I like owning things

Cloud will buy less NVDA chips, and since they're related goods, prices will drop.

Pricing is based on competitors more than demand.

You own any GB200s?

You will own nothing and you will be happy.

Seriously yes. I don’t want to own rapidly depreciating hardware if I don’t have to.

I don’t want to own open source software and I’d prefer if culture was derived from and contributed to the public domain.

I’m still fully on board with capitalism, but there are many instances where I’d prefer to replace physical acquisition with renting, or replacing corporate-made culture with public culture.

Nvidia cloud instances have competition. What happens when you get vendor lock in with TPU and have no exit plan? Arguably it's competition that drives value creation, not capitalism.

Just don’t let yourself get stuck behind vendor lock-in, or if you do, never let yourself feel trapped, because you never are.

Whenever assessing the work involved in building an integration, always assume you’ll be doing it twice. If that sounds like too much work then you shouldn’t have outsourced to begin with.

Switching costs are a thing everywhere in the economy. Measure twice cut once. Planning on doing the work twice is asinine.

Then you shouldn’t have outsourced to begin with.

Oh, you own the generator for you GPU as well?

And time, energy to maintain that.

It's only competitive with nvidia if you believe Google won't kill this product like everything else.

I love to hate on google, but I suspect this is strategic enough that they wont kill it.

Like graviton at AWS its as much of a negotiation tool as it is a technical solution, letting them push harder with NVIDIA on pricing because they have a backup option.

Google has done stuff primarily for negotiation purposes (e.g. POWER9 chips) but TPU ain't one. It's not a backup option or presumed "inferior solution" to NVIDIA. Their entire ecosystem is TPU-first.

Was Gemini 2.5 trained on TPUs thought? I seem to be struggling to find that information. Wouldn't they want to mention it in every press release?

Pretty sure the answer is yes. I have no direct knowledge of the matter for Gemini 2.5, but in general TPUs were widely used for training at Google. Even Apple used them to train their Apple Intelligence models. It’s not some esoteric thing to train on TPU; I would consider using GPU for that inside Google esoteric.

P.S. I found an on-the-record statement re Gemini 1.0 on TPU:

"We trained Gemini 1.0 at scale on our AI-optimized infrastructure using Google’s in-house designed Tensor Processing Units (TPUs) v4 and v5e. And we designed it to be our most reliable and scalable model to train, and our most efficient to serve."

Google's been doing custom ML accelerators for 10 years now, and (depending on how much you're willing to stretch the definition) has been doing them in consumer hardware for soon to be five years (the Google Tensor chips in pixel phones).