1) the simplest explanation whenever a bunch of people leave [x] at a company at the same time, is that [x] is becoming less important to that company moving forward. It's not proof, but you know, everyone is trying really hard to say that's not what's happening here. Sometimes the simplest explanation is correct.
2) rumor is that Sergey Brin, co-founder of Google, got bored during pandemic lockdown and eventually returned to Google in a lower profile role, related to AI. I have to think that he still has a lot of say in what goes on in Google relative to his interests.
3) Yahoo Finance article says Hassabis "has long prioritized research over profits", so his replacement might indicate that Google has decided it is time for this stuff to pay for itself?
There is another universe where Google is hard AGI forward, freeing up as much compute as possible for Deepmind, turning away OAI and Anthropic, and offering the uncontested premier AI research lab, by probably an order of magnitude or more compute. Gemini 3.5 ultra is limited availability with unreal abilities.
But their balance sheet becomes terrifying, and it's all or nothing that this plays, er pays, out.
Right now though Google is pretty well hedged. Even Chinese models likely just mean more compute sold.
Instead, it seems Pichai has fumbled what they had with DeepMind, and they'll now just be a me-too LLM competitor chasing OpenAI and Anthropic from behind, in a race that will never get to AGI.
At least Hassabis has an understanding that AGI will require more than an LLM, and understands some of what is missing. He has never spoken publicly about any vision of what an AGI architecture would look like, other than requiring some more "Transformer-level" breakthroughs, so it seems hard to say that he would have failed, other than his 2030 projection seeming unrealistic.
My only criticism of his AGI direction was that he has talked about retaining an LLM as a component of that, but it's hard to tell if that is/was just short-term pragmatism, and a product-based path, or if he really believed this was the best direction. On the face of it having a pre-trained LLM at the heart of an attempt to build a human brain (build true AGI) is an admission you have failed, since if you build a powerful enough (human level) learning architecture it would be able to learn language for itself, not need to have it baked-in. If your version of AGI is not capable of learning language, then what else is it incapable of learning? It would certainly reflect sub-human rather than super-human capability.