Not really, but there is a finite amount of data to train models on. I found it rather interesting to hear him talk about how Gemini has been better at getting results out of the data than their competition, and how this is the first insights into a new way of dealing with how they train models on the same data to get different results.

I think the title is an interesting thing, because the scaling isn't about compute. At least as I understand it, what they're running out of is data, and one of the ways they deal with this, or may deal with this, is to have LLM's running concurrently and in competition. So you'll have thousands of models competing against eachother to solve challenges through different approaches. Which to me would suggest that the need for hardware scaling isn't about to stop.