---
title: 'Demis Hassabis: Agents, AGI & The Next Big Scientific Breakthrough'
source: 'https://youtube.com/watch?v=JNyuX1zoOgU'
video_id: 'JNyuX1zoOgU'
date: 2026-06-15
duration_sec: 0
---

# Demis Hassabis: Agents, AGI & The Next Big Scientific Breakthrough

> Source: [Demis Hassabis: Agents, AGI & The Next Big Scientific Breakthrough](https://youtube.com/watch?v=JNyuX1zoOgU)

## Summary

Demis Hassabis, CEO of Google DeepMind, discusses the path to AGI, the current state of AI agents, and the future of scientific discovery. He shares insights on what's missing in current AI systems and how to build defensible deep tech startups.

### Key Points

- **Missing Pieces for AGI** [00:00] — Continual learning, long-term reasoning, and aspects of memory are still unsolved and required for AGI.
- **Current Paradigm Components** [01:53] — Large-scale pre-training, RLHF, and chain of thought are likely part of the final AGI architecture, but one or two big ideas may still be missing.
- **Continual Learning and Memory** [03:47] — Current systems use 'duct tape' like large context windows, but true continual learning inspired by the brain's hippocampus and sleep is needed.
- **Reinforcement Learning Underrated** [06:09] — RL and search (e.g., Monte Carlo tree search) from AlphaGo/AlphaZero are being revisited and scaled for general foundation models.
- **Distillation and Small Models** [08:00] — Distillation packs frontier capabilities into smaller models; no theoretical limit yet, and edge models are crucial for privacy and robotics.
- **Agents and Continual Learning** [12:34] — Lack of continual learning holds back agents from full autonomy; they need to adapt to specific contexts to be 'fire and forget'.
- **Reasoning Gaps** [13:27] — Models overthink and make basic errors; introspection and monitoring of chain-of-thought are needed to fix 'jagged intelligence'.
- **Agent Capabilities and Hype** [15:28] — Agents are just getting started; we're in the experimentation phase, and a 'triple-A' game or killer app hasn't emerged yet.
- **Creativity and Invention** [18:37] — Current systems can't invent something like the game of Go from a high-level description; true creativity remains missing.
- **Open Source and Edge Models** [20:20] — DeepMind is committed to open source (e.g., Gemma) for edge devices, balancing openness with strategic advantages.
- **Multimodal Gemini** [22:20] — Gemini's native multimodality is a competitive advantage for world models, robotics, and digital assistants.
- **Inference Cost and Efficiency** [24:03] — Inference will never be free due to Jevons' paradox; physical chip constraints will remain a bottleneck.
- **Virtual Cell and Scientific Breakthroughs** [25:18] — A full virtual cell is about 10 years away; data and imaging challenges remain, but AI will transform many scientific domains.
- **Defensible Deep Tech Startups** [30:40] — Combine AI with deep tech (atoms, materials, medicine) for defensibility; interdisciplinary teams are key.
- **Pattern for AlphaFold-style Breakthroughs** [33:24] — Massive combinatorial search space, clear objective function, and enough data/simulator are the recipe.
- **AI for Scientific Discovery** [35:32] — AI hasn't yet made a truly novel discovery; the 'Einstein test' (recreating 1905 physics) is a benchmark for creativity.
- **Advice for Builders** [38:14] — Pursue hard, deep problems; consider AGI appearing mid-journey; build systems that leverage specialized tools.

### Conclusion

Demis Hassabis emphasizes that while current AI systems have made remarkable progress, key capabilities like continual learning, true reasoning, and creativity are still missing. He advises builders to tackle deep tech problems and plan for AGI's arrival within their journey.

## Transcript

continual learning, long-term reasoning,
[music] uh some aspects of memory, these
are still unsolved. I think all of these
are going to be required for AGI.
Depending on what your AGI timeline is,
you know, mine's like 2030 or something
like this, then [music] if you start off
on a deep tech journey today, you have
to just consider AGI appearing in the
middle of that journey. It's not bad
necessarily, but you have to take that
into account. [music] You have to have
an active system uh that can actively
solve problems for you to get to AGI.
So, agents are that path, and I think
we're just getting going.
Demis Hassabis has had one of the most
unusual careers in tech. He was a chess
prodigy as a kid, then designed his
first hit video game, Theme Park, at 17.
He then went back to school, got a PhD
in cognitive neuroscience, published
foundational work on how memory and
imagination work in the brain, and then
in 2010 co-founded DeepMind with one
mission, solve intelligence.
And I think they've done it.
Since then,
uh his lab has gone on to do things most
people thought were decades away.
AlphaGo beat a world champion at Go.
AlphaFold cracked protein structure
prediction, a 50-year grand challenge in
biology, and they gave it away for free
to every scientist on Earth.
That work won him the Nobel Prize in
chemistry last year. Today, Demis leads
Google DeepMind, where he's building
Gemini and pushing toward the same goal
he set when he was a teenager,
artificial general intelligence. Please
welcome Demis Hassabis.
So, you've been thinking about AGI
longer than almost anyone. Uh when you
look at the current paradigm,
large-scale pre-training, RLHF, chain of
thought, how much of the final
architecture for AGI do you think we
already have, and what's fundamentally
missing right now? Well, first of all,
thank thanks, Gary, for that great
introduction, and it's great to be here.
Thanks for for welcoming here. It's
amazing space, actually. I'll have to
come back here often. Very inspiring
that you all get to work in in in this
space. So, the question is I think the
the components that you just mentioned,
I'm pretty sure will be part of the
final architecture for AGI. So, I think
they've come such a long way now, uh and
we've proven out so many things about
what they can do.
Uh I can't see a world in which we'll
sort of realize in a couple of years
this was a dead end. That doesn't make
sense to me. But, there still might be
one or two things missing on top of uh
of of of what you've you know, what we
already know works. So, um
continual learning, long-term reasoning,
uh some aspects of memory, these are
still unsolved. Um and how to get the
systems to be more consistent across the
board. Um I think all of these are going
to be required for AGI. Now, it might be
that the existing techniques can just
scale up to that with some innovation
and some incremental innovation. Um but,
it could be that there's still one or
two big ideas left uh that need to be
cracked. I don't think it's more than
one or two if there are out there. And I
think, you know, my betting is uh about
50/50 if that's the case. So, of course,
at DeepMind at Google DeepMind we work
on both those things. I guess that's all
I mean, working with a bunch of
identical systems. The wildest thing to
me is to what degree It's the same
weights ev- over and over. So, this idea
of continual learning is so interesting
because like you know, right now we're
sort of cobbling it together with duct
tape, you know? Yes. These dream cycles
at night and things like that.
>> Yeah. It's pretty cool, the dream
cycles, and we we used to think about
this with consolidation with episodic
memory. It's actually that's what I
studied for my PhD is how the
hippocampus works and integrates, you
know, new knowledge gracefully into the
existing knowledge base. So, the brain
does that amazingly well. It it it does
it through you know, during sleep uh
especially things like REM sleep,
replaying back episodes that that are
important so that you can learn from it.
In fact, our very first Atari program
DQN, one of the ways it was able to
master Atari games was by doing
experience replay. So, we sort of
borrowed that from from neuroscience and
replayed successful trajectories
uh many times, you know, that's
way back in 2013 now in the in the dark
ages of AI. It was uh a really important
thing. And and I agree with you, we're
kind of using duct tape right now. So,
like shove it all in the context window.
Um this but this seems a bit
unsatisfying, right? And actually, even
though uh we're working on machines, not
biological brains, and so you
potentially you could have, you know,
millions or tens of millions size
context window or memory, and it can be
perfect, there's still a cost to looking
it up and finding the right thing uh
that that's actually relevant for the
specific uh decision you've got to make
right now. And that's non-trivial that
cost, even if you can potentially store
it all. I think there's actually a lot
of room for innovation in in areas like
memory. Yeah. I mean, the one thing is
like it feels like a million
token context one is actually bigger
than I mean, it's plenty big, honestly.
You can do so.
It's plenty big for for for most things
that it should be used for. I mean, if
you think about the context window is
sort of equivalent to working memory,
you know, humans have we have like a few
digits, you know, it's like a a dozen
digits maybe, you know, average of
seven. We got million or, you know, 10
million context windows, but the problem
is is that we're trying to store
everything in that. You know, things
that aren't in not important, things
that are wrong. It's pretty brute force
currently, and that doesn't seem uh
right. And then the problem is if you're
an agent trying to try and process live
video, and you're just going to naively
record all the tokens, then actually a
million tokens isn't that much. It's
only like 20 minutes. So, actually you
need more if you want something that's
going to understand your, you know, your
what's going on in your life over maybe
a month or two. DeepMind has
historically leaned into reinforcement
learning and search.
AlphaGo, AlphaZero, and MuZero. How much
of that philosophy is actually embedded
in how you're building Gemini today? Is
RL still underrated? Yeah, I think
potentially it is. It sort of goes in in
abs and way waves. You know, we've
worked on agents since the beginning of
DeepMind. In fact, we also That's what
we said we were working on. And so, all
of the Atari work and AlphaGo, most
specifically, they're agent systems. And
what we meant by that is systems that
are able to, you know, accomplish goals
on their own.
And make active decisions and and make
plans. And so, of course, we were doing
it in the domain of games to to to make
it tractable. And then doing
increasingly complex games, things like
StarCraft after AlphaGo, AlphaStar. So,
we basically did all the games that are
out there.
And then of course, the question is can
you generalize those models to be world
models or models of language, not just
models of simple games or even complex
games. And that's what the last few
years has been about. But really, you
can think of a lot of the things we're
doing today, all the leading models with
thinking modes and chain of thought
reasoning as aspects of what was sort of
pioneered with AlphaGo coming back now.
And I actually think there's a lot of
work we did back then that is relevant
today. And we're sort of re-looking at
some of those old ideas
at scale today in a more general way.
Including things like Monte Carlo tree
search and other other ways of doing
augmenting the RL
on top of the the reinforcement learning
we're ready to do today. And I think a
lot of those ideas both from AlphaGo and
AlphaZero are really really relevant to
to where we are with today's foundation
models. And I think a lot of that is
what we're going to see of the advances
the next few years. One question I would
have like obviously today you need
bigger and bigger models to be smarter
and smarter, but then we're also seeing
distillation working and then smaller
models can be like quite a bit faster. I
think you know, you guys have incredible
flash models that are like nine like
you're finding that they're 95% as good
as the frontier and at like 1/10 the
price. Is that right? I think that's one
of our core strengths is I mean you have
to build the biggest models to to to to
have the frontier capabilities, but I
think one of our biggest strengths has
been
distilling and packing that power into
smaller and smaller models very quickly.
Obviously we we you know, we invented
the kind of distillation process and and
people like Jeff and Oriol and and
others and we're still world experts in
that. And we also have a huge need to
do it because we've got to serve the
biggest probably AI surfaces there are.
Obviously there's search with AI
overviews and AI mode and there's Gemini
app and now increasingly every single
product at Google has you know, maps and
YouTube and so on has some aspect of
Gemini or Gemini related technology in
it. And so that's billions of users a
dozen more than a dozen billion user
products
and they have to be served extremely
fast, extremely efficiently and cheaply
and with low latency. So that that gives
us a really important incentive to to
make these flash and even smaller models
flashlight models extremely efficient.
And hopefully that ends up then being
really useful for many of the workloads
that all of you use for. I'm curious
about how much smarter these smaller
models can actually be. Like, are there
limits to the distillation process?
Like, could a 50B or 400B model be as
smart as like a mythos for today? Yeah,
I didn't I didn't see any I don't think
we've got to any kind of or at least
none of us know yet if we've got to any
kind of informational limit. I mean,
maybe at some point that will be the
case where there's just an information
density that can't we can't get beyond.
But, I think for now there's the
assumption we make is that you know, a
year later one of our
leading, you know, pro models or
frontier models goes out, half a year
later, a year later you'll have them in
the the really tiny almost edge models.
And you'll also see some of that
goodness in our Gemma models, which
hopefully you're all enjoying our Gemma
four models, which I think are really
amazing power for their sizes. So,
again, that uses a lot of this these
distillation techniques and and the idea
of how to make things really efficient
in these very small models. So, I don't
really see any limit yet in terms of
like some kind of theoretical limit. I
think we're still pretty far off of
that. That's a mean I mean that is
really good.
>> Yes.
Uh you know, one of the weirder things
that we're seeing right now is like
engineers can do like 500 to 1,000 times
the amount of work that they were doing
like 6 months ago, I guess. I mean, the
people in this room there are people who
are doing about like a thousand X the
work that like I Steve Yegge talks about
this. It's like a thousand X the work
that a Google engineer from the 2000s
was doing. I think it's very exciting. I
mean, I think models have many uses. One
is obviously cost, but the speed can
allow, you know, if you think about
coding even or other things, you can
iterate a lot faster. Also, especially
if there's if you're collaborating with
the system. I think there's a there's a
a lot of need for having fast systems
that maybe are not quite frontier. Like
you said, like 95% 90%, but that's
plenty good enough and actually gain
back more than the 10% on the the
iteration speed. So, and then the other
big thing I think is running these
things on the edge. Again, for
efficiency reasons, but also for privacy
and security reasons, too. Um if you
think about different devices that you
might run these systems on that that,
you know, process very personal
information, can also think about
robotics, as well. Um you know, robots
in your house. I think you're going to
want very efficient, uh very powerful,
uh local models, which maybe are
orchestrated
you know, with some bigger models,
frontier models that are in the in the
cloud, but you only delegate to that in
certain circumstances. And perhaps you,
you know, you process all of the
audio-visual feed, let's say, locally,
and that stays local. I could imagine uh
that would be a very good sort of um end
state. Y Combinator Startup School is
back. We're hand-selecting the most
promising builders in the world and
flying them out to San Francisco on July
25th and 26th to discuss the cutting
edge of tech. Apply now for a spot.
Okay, back to the video. Going back to
context and memory, models currently
stateless, but, you know, continue like
what would the developer experience even
be like for someone who's using a
continual learning model? Like, you
know, any idea like how you'd steer it?
I think it's really interesting. I think
that's one of the not having continual
learning currently is one of the things
holding back agents from doing full
uh tasks, you know? I think they're
really useful for aspects of tasks right
now, and you can patch them together and
do some really cool things, but they
don't adapt well with the context that
you're in. And I think that's the
missing piece for them being really kind
of fire and forget, and they'll figure
it out themselves. You know, I think
they need to be able to learn um about
the specific context um that you're
going to put them in. So, um I think we
have to crack that to get full general
intelligence. Where are we on reasoning?
So, models can do really impressive
chain of thought now, but they still
fail on things a smart undergrad
wouldn't. What specifically needs to
change and what progress do you expect
in reasoning? There's a lot of
innovation left in in think the thinking
paradigms, I would say. Again, I think
we're fairly we're doing fairly
simplistic things, fairly brute force.
One could imagine
I think there's a lot of scope for
example in monitoring the chain of
thought, maybe interjecting midway
through a thought process. I often get
the impression with our systems and and
our competitor systems that they're
almost overthinking. They're almost
getting into sort of loops of things.
Like one thing I sometimes like to do is
is play chess against Gemini. And you
know, it's the all the leading
foundation models are pretty poor at
games, which is quite interesting. It's
very
cool to kind of look at the thinking
traces cuz obviously these are can be a
well-understood. You know, I can tell
quite quickly if it's going off on a
tangent and it's very sort of provable
what the what the the thinking is doing,
whether it's useful or not. And so, what
we see is that, you know, sometimes it
will it will it will consider a move. It
will realize it's a blunder, but it
can't find anything better, so it kind
of goes back to that move and does it
anyway. So, it you know, you just
shouldn't be seeing that
happening in a in a very precise
reasoning system. So, there's just sort
of huge gaps, I think, still, but it may
only be one or two tweaks that are
required to fix those kind of gaps just
to be clear, but I think that's pretty
pretty obvious they're all there. And
that's why you get this kind of jagged
intelligence. You know, on the one hand,
it can solve gold medal problems in IMO,
which is super hard, but on the other
hand, as we've all seen, it can still
make basic elementary maths errors if
you pose the question in a certain way,
right? So, or elementary reasoning
errors. So, there's just something to me
about the almost an introspection about
its own thought process that I feel like
there's there's something maybe missing
there. Agents are really big. Some would
say they're hyped. I personally think
they're just getting started. It's
[laughter] totally insane. What does
DeepMind's internal research tell you
about where agent capabilities actually
are right now versus, you know, sort of
the hype out there? I think we are I
agree with you. I think we're just at
the beginning. You have to have an
active system that can actively solve
problems for you to get to AGI. That was
always clear to us. So, agents are that
path and I think we're just getting
going. I think all of us are getting
used to how do we best work and you're
leading the way in a lot of this in your
own personal experiments. I'm sure many
of you are doing that. I think how do
you incorporate it into your
workflow in a way that isn't just sort
of a nice to have, but actually starting
to do fundamental things. My I think My
impression is at the moment we're all
experimenting we're experimenting a lot
of things, but we're only in the maybe
the last couple of months starting to
find the really valuable places. And the
technology probably only getting good
enough for that to be the case, right?
Where that it's not a kind of toy nice
demonstration, but actually really
adding value to your to your to your
time and efficiency.
I had often wondered I see a lot of
people working on
like setting off, you know, dozens of
agents for like 40 hours, but I'm not
sure I've seen the output that yet of
that quite justify that level of input
going in, but I think it will come. So,
I still think we're in the
experimentation phase. We haven't seen a
triple-A game that tops the App Store
charts that was sort of vibe coded yet,
right? I've seen and I've programmed and
I'm sure many we've all done little nice
demonstrations and it's like amazing. I
can do a prototype of theme park in half
an hour now which took me six months
back when I was 17. It's kind of
mind-blowing and I and I wish I I got
this feeling if I spent the whole summer
working on it, you could make something
really incredible, but it still needs
craft and, you know, human sort of soul
into it and taste. I think that's that's
something that can that's you have to
make sure you still bring that to to
whatever it is you're building. And I
think it still shows like it's not quite
there yet because why haven't we seen
a kid making a hit game that's that
sells 10 million copies, right? That
should be possible given the effort
that's gone in. So something's still
somehow missing. Maybe it's to do with
the process, or maybe it's to do with
the tools. I'm not quite sure. You will
probably know better than me cuz I'm
sure you're all experimenting on that.
But I haven't seen the result yet which
I would expect once this is really
delivering that full value. Which I
think will come in the next 6 to 12
months. Some of it is like how much of
it will be autonomous versus I mean, I
don't think we'd see autonomous first.
We would actually probably see people in
this room operating at 1000X, and then
That's what you should see first, and
then many of you, you know, they'll be
like games companies or, you know, other
types of companies that have built some
kind of best-selling app, best-selling
game using these tools. That's what you
should see first, and then more of that
will get automated. I mean, some of it
is like there's a human in there, and
then the human doesn't want to say that
the the the agents did it yet. I think
part of it might be though that um this
if we want to discuss like creativity,
what I often say about that is like if
we look at the things we've done like
AlphaGo. So obviously very famously
you'll all know about the move 37 in
game two, and for me I was waiting for a
moment like that to start the science
projects like AlphaFold. We started
AlphaFold like the day we got back from
Seoul, which is 10 years ago now. I'm
going to Korea after this to celebrate
the 10-year anniversary of AlphaGo. But
it's not enough to come up with move 37.
Like that's pretty cool, very useful,
um but can it invent go?
That's what I want a system that can
invent go if you give it a high-level
description, you know, like a game you
can learn the rules of in 5 minutes, but
it takes a many lifetimes to master.
It's beautiful aesthetically,
um but you can play it in a few hours in
an afternoon. So, you know, maybe you
could imagine that would be the
high-level description I would give and
then I'd want the the return the thing I
get back is go.
Right? And um clearly today's systems, I
think can't do that. So, the question is
why? Um and I think there's something
still missing there. Well, someone in
this room might might make it.
>> Then the answer would be there's nothing
missing. It just was the way we were
using the systems. And that might
actually be the answer. It might be that
today's systems are capable of that with
a brilliant enough creative person using
it and providing that impetus that the
soul of the project and being able to
probably being
au fait enough with the tools to like
almost be at one with the tools. I could
imagine that would be happening if you
experimented with the tools all day and
all night like probably many of you are
doing that and you combine that with
proper deep creativity.
Um something, you know, more incredible
could be done. Switching gears to open
source, I mean or open open and open
weights. I mean, the recent release of
Gemma, you're making highly capable open
and accessible ones that can actually
run locally. What do you think that
means for you will AI be something that
is in the hands of the users instead of
primarily in the cloud? And does that
change who gets to, you know, build with
these models? We're huge proponents of
in general of open source and open
science. And you mentioned AlphaFold at
the beginning, you know, we put that all
out there for free. And all of our
science work even still today we publish
in, you know, the big journals. We
wanted to create uh world-leading models
for their their sizes. Right? And so,
that's what we hopefully we've done with
Gemma. And we're, you know, very
committed to that path. And hopefully
you will experiment and build and and
enjoy and using Gemini. I think it's
been like 40 million downloads now and
uh it's just in you know 2 and 1/2
weeks. So we're really excited about
that. And I also think it's important
for there to be Western stacks on open
source. You know, obviously a lot of the
Chinese models are excellent and and
they're currently well well leading in
open source and we think Gemini is very
competitive for its sizes
uh in in all those respects. And for us,
I mean there is a question of resources,
talent, and compute. Like nobody has
enough spare compute to just make two,
you know, uh frontier models at maximum
size, right? With different attributes.
So that's pretty difficult. But also for
what for now what we've we've decided is
that our edge models, the things we want
to use for Android and glasses and
robotics, um it's best that they're open
models because they're vulnerable anyway
on the once you put them out on the
surfaces. So they might as well be
actually fully open, right? So we've
sort of made a decision to kind of unify
that
uh at the at the kind of we call it nano
size level. So that actually works for
us uh strategically as well. Um and you
know, we hope as many people as possible
build on it. And of course, we'll be
building on that, too. Earlier uh before
we came on, I got to show you a demo of
uh my version of Samantha from Her,
which is Yes. uh harrowing for me to try
to demo something to you. Yeah, very
good. Um and it worked, which is
amazing. Gemini was built multimodal and
I spent a lot of time with a bunch of
the models and I mean the depth of the
context and the tool use with speech
directly to model, like there's nothing
like bar none, like the best one
actually.
>> Yeah. Yeah, I think I think that's the
sort of still a slightly
underappreciated aspect of of of the
Gemini series is we we started it being
multimodal from the start. That made it
a little bit more difficult actually to
begin with cuz then just focusing on
text, for example. But I we believe
we're going to gain from that in the
long run. And I think we're seeing that
now for
things like world model building, so
stuff like Genie that we build on top of
Gemini. I think it's going to be really
important for things like robotics. So
this is why Gemini robotics which many
of you probably played around with, I
think it's going to be built on
multimodal foundation models, the
robotics models. And we think we have a
sort of competitive advantage with with
Gemini being so strong at multimodal.
We're using it increasingly in things
like Waymo. Um but also if you imagine
devices and assistants that digital
assistants that come with you into the
real world, you know, maybe on your
phone or glasses or some other device,
it needs to understand the physical
world around you and intuitive physics
and and the and the physical context
you're in. And that's what our systems
are extremely good at and I think you
found that's why you've enjoyed using it
in your setup. We're planning to
continue on that and I think we're
far and away the strongest models on on
those types of
problems. So the cost of inference is
dropping fast. What becomes possible
when inference is essentially free and
how does that change what your team is
actually optimizing for? Yeah, I'm not
sure inference
will ever be essentially free. I mean
there's sort of Jevons' paradox and
other things about like I think we'll
just end up using all of us will end up
using whatever we can get our hands on
and you could imagine
millions of agents, swarms of agents
working together on things. So that's
one way to use the inference or you
could imagine
single agents or smaller groups of
agents thinking for in multiple
directions and then ensembling that. So
we're experimenting with all these
things, probably many of you are. All of
that will use up any inference I think
that's available. I mean one day maybe
it can be almost cost zero, certainly
the energy if we solve fusion or you
know, superconductors or you know,
optimal batteries or some set of those
things which I think we will do with
material science, And energy costs will
be essentially zero, but there'll still
be the physical creation of the chips
and other things. There'll There'll be
some bottleneck um at least for the next
few decades, I think. And so if that's
the case, there'll still be rationing on
the inference side. You still have to
use it, I think, efficiently. Yeah.
Well, luckily the smaller models are
getting smarter and smarter, which is
fantastic. Uh we got a lot of bio and
biotech founders in the audience. I can
see a few. AlphaFold 3 took us beyond
proteins to a broad spectrum of
biomolecules. Uh how close are we to
modeling full cellular systems, or is
that still a fundamentally harder
problem in a class of its own? Well, I
Isomorphic Labs, which we spun out from
from from from DeepMind after we did
AlphaFold 2,
um it's it's which is going amazingly
well. It's it's it's trying to build out
uh not just AlphaFold. It's just one
piece of the drug discovery process, uh
as many you know, but we're trying to do
the the adjacent biochemistry and
chemistry to design the right compounds
with the right properties and so on.
We'll have some big announcements for
you know, very soon to talk about on the
on that front. I think that's going
really well. Eventually, you want a
whole virtual cell. So I've talked about
this in many of my science talks about a
full working simulation of a cell that
you can perturb, and then the you know,
the the outputs of that would be close
enough to experimental that it's useful,
right? You could skip out a lot of the
the search steps and generate lots of
synthetic data to train other models
that then would predict things about,
you know, real cells. And um
I think we're about 10 years away
probably from something like a virtual
cell, like a full virtual cell. You
know, we're starting out This is we're
working on the DeepMind side, science
side, on a you know, virtual nucleus,
cell nucleus first cuz relatively
self-contained. The trick with all of
these things is can you pick uh a slice
of the complexity, you know, eventually
you want to want to model a human body,
but can you model it down to the right
level of detail and what slice can you
take out of it that will be
self-contained enough? You can kind of
model and approximate the inputs and
outputs into that self-contained system
and then just focus on the
self-contained system. So, a nucleus is
quite interesting from that perspective.
Um, then the other issue is just there's
not enough data yet. So, you need data
and I talked to various, you know, top
scientists about who work on electron
microscopes and other imaging things. If
we could image a live cell without
killing the cell, that would be
game-changing obviously cuz then you
could convert it into a vision problem
which we would know how to solve. Right?
And but at the moment, there are at
least I'm not aware of any techniques
that can give you a kind of, you know,
nanometer resolution
but without destroying but in, you know,
in a live dynamic cell. So, you can see
all the interactions. Right? You can
take static images at that resolution
obviously.
Really detailed now and that's quite
exciting but it's not enough to turn it
just into just into a complex vision
problem.
So, that's one way it could be solved.
So, it could be a hardware driven data
driven solution or it could be that we
build better
learn simulators of these dynamical
systems. So, that's that's the more
modeling way of solving it. You've been
looking at all kinds of science and not
just bio. There's material science, drug
discovery, climate modeling,
mathematics. If you had a rank which
scientific domain will transform the
most dramatically the next 5 years,
what's in your list?
>> all sounds exciting and that's why, I
mean, that that for me has been my main
passion and always the reason why I've
worked on AI for my whole career for 30
plus years now is to use AI as the
ultimate tool. I always thought AI would
be the ultimate tool for science and to
invite such advanced scientific
scientific discovery, and things like
medicine, and just our understanding of
the universe around us. So, actually,
when you mentioned our original way we
used to articulate our mission
statement, which is still the way we
think about it, is there was two steps
to it. One was Step one was solve
intelligence, i.e. build AGI, and then
step two was use it to solve everything
else. We had to change that a bit over
time cuz people were like, "Do you
really mean solve everything else?" And
we did mean that, and I think people are
sort of understanding what that means
today. But, specifically, I was meaning
solve other what I call root node
problems in science. So, areas of
science that would unlock whole new
branches or avenues of discovery. And
AlphaFold is the prototypical example of
what we want to do. So, over 3 million
researchers around the world, pretty
much every biology researcher in the
world uses AlphaFold now. And I was told
by some of my, you know, former
executive friends that, you know, almost
every drug discovered from now on will
have used AlphaFold at some point in its
in the drug discovery process. So,
that's something we're very proud of,
and it's the sort of impact that we hope
to have with with AI. But, I do think
it's just the beginning. I I don't
really see any area of science or
engineering that this won't be able to
help be helpful with. And the ones you
mentioned, I think we're almost like an
AlphaFold one moment. So, it's we've got
very promising results, but it's not
quite solved the the grand challenge yet
in that domain. But, I think we're going
to have a lot to talk about in the next
couple of years on all those areas you
mentioned, materials, which I I think is
very exciting, all the way to
mathematics. In in science, I mean, it
feels Promethean. It's like, here is
this capability, and you I think so. I
mean, of course, along with that,
including including what the the the
parable of Prometheus, we have to also
be careful with how we use that and what
we use it for, and also the misuse that
can happen with those same tools. A lot
of people in this room are trying to
build companies applying AI to science.
For them, what's the difference between
a startup that actually advances the
frontier in your view versus one that's
just wrapping an API around a foundation
model and calling it AI for science?
Well, look, I think there's one of the
things I would recommend. I'm trying to
think about and I think you mentioned
this to me before. What would I do today
myself if I was sitting in your place in
Y Combinator, you know, looking at
things. One thing you have to do is
obviously intercept where the AI tech is
going. So, that's one hard part of it.
But, I do think there's huge scope for
combining where AI is going with some
other deep technology area. I just think
that that sweet spot is is whether it's
materials or medicine or other really
hard areas of science. I think that
those kinds of interdisciplinary teams,
especially if it involves the world of
atoms as well,
there's not going to be a shortcut to
that, at least in the foreseeable
future. Those areas that are pretty safe
from just getting swamped by whatever
the next update is to the foundation
models. So, I think if you're looking
for things like that, that's one of the
more defensible areas I would say. And
I've always loved deep tech, so I'm kind
of biased towards deep tech things. I
think nothing
that's really long-lasting and
worthwhile is easy. And so, I'm always
been drawn to to deep technologies.
Obviously, AI was like that back in 2010
when we started out, right? It was It
was thought to just we know we know it
doesn't work kind of thing is what I was
told by investors and even in academia
it was considered to be a very niche
subject that we sort of tried in the
'90s and we know doesn't work. But, if
you, you know, if you have belief and
conviction in your idea why it's
different this time or what special
combination from your background that
you had, ideally you're expert in both
those areas, both the machine learning
and the other area you're applying it to
or you can create a founding team with
that expertise, I think there's huge
impact to be made there and huge value
to be built there. That's of important
message. I mean, even I mean, it's hard
it's easy to forget. Like, basically,
once you've done it, you've done it.
But, before you've done it, people are
arrayed against you. Oh, sure. I mean,
no one believes in it, which is why I
think you got to you've also got to work
in things that you're genuinely
passionate about. Like, for me, I would
have worked on AI no matter what
happened. I just decided from a very
young age it was the thing that um could
be the most consequential thing I could
think of. It's turned out that way, but
it might not have. Maybe we would have
been 50 years too early. And it was also
the most interesting thing I could think
of working on. And so, I would have
still be working on AI today even if we
were still, you know, in a little garage
somewhere and it still wasn't quite
working. I would have still been trying
to find Maybe I'd have been back in
academia or something, but I would have
found some way of of continuing to work
on it. So, I mean, AlphaFold was like an
example of a spike that you pursued and
it worked. You know, what makes a
scientific domain ripe for an AlphaFold
style breakthrough? And is there a
pattern, a certain objective function?
>> The way I I I I should write this up at
some point when I have 5 minutes spare,
but the lesson I've learned from all the
Alpha projects we've done, specifically
AlphaGo and AlphaFold, is um I think the
techniques we have and the problems I
look like to look for are great in if
this if the situation can be described
as massive combinatorial search space.
The more massive the better in some
ways. So, no brute force or special case
algorithm will will solve it. And that's
true of Go moves and of, you know,
different configurations of proteins,
far more than the atoms in the universe,
both of those. And then, um you have a
clear objective function. So, you know,
you can think of it as minimizing the
free energy in the proteins or, you
know, the winning the game of Go. So,
you need to be able to you need to
specify your objective function clearly
so you can hill climb. And then, um
enough data and or simulator that can
generate you uh lots of uh in
distribution
uh uh synthetic data. If those things
are true, then I think um with today's
methods, you can go a long way into
tackling and finding the kind of needle
in the haystack that you need uh to for
the solution that you're trying to look
for. And I think of just drug discovery,
by the way, in the same way, right?
There is a compound out there that would
solve this disease if one could find it,
if one could only find it, right? And
that wouldn't have any side effects and
so on. And as long as the laws of
physics allows it, then the only
question is how do you find it in an
efficient way, in a tractable way? I
think we showed for the first time,
actually, with AlphaGo, that these
systems could uh find those kinds of
needles in a haystack, in that case, you
know, the perfect Go move. I guess uh to
get a little meta, I mean, we're we're
talking about humans using these methods
to create AlphaFold, but then there's a
meta level, which is humans using AI to
explore the space of possible
hypotheses. How close are we to AI
systems that can do genuine scientific
reasoning, not just pattern matching on
data?
>> we're close. Um
we're working on these general systems
like that like I think we we have this
system called co-scientist, and we have
other algorithms like AlphaFold that can
go a little bit beyond what the basic
Gemini will do. And obviously, all the
frontier labs are experimenting in this
way. I've yet to seen anything so far,
and we we all tinker with the same
things, you know, some math problems
that are a little bit harder than IMO
and so on. I haven't seen anything yet
um that is a true genuine, you know,
massive discovery. That's my personal
opinion. I think it's coming. I think it
may be related to uh this earlier
this thing we discussed about
creativity, and and actually going on
beyond the bounds of what's known. So,
clearly, that's just not pattern
matching at that point, cuz there is no
pattern to match to, and it's a bit more
than extrapolation. It's some kind of
analogical reasoning, and I don't think
these systems have that, or at least
we're not using them in the in the right
way to do that. So, the way I often say
that in science is can it come up with a
hypothesis that's really interesting,
not just solve one. When I say just,
we're not talking about just like
solving the Riemann hypothesis or
something. This would be obviously
amazing, or one of the Millennium Prize
problems, and maybe we're a couple of
years out from doing that. Um but, I'd
like to solve P equals NP. That's That's
my favorite one. But, can you But, even
harder than that would be to come up
with a new set of of Millennium Prize
problems that were regarded by top
mathematicians to be as, you know, deep
and meaningful and worthy of lifetime of
study and effort to solve. Right? I
think that's another level harder. And
uh we don't have um you know, I still
don't think we know how to do that. I
don't think it's it's magical, though. I
do think these systems will be
eventually be able to do that. Maybe
we're missing one or two things. And
then, the way we would test that is, you
know, I sometimes call it my Einstein
test, which is, you know, can you train
a system with the knowledge of cutoff of
1901, and then will it come up with you
know, what Einstein did in 1905,
including special relativity, you know,
his annus mirabilis. Can Can it do that,
right? Uh and then, I think we could run
that test. May- Maybe we should just run
that test and keep seeing if that's
possible. And once that is, then I think
we're on the verge of these systems
being able to invent something new,
truly novel. So, last last question. For
the people who are deeply technical in
this room who want to work on something,
you know, even close to the scale that
what you have created with you know,
it's one of the largest AI efforts in
the world, and you've been a pioneer for
all these years. So, for that, I think
everyone in this room thanks you and the
folks at DeepMind very, very deeply from
the bottom of our hearts. Thank you.
What's the thing that you know now about
building at the frontier that you wish
you'd known at 25?
I think we covered some of it in terms
of actually you you work out that going
after hard problems and deep problems um
it's no more difficult in some ways than
than going after a shallower, simpler,
more superficial problem. They're
they're they're just differently
difficult. There's different things that
are hard about each of those things, but
I think given life's very short and you
you know, you only have so much time and
energy, you might as well put your life
force into something that will really
make a
difference if you hadn't done it, if you
hadn't been there to push it. So, I
would just think of it through that
lens. And then the other thing is if
you're if you are and then we talked
about deep tech and I love
interdisciplinary
uh work and I think that's going to be
even more prevalent in the next few
years in combinations of fields and uh
uh finding the the the the connections
between those fields. And it's going to
be even easier to do that with AI. And
then the only other thing I would say is
if you know, if you have your depending
on what your AGI timeline is, you know,
mine's like 20 30 or something like
this, then if you start off on a deep
tech journey today
usually that you're talking about a
10-year journey for for true deep tech
in my opinion. So, then now you have to
just consider AGI appearing in the
middle of that journey. So, what does
that mean? It doesn't it's not bad
necessarily, but you have to take that
into account, right? To will it be able
to leverage it? What will the AGI system
do with it? And it goes a little bit
back to what you said earlier about
AlphaFold and general AI systems. So,
one thing I can think see happening is
Gemini, Claude, or one of these general
systems making use of AlphaFold like
specialized systems as tools. I don't
think we're going to have it just in one
giant brain cuz it will have too much
regression in if I put all the proteins
into you know,
Gemini, that wouldn't make sense. We
don't need Gemini to do protein folding.
Going back to your information
efficiency, it will definitely affect
its language skills or something like
that, right? In a bad way. So, much
better I think is to have really good
general purpose tool usage models that
will then
maybe they could even train those
specific tools, but they would be in a
separate
system. So, I think that's kind of
interesting to think through the
implications of that and then what you
might build today. Also, physical things
too like what kinds of factories would
you build, what sorts of
you know, finance systems and so on. So,
I just think you need to really take
that seriously and and and on the one
hand is like an imagine what that world
would look like and then build something
that would be useful if that comes in
halfway through.
Demis Hassabis everyone.
>> [applause]