Even then, I think that their primary use case is going to be consumer grade good AI on phones. I dunno why Gemma QAT model fly so low on the radar, but you can basically get full scale Llamma 3 like performance from a single 3090 now, at home.

Google has already started the process of letting companies self-host Gemini, even on NVidia Blackwell GPUs.

Although imho, they really should bundle it with their TPUs as a turnkey solution for those clients who haven't invested in large scale infra like DCs yet.

And also, Google's track record with hardware.

My point was a bit more specific though. To elaborate, I know of a number of publicly traded companies (USD $200M+ market cap) globally which have identified use cases for onprem AI and want to implement them actively but cannot, because they lack the knowhow to work with onprem, and hiring talent to implement that is just extremely expensive. Google should simply provide it as a turnkey bundle and milk them for it.

They’ll definitely have the best model, but there is a chance they will f*up the product / integration into their products.

But then again even there, their reputation for abandoning products, lack of customer service, condescension when it came to large enterprises’ “legacy tech” lets Microsoft who is king of hand holding big enterprise and even AWS run rough shod over them.

When I was at AWS ProServe, we didn’t even bother coming up with talking points when competing with GCP except to point out how they abandon services. Was it partially FUD? Probably. But it worked.

there are few groups as talented at losing a head start as google.

What are the chances of abandoning TPU-related projects where the company literally invested billions in infrastructure? Zero.

All things that Google is remarkably bad at.

Penny-wise pound-foolish.

https://tomforsyth1000.github.io/blog.wiki.html#%5B%5BWhy%20...

So it isn't like Google designed a TPU for a specific model or architecture. They're pretty general purpose in a narrow field (oxymoron, but you get the point).

The set of operations Google designed into a TPU is very similar to what nvidia did, and it's about as broadly capable. But Google owns the IP and doesn't pay the premium and gets to design for their own specific needs.

Personally right now I see one clear leader and one group going 0-99 like a five sigma cosmic ray: Anthropic and the PRC. But this is because I believe/know that all the benchmarks are gamed as hell, its like asking if a movie star had cosmetic surgery. On quality, Opus 4 is 15x the cost and sold out / backordered. Qwen 3 is arguably in next place.

In both of those cases, extreme quality expert labeling at scale (assisted by the tool) seems to be the secret sauce.

Which is how it would play out if history is any guide: when compute as a scaling lever starts to flatten, you expert label like its 1987 and claim its compute and algorithms until the government wises up and stops treating your success persobally as a national security priority. It's the easiest trillion Xi Xianping ever made: pretending to think LLMs are AGI too, fast following for pennies on the dollar, and propping up a stock market bubble to go with the fentanyl crisis? 9-D chess. It's what I would do about AI if I were China.

Time will tell.

All the LLM vendors are going to have to cope with the fact that they're lighting money on fire, and Google have the paying customers (advertisers) and with the user-specific context they get from their LLM products, one of the juciest and most targetable ad audiences of all time.

I would only offer one disagreement with your post: There will not be a single winner in LLMs. The landscape is so large that we will have multiple winners in different areas. Example: Google might fail in B2C LLM (chatbot that answers your questions), but will certainly be (wildly?) successful in B2B for adverts.