It seems like there are some credible rumors that Google is actually winning in terms of actually building models that work and don't lose money- between how they're able to price them, the TPU advantage and their capex advantage (being able to raise debt + just having a lot of cash - well I said not lose money... more like not go bankrupt).

From the outside they look like they're behind in terms of frontier models, but I think they might be the best positioned to not go out of business when the bubble pops.

Also look at the fact that they've been able to deploy AI-assisted search at google scale. It must be another order of magnitude larger (at least) than the model deployments for OpenAI and Anthropic.

Of course unless you're inside Google it's impossible to know for sure.

In terms of open models, Gemma 4 beats the pants off everything else to the point that paying for APIs becomes hard to justify. Qwen has the meme-share for coding, but it feels much less well rounded. I have no doubt that Google have both the infrastructure and the expertise to curb stomp everyone else, should they resolve in earnest to do so.

Lest we forget, "Attention is All You Need" came from Google.

That "TPU advantage" might be slowing Google down (though likely not as much as their internal bureaucracy).

Porting CUDA-based research, debugging, and overall experimentation speed is likely slower.

The GPU is still king for training.

They basically don't exist in the currently most profitable LLM market (coding).

Yes, subs like codex are heavily subsidized. But API billing has massive margins and that's what enterprises pay.