Google DeepMind’s Dennis on Gemini 3, the AI Race, Multimodal Futures, and the Path to AGI

Name: Google DeepMind chief warns AI investment looks ‘bubble-like’ | FT Interview
Uploaded: 2026-01-26T12:57:20.183811+00:00
Channel: Financial Times
Description: Summary and key takeaways on Google DeepMind’s Dennis on Gemini 3, the AI Race, Multimodal Futures, and the Path to AGI, covering Introduction Dennis, head of

Financial Times

Jan 26, 2026

•

3 min read

YouTube video ID: -RPbxvz6sB8

Source: YouTube video by Financial Times — Watch original video

PDF

Introduction

Dennis, head of Google DeepMind, discusses the launch of Gemini 3, Google’s position in the AI race, and the broader landscape of artificial intelligence.

Gemini 3 and Google’s Momentum

Gemini 3, released a few months ago, is topping most leaderboards and receiving strong feedback from users and enterprise customers.
Compared to Gemini 2.5 (launched in mid‑2023), Gemini 3 cements Google’s lead on the frontier of AI capabilities.
Monthly active users of the Gemini app have reached 650 million, and AI‑powered features now serve 2 billion users worldwide, making it the most used AI product globally.

Competitive Landscape

Anthropic is gaining attention with its Claude code model, especially among developers.
The competition is “ferocious and intense,” with all players pushing hard on research, talent, and product integration.
Google emphasizes focusing on internal quality and speed of delivery rather than external hype (e.g., Sam Altman’s “code red”).

Multimodal Vision

Gemini has been multimodal from the start, handling text, images, video, and audio as native inputs/outputs.
Multimodal AI is essential for future assistants that operate on glasses, phones, or robots, understanding real‑world context.
Google is exploring smart‑glass partnerships (e.g., with WBY Parker and Gentle Monster) to create a universal digital assistant.

Talent War and Culture

The AI talent market is extremely competitive, with offers reaching $100 million for top researchers.
Google retains talent by emphasizing mission‑driven work: building products that impact billions, advancing AI for science and medicine, and offering the chance to ship breakthroughs quickly.

Safety, Misuse, and Industry Responsibility

Google is proactive with safeguards such as SynthID watermarking and usage guardrails for Gemini.
The company aims to be a role model for responsible AI deployment and calls for broader industry and governmental coordination.

The AI Bubble Debate

Dennis argues the overall AI market isn’t a bubble due to unprecedented demand and limited chip supply.
However, certain segments (e.g., oversized seed rounds for unproven startups) may see corrections.
Google’s diversified business (search, Gmail, Chrome) provides resilience regardless of market fluctuations.

China vs. West Approaches

Chinese AI firms focus more on near‑term applications and efficiency, whereas Western labs (Google, DeepMind) pursue long‑term AGI goals.
The gap between the two is likely months, not years, but Chinese labs have yet to demonstrate breakthroughs beyond the current frontier.

AGI Timeline and Technical Hurdles

Dennis estimates 4‑8 years (around 2030) for a plausible AGI, aligning with a 5‑10 year horizon.
Missing capabilities include continual/online learning, personalization, and self‑improvement—areas where DeepMind has early research (e.g., AlphaGo, AlphaZero).

AI‑Driven Drug Design & Material Science

Google DeepMind is in pre‑clinical trials for AI‑designed drugs, with partnerships with J&J, Eli Lilly, and Novartis; clinical trials are expected in the next few years.
A new UK‑based material‑science lab aims to automate testing of AI‑generated compounds for semiconductors, batteries, and superconductors.

Future Outlook

Dennis remains passionate about research, balancing leadership duties with hands‑on scientific work.
He does not aspire to become CEO but wants to stay close to cutting‑edge science while guiding product integration.

Key Takeaways

Gemini 3 solidifies Google’s lead, but the AI race remains highly competitive.
Multimodal AI and real‑world assistants (e.g., smart glasses) are central to Google’s next wave of products.
Talent, safety, and responsible deployment are critical pillars for long‑term success.
AGI is projected within the next decade, contingent on solving continual learning and self‑improvement challenges.
AI is already reshaping drug discovery and material science, with tangible commercial partnerships on the horizon.

Google DeepMind’s Dennis paints a picture of a company that is ahead in AI performance with Gemini 3, deeply invested in multimodal assistants, and cautiously optimistic about reaching AGI within a decade, all while navigating fierce competition, talent wars, and the responsibility to deploy AI safely.

Frequently Asked Questions

Who is Financial Times on YouTube?

Financial Times is a YouTube channel that publishes videos on a range of topics. Browse more summaries from this channel below.

Does this page include the full transcript of the video?

Yes, the full transcript for this video is available on this page. Click 'Show transcript' in the sidebar to read it.

Helpful resources related to this video

If you want to practice or explore the concepts discussed in the video, these commonly used tools may help.

Artificial Intelligence: A Guide For Thinking Humans Book Recommended

Provides a clear, accessible overview of AI concepts and ethical considerations, helping readers understand the context of Gemini and AGI discussions

Amazon →

Deep Learning With Python Book

Offers hands‑on tutorials for building and training neural networks, useful for developers interested in multimodal models like Gemini

Amazon →

Google Nest Hub Max Smart Display

A physical device that integrates Google’s AI assistants, illustrating how multimodal AI can be experienced at home

Amazon →

Links may be affiliate links. We only include resources that are genuinely relevant to the topic.

Summarize another video

Full Transcript YouTube

Dennis, Google launched its most
powerful model, Gemini 3, just just a
few months ago. It was received with a
lot of excitement. Where do you think um
Google is right now on the on the AI
race?
>> Well, we're we're we're very happy, as
you say, with the last model we
released, Gemini 3. Uh it's topping, you
know, pretty much all the leaderboards.
So, it's a great model. Uh feedback's
been great from our users and and and
enterprise customers. Um but I think
overall we have had a really good year
last year when we look back on it. Um I
think the trajectory of progress we've
been making is the fastest of anyone in
the industry. If you look at uh you know
Gemini 2.5 the previous version that we
released in April May last year that was
already becoming very competitive I
think at the at the top of the uh at the
frontier. Uh and then I think we
cemented that with Gemini 3. But of
course it's a ferocious intense
competition as you know and um
everyone's pushing uh as hard as they
can and we've got to make sure we
deliver this year too. It
>> is it why um Sam Alman declared uh code
red?
>> Well, apparently that's what what was
being reported and uh
>> how do you feel about it?
>> Uh you know it's it's fine. we just
focus on ourselves that uh that and and
I think that's uh what we got to do is
kind of block out the noise and just
execute uh focus on the quality of our
research and then making sure we're
shipping that quality fast enough into
our product surfaces and I think that's
what you've seen with the like our share
of the chatbot space with the Gemini app
has gone up um you know 650 million
monthly users now and then uh you know
things like AI overview 2 billion users
I think it's the most used AI product in
the world so we're really proud and
pleased with how that's going, but I
think we're just scratching the surface
of what we can really do when we when
we're fully in our groove
>> and and we're we're going to get to to
that. But when you look at the industry,
what do you think rivals are doing best?
What do you think is really interesting
right now?
>> Well, I think um what Anthropic's doing
with code is very interesting with their
claw code. There's a lot of excitement
around that in the developer market.
We're pleased with the the performance
of Gemini 3, but they've done something
special there. I think other than that,
I'm I'm very excited about the stuff
that we're doing on multimodal. I feel
like um
>> do you want to explain that?
>> Yes. Multimodal being uh Gemini from the
beginning has been multimodal. And by
that I mean being able to deal with more
than just language and text, but
actually image, video, audio as a native
input and output. And um and we're
bringing that all together. That's
always been our strength. And the reason
we want to do that, and I think that's
what I'm excited about this year, is
that's what you would need for uh a kind
of an assistant that travels around with
you in the real world, maybe on your
glasses or your phone. Uh it needs to
understand the world, the context around
you, the physical world. And of course,
for robotics, that's critical, too. And
I think I've been spending quite a lot
of time on that last year, and I think
that's going to be uh have some big
moments in the next couple of years.
>> Can you talk a little bit about these
big moments? Is it is it a question of
uh trying to create devices that would
sort of you know the the new iPhone?
>> Yeah.
>> Or the glasses. There's actually so many
uh uh simultaneous things one has to do
which why it's it's very exciting but
also quite daunting at the moment is um
at least from our perspective as Google
deep mind as kind of like you know we
like to think of oursel we describe
ourselves internally as the kind of
engine room of Google and we're
providing the engine which is these you
know these models like Gemini and VO and
Nana Banana all these kind of um
state-of-the-art models and um then we
got to figure out you know how do we
want to incorporate them into features
in products that are really useful to
the end user. So there's that whole
aspect of work which is enhancing what
already exists from email to your Chrome
browser to search. Um but then there are
also you know all these very exciting
new green field areas uh of uh you know
digital assistants like the Gemini app.
But what does that become over time and
that including uh new devices and we're
working on and we've announced recently
you know partnerships with WBY Parker
and Gentle Monster on new types of smart
glasses. Obviously, Google has a long
history with smart glasses. Um, but I
think maybe we were a bit too ahead of
our time when we first started this 10,
you know, 10 plus years ago at Google,
um, with the devices, but now I think,
you know, I think what was missing was a
killer app for that. And I think, uh, a
kind of universal digital assistant that
helps you in your everyday life could
well be that killer app uh, for things
like a smart glasses that's connected to
your phone.
This is an area where a lot of your
competitors are are also um are also
working on is it
>> why do you think you will be able to
compete very effectively?
>> Well, I think it starts with the quality
of your research and models. So, I think
we have by far the deepest and uh
broadest research bench. I think we have
the most talent in the industry. Um, and
I think that then will translate to the
quality of our breakthroughs and and
research innovations and then that
underpins what you can do with these new
products.
>> Speaking of um talent, there's a real
talent war uh in the industry. Some
researchers are uh getting offers for
$100 million.
>> Uh how are you holding on to your
researchers? Are are you having to pay
more than that? Look, I mean, of course,
there's it's it's another part of the
ferociousness of the competition is the
talent wars. Um, but I think that most
top researchers, you know, every they're
of course fabulously well paid, but it's
then beyond that is like the mission.
What are you um trying to do with your
skills? You know, these are phenomenally
smart people. They could do anything
with their skills. Are you doing good in
the world? Are you building products or,
you know, applying in our case as well
AI for science, scientific ends that
actually you'd be proud of? and your
friends and family will be proud of and
you're you're overall benefiting
society. And I think um we're very lucky
at Google that we we have that those
product surfaces that that people love
and use every day from maps to email
that we are uh enhancing with you know
our AI work. So it's very you know
motivating to when you make a research
breakthrough some you can kind of ship
it and then immediately a billion users
can can take advantage of that. So my
expectation is that this year we're
going to hear a lot more about a tech
lash because there are growing concerns
in in society, but there are also safety
misuse um issues and we've seen several
several examples uh examples of that.
How concerned are you and how do you how
do you protect against it?
Look, I think society is right to be
worried about these things and and I
think of course as you know I spent my
whole career working on AI because I'm I
really believe in all the benefits that
are going to come from science and
medicine medicine advances things like
Alpha Fold that we've done. Um but um
you know we need to also worry about
these harmful use cases. We've tried to
um be, you know, kind of get ahead of
that with things like synth ID, like
watermarking technology for things like
deep fakes. Um getting the right guard
rails around like the usage of Gemini.
Um and we take that responsibility very
seriously for you know uh uh all the
users that we have. Um and we try to be
role models beyond what we do. So
there's what we can control and then
beyond that we try to be role models for
what responsible use of these kind of
tech deployment of these technologies
looks like. And then as far as society
goes in the average person is we need to
show as an as an industry as a
scientific field what the unequivocal
benefits are more clearly more quickly
and I think for us that's doubling down
on our AI for science and AI for
medicine work um and things like that
that are kind of unequivocal goods in
the world. Uh I'm going to get to uh to
that with is a morphic but just staying
with the misuse and and safety uh issue.
Whatever happens in in this industry and
if there is a a growing tech lash it
will affect all the companies. So is
that something I mean are you all not
getting together to discuss this? Is is
any is there any effort underway to to
address it as an industry? Um there are
some industry groups but and of course
you know most of the lab uh heads know
each other quite well but I think um
you're seeing different frontier labs
you know do different things and um I
think uh we'll have to see how that
works out or we can control is what we
do at Google deep mine and we try to
broadcast that places like this and and
show the way forward that I think you
know gets most of the benefits but
mitigates the risks and we hope others
will will follow in that path but um you
know it would need something
governmental I think to to create the
whole of the industry to do that and
then there's also the international uh
uh cooperation question too.
>> Um the other big risk this year is the
the bubble bursting is
>> are we in an AI bubble?
>> Well yeah look look I for me it's not a
binary question yes or no. Um the AI
industry is very big now as you know and
it's it's it's sort of uh
multiffactorial. So I think my guess is
um I mean from our point of view we're
seeing more usage than ever, incredible
demand for our models and our you know
the the AI features um we you know we
can barely satisfy that right there
aren't enough chips to go around and um
so I think from that perspective and
also overall there's not going to be you
know it's going to be the most
transformative technology probably ever
invented. So I think from that
perspective there can't really be a
bubble. Um but on the other hand I think
there are parts of the industry that you
know do look bubble-l like for example
seed rounds multi-billion dollar seed
rounds in in you know new startups that
don't have a product or technology or
anything yet does seem a little bit
unsustainable. So there may be some
corrections in some parts of the market.
Uh and then we have to see for my I
don't worry too much about that from
from from from our day-to-day. I focus
on um our technology and delivering
that. And my job as head of Google
DeepMind is to make sure we're well
positioned no matter what happens. If
the bubble bursts uh we will be fine.
We've got an amazing business that we
can add AI features to and and get more
uh productivity out of. And also if the
the bull case continues um then we've
also got these amazing AI first AI
native products like the Gemini app.
>> You've also spoken about the AI race and
the competition with with China. Um from
what I can see in in China there is the
there is no AI race. It's ve it's very
different from what you hear there's no
sort of uh race to reach uh AGI. there
is a lot more focus on applications and
um and finding efficiencies.
>> Is that perhaps the more sort of um
realistic approach?
>> Um look, it's it's it's per perhaps the
more um I don't know about realistic,
but uh uh the the less risky approach
perhaps. Um, and I think, by the way, I
think the Chinese market, from what I
understand, is is just as intensely
competitive as the Western companies are
with each other. It's just that I think
you're right. They're more focused on
the near-term applications. What can you
concretely do right now rather than
maybe these um more research heavy
frontier capabilities that would get you
to AGI? Um, I think that's fine. I I I
started Deep Mind and our job is now at
Google Deep Mind and and Alphab, we want
to build AGI. We think that's the
ultimate goal. and then that will um
unlock so many opportunities and
possibilities in the world that we've
talked about many times. So I think
that's really the north star and um and
on the way we'll create lots of useful
technologies but I think um you've got
to have that as a north star if you want
to progress the research uh in as
innovative way as possible and I think
that's why in my opinion the western
companies are still in the lead on that.
>> How many months are you ahead? Is it a
matter of months or
>> I think probably it's only a matter of
months now would be my guess. Although
interestingly, uh, some of the Chinese
leaders, entrepreneurs I talked to, they
they feel like they're more they're
further behind than that. I'm not sure
that's the case. You know, maybe it's
only a matter of 6 months or so now, but
I think it's important that because I
think what um even things like Deep
Seek, which was I think was a bit of an
overreaction in the West to that
actually is a bit overblown. Um, there's
uh they still the Chinese sort of labs
haven't proven they can innovate beyond
the frontier yet. They're getting faster
and faster at catching up to the
frontier. what the Frontier Labs are
doing, but they haven't sort of
innovated beyond that, like the next
Transformers or something like that.
They haven't proven they have that
capability yet.
>> Do you think they are as focused on it
though?
>> They're probably not, and that might be
one reason why.
>> Um, there is there was in the last um uh
in the last few months a debate about uh
AGI. Um, and you disagreed with Yan Lun
who said that uh there is no such thing
as as general intelligence. You're a
real expert on the brain.
>> Yes.
>> So, explain to me why you disagreed with
him.
>> Yes. Um, yeah, we have many fun debates,
Yan and I, at conferences and things and
and uh but uh this was an online one.
Yeah. I just think it's kind of
ridiculous uh his argument on that. I
think he's confusing two things which is
general intelligence which I think
clearly we have as humans and our brain
has that um and uh and universal
intelligence something that can
understand anything that could be
possible and um and the thing is it's
obvious our brains are very general
because look at modern civilization
we've built and we're basically uh you
know tool making uh creatures right
that's what separates us from from other
animals is we build tools um from all
the modern uh things around us vehicles
747s, but also computers and and I'd
include AI in that as well. It's kind of
like the ultimate expression of the
computational tool. So, if you include
all of that and the science that we do,
it's unbelievably general. Um, it's not
everything that could possibly happen to
your retina and all these arguments he
makes, but it's it's clearly general.
And then the other argument I make is
more from Alan Turing, who's one of my
all-time scientific heroes, you know,
and he proved that Turing machines um
could compute anything that was
computable. So that's super general
class of machine and all modern
computers are based around that. But
also I think most neuroscientists would
agree that our brains are an approximate
cheuring machine or or approximately
cheuring powerful which means that we
you know we can do uh in theory uh
understand almost anything. So uh that's
computable and so the idea is that we
have our brains are a general system
that can in theory learn almost
anything. Uh not that we already know
that. Right? So it's it's a question of
is it can do you have the learning
capability versus the actual full
knowledge. Obviously our brains are
limited. We can't know everything a
single human but in totality our brains
are very very powerful
>> and very flexible
>> and extremely flexible. Right.
>> Uh so what is it going to take to get to
AGI recursive uh self-improvement where
essentially AI models can teach uh
themselves? We're not we're not there
yet. How far are we from it?
>> Yeah. Yeah. So, and is that the main
breakthrough that you're looking at?
>> Well, that's one. I mean, there are I
think there are I think there are quite
a few capabilities missing from today's
systems that will be needed for uh
something that could probably, you know,
pass as AGI and um continual learning is
one of those things like online learning
after you've been after it's been
trained, can it learn new things from
the user or from experience? Uh so, it's
sometimes called continual learning or
online learning. Um
>> and for that, you need the
personalization, right? So, that yes,
that will be part of it. So if you want
it to personalize then that would have
that would be a form of online learning.
Um but also self-arning and
self-improvement can also be part of
that. So that's sort of a closed loop
version of like experiencing something
in the world and then updating your
knowledge base directly uh and
automatically. And we actually pioneered
a lot of that work back 10 plus years
ago now with Alph Go and Alpha Zero our
games playing programs. But the of
course the question is in games that's
much simpler. The real world's made much
messier much more complex. So the
question is, can you translate some of
those techniques to the messy real
world?
>> Um, let's get to isomorphic because
originally, um, I think the company said
that you'd be going to clinical trials
in Q4 of 2025.
Um, but then it was preclinical. So what
happened? And when will the drugs go to
>> Sure. No, nothing happened. I misspoke
it. I think it was one interview I gave
a couple years ago. It was pre-clinical.
We were um entering last year. It was
was the plan. Yeah. And uh so I misspoke
then. And basically our pre we're in
preclinical trials with a few of our um
drug programs. It's going very well.
We're advancing very well. And then as
soon as that's ready, we'll move that
into clinical.
>> So when will we have the first AI
designed drug?
>> Well, I hope in the next few years, but
it's it's depends on the how the
preclinical trials go in the clinical
trials.
>> Has it been harder than you you would
expect?
>> Not at all. It's it's been we're we're
actually doing phenomenally well and we
just announced a a new deal with a new
partnership with J&J yesterday. Um so we
now work with J&J, Eli, Lily, and
Novartis, three of the the best farmers
in the world. And we also have our own
internal programs. Uh so we have like
about 17 programs in total. So and the
the we're going to talk a lot more about
that. You'll see a lot more news from us
this year uh first half of this year on
on our progress which is going very
well. You also announced that you were
building a material science lab in the
UK. Um c can you give me
>> yes
>> some more detail about that?
>> A little bit more. I mean we're still
quite early stages with that. Um but I
think material science and AI designing
new materials semiconductors
superconductors uh batteries these kind
of things is going to be a huge part of
what AI the benefits AI will bring to
the world. And I think we're at uh maybe
like an alpha fold one level kind of an
some promising research prototypes, but
we need to go further. And for part of
that is we need to be able to test our
materials that our AIS are designing
quickly. And so we're we're thinking
about creating kind of automated lab um
uh in in the UK to basically test these
uh theoretical compounds that um the AI
systems are coming up with. Every time I
see you, I ask you what is your
expectation of the what's the timeline
for AGI? And I've noticed that lately
all of you are not talking so much about
timelines. In fact, Sam Sam Oldman even
says there is no we are in almost in uh
at that at that stage. So I am going to
ask you what is what is your timeline?
>> Well, mine's been very consistent. I
think we're about you know 5 to 10 years
away. Um so maybe it's now like 4 to 9
years.
So now it's like 4 to 8 years. So you
know I think 2030 uh is probably the
earliest you know it could be u maybe
50% chance over that that kind of time
zone. So um I I'm still sticking with my
timeline. I think others who've had more
aggressive timelines maybe are updating
to you know to be a little bit longer
and a little bit more realistic. But for
me things always take a little bit
longer than than ones assumes even at
the pace that we're all going at. I mean
that's still phenomenally that's still
extremely soon. Um, I just think it's
not going to be sort of like next year.
>> And your job has evolved a lot from deep
mind to actually handling all of uh
Google's uh AI. I'm just wondering where
you see your future is uh do you want to
be CEO?
>> No, look, I love the I'm very happy what
I'm doing. I I I love being close to the
science and the research. So, I still
try and carve out time to do that even
though I'm running, you know, a lot of
things, including some products now. Um,
but look, I can get pretty excited about
I'm very general in my interests and I
can get very excited about anything
that's cutting edge. Um, especially if
it has a especially if it has a
leaderboard attached to it. Yeah, I
think you know there's only so much one
can do in the day and still leave enough
time for uh serious thinking uh which I
do at nighttime and I quite like that
routine. So I hope to be able to stick
with that.
>> You didn't say no.
>> No. Okay, so there we go.
>> Uh, thank you. Yeah.
>> Thank you, Dennis.
>> Thank you.