The AI labs have been subsidising. When they try turn a profit, people will move to the fast followers. The only people that won’t are those that compete on leveraging the very latest models and even then, once spend and scale goes to the cheaper providers, we’ll see deeper research from those providers too. Think “PC compatibles beat IBM, Sun, SGI eventually”.

> Domestic China is the only very large audience for their own models

I don't think so. US models are very expensive, and not available in every country. I am not willing to pay $50/1M tokens for writing my pet projects.

without any hard data one way or another your comment is worthless. "pile up subscriptions" - based on what? neither company is public. "piling up subscriber counts", "piling up API usage"? cool. how much money are they making? oh you don't know because they're not public.

the reality is one way or another that as long as there exists an alternative that a USA company could serve with the same compute rented from hyperscalers, this represents a threat, even if the extent to which is unknown

But what does that mean for Google if their model isn't as good as OpenAI's and Anthropic's?

Why wouldn't the hyperscalers run these open models since they're much better than OpenAI and Anthropic at operating compute at scale?

I think the reason OpenAI and Anthropic stay ahead in revenues right now is because the models are improving too quickly to reliably compete with them on cost.

But, once model performance reaches a plateau -- they have to at some point, though perhaps years away -- that's when ability to operate compute infrastructure at scale becomes the secret sauce.

The big AI labs are likely safe until models stop improving fast enough to protect them from competition on cost.

This similar pattern has repeated in most technical booms prior to this.

When hard drive technology was improving fast enough that old hard drives were quickly obsolete, IBM could maintain good margins making hard drives. But once hard drives got good enough and advances were slow enough that innovation was not the only factor considered by drive purchasers, commodity hard drives started to take over and IBM had to exit those businesses.

The same is likely to happen once model improvement slows.

You’re absolutely right and it’s heartening to see. I maintain a client with ~every provider you can think of and llama.cpp and it was really tiring the last few days to see people laundering other stuff through Kimi and Qwen. They’re not even open yet, the hype was based on their own blog posts, no one’s actually running these locally, the Qwen Max’s have never been open, Kimi’s API was 1/2 the speed the benchmarks was based on, when it was up, and had 60% downtime before they had to stop accepting new accounts, and their EULAs are “your inputs and outputs are ours.” May being clear-eyed benefit us both in the long run.

> is the same exact reason the Kimi crowd is wrong now. And it's very obvious that they're wrong, but they have intense emotional blinders on.

On the very link on the top comment of this thread, which I repost here:

https://artificialanalysis.ai/models/gemini-3-6-flash

Kimi K3 is ahead of Fable 5 on several benchmarks.

So basically the angle went from "China cannot ever compete" to "China is six months behind" to "China is six weeks behind" to "China is six days behind but that's because they're distilling" and now you're saying "Yup sure, Kimi K3 is ahead on several benchmarks but you cannot host it yourself so this thing will go absolutely nowhere".

I mean: is it not a bit early to draw conclusions? It's been days since a chinese model is ahead of the very best / frontier US model on several benchmarks and you compare it to models who were clearly behind on everything.

Give it some time.