Just because Anthropic and OpenAI really want there to be an arms race justifying the outsized investment, doesn't mean the optimal play is to build larger, more expensive, models.

The capital infusion the frontier labs have received has gotten to a size where many believe it may not be possible to recoup this investment without some very unrealistic things happening.

I think it's reasonable to not completely drain one's cash reserves trying to stay ahead in a race where participants may very clearly be about to run straight off of a cliff.

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gbriel an hour ago | parent | next [-]

Google doesn't have a good coding model. This is a HUGE problem. They don't need "larger more expensive models", they need a good coding model because it's a competitive advantage.

	▲	dbbk 13 minutes ago \| parent [-]
		Competitive advantage why? Will it really make them more money? They already have Google Cloud.

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dbbk 14 minutes ago | parent | prev | next [-]

All Google has to do is build a model that works good enough for the Gemini app and for Spark. And they have it.

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msabalau 2 hours ago | parent | prev | next [-]

And it doesn't have to be either/or. They could make larger, more expensive models, just at a slower cadence.

Sure downside would be not learning from people using your model for coding, if we're on the cusp of huge leaps in self-improvement. But there is a reasonable case for avoiding desperate scramble, especially if other parts of the business can also create value with the compute.

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guilhermeasper 2 hours ago | parent | prev | next [-]

Yes, I agree with you that the race all the AI companies are running doesn't make sense, but at the same time, there are rumors that Google has produced newer versions of Pro without releasing them to the public.

Version 3.1 has plenty of room for improvement, yet they don't seem to be giving the attention it deserves or at least communicating accordingly.

	▲	SimianSci an hour ago \| parent [-]
		There is more to the cost of a model than its training. While training is a significant Capex expenditure, it has very low Operational cost after training unless it is deployed for public inference. It may be that they wish to slow their cadence of releases, or develop their models to focus more in a different direction, etc. No matter what the actual reasoning, they have chosen to not compete in the same race, and I cannot say I fault them.

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jimmoores 2 hours ago | parent | prev | next [-]

If the Chinese labs can compete on a shoestring budget with access to much less powerful hardware, Google should be able to compete as well. They're becoming almost irrelevant for agentic coding right now.

	▲	bitexploder 2 hours ago \| parent \| next [-]
		AI / LLM is about more than agentic coding. It is one of the least interesting use cases to me, thinking more broadly. HN may be over-indexed on it.
	▲	SimianSci an hour ago \| parent \| prev [-]
		It's not much of a shoestring budget to be receiving regular injections of investment from state lenders along with cheap credit. I don't think the comparison holds.

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onlyrealcuzzo 2 hours ago | parent | prev [-]

Google paid for 3.5 Pro training. They just didn't release it.

They never gave an official answer as to why, so I'll let you draw your own conclusions.

They did not decide it wasn't worth spending the money to train.

They absolutely spent the money.

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bitexploder 2 hours ago | parent | next [-]

I work there. I have zero internal knowledge about the model. Opinion my own, etc. I don't think it is worth fighting to win on a month to month time horizon. When you step back and look an inch above this market, Gemini Pro 3.1 as a product was released in February. 6 months. It feels like forever and that Google is behind, but on a 2-3 year horizon? The models are going to stay similar.

Also, look at Flash 3.5 to 3.7. Flash 3.7 is a genuinely decent Sonnet 5 class model. Flash 3.7 is quite efficient too. Also, whatever was spent training 3.5 pro is probably not wasted. However, as a strategy, when I see models like Kimi K3, Fable, Sol. If you discard "because the model sucked" what other alternatives or potential options might exist?

I thought of a quite a few and they are far more compelling and interesting to me.

(Also Gemini models tend to be pretty decent at more than just programming. Enterprise AI use is more than just software eng / programming)

	▲	mh- an hour ago \| parent [-]
		I'm the CTO of a GCP shop with an 8 figure annual commit. If you'd told me at the end of Cloud Next 2025 that by now Google still wouldn't have a competitive offering to agentic coding offerings from Anthropic (Claude Code + Fable) or OpenAI (Codex + Sol), I wouldn't have believed you. In our non-coding use cases where we're embedding models in our product, we're also not reaching for GCP stuff. Because Anthropic has the mindshare of our engineers and product folks, since it's what they use every day.

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WarmWash an hour ago | parent | prev [-]

3.5 was almost certainly a 3.1 post-train, so likely a small investment on Google's part.

They mentioned that they have already started pretraining Gemini 4, which will be the full ground up rip-your-face-off-expensive training that is often discussed.