Google seems to have anorexia when it comes to model intelligence. They have an internal hard constraint on price per token it seems, and they are trying to squeeze out intelligence with limited compute.

I wonder if there is something with their TPU cycles that makes them want to postpone training a new model. My guess is that they have been on the same base model for 6 months and they may have waited for the next gen TPUs to train Gemini 4, which greatly limits how much intelligence they can increase and forces them to do cost efficiency increases.

Could it be that they have to serve their models to billions of users?

I'd guess they did model-hardware codesign but the design ended up limiting the scaling capability of the model (i.e. they overoptimized too soon).

Google Cloud is probably Google Deepminds biggest competitor. Big company kinda bullshit.