human-like AI or anthropomorphic AI. - Sorry for use of the term AGI, and I know it's triggering for you. - 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, 'cause 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, 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. - So you can get closer to truth by grounding of the AI to physics, 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. Would that be fair to say? - Yeah, no, you wouldn't, you know, try to extrapolate the
stock market with an LMM trained on texts from the internet, right? It's not gonna be a very accurate model. It's not gonna model its priors or its uncertainties about the world very accurately, right? So you need a different type of AI to compliment sort of this
text extrapolation AI, yeah. - You mentioned singularity earlier. How far away are we from a singularity? - I don't know if I believe
in a finite time singularity as a single point in time. I think it's gonna be asymptotic, and sort of a diagonal sort of asymptote. Like, you know, we have the light cone, we have the limits of physics restricting our ability to grow. So obviously can't fully
diverge on a finite time. I think my priors are that, you know, I think a lot of people on
the other side of the aisle think that once we reach human level AI, there's gonna be an inflection
point and a sudden, like foom like suddenly AI is gonna
grok how to, you know, manipulate matter at the
nanoscale and assemble nanobots. And having 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. There's a sort of minimal cost computationally and thermodynamically to acquiring information about the world in order to be able to
predict and control it. And that keeps things in check. - It's funny you mentioned
the other side of the aisle. So in the poll I posted
about Pdoom yesterday what's the probability of doom. There seems to be a nice like division between people think it's
very likely and very unlikely. I wonder if in the future, there'll be the actual Republicans
versus Democrats division blue versus red, is the AI doomers versus
the e/acc-ers, e/acc. - 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 scale. - [Lex] Which one is the up, okay. - Civilization, right? - [Lex] All right. - 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 for them
to give up more control and give the government more power. But fundamentally we're
not left or versus right. I think, we've done polls of people's alignment within e/acc. I think it's pretty balanced. So it's a new fundamental
issue of our time. It's not just centralization
versus decentralization. It's kind of do we go, it's like tech progressivism
versus technoconservatism, right? - So e/acc 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,
insightful to you about them, and what is negative? - Right, I think like
people trying to do good from first principles is good. - 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 the 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 wanna minimize it. And there's ways that that can go wrong, as any optimization can. And so it's interesting to explore like how things can go wrong. - 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? Their loss function is
sort of hedons, right? Units of hedonism, 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. Or you can end up with wireheading
where you just, you know, either install a neural link
or you scroll TikTok forever and you feel good on
a short-term timescale because of your neurochemistry. But on long-term timescale, it causes decay and death, right, 'cause you're not being productive. Whereas sort of e/acc measuring
progress of civilization, not in terms of a subjective
loss function like hedonism, but rather an objective measure, a 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 did it in terms
of like GDP or a currency that's pinned to a certain
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. - Is there a degree, and 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. Is there a degree that
we should be skeptical of the tyranny of an equation, of a loss function over which to optimize? Like having a kind of
intellectual humility about optimizing over loss functions? - 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 gonna 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. And we're just trying
to point out what is, and try to orient ourselves,
and chart a path forward, given this fundamental truth. - But there's still some uncertainty, there's still 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 to fool the populace into doing bad. I just, I guess that this is something that should be skeptical
about in all movements. - That's right. So we
invite skepticism, right. - 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? - 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 were literally controlling the board of OpenAI, right? And look over to Anthropic. I think they all have some
control over that too. And so I think, you know, the assumption of e/acc
is more like capitalism is that every agent
organism and metaorganism is gonna act in its own interests. And we should maintain sort
of adversarial equilibrium or adversarial competition to keep each other in check
at all times, at all scales. 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. - 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 the perfect day? - Okay, so I have a particular regimen. I would say my favorite
days are 12 pm to 4 am, 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 interviewing potential candidates. And usually I'll have
ketones, exogenous ketones. - [Lex] So you on a keto diet 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. - You do the social
interactions earlier in the day, without food. - Front load them, yeah, yeah. Like right now I'm on
ketones and Red Bull. 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, an 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 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 work with engineers on
very technical problems. - So it's like the, the 9 pm to 4 an, whatever, that range of time. - Yeah, yeah, that's the perfect time. The emails, the things that
are on fire stop trickling in. You can focus, and then
you have your second wind and you know, I think Demis Hassabis has a similar work day to some extent. So I think that that's
definitely inspired my workday. But yeah, that 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
do technical work at night. - Exercise, sleep, those kinds of things. Said football used to play football. - 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 lift. So it's funny to me how
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 a shorter timescale. So I've learned a lot of things.
I've followed my curiosity. You're naturally, if you're passionate
about what you're doing, you're gonna learn faster, you're gonna become smarter faster. And if you follow your curiosity, you're always gonna 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. - And caring about a thing, like you said, which is interesting, it
works for me really well. Is like tricking yourself
that you care about a thing. And then you start to
really care about it. So it's funny, the motivation is a really good catalyst for learning. - Right, and so at least part
of my character as Beff Jezos is kind of like. - [Lex] Yeah, the hype man. - Yeah, just hype. But I'm like hyping myself up, but then I just tweet about it. And it's just when I'm
trying to get really hyped up and in like an altered
state of consciousness where I'm like ultrafocused,
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 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 maximum strength, right? And I've engineered that switch
through years of lifting. If you're gonna 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'll wake you right up. And that sort of skill I've
carried over to like research when it's go time, when
the stakes are high, somehow I just reach another
level of neural performance. - So Beff Jezos is your
sort of embodiment, representation of your intellectual Hulk. It's your productivity
Hulk that you just 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. - 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, Beff is just 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 Beff, 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 doxx, it was already starting
mentally that, you know, I am this character, it's part of me. - Would you recommend
people sort of have an alt. - [Guillaume] Absolutely. - Like young people,
would you recommend them to explore different
identities by having alts, alt accounts? - It's fun. It's like writing an essay
and taking a position, right? It's like you do this in debate. It's like you can have
experimental thoughts, and by having, by the stakes being so low because you're an anon
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 I feel like
we've lost something there. So I hope, you know,
platforms like X and others like really help support people trying to stay synonymous or anonymous, because it's really important for people 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. - 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 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 the Nietzsche
gaze long into the abyss, the abyss gazes into you. You have to be careful. - Breaking Beff. - Yeah, right. Breaking Beff. Yeah, you wake up with
a shaved head one day, and it's just like, 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 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? - Hmm, 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. And so not everybody has
to study mathematics, but I think it's really
a huge cognitive unlock to learn math and some
physics and engineering. - Get as close to the base
of the stack as possible. - Yeah, that's right. 'Cause 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. - I guess the closer you are
to the base of the stack, the easier the transfer
learning, the shorter the jump. - 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 aren't necessarily physics. And I guess like the
e/acc writings, you know, the principles and tenet posts
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 e/acc and this movement. - If you look at you're
one cog in the machine, in the capitalist machine, one human, and if you look at yourself, do you think mortality
is a feature or a bug? Like would you want to be immortal? - No, I think fundamentally in 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. 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, 'cause 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 than 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? Like 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 them 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 and nature. So yeah, I am for, I'm for death, but I do think that longer lifespan and longer time for
neuroplasticity, bigger brains, should be something we should strive for. - Well, and that Jeff Bezos and Beff 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, 'cause it's so damn difficult
to keep reinventing. Are you afraid of your own death? - 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. - So you're not attached
to this particular body and mind that you got? - No, I think I'm sure there's
gonna be better versions of myself in the future or. - [Lex] Forks. - Forks, right. Genetic
forks or other, right? I truly, I truly believe that. I think there's a sort of a
evolutionary-like algorithm happening at every bit or NAT in the world is sort of adapting through this process that we describe in e/acc. 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 gonna be
greater optima in many ways. - What do you think is
the meaning of it all? What's the why of the
machine, the e/acc machine. - The why? Well, the
why is thermodynamics. 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, and 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 or create pocket universes
and set the hyperparameters? So there is some mutual information between our existence and 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. - And above that, why is 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 does that puzzle
solving creative force that underpins all of it, it seems like. - I think we seek, just like an LLM 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 be able to
understand the world better in order to best steer, steer
it or steer us through it. And in general, it's the
capability that has evolved because the better you
can predict 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 Kardashev scale. And to us, that's the great
challenge of our times. - And when you're not sure where to go, let the meme pave the way. - That's right. - Guillaume, Beff, 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. - Thank you so much for having
me, Lex, it's a pleasure. - 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.