They

were doing like what, just like RL? I think when you're in a startup, when you are, when you are doing research, everyone does everything right.

There were like three, four deep learning labs at the time.

Bringing that deep learning background together with Carl into DeepMind.

It was an amazing environment.

There were, I remember like, I think in research, there were like 10, 12 people in research and everyone is working on some, some really interesting idea from their own background, like a really, really interesting group of people.

At some point, there was a discussion of, okay, let's pick an ambitious task that we can corral the whole research team around and try to show progress on that task.

Yes, I

Yeah.

At some point, like research, like research in AI was a lot more academic, right? Like you go back like ten years, a lot more academic research.

And at that point, like the-- how you organize it is not really the key element.

It is more about the exploration, and the speed of exploration is important.

But as things get more and more focused, what you want is to really, to Jeff's point, this focused operation where rather than us trying to build things in parallel, because these things require a lot more focus, effort, and each one of them is like a major operation in terms of many researchers coming together, solving many problems.

At that point, I think it was really, really a good idea to, okay, this is the moment that we need to change.

I think like, um, both organizations acted with like great urgency on that and enabled it.

... sort of that

So talk to me about what investors are missing today.

Thank you very much for having me.

What you are seeing here today is what we have been developing for building intelligence.

And these are the recent developments that we are sharing.

And I think there are two main updates in terms of AI technology that we are bringing.

First one is Gemini 3.5 Flash.

It's making a huge jump in terms of capabilities in coding and agentic workflows.

So one, how can you afford this new AI strategy? But two, and perhaps more importantly, how do you think about the value of these billions of consumers over the long term?

Yann emphatically stated, uh, "There is no way the AI industry is gonna reach human-level intelligence," which is his term for AGI, "just by scaling up LLMs." Do you agree?

Well, I mean, I think, um, that's a hypothesis, right, that might turn out to be true or not.

But also, I don't think that there's any research lab that is trying to only do scaling up the LLM.

So like, I don't know if anyone is actually trying to negate that hypothesis or not.

I mean, we are not.

From, from my point of view, we are investing in such a broad spectrum of research that I think that is what is necessary, and clearly, I think, like many of the researchers that I talk to, and me, myself, I think that, um, there is a lot more, um, critical elements that needs to be invented, right? So there is critical innovations on our path to AGI that we need to, uh, we need to get through.

That's why we are still looking at this as a very ambitious research problem, and I think it is important to keep that kind of critical thinking in mind.

And is this like, you know, I kinda asked you, what do we get at 10%, 50%, but is this kind of that perfect example of, um, the model getting better producing something that goes from, "You know, that's a fun little video," to like, "Oh, I could really use this now."