Some do, unfortunately. But that's not all of them.

My country has no frontier models; since I've no national grounds to support one provider over another, a strong degree of provider diversity is an obvious benefit to me. I also think a highly competitive global market is a good thing for mankind in general, any hegemonic power having a monopolistic or oligopolistic position in AI provision is probably the 'bad ending' for most of us since it represents an enormous concentration of democratically unaccountable power regardless of the power which 'wins'. I'd argue it's rational, not spiteful to favour provider diversity on those grounds.

Someone like me who spends a lot of time in the anglosphere puts faces on companies like OpenAI or Anthropic. I doubt that's true for most people, especially outside of the USA. They're just big companies, like 3M, DHL or Toyota. Who knows what the management of these companies think of the future of humankind?

I would expect than personality-driven rejection of American tech from abroad nowadays is driven mainly by Trump and, to a much lesser extent, Musk. Everybody else is just "some guy" to 99% of the population.

So I'm doing what I can - I only use local models for 100% of my personal coding. (And their capability is amazing! Qwen 3.8 Flash Next leaves me wanting for nothing at this point.)

And I know open weights aren't enough. I'm ready to support open training if/when we get around to that. I hope once hardware prices ease, we can see homelabbers building small AI hosts (small by homelab standards yknow. So let's say 512GB host ram and 1-2 32GB cards, though i hope we also start to see AI cards with lots of vram but less compute cores). Then maybe we can do folding@home style distributed training.

In a time when software freedom is perhaps more threatened than ever, I'm happy to see that information still WANTS to be free, perhaps even more than ever. Look at how easy it is to distill claude! Despite, presumably, anthropic working very hard to prevent it.

https://www.astralcodexten.com/p/ai-safety-is-not-a-corporat...

Is engineering the factories that make the hardware not nuclear engineering? Even if not, considering there exists only 1 supply chain in the world capable of making them, it seems pretty revolutionary. And that moat seems pretty big, given that it hasn't been crossed for many years.

https://www.astralcodexten.com/p/ai-safety-is-not-a-corporat...

The risk is that USA and China will come together on a joint "AI non-proliferation" agreement in the same way that nuclear technology became restricted by the countries that already had the capability. This will lead to a de facto techno-colonization of the rest of the world just between themselves.

This was a topic of discussion at Trump and Xi's AI summit just last month.

Not unless they break their own TOS. The temptation must be very large to do so and is probably why those settings keep mysteriously shifting back to 'yes, I give all my data to you to do with as you please'.

I don't think the evidence supports this claim. All the important capability gains now come from RL rollouts in real environments. You can't fake that by generating text from a teacher model. Anthropic makes a lot of noise about how China is distilling their models, but all Chinese labs can access is text outputs. They can't even get the logits. Now, compare all of the recent US open-weights models that are far behind the Chinese frontier. Why haven't they simply "distilled" all of those powerful open weight models that are available to them?

Isn't this somewhat akin to a model learning from its own inputs? Doesn't that behavior lead to model collapse? Either the model is trained on pristine data, in which case it stagnates, or it's distilled from another model, which leads to model collapse.

In fact, I'd assume this direction has been widely discussed and considered at Anthropic. I think Fable's release was an experiment to test this.

The biggest risks though are China and Moore's law. In the short term, if you don't offer a competitive model as the market grows, you fall behind in marketshare. Anthropic's edge is that it's offering is still better than Chinese offerings. If they offer anything less than that, something not better but just comparable for example, they lose on price. In the long term, as computing capacity continues to increase, local models potentially pose the same challenge.

So there's plenty of pressure for Anthropic to keep widely offering the best model they can.

I am under the impression that curated training pipelines are driving the gains, not utilizing random usage data in nefarious ways.

So like electricity, where most people will probably just use the network, but a few will opt to invest upfront and generate their own.