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> While they'd love to reduce their actual costs, they'd only want to do it to the extent they are certain they can keep it secret.

So you are saying that frontier AI labs are spending billions of dollars on datacenters as a form of marketing. And they are colluding to hide the fact that they don't need to.

Of course they profit more if they are in front, but bleeding money to pretend to be in front is not a winning strategy. They can't fool the market if their models are not actually better, and they know this.

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Given that tokens are supply constrained right now for Anthropic and OpenAI (especially a problem for Anthropic), stepwise efficiency advances for either would give it a leg up on the other. It would also help them better compete on price with Chinese models.

Given that neither company releases parameter counts, that sort of information would be slow coming out anyway. The most important thing is improvements in actual performance/ benchmark numbers, which allow them to preserve their price points as much as possible.

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Google seems pretty happy to release smaller, faster models. 3.5 Flash is pretty clutch isn't it?
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At 6x the cost of its predecessor!
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Google, who has invested in their own hardware supply chain and is already solvent in their own right, seems to be best positioned to force the other players to implement SOTA optimizations in their product offerings.
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Google can definitely play a spoiler role here not only due to their compute infrastructure and ability to play the long-game financially but they also have more existing ways to monetize with their other businesses.

The ideal pro-consumer scenario is OAI and Anthropic are prevented from extracting monopoly rents between 'close-enough' self/cloud-hosted open source on one side and Google on the other. I'm really hoping that's how it plays out. Of course that will be somewhere between bad and disastrous for all the VCs and hedge-funds who financed the mad AI build-out far in advance of demand, and then kept funding it as prices went vertical.

However, I'm shedding no tears for them as I look forward to the fire sales when all the GPUs and RAM they pre-bought flood back onto the spot market. :-)

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Google has also built a Knowledge Graph Ontology project which has stored facts. So LLMs could just incorporate facts requirements from there. All they need is a proper reasoning model which is reason heavy and fact lean.
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Yeah just watch out, they're trying to eat your 401k and they've got a powerful easily influenced friend.
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Priced like a much larger model
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I’ve shockingly quite enjoyed coding with it using antigravity. I only really use 3.5 flash and gpt5.5 xhigh
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I've not been impressed with the latest flash model at all. :\
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