Google is systematically reallocating capital away from frontier model development and toward compute infrastructure and data centers, which is why its top AI scientists are leaving to start new labs.

3:53billion in capex this year in AI infrastructure data center buildout. Because of the capex and accelerated depreciation, making an investment in AI compute in the US right now is hugely tax advantaged. And because of the extreme demand for compute, it's a pretty obvious kind of ROIC model, return on invested capital. So if you make this sort of an investment, you have significant demand for that compute infrastructure. You're very good at

4:23running the compute infrastructure. That capital can deliver massive profit returns for you with very high confidence in some forecasted period. Building the most advanced frontier lab driven model also takes tens of billions of dollars of capital. And the question really is can you deliver the profits from the model? And in a world where open-source is becoming so good and open weights models are catching up so quickly and all the frontier labs are

4:53catching up to each other so quickly, does it really make as much sense to deploy tens of billions of dollars against building a model? And I think that the scientists that we're seeing transition out are the scientists that have been at the core of model development of making these frontier models. And they were certainly first out the gate. >> You can look at some of the early

5:12interviews with Jeff Dean from a couple years ago where they actually had a chat GPT equivalent internally a year before chat GPT came out from OpenAI. Google chose not to release it for fear of cannibalizing search and so on. That's when Sergey stepped in and there was this whole kind of revitalization. But as time has gone on and as everyone has competed on models, as we've talked

5:33about many times on the show, I think it's pretty obvious that it is very hard to get the same sort of return on capital invested in model development as it is in capital invested on compute infrastructure and being model agnostic. What Google has is probably one of the greatest install enterprise bases in the world for compute. So they have the most enterprise customers. They have the most

5:56consumers. And in both cases, they don't necessarily need to have the best model to make an incredible business. They can be model agnostic. They can work with anthropic. They can work with OpenAI. They can work with SpaceX. They have a significant ownership stake in SpaceX and in Anthropic and they can work with all the open weights models. They can host them all. So now if you're one of

6:16the great computer scientists, you're Demis, you're Jeff Dean, you're this whole crew, and you're inside at Google and they're allocating capital not to your models, not to the things that you're most interested in, but they're allocating capital to infrastructure and data centers and supporting the broad ecosystem of models, you start to say, well, given the fact that I can go down the road and visit Brad Gersonner and a

6:36couple other people and raise a couple billion dollars at a multi-billion dollar pre- money with a PowerPoint deck because I'm the greatest in the world at doing this, that might be a better path. for me and I think that that's the moment. So I the way I would frame it is capex is high alpha low beta in data center infrastructure that capital and model development theoretically could be