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Comment by anthonypasq | Hacker News Reader

Sergey better really go into "founder mode" or else Thomas Kurian (head of GCP) will continue to eat Deepmind's lunch.

Between selling TPUs to external companies and now hosting competitor models, Gemini just doesnt have the full faith of the Alphabet capital allocators, and I can't really blame them. A mature company like google would rather just expand their extremely profitable cloud business rather than dump billions into a black hole on the off chance they achieve RSI or superintelligence or whatever.

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> Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development

Sundar Pichai just this week.

https://finance.yahoo.com/technology/ai/articles/google-ceo-...

This can be just as well phrased as:

> Our frontier AI business is at any given moment in time forgoing short-term development because we are making compute trade-offs to support our cloud business revenues.

Exactly, the definition of tradeoffs

Infact all companies, at all times are doing tradeoffs. You and I can be doing something else right now but I'm here on HN replying to you - and that's a tradeoff.

If someone suggests -just do X then you don't have to do a tradeoff, that just sounds wrong to me.

The primary trade off amounts to CEOs enriching themselves by sucking resources out of the company they control. They trade the company's future and their employee's incomes for personal wealth. Investors are the bag holders for CEOs and their insiders.

Google is a multi trillion dollar company. Sundar Pichai is only recently a billionaire.

The investors are the ones whose net worth is measured in trillions, and most of the individuals are invested through 401k and pension accounts.

We’ve been seeing compute allocation failures in GCP us-central1 for about a month now- that can’t be related can it? These aren’t TPUs, this is normal compute for dataflow/etc jobs.

Frontier AI development via agents uses plenty of CPU compute. Maybe in a lopsided way during development as opposed to training?

10 bucks say it's just a high-demand region and GCP's hardware supply chains are at capacity. So you see stockouts.

Infra might very well be the right play. Over the past year, it seems like the evidence is on the side that there's no "secret sauce" in AI, and models will all converge towards similar capabilities. Not much margin to be had for having the best model for a month or less until another lab leapfrogs you by 3%.

I think RAM turned out to be the secret sauce all along!

I keep saying LLMs are the new VMs and everyone gets mad at me, but time will tell.

If there's no point in having the best model, there's also no point in leapfrogging the current best model. Conversely, labs trying to leapfrog each other indicates that they consider the advantages of having the best model (or at least a better one than they had before) worth it, even taking the competition into account.

Suppose the big 2 labs didn't think the expense was "worth it."

Wouldn't they keep spending anyway because they need to hype things up before IPO?

I think it shows that they consider their to be advantages to being seen as having the best model, which is a subtly different thing.

"4 out of 5 dentists prefer DentifreshAI"

I mean the labs are in a tricky spot where they have to justify massive valuations. "We're going to stay where we are and focus on inference" isn't the growth story you need when you're trying to justify a 2T valuation on 80B in revenue (unless you dress it up as a safety concern).

Google can afford to take their foot off the gas without killing their business.

What does “capital allocators” mean here? If your implication is that the Gemini org is resource starved and a few extra dollars will put their model in the lead that is 100% incorrect. If anything it’s the bloat and bureaucracy keeping it behind.

Signing partnerships and hosting third party models costs Google nothing, and in fact brings in a ton of revenue that will eventually go back into Gemini.

Google uses Gemini for its AI Overview on Google Search results, though. And it seems to me that this is an area where they really have a big incentive to run their own AI that they completely control. AI Overview sits at the top of the Google Search results first page, so when it comes to marketing it's sitting in the most coveted place on the entire Internet.

It's also marketing that their AI is hilariously terrible because AI Overview is the biggest posterchild for AI slop and often used as an example of how bad Google is at AI. Gemini proper isn't that bad, just the ultra cost cutting version on search is.

To be fair the search version of Gemini is nowhere near as bad as it used to be. The bar is in the floor I know.

> The bar is in the floor I know.

People don't seem to realize, this is how successful businesses happen. Go take a look at Windows 1.0, say.

It has gotten a lot better and it's useful for information on most normal search queries now.

Yes, I'm realistic about it too. For simple queries it nails it more often than not. It's only asking it more complex technology related questions that it falls apart for me, and in those cases I already know I should be talking to one of the big boi models for certain lines of questioning.

Google certainly ain't gonna run a several T model for every search that's for sure (well, not yet).

Do you think most people around the world want access to ultrathink?

For a lot of folks, something low cost and low intelligence is what they want. And for a few that needs high intelligence, there's stronger models.

Google has both bases covered. It is brilliant.

I've had the Search version of Gemini give substantially better responses than (free) ChatGPT in some cases.

im talking about research compute allocation

It’s not called Gemini Overview…

Google are now offering Claude models in the Gemini "app". https://cloud.google.com/blog/products/ai-machine-learning/w...

I think it makes sense for them to separate the application/productivity layer from the model layer. And offer models from many model providers, including OpenAI.

To stay competitive with Microsoft and other productivity suites.

The tragic truth is that we live in a world where open models are perpetually only a few months behind frontier models.

Opus 4.6 was "good enough." Having a frontier model is no longer a requirement to print cash with a GPU.

AI hosting currently generates revenue, so the choice is trivial. Building an LLM model is company PR, while hosting another’s model provides immediate income.

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