---
title: 'The future of intelligence | Demis Hassabis (Co-founder and CEO of DeepMind)'
source: 'https://youtube.com/watch?v=PqVbypvxDto'
video_id: 'PqVbypvxDto'
date: 2026-06-19
duration_sec: 0
---

# The future of intelligence | Demis Hassabis (Co-founder and CEO of DeepMind)

> Source: [The future of intelligence | Demis Hassabis (Co-founder and CEO of DeepMind)](https://youtube.com/watch?v=PqVbypvxDto)

## Summary

In this podcast episode, Hannah Fry interviews Demis Hassabis, CEO of Google DeepMind, about the rapid progress in AI over the past year, including the release of Gemini 3, advances in world models, and the pursuit of AGI. Hassabis discusses the balance between scaling and innovation, the importance of consistency in AI systems, and the societal implications of AGI, emphasizing the need for international collaboration and new economic models.

### Key Points

- **Effort Allocation** [00:00] — 50% of effort is on scaling, 50% on innovation; both are required to reach AGI.
- **AGI as Simulation** [00:22] — Building AGI will help compare artificial and real minds, revealing what is special about human consciousness, emotions, and creativity.
- **Year in Review** [00:55] — 2024 saw a shift from LLMs to agentic AI, acceleration in drug discovery, and multimodal models integrated into robotics and driverless cars.
- **Rapid Progress** [01:47] — The year felt like 10 years packed into one, with major advances in models like Gemini 3 and world models.
- **Root Node Problems** [02:32] — AlphaFold proved that AI can solve root node problems, unlocking downstream benefits across science.
- **Material Science Goals** [02:57] — Hassabis aims to solve problems like room-temperature superconductors, better batteries, and materials for fusion.
- **Fusion Partnership** [03:10] — DeepMind announced a deeper partnership with Commonwealth Fusion to accelerate tokamak reactors using AI for plasma control and material design.
- **Jagged Intelligence** [05:18] — AI systems can win Math Olympiad gold but make basic high-school mistakes; performance is uneven and inconsistent.
- **Need for Consistency** [07:05] — Consistency and reasoning are key missing pieces; thinking systems exist but are only 50% of the way there.
- **Missing: Online Learning** [09:29] — Current systems cannot learn continuously after deployment, a critical gap for AGI.
- **Slower Route?** [09:43] — Hassabis wished AI had stayed in the lab longer to do more AlphaFold-like science, but commercial acceleration brought resources and public awareness.
- **Scaling Wall** [13:01] — No hard wall observed; only diminishing returns. Synthetic data and research innovations keep progress going.
- **Confidence Scores** [15:50] — Models need to output confidence scores, like AlphaFold, to reduce hallucinations and improve reliability.
- **World Models and Simulations** [17:43] — World models (Genie, Simma) capture spatial dynamics and physics, enabling training agents in simulated environments.
- **Physics Hallucinations** [23:38] — Current video models approximate physics well visually but are not accurate enough for robotics; a physics benchmark is needed.
- **Evolution Simulations** [26:13] — Hassabis is interested in running evolution simulations to understand origins of life and consciousness.
- **AI Bubble?** [28:31] — Parts of the AI ecosystem (e.g., startup valuations) may be in a bubble, but big tech has real underlying business.
- **Avoiding Echo Chambers** [31:54] — Gemini 3's persona is designed to be scientific, helpful, and push back on false ideas, avoiding sycophancy.
- **Industrial Revolution Lessons** [37:33] — AI transformation will be 10x bigger and 10x faster; society must learn from past dislocations and prepare new economic models.
- **Post-AGI Economics** [40:37] — New systems like direct democracy voting with credits or UBI may replace current labor-for-resources models.
- **Limits of Computation** [44:42] — Hassabis believes everything may be computable; the Turing machine question is central. Information may be the fundamental unit.
- **Mission and Sabbatical** [54:30] — Hassabis's mission is to help steward AGI safely; after that, he might take a well-earned sabbatical.

## Transcript

We effectively you can think of as 50% of our effort is on scaling, 50% of it
is on innovation. My betting is you're going to need both to get to AGI. I've
always felt this that if we build AGI and then use that as a simulation of the
mind and then compare that to the real mind, we will then see what the
differences are and uh potentially what's special um and remaining about
the human mind, right? Maybe that's creativity, maybe it's emotions, maybe
it's dreaming. There's a lot of consciousness. There's a lot of um
hypotheses out there about what may or may not be computable. And this comes
back to the chewing machine question of like what is the limit of a chewing machine.
>> So, there's, nothing, that, cannot, be, done within the sort of computational.
>> Well,, no, one's, put, it, this, way., Nobody's found anything in the universe that's
that's non-computable >> so, far. >> So, far. Welcome to Google Deep Mind the podcast
with me, Professor Hannah Fry. It has been an extraordinary year for AI. We
have seen the center of gravity shift from large language models to agentic
AI. We've seen AI accelerate drug discovery and multimodal models
integrated into robotics and driverless cars. Now, these are all topics that
we've explored in detail on this podcast. But for the final episode of
this year, we wanted to take a broader view, something beyond the headlines and
product launches to consider a much bigger question. Where is all this
heading really? What are the scientific and technological questions that will
define the next phase? And someone who spends quite a lot of their time
thinking about that is Demis, CEO and co-founder of Google DeepMind. Welcome
back to the podcast, Deis. >> Great, to, be, back. >> I, mean,, quite, a, lot's, happened, in, the
last year. >> Yes., What's, what, sort, of, the, biggest shift do you think?
>> Oh, wow., I, mean, um, it's, just, so, much, has happened as you said it's just it feels
like we've packed in 10 years in one year. I think a lot's happened. I mean
certainly for us uh the the progress of the models um we've just released Gemini
3 which we're really happy with. Um the the cap multimodal capabilities all of
those things have just advanced really well. And then probably the thing I I
guess over the summer that I'm uh very excited about is world models being
advanced. I'm sure we're going to talk about that. Yeah, absolutely. We will
get on to all of that stuff in a bit more detail in a moment. Um, I remember
the very first time that I interviewed you for this podcast and you were
talking about the root node problems about this idea that you can use AI to
kind of unlock these downstream benefits. Um, and you've made pretty
good on your promise, I have to say. And do you want to give us an update on on
where we are with those where what are the things that are just around the
corner and and the things that we've that you've sort of solved or near solved?
>> Yeah., Well,, of, course,, obviously, the, big proof point was was AlphaFold and sort
of crazy to think we're coming up to like 5year sort of anniversary of of
AlphaFold being sort of announced to the world, Alpha Fold 2 at least. So that
was the proof I guess that it was possible to do these root node type of
problems. And we're looking we're exploring all the other ones now. I
think material science uh I'd love to do a room temperature superconductor um and
uh you know better batteries these kinds of things. I think that's that's on the
cards. uh better materials of all sorts. We're also working on fusion um
>> because, there's, a, new, partnership, that's been announced fusion.
>> Yeah,, we've, just, announced, partnership with a deep one. We we already were
collaborating with them, but it's a much deeper one now with Commonwealth Fusion
who, you know, I think are probably the best startup uh uh working on at least
traditional TOKAC uh reactors. So they're probably closest to to having
something uh uh viable and we want to help accelerate that uh you know helping
them contain the plasma in the magnets and maybe even some material design
there as well. So that's exciting. And then we're collaborating also with our
quantum colleagues which they're doing amazing work uh at the at the quantum AI
team at Google and we're helping them with error correction codes uh where
we're using our machine learning to help them and then maybe one day they'll help us.
>> That's, perfect., Exactly., The, fusion, one is particularly I mean the difference
that that would make to the world that would be unlocked by that is gigantic.
>> Yeah., I, mean, fusion's, always, been, the holy grail. Of course I think solar is
very promising too, right? Effectively using the fusion fusion reactor in the
in the in the clouds in the sky. But um I think if we could have uh modular
fusion reactors, you know, this promise of uh almost unlimited renewable clean
uh energy uh would be obviously transform everything. And that's the
holy grail. Of course, that's one of the ways we could we could um uh help with climate
>> does, make, a, lot, of, our, existing, problems sort of disappear if we can if we can
>> definitely, I, mean, it, opens, up, many, this is why we think of as a root node. Of
course it helps directly with energy and and pollution and and so on um and helps
with the with the climate crisis. But also, if energy really was renewable and
clean and and and super cheap or almost free, then many other things would
become viable. Um, like, you know, water access cuz we could have desalination
plants pretty much everywhere. Uh, even making rocket fuel. Uh, you know, it's
just there's lots of seawater that contains hydrogen and oxygen. That's
basically rocket fuel, but it just takes a lot of energy to split it out into
hydrogen and oxygen. But if energy is cheap, uh, and and renewable and sort of
clean, then why not do that? you know you could have that producing 247.
>> You're, also, seeing, a, lot, of, change, in the uh the AI that is applying itself to
mathematics, right? That you know winning medals in the International Math
Olympiad and yet at the same time, these models can make quite basic mistakes in
high school math. Why is there that paradox? >> Yeah,, I, think, it's, fascinating, actually.
One, of the, most, fascinating, things, and probably that needs to be fixed uh as
one of the key things while we're not at AGI yet. Um, as you said, we've had a
lot of success in other groups on getting like gold medals at the
International Mass Olymp. You look at those questions and they're they're
super hard questions that only the top students in the world can can do. And on
the other hand, if you pose a question in a certain way, we've all seen that
with with experimenting with chat bots ourselves uh in our daily lives that it
can make some fairly trivial mistakes on logic problems. They can't really play
decent games of chess yet, which um is surprising. So there's something missing
um uh still from these systems in terms of their consistency. And I think that's
one, of the, things, that's, you, would expect from a general intelligence and
art, you know, an AGI system is that it would be consistent across the board.
And so sometimes people call it jagged intelligences. So they're really good at
certain things, maybe even like PhD level, but then other things they're
like not even high school level. So it's very uneven still the performances of
these systems. They're very very impressive in certain dimensions. Um but
they're still pretty uh uh basic in others and we've got to close those
gaps. And you know there are theories reasons to why and depending on the
situation it could even be uh the way that an image is is perceived and
tokenized. So sometimes actually it doesn't even get all the letters that
you you know so when you count letters in words um it sometimes gets that wrong
but but it may not be seeing that each individual letter. So there's sort of
different reasons for some of these things. uh and each one of those can be
fixed and then you can see what's left. Um but I think consistency I think
another thing is reasoning and thinking. So uh we have thinking systems now that
at inference time they spend more time thinking and they're better they're
better at outputting their answers. Um but it's not sort of super consistent
yet in terms of like is it using that thinking time in a useful way um to
actually double check and use tools to double check what it's outputting. I
think we're we're on the way, but maybe we're only 50% of the way there.
>> I, also, wonder, about, that, that, story, of of Alph Go and then Alpha Zero where you
sort of took away all of the human experience and found that the model
actually improved. Is there a sort of is there a scientific or a maths version of
that in in the the models that you're creating? >> I, think, what, we're, trying, to, build
today, it's more like Alph Go. So, you know, you effectively these these large
language models, these foundation models, they're starting with all of
human knowledge. you know, what we put on the internet, which is pretty much
everything these days, and um compressing that into some useful
artifact, right, which they can look up and and generalize from. But I do think
we're we're still in the in the early days of having this uh uh search or
thinking on top like AlphaGo had to kind of uh use that model to direct in useful
reasoning traces, useful planning uh ideas. Um and and then come up with the
best, you know, solution to whatever the problem is at that point in time. So I I
don't feel like we're constrained at the moment with the kind of limit of human
knowledge like the internet. I think the main issue at the moment is we don't
know how to use those systems in a reliable way fully yet in the way we did
with Alph Go. Um, but of course that was a lot easier because it was just it was
a game. I think once you have Alph Go there, uh, you could go back just like
we did with the the Alpha series and do an Alpha Zero where it starts sort of
discovering knowledge for itself. I think that would be the next step, but I
I that's obviously harder and so I think it's good to try and create the first
step first with some kind of alphike system and then we can think about an
alpha zero like system. But that is also one, of the, things, missing, from, today's
systems is the ability to online learn and continually learn. So you know we
train these systems, we balance them, we postrain them and then they're out in
the world but they don't do they don't continue to learn out in the world like
like we would. Um, and I think that's another missing critical missing piece
from from these systems from from, you know, that will be needed before AGI.
>> In, terms, of, all, of, those, missing, pieces, I mean, I know that there's this big
race at the moment to release commercial products, but but I also know that that
Google Deep Mind's roots really lie in in that idea of scientific research. And
I I found a quote from you where you recently said, "If I had had my way, we
would have left AI in the lab for longer and done more things like AlphaFold
maybe cured cancer or something like that." Mhm. >> Do, do, you, think, that, we, lost, something
by not taking that slower route? >> Um, I, think, we, lost, and, gained, something.
So I feel like that would have been the more, pure, scientific, approach., At least
that was my original plan say 15 20 years ago that you know when almost no
one was working on AI. We just started we were just about to start Deep Mind.
People thought it was a crazy thing to work on. Um but we believed in it and
and and and I think that the idea was if we would make progress we would continue
to sort of um incrementally build towards AGI be very careful about what
each step was and and the safety aspects of it and so on analyze what the system
was doing and so on. But in the meantime you wouldn't have to wait till AGI
arrived before it was useful. You could branch off that technology and use it in
really beneficial ways to society namely advancing science and medicine. So
exactly what we did with AlphaFold actually which um it's not it's not a
foundation model itself general model but it uses the same techniques you know
transformers and other things and then blends it with um uh more specific
things to that domain. So I imagined a whole bunch of those things getting done
while which would be hugely you know you'd release to the world for just like
we did with AlphaFold and indeed do things like cure cancer and so on. um
whilst we were working on the sort of more the AGI track in the lab. Now it's
turned out uh that chat bots were possible at scale and people find them
useful and then they've now morphed into these foundation models that can do more
than chat and text obviously including Gemini. Uh they can do images and video
and all sorts of things and um that's also been very successful commercially
and in terms of a product and I love that too. Like I've always dreamed of
having the ultimate assistant that would help you in everyday life, make it more
productive, maybe even protect your brain space a bit as well from an
attention so that you can focus and be in flow and so on cuz you know today
with social media it's just noise noise and I think AI can actually that works
for you could help us with that. Um, so I think that's good, but it has created
this pretty crazy race condition where there's many commercial organizations
and even nation states all rushing to you know, improve and overtake each
other and that makes it hard uh to do sort of rigorous science at the same
time. We try to do both and I think we're getting that balance right. On the
other hand, there are lots of pros of the way it's happened which is of course
there's a lot more resources coming uh into the area. So that's definitely
accelerated progress. Um and also um I think the general public are actually
interestingly only a couple of months behind the absolute frontier in terms of
what they can use. So everyone gets the chance to sort of feel for themselves
what, AI, is, going to, be, like., And, I, think I think that's a good thing and then
governments sort of understanding this better. The thing that's strange is that
I mean this time last year I think there was a lot of talk about um you know
scaling eventually hitting a wall about us running out of data and yet you know
we're recording now Gemini 3 has just been released and it's leading on this
whole range of different benchmarks. Um how how has that been possible? Like
wasn't there supposed to be a problem with scaling hitting a wall? I think a
lot of people thought that especially as other companies have sort of had slower
progress should we say but I think we've never really seen any wall as such like
what I would say is um maybe there's like diminishing returns and people when
I say that people think only think like oh so there's no returns like it's zero
or one it's either exponential or or it's asmtopic no actually there's a lot
of room between those two regimes and I think we're in in between those so it's
not like you're going double the performance on all the benchmarks every
time you release a new iteration. Maybe that's what was happening in the early
very early days, you know, three four years ago. But you are getting
significant improvements like we've seen with Gemini 3 that are well worth the
investment and the return on that investment and doing. So I that we
haven't seen any slowdown on. There are issues like are we running out of just
available data but there are ways to get around that you know synthetic data uh
generating your you know these systems are good enough they can start
generating their own data especially in certain domains like coding and math
where you can verify the answer in some sense you could produce unlimited data
so all of these things though are research questions and I think that's
the advantage that we've always had is that um we've we've always been sort of
research first and We I think we have the broadest and deepest research bench
always have done. Um and if you look back at the last decade of advances
whether that's transformers or alpha zero any of the things we just discussed
that they all came out of Google or deep mind. So I've always said like if if
more innovations are needed uh scientific ones then I would back us to
be the place to do it just like we were you know in the previous sort of 15
years for a lot of the big breakthroughs. So I think that's just
what's transpiring and I actually really like it when the terrain gets harder
because then it's not just worldass engineering you need which is already
hard enough um but you have to ally that with worldclass research and science
which is what we specialize in uh and on top of that we also have the advantage
of world-class infrastructure with our TPUs and and other things that we've
invested in a lot for a long time um and so that combination I think allows us to
uh uh sort be at the frontier of the innovations as well as the scaling part
and we effectively you can think of as 50 50% of effort is on scaling 50% of it
is on innovation and I think my betting is you're going to need both to get to AGI
>> I, mean, one, thing, that, we, are, still seeing even in Gemini 3 which is an
exceptional model is uh this idea of hallucinations so I think um there was
one metric that said uh it can still give an answer when actually it should
decline um I mean could you build the system where Gemini gives a confidence
score in the same way that Alpha Fold does. >> Yeah,, I, think, so., And, I, think, we, need
that actually. And I think that's sort of one of the missing things. I think
we're getting close. I think the better the models get, the more they know about
what they know, if that makes sense. And so, and I think the more reliable we
could sort of rely on them to actually introspect in some way or do more
thinking and actually realize for themselves that they're uncertain or
there's there's there's uncertainty over this answer. Uh, and then we've got to
sort of work out how to train it in a way that where it can it can output that
as a as a reasonable answer. Um, we're getting better at it, but it still sometimes, you know
it sort of forces itself to answer when it probably shouldn't. Um, and then that
can lead to a hallucination. So, I think, you know, a lot of the hallucinations are of that type
currently. So, there's a missing piece there that that sort of has to be
solved. And you're right, as we did solve it with Alpha Fold, but in in
obviously a much more limited way >> cuz, presumably, behind, the, scenes, there
is some sort of measure of probability of whatever the next token might be.
>> Yes,, there, is, of, the, next, token., That's how it all works. But that doesn't tell
you the overall arching piece is this is you know how confident are you about
this entire fact or this entire um statement. And I think that's why you'll
need this. I I think we'll need to use the thinking steps and the planning
steps to go back over what you just output. At the moment, it's a little bit
like the systems are just it's like talking to some a person and they just
you know, when when they're in on a bad day, they're just literally telling you
the first thing that comes to their mind., Most, most, of the, time, that, would
be okay. But then sometimes when it's very difficult thing, uh you'd want to
like stop pause for a moment and maybe go over what you were about to say and
adjust what you were about to say. But perhaps that's happening less and less
in the world these days, but um that's still the better way of having a
discourse. So, you know, I think you can think of it like that. These models need to do that better.
>> I, also, really, want, to, talk, to, you, about um the the simulated worlds and putting
agents in them because we got to talk to your genie team earlier today.
>> Tell, me, why, you, care, about, simulation. What What can a world model do that that
a language model can't? >> Well,, look,, I, it's, it's, actually, been
it's probably my longest standing passion is world models and simulations.
uh in addition to AI and of course it's all coming together in our most recent
work like Genie and I think um language models are able to understand a lot
about the world I think actually more than we expected more than I expected
because language is actually probably richer than we thought it contains more
about the world than we maybe even even linguist maybe imagined and that's you
know proven now with these new systems but there's still a lot about the the
spatial dynamics of the world you know how spatial awareness um and the cont
the physical context we're in um and how that works mechanically that um isn't is
hard to describe in words and isn't generally described in in corpuses of of
words and a lot of this is allied to learning from experience online
experience there's a lot of things which you can't really describe something you
have to just experience it um maybe the sensors and so on are very hard to put
into words you know whether that's you know motor angles and smell and you know
these kind of sensors it's very difficult to describe that in any kind
of language. So I think there's a whole set of things around that and I think if
we want robotics to work or a universal assistant that maybe comes along with
you in your daily life maybe on glasses or you know on your phone um and helps
you in your everyday life not just on your computer um you're going to need
this kind of world understanding and uh world models are at the core of that. So
this what we mean by a world model is this this sort of model that understands
the causitative and effect of of the mechanics of the world right intuitive
physics but um how things move how things behave. Um now we're seeing a lot
of that in our video models actually and one way to show how do you test you have
that kind of understanding well can you generate realistic worlds cuz if you can
generate it then in a sense you must have understood uh uh the system must
have encapsulated a lot of the mechanics of the world so that's why Genie and VO
and these models are our video models and our sort of interactive world models
are really uh impressive but also important steps towards showing we have
generalized models and then hopefully some point we can apply it to, you know
robotics and and and universal assistance. And then of course, one of
my favorite things I'm definitely going to have to do at some point is
reapplying it back to games and and uh you know, game simulations and create
the ultimate games, which of course was maybe always my subconscious plan. >> All, of, this.
>> Yeah., All, of, the, time., Exactly. >> What, about, science, too, though?, Could, you
use it in that in that domain? >> Yes,, you, could., So, uh, science, you, know
again I think building models of scientifically complex uh uh domains uh
whether that's materials on atomic level um you know in biology uh but also like
some physical things as well like weather one way to um understand those
systems is to build simula learn simulations of those systems from the
raw data right so you have a bunch of raw data let's say it's about the
weather and obviously we have some amazing weather projects going on um and
then you have a model that kind of learns those dynamics and can recreate
those dynamics uh more efficiently. So uh than doing it by brute force. So I
think there's huge potential for simulations and uh kind of world models
maybe specialized ones for aspects of of science and mathematics.
>> But, then, also, I, mean, you, can, drop, an agent into that simulated world too right?
>> Yes., your, Genie, 3, team,, they, had, this really lovely quote which was almost no
prerequisite to any major invention was made with that invention in mind. And
they were talking about dropping agents into these simulated environments and
allowing them to explore with sort of curiosity being their main motivator
>> right?, And, so, that's, that's, another really exciting use of these these uh
world models is you can we have another project called Simma. We just we just
released Simma 2. sim, you know simulated agents where you have an
avatar or an agent and you put it down into a virtual world. It can be a
normal, it can be a kind of actual commercial game or something like that
very complex one like No Man's Sky, kind of open world space game. Uh, and then
you can you can instruct it with because it's got Gemini under the hood, you can
just talk to the agent and and give it give it tasks. But then we thought
well, wouldn't it be fun if we plug Genie into Simma and sort of drop
Simmer, a Simma agent into a another AI that was creating the world on the fly.
So now the the two AIs are kind of interacting in the minds of each other.
So Simmer's, you know, the Simma agents trying to navigate this world and Genie
is, as far as Genie is concerned, that's just a player and uh an avatar doesn't
care. There's another AI. So it's just generating the world around whatever Sim
is trying to do. So, so that it's kind of amazing to see them both uh
interacting together. And I think this could be the beginning of an interesting
training loop where uh you almost have infinite training uh examples because uh
whatever the simmer agent's trying to learn, Genie can basically create on the
fly. So, I think that you could imagine a whole world of like uh setting and
solving tasks, just millions of tasks automatically, and they're just getting
increasingly more difficult. So, we might try to set up a kind of loop like
that. Um, as well as obviously those simmer agents could be great as game
companions. Um, also some of the things that they learn could be useful also for robotics.
>> Yeah., The, end, of, boring, NPCs, basically. >> Exactly., It's, going, to, be, amazing, for
these games. Yeah. >> Those, worlds, that, you're, creating though, how do you make sure that they
really are realistic? I mean, how do you ensure that you don't end up with
physics that looks plausible but is actually wrong? >> Yeah,, it, that's, that's, a, great, question
and and and and can be an issue. It's basically hallucinations again. So some
hallucinations are good cuz cuz you it also means you you might create
something interesting and new. So in fact sometimes if you're trying to do
create creative things or trying to get your system to create new things, novel
things, um a bit of hallucination might be good, but you want it to be
intentional, right? So not uh so you kind of switch on the hallucinations
now, right? Or the the creative um exploration. But yes, with with the with
when you're trying to train a simmer agent, you don't want genie
hallucinating kind of physics that are wrong. So actually what we're doing now
is we're almost creating a physi physics benchmark where um we can use game
engines which are very accurate with physics to create lots of like um fairly
simple like the sorts of things you would do in your physics A level uh lab
uh lessons, right? like you know rolling little balls down different tracks and
seeing how fast they go and so like really teasing a part on a very basic uh
level like Newton's three laws of motion has it encapsulated it um whether that's
VO or Genie have these models encapsulated the physics of that 100%
accurately and right now they're not they're kind of approximations and they
look um realistic when you just casually look at them but they're not uh they're
not accurate enough yet to rely on for say robotics So now we've got these really
interesting models.
Um, and with physics, I think that's going to probably involve generating
loads and loads of ground truth. Simple videos of pendulums, you know, what
happens when two pendulums go around each other, but then very quickly you
get to like three body problems which are not solvable anyway. So I think it's
going to be interesting. But what's amazing already is when you look at the
the video models like VO and just the way it treats reflections and liquids
it's pretty unbelievably accurate already,, at least, to, the, naked, eye., So
the next step is actually going beyond what a human can amateur can perceive
and uh would it really hold up to a proper physicsgrade experiment? I know
you've been thinking about these simulated worlds for a really long time
and uh I went back to the transcript of our first interview and in it you said
that you really like the theory that consciousness was this consequence of evolution
>> um, that, you, know, at, some, point, in, our evolutionary past there was like an
advantage to understanding the internal state of another and then we sort of
turned it in on ourselves. >> Does, that, make, you, curious, about, running
sort of an a agent in evolution inside of a simulation? Sure. Um, I mean, I'd
love to run that experiment at some point. Kind of re rerun evolution, rerun
um almost social dynamics as well. Like the the Santa Fe used to run lots of
cool experiments on little grid worlds. I used to love some of these, but
they're mostly economists and they were trying to like, you know, run like
little uh artificial societies and they found that things all sorts of
interesting things got invented like that uh if you let agents run around for
long enough with the right incentive structures. markets and banks and all
sorts of crazy things. So I think it would be really cool and also just to
understand the origin of life and the origin of consciousness. And I think
that is the one of the big passions I had for for working on AI from the
beginning was I think you're going to need these kinds of tools to really
understand where we came from and what these phenomena are. Um, and I think
simulations is is is one of the most powerful tools to do that because you
can then do it statistically because you can run the simulation many times with
control slightly different initial starting conditions and then um maybe
run it millions of times and then understand what the slight differences
are in a very uh controlled experiment sort of way which of course is you know
very difficult to do in the real world for any of the really interesting
questions we want to answer. So I think accurate simulations will be an unbelievable boon to science
>> given, you, know, what, we've, discovered about sort of emergent properties of
these models right having sort of conceptual understanding that we weren't
expecting do you also have to be quite careful about running this sort of simulation
>> uh, I, think, you, would, have, to, be, yes, but that that's the other nice thing about
simulations you can run them in you know pretty safe sandboxes maybe eventually
you want to air gap them uh and you can of course monitor what's happening in
the in the in the simulation 24/7 uh and you have access to all the data.
So we may need AI tools to help us monitor the simulations because um
they'll be so complex they'll be and there'll be so much going on in them. If
you imagine loads of AIs running around in a simulation uh uh it will be hard
for any human scientist to keep up with it on but we could probably use other AI
systems to help us analyze and flag anything interesting or worrying in
those simulations uh automatically. I mean this I guess we're still talking
sort of medium to long term in terms of this stuff. So so just going back to the
trajectory that we're on at the moment. Um I also want to talk to you about the
the impact that AI and AGI are going to have on on wider society.
>> Um, and, last, time, we, spoke, you, said, that you thought AI was overhyped in the
short term but underhyped in the long term. >> Um, and, I, know, that, this, year, there's
been a lot of chatter about an AI bubble. >> Yes.
>> What, happens, if, there, is, a, bubble, and, it bursts? What happens? Well, look, I I
think yes that I still subscribe to it's overhyped in the short term still and
still underappreciated in the in the medium to long term what's going to you
know how transformative it's going to be. Um yeah, there is a lot of talk of
course right now about AI bubbles. Um in my view uh I I think it there isn't it's
not one thing binary thing are we or aren't we? I think there are parts of
the AI ecosystem that are probably in bubbles. What one example would be, you
know, just seed rounds for startups uh that basically haven't even got going
yet and they're raising at tens of billions of dollars uh valuations just
out of the gate. It's sort of interesting to see how how can that be
sustainable? Um you know, my guess is probably, not, uh, at least, not, in, general.
Um so there's that area. Then the people are worrying about obviously there's
there's the big tech valuations and other things. I think there's a lot of
real business underlying that. So um but it remains to be seen. I mean I think
maybe for any any uh new unbelievably transformative and profound technology
of which of course AI is probably the most profound. Uh you're going to get
this uh overcorrection in a way. So when we started Deep Mind no one believed in
it. No one thought it was possible. People were wondering what's AI for
anyway. And then now fast forward 10 15 years and now obviously it seems to be
the only thing people talk about in business. And um so it's a but you're
sort of going to get it's almost an overreaction to the underreaction. Um so
I think that's natural. I think we saw that with the internet. I think we saw
with mobile and I think we're we're seeing or going to see it again with AI.
Um I don't worry too much about are we in a bubble or not because from my
perspective as you know leading Google deep mind and also obviously with Google
as as and alphabet as a whole our job and my job is to make sure either way we
uh are come out of it very strong and I think and we're very well positioned and
I think we are tremendously well positioned either way. So if it continues going like it is now
fantastic. we'll carry on, you know, all of these great things that we're doing
and experiments and progress towards AGI. If there's a retrenchment, fine.
Then also, I think we're in a great position because, uh, we have our own
stack with TPUs. We also have, um, all these incredible Google products and
you know, the profits that all makes to plug in our AI into. And we're doing
that with search is totally revolutionized by AI overviews, AI mode
with Gemini under the hood. We're looking at workspace at email, you know
at YouTube. So, there's all these amazing things in Chrome. There's a lot
of these amazing things that um AI we can see already are lowhanging fruit to
apply uh Gemini 2 as well of course as Gemini app which is doing really well as
well now and and and the idea of universal assistant. So, there's new
products and I think they will in the fullness of time be super valuable, but
we don't have to rely on that. we can just power up our existing uh ecosystem.
Uh which is all sort of I think that's what's happened over the last year.
We've got that really efficient now. >> In, terms, of, the, the, AI, that, people, have
access to at the moment, I I know you said recently how important it is not to
build AI to maximize user engagement just so we don't repeat the the mistakes
of social media. But I but I also wonder whether we are already seeing this in a
way. I mean people spending so much time talking to their chat bots that they end
up kind of spiraling into self-radicalizing. Yeah. >> Um,, how, do, you, stop, that?, How, do, you
build AI that that puts users at the center of their own universe, which is
sort of the point of this in a lot of ways, but without creating echo chambers of one?
>> Yeah,, it's, a, very,, you, know,, um,, careful balance that, you know, I think is one
of the most important things that we as an industry have got to get right. So I
think we've seen what happens with uh you know some systems that were overly
syopantic or you know then you get these these sort of echo chamber
reinforcements that are really bad for the person. So I think part of it is and
actually what we want to build with with Gemini and I'm really pleased with the
Gemini 3 persona that we had a great team working on and I helped with too
personally is um just this sort of almost like a scientific uh personality
that's um it's warm, it's helpful, it's light, but it's it's it's succinct to
the point and it will push back on things in a friendly way that don't make
sense. you know, rather than trying to reinforce you, you know, the idea that
the earth's flat and you said it and it's like wonderful idea, you know, I
don't think that's good in general for society if that were to happen. Um, but
you got to balance it with what people want cuz people want uh these systems to
be supportive um to be helpful with their with with their ideas and their
brainstorming. So, you've got to get that balance right. And I think I think
we are we're sort of developing a science of of personality and persona of
like how to to to kind of measure what it's doing and where do we want it to be
like on authenticity on humor you know these sorts of things. And then you can
imagine there's a kind of base personality that it ships with. And then
everyone has their own preferences. You know do you want it to be more humorous
less humorous or or more succinct or more verbose? People like different
things. So you add that additional personalization layer on it as well. But
there's still the core base personality that everyone gets, right? Which is
trying to try and adhere to the scientific method, which is the whole
point of these. And we want people to use these for science and for medicine
and health issues and so on. Uh and so um I think it's it's it's part of the
science of getting these uh large language models right. And um I'm I'm
quite happy with the direction we're going in currently. uh we got to talk to Shane Le a couple
weeks ago um about uh AGI in in particular across everything that's
happening in in AI at the moment the language models the world models you
know and so on what's closest to your vision of AGI I think actually the
combination of obviously there's Gemini 3 which I think is very capable but the
Nano Banana Pro system we also launched last week which is an advanced version
of our image creation tool what's really amazing about that it has also Gemini
under the hood. So, it can understand not just images, it sort of understands
uh what's going on semantically in those images. Uh and people have been only
playing with it for a week now, but I've seen so much cool stuff on on social
media about uh what people are using it for. So for example um you know you can
give it a picture of a of of a of a complex plane or something like that and
it can label all the diagrams of uh you know all the different parts of the
plane and even visualize it in in a for like with all the different parts sort
of exposed. Um so it has some kind of deep understanding of mechanics and and
what make what you know makes up parts of objects what's materials. So it's a
sort of um and it can you know render text really really uh accurately now. So
I think that's sort of um it's getting towards a kind of AGI for imaging. Um I
think it's uh a kind of general purpose system that can do anything across
images. So I think that's very exciting. And then the advances in in world
models, you know, Genie and Simma and what we're doing there. And then
eventually we got to kind of converge all of those different they're kind of
different projects at the moment and they're they're they're intertwined but
we need to you know converge them all into one one big model and then that
might be start becoming you know candidate for protoagi.
>> I, know, you've, been, reading, quite, a, lot about the industrial revolution
recently. Um, are there things that we can learn from what happened there to
try and mitigate against the the sort of some of the disruption that that we can expect
>> AGI, comes? >> I, think, there's, a, lot, we, can, learn., It's
it's something you sort of study in school, at least, in, the, in, in, Britain,
but but on a very superficial level like it was really interesting for me to look
into how it how it all happened, what it started with, the reasons behind the
economic reasons behind that which is like the textile industry and then the
first computers were really the sewing machines, right? And then they became
punch cards for the early forran computers, mainframes. And for a while
it was very successful in Britain became like the center of the the textile world
because they could make these amazingly high quality things for very cheap uh
because of the automated systems. Um and then obviously the steam engines and all
of those things came in. I think there's a lot of um incredible advances that
came out of industrial revolution. So um child mortality went down and all of
modern medicine uh and and um sanitary conditions, the kind of work life uh uh
split and how that all worked was kind of worked out during the industrial
revolution. But it also came with a lot of challenges like in it took quite a
long time um roughly a century and um different parts of the labor force were
dislocated at certain times and then new uh things had to be created new
organizations like unions and other things had to be created in order to
rebalance that. So like it was fascinating to see the whole of society
sort of had to over time adapt and then you've got the modern world now. So
there were I think there were lots of obviously pros and cons of the
industrial revolution why it was happening but no one would want if you
think about what it's done in total like abundance of you know people you know of
food and in the western world and and modern medicine and all these things
modern transport um that was all because of the industrial revolution. So, we
wouldn't want to go back to pre-industrial revolution, but maybe we
can figure out ahead of time by learning from it what those dislocations were and
maybe mitigate those um earlier or more effectively this time. And we're
probably going to have to because the difference this time is that it's
probably going to be 10 times bigger than industrial revolution and it'll
probably happen 10 times faster. So more like a decade then unfold over a
decade than a century. One of the things that Shane told us was that that the
kind of current economic system where you know you exchange your labor for
resources effectively, it it just won't function the same way in a post AGI
society. Do you have a vision of of how society should be reconfigured or might
be reconfigured in a way that works? >> Yeah,, I'm, spending, more, time, thinking
about this now and Shane's actually leading an effort here on that to sort
of think about what a post AGI world might look like and what we need to
prepare for. But I think society in general needs to spend more time
thinking about that. Economists and social scientists and governments
because, I, I I, as, with, the, industrial revolution you know the whole working
world and working week and everything got changed from from pre-industrial
revolution war agriculture and I think that's going to at least that level of
change is going to happen again. So it's not surprising. I don't would not be
surprised if we needed new economic systems, new economic models to uh to
basically um help with that transformation and make sure for example
the benefits are widely um distributed and maybe things like universal basic
income and things like that are part of the solution. But I don't think that's
the complete uh I think that's just what we can model out now, right? Because
that would be a almost an add-on to what we have today. But I think there might
be something way better systems where um more like direct democracy type systems
where you can you know vote with a certain amount of of credits or
something for what you want to see. It happens actually on local uh community
level. You know here's a bunch of money. Do you want a playground or a tennis
court or an extra classroom on the school? And then you let the community
um sort of vote for it, right? So, and then and then maybe you could even
measure the outcomes and then and then the people that sort of consistently
vote for the for for things that that end up being um more wellreceived, they
they have proportionally more influence for the next vote. So, there's there's a
lot of interesting things I hear, you know, economist friends of mine who are
are kind of brainstorming this and I think that would be great if we had a
lot more work on that. And then there's the philosophical side of it of like
okay so jobs will change and other things like that but then um but maybe
we'll have fusion will have been solved and so we have this sort of abundant
free energy so we're post scarcity so what happens to money um maybe
everyone's better off but then what happens to purpose right because a lot
of people get their purpose from you know their jobs and then providing for
their families uh which is a very noble purpose so if that's you know so there's
a lot of I I think some of these questions blend from economic questions
into almost philosophical questions. >> Do, you, do, you, worry, that, people, don't
seem to be paying attention sort of or moving as quickly as you'd like to see?
What would it take for for people to sort of recognize that we need
international collaboration on this? I am worried about that and I wish that
and and again in a sort of ideal world there would have been a lot more
collaboration already and international specifically uh and a lot more research
and and sort of um I guess exploration and discussion going on about these
topics. I'm actually pretty surprised there isn't more of that being discussed
given that you know even our timelines which were there are some very short
timelines out there but even ours are 5 to 10 years which is not long for for
for for institutions or things like that to be built to to handle this. Um and
one of the worries I have is that the institutions that do exist they you know
seem to be very fragmented and not very influential to to the level that you
would need. Um, so it may be that that that that there are there aren't the
right institutions to deal with this currently. And then of course if you add
in the geopolitical tensions that are going on at the moment around the world
it seems like collaboration, cooperation is harder than ever. Um, just look at
climate change and and um how hard it is to get any agreement on anything to do
with that. So um so we'll see. I think as the stakes get higher and as these
systems get more powerful and maybe this is one of the benefits of them being in
products is uh the the you know everyday uh person that's not working on this
technology will get to feel the increase in the power of these things and the
capability and so that will then reach government and then maybe um uh they'll
see sense as we get closer to to AGI. >> Do, you, think, it, will, take, a, moment, an
incident for everyone to sort of sit up and pay attention?
>> I, don't, know., I, mean,, I, hope, not., Most of the main labs are pretty pretty
responsible. We try to be as responsible as possible. You know, that's always
something we've, as you know, if you followed us over the years, that's been
at the heart of what everything we do. Doesn't mean we'll get everything right
but we try to be as thoughtful and as scientific in our approach as possible.
Um, I think most of the major labs are are trying to be responsible. Also
there's good commercial pressure actually to be responsible. If you think
about agents, uh, and you're renting an agent to another company, let's say, to
do something, um, that other company is going to want to know what the limits
are and the boundaries are and the guardrails are on those agents, you
know, in terms of what they might do and not just mess up the data and all of
this stuff. So, I think that's good because the pe the more kind of carboy
operations, they won't um get the business because the enterprises won't
choose them. So I think the kind of capitalist system will actually be useful here to reinforce
responsible behavior which is good but then there will be rogue actors um maybe
rogue nations maybe rogue organizations um maybe people building on top of open
source I don't know like obviously it's very difficult to stop that then um
something may go wrong and uh hopefully it's just sort of medium-sized and then
that will be a kind of warning shot to to to humanity across the bow and then
that might be the moment to kind of um advocate for uh international uh
standards or international cooperation or collaboration at least on some the
high level basic or you know kind of like what's the basic standards we we we
would want and and and agree to I'm hopeful that that will be possible
>> in, the, long, term, so, beyond, AGI, and, and towards ASI right artificial super
intelligence do you think that there are some things that that humans can do that
machines will ever be able to manage? >> Well,, I, think, that's, the, big, question
and I feel like this is related to as you know, one of my favorite topics is
cheuring machines. I've always felt this that if we build a GI and then use that
as a simulation of the mind and then compare that to the real mind, we will
then see what the differences are and uh potentially what's special um and
remaining about the human mind, right? Maybe that's creativity, maybe it's
emotions, maybe it's dreaming. There's a lot of consciousness. There's a lot of
um hypotheses out there about what may or may not be computable. And this comes
back to the chewing machine question of like what is the limit of a chewing
machine? And I think that's the central question of my life really ever since I
found out about chewing and chewing machines. And um you know I think that's
that's I fell in love with that. That's my core passion. And I think um
everything we've been doing is been sort of pushing the notion of what a cheuring
machine can do to the limit including you know folding proteins right and so
it turns out I'm not sure what the limit is maybe there isn't one right and of
course the my quantum computing friends would would say there are limits and and
you need quantum computers to do quantum systems but I'm really not so sure and
I've actually you know discussed that with some some some of the quantum folks
and it may that we need data from these quantum systems in order to create a
classical simulation. Um and then that that comes back to the mind which is is
it all classical computation or is there something else going on you know like
Roger Penrose believes you know there's quantum effects in the brain. If there
are then and that's what consciousness is do with then machines will never have
that, at least, the, the, the, classical machines we'll have to wait for quantum
computers. Um but if they if there isn't then there may not be any limit maybe in
the universe everything is computationally tractable and therefore
if you look at it in the right way and therefore chewing machines might be able
to model everything in the universe I I'm currently if you were to get make me
guess I would guess that and I'm working on that basis until physics um shows me otherwise
>> so, there's, nothing, that, cannot, be, done within these sort of computational
>> well, no, one's, put, it, this, way, nobody's found anything in the universe that's
that's non-computable So far >> so, far, >> right?, And, I, think, we've, already, shown
you can go way beyond the the usual complexity theorist P= MP view of like
what a classical computer could do today. Things like protein folding and
go and so on. So I don't think anyone knows what that limit is. And that's
really if you boil down to what we're doing at Deep Mind and Google and what
I'm trying to do is is find that limit. But then in the limit of that though
right, is that in the limit of that idea is that, you know, we're sitting here
sort of there's like the warmth of the lights on our face. We kind of hear the
wear of the machine in the background. There's like the feel of the desk under our hands.
>> All, of, that, could, be >> replicable, by, a, classical, computer.
>> Yes., Well,, I, think, in, the, end,, my, view on this is why I love K as well is all
all, of, all all, of, my, two, favorite philosophy
is a construct of the mind. I think that's true. And so, yes, all of those
things you mentioned, they're coming into our sensory apparatus and they feel
different, right? The light, the warmth of the light, the feel, the touch of the
table, but in the end, they're it's all information. And we're information
processing systems. And I think that's what biology is. This is what we're
trying to do with isomeorphic. That's how I think we'll end up curing all
diseases is by thinking about biology um as an information processing system. And
I think in the end that's going to be and I'm working on my spare time, my 2
minutes of spare time, you know, physics theories about uh things like
information being the most fundamental unit, should we say, of the universe
not energy, not matter, but information. And um so it may be that these are all
interchangeable in the end, right? But we just sense it. We feel it in a
different way. Um but you know, as far as we know, this is still all these
amazing sensors that we have, they're still computable by a chewing machine.
But this is why your simulated world is so important, right?
>> Yes., Exactly., Because, that, would, be, the way to get one of the ways to get to it.
What's the limits of what we can simulate? Because if you can simulate
it, then in some sense, you've understood it. >> I, wanted, to, to, finish, with, some, personal
reflections >> of, of, what, it's, like, to, be, at, the forefront of this. I mean, does the
emotional weight of this ever sort of weigh you down? Does it ever feel quite isolating?
>> Yes., Um,, look,, I, I, don't, sleep, very much, partly because it's too much work
but also I have trouble sleeping. It's very complex emotions to deal with
because it's unbelievably exciting. Um you know, I'm I'm basically doing
everything I ever dreamed of. And we're at the absolute frontier of science on
in so many ways, um, applied science as well as machine learning. And that's
exhilarating as all scientists know that that feeling of being at the frontier
and discovering something for the first time. And that's happening almost on a
monthly basis for us. So, which is amazing. Um, but then of course we as as
and Shane and I and others who've been doing this for a long time, we
understand it better than anybody. Um the enormity of what's coming and this
thing about is still under actually appreciated. In fact, what's going to
happen in more of a 10-year time scale. Um, including to things like the the
phil philosophical uh, you know, what it means to be human, what's important
about that? all of these questions are going to come up. Um, and so it's it's
it's a big responsibility. Um, but we have an amazing team thinking about
these things. Um, but also it's something, I, guess, at least, myself, I've
trained for my whole life. So, you know ever since my early days playing chess
and and then working on computers and games and simulations and neuroscience
it's all been for uh this kind of moment. Um, and it's roughly what I
imagined it was going to be. So, that's partly how I cope with it is just training.
>> Are, there, parts, of, it, that, have, hit, you harder than you expected though?
>> Uh,, yes,, for, sure., On, the, way,, I, mean, even the Alpha Go match, right? Just
seeing you know that how we managed to to crack Go, but Go was this beautiful
mystery and it changed it. And so, that was that was interesting and kind of
bittersweet. I think even the the more recent things of like language and then
imaging and you know what does it mean for creativity uh I I'm you know have
huge respect and passion for the creative arts and having done game
design myself and you know I talked to film directors and it's it's an
interesting dual moment for them too. There's like first on one hand they've
got these amazing tools that speed up prototyping ideas by 10x but on the
other hand um is it replacing certain creative skills? So I think there's
there's sort of these trade-offs going on um all over the place which um I
think is inevitable with something as uh a technology as powerful and as
transformative as as AI is as in the past electricity was and internet and
we've you know we've seen that that is the story of humanity is we are tool
making uh animals and that's what we love to do and for some reason we also
have a brain that can can understand science and do science which is amazing.
but also sort of insatiably curious. I think that's the heart of what it means
to be human. And I think I've just had that bug from the beginning. And my
expression of trying to answer that is is to build AI.
>> When, you, and, the, other, AI, leaders, are, in a room together, is there sort of sense
of solidarity between you that that this is a group of people who all know the
stakes, who all really understand their things, or or does the competition kind
of keep you apart from one another? >> Well,, we, all, Yeah,, we, all, know, each
other. I get on with pretty much all of them. Some of the others don't get on
with each other. Uh and there is it's hard because that we're also in the most
ferocious uh uh capitalist sort of competition there's ever been probably.
You know, investor friends of mine and VC friends of mine who who who were
around in the dotcom era say this is like 10x more ferocious and intense than
that was. In many ways, I love that. I mean, I I live for competition. It's
it's it's it's you know I've always loved that since my chess days but
stepping back uh I understand I hope everyone understands that there's a much
bigger thing at stake than just you know company successes and and and you know that type of thing
>> when, it, comes, to, the, next, decade, when you think about it are there big moments
coming up that you're personally most apprehensive about
>> I, think, right, now, the, systems, are, you know I I call them passive systems you
you put the energy in as the user you know the question or the what's the task
and then they uh these systems kind of provide you with some summary or some
answer. Um so very much it's it's human directed and human energy going in uh
and human ideas going in. The next stage is agent-based systems, which I think
we're going to start seeing. We're seeing now, but they're pretty
primitive. Like in the next couple of years, I think we'll start seeing some
really impressive reliable ones. And um I think those will be incredibly useful
and capable if you think about them as an assistant or something like that, but
also they'll be more autonomous. So I think the risks go up as well uh with
those types of systems. So I'm I'm quite worried about uh what those sorts of
systems will be able to do maybe in two, three years time, you know. So
we're working on cyber defense in preparation for uh a world like that
where maybe there's millions of agents you know, roaming around on the internet.
>> And, what, about, what, you're, most, looking forward to? I mean, is there is there a
day when you'll be able to retire sort of knowing that your work is done or or
is there more than a lifetime's worth of work left to do? >> Yeah,, I, always, Well,, I, I, could
definitely do with sabbatical um and I would spend it doing stuff. Yeah, a week
off for even even a day would be good. Um, but look, I think my mission has
always been to get to kind of help uh the world steward AGI safely over the
line for all of humanity. So, I think when we get to that point, of course
there's then there's super intelligence and there's post AGI and there's all the
economic stuff we were discussing and societal stuff and maybe I can help in
some way there. But I think um that will be my core part of my mission, my life
mission uh will be done if it's a I mean it's only a small job, you know, just
get that over the line or help the world get that over the line. You know, I
think it's going to require collaboration like we talked earlier. Um
and I'm quite a collaborative person. So I hope I can I can help with that from
the position that I have. >> And, then, you, get, to, have, a, holiday
>> and, then, I'll, get, I'll, have, the, Yeah, exactly. a well- earned sbatical.
>> Yeah,, absolutely., Deis,, thank, you, so much. helpful as always.
>> Well,, that, is, it, for, this, season, of, Goo Deep Mind the podcast with me, Professor
Hannah Fry. But be sure to subscribe so you will be among the first to hear
about our return in 2026. And in the meantime, why not revisit our vast
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