Demis Hassabis

And then of course that gets monitored.

And then at the end of the pre-training, then there's all the post-training.

There's many different ways of doing that, different ways of patching it.

So there's a whole experiment and phase there, which you can also get a lot of gains out.

And that's where you see the version numbers usually referring to the base model, the pre-trained model.

And then the interim versions of 2.5, you know, and the different sizes and the different little additions, they're often patches or post-training ideas that can be done afterwards.

off the same basic architecture.

And then of course, on top of that, we also have different sizes, pro and flash and flashlight that are often distilled from the biggest ones, you know, the flash model from the pro model.

And that means we have a range of different choices.

If you are the developer of, do you want to prioritize performance or speed, right?

And cost.

And we like to think of this Pareto frontier of, on the one hand, the y-axis is like performance, and then the x-axis is cost or latency and speed, basically.

And we have models that completely define the frontier.

So whatever your trade-off is that you want as an individual user or as a developer, you should find one of our models satisfies that constraint.

Exactly.

And then you're constantly this process of converging upstream.

We call it, you know, ideas from the, from the product surfaces or, or, or from the post-training and, and even further downstream than that, you, you kind of upstream that into the, the core model training for the next run.

So then the main model, the main Gemini track becomes more and more general.

And eventually, you know, AGI.

One hero run at a time.