I don't doubt that's an unregretted side-effect for political leaders in China.

But the major motivation is to accelerate diffusion within their own massive economy in the pursuit of an across the board productivity boost in the face of an aging population.

The main difference here is if a startup goes underwater all the tech is usually lost. The Chinese weights are not going anywhere if the labs fail.

1) They sell compute: chips (Nvidia), data centers (AWS, Microsoft, Google, SpaceX, etc), or even end-user device manufacturers like Apple (e.x. M7 rumored to have 1.5TB of unified memory). If Jevon's paradox holds, then cheaper (or free) models means more demand. But compute is likely supply-constrained for years anyway.

2) Their product isn't AI but depends on AI being cheap, or they don't want competitors to capture that value, i.e. "commoditize your complement" https://gwern.net/complement

It probably doesn't make sense for these companies to invest a lot of money training models that will be obsolete in a few months anyway. When progress starts to plateau I'd expect more companies to start training models they give away for free.

I often see the sentiment: "the Chinese strategy only makes sense in the context of undercutting American labs' profit margins".

If, for example, you are a company with a near-monopoly on "serving video content", and you feel reasonably confident about retaining a decent slice of the serving-video-content market (Google in the west is an example, Tencent in the east), then training video models on your dataset - and releasing them freely - makes an awful lot of sense.

Free tools to create with mean more video content. In this hypothetical, you're reasonably certain that any video content which does get created will also be watched on your platform.

That is a net positive. The question becomes: How many watch-hours earns back the cost of training a model? It's probably not really that many, especially when you have a near-monopoly on a billion sets of eyes.

It's also a net-positive if people build better video models from research you release, because - again - you are reasonably certain that the even-more-innovative content those models produce will be watched on your platform.

It really begins to make strategic sense if your company is in a GPU-poor environment. Your costs cease at the point you upload a model if your users are running it themselves. You don't have to serve the model. The content is still created.

You are also less likely, I think, to alienate human creators whose work the model was trained on if the model is not sold back to them as a subscription, or by the token, but given for free as a tool.

This frames the conversation very differently. It creates, I think, less of an "us vs them" dynamic, and more of a rising tide.

It's true that it is also beneficial that these models undercut (especially in language models) American companies. But, generally, Americans are not the customers of Chinese companies releasing models. They are already serving a huge volume of customers in a complex, existing marketplace.

The full picture is much more nuanced than simply a geopolitical desire to undercut US labs, and there are several other reasons the strategy can make logical sense.

I agree with this sentiment and think it's echoed in Fareed Zakarias take here: https://youtu.be/VBblUjLw5lE

China seems to perceive AI as a much more sensible technology than the US and seems to be integrating it in far more industries than the US.

I'm not sure the American mind can understand the distributed benefits afforded to the Chinese economy from opening their AI models, I think it's pretty reductive to assume it's purely a strategy of undercutting American frontier labs.

Releasing the models for free accelerates the trend but if you're a startup that needs leverage it's a good way to build brand and customer momentum that will be relevant in the more established future market.

I can see an American company taking on the same strategy, and in fact Thinking Machines based out of San Francisco did that just a few days ago by releasing their first model with open weights.

There are people that spend tens of millions of dollars on paintings and artwork. I can see plenty of reasons why organizations and individuals will continue to want to drop a few million on an AI model just for the fun and prestige.

Is that maybe spilled milk?

Maybe today's US frontier models provide enough information content, so that the momentum suffices to use them as a base for every coming generation of distilled and later fine-tuned models?

A pittance frankly. Something that could easily be covered by oh I dunno, let's call it a National Science Foundation who's in charge of subsidizing important basic research for a nation's interests.

Anywho, when the market is trillions (and of potential nation state concern), it is pretty inconsequential and very much worthwhile.

Aside, I think your scale is a bit off, I think Moonshot has raised $5B and potentially they get other breaks from China, not sure. So to produce something like SOTA takes billions, not tens of millions. I'd still argue it is worthwhile to subsidize and invest in open versions, imagine spending $5B to unlocking a few percentage point increases in your country's productivity.

Imagine if, in 10 years time, every school kid is learning the causes of the US civil war from an LLM, getting their essays on hiroshima and nagasaki graded by an LLM, and a million other things.

A country with competitive LLMs gets to decide whether "it was more complicated than just slavery", and whether "it was tragic but necessary, saving lives over all".

Countries without competitive LLMs are effectively going to be buying all their history, economics and sociology textbooks from abroad.