Your guess is as good as mine for China though.

[1] https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/

In China, it's because they are being heavily subsidized to do the research activity. It's not really complicated -- if you allocate public money for people do to a thing, they will do it.

People are happy to pay $50/year per seat to have the extra features and to not have to deal with stuff.

There's an issue at the margins here:

$1000/employee is a massive cost - it has to be deeply justified. $50/employee is like ... $2 out of your pocket. It's an incremental cost. The CFO is happy to pay it if there is a lot of value.

A lot of software is in that later category.

Imagine if gasoline was 1 cent per litre - and there was 'free gas' but it was a pain to use, and you had to check a bunch of things. You may just pay the 1 cent.

AI is not quite that yet, but these dynamics will play out eventually, for a lot of things.

if you do the training then you're in control of the output. For example, recommending your products/services or failing to mention your competitors. You could also automatically introduce backdoors into code deemed interesting, i'm sure all governments are very interested in having that influence.

There’s a lot of value in the same sense there is a lot of value in controlling what Google search results are shown and what people see in the Twitter feed.

Some companies (most notably Deepseek) also manage to host their own LLMs so efficiently they undercut all third-party hosting services.

They need their models to be good enough and cheap enough. Then the rest will follow. Companies will figure out how to host them for you efficiently, and you pay them monthly.

I don’t think I will ever want to set up a home server, no matter how inexpensive the hardware gets. At work I still use Cursor (with Anthropic models usually) because it’s paid by my employer, but for private stuff, I’m already using cheap models with OpenCode, and it’s extremely cheap and surprisingly capable.

I think there was around half a year between where the best models became good enough (last year December?) and where the cheap models became good enough (couple of months ago?).

I also think the restrictions on OpenAI and Anthropic are somewhat short sighted. In that the guardrails dramatically limit efforts towards securing your own software in many ways. Yes, it's also "dangerous" and maybe there should be a means of identifying "domestic" or otherwise "secure" accounts for those allowed to use the models without the same guardrails in place.

Americans used to do that too, spectacularly.

Just consider the two alternatives: one is your industrial sector with this incredible new automation and analysis tool available for free. The other is one where it has to pay huge chunks of its resources to overseas companies.

> When entrepreneurs walk into the offices of Andreessen Horowitz (a16z), a big American venture-capital firm, the odds these days are that their startups are using AI models made in China. “I’d say 80% chance [they are] using a Chinese open-source model,” says Martin Casado, a partner at a16z.

This is very different from what the author portrays. It may be the case that many pre-funded startups are using Chinese open-source models (somewhere in their workflow). But what percent of startups that survive more than a year (either with funding or revenue) are still doing this?

I imagine their pitch is: "look at how well we're doing using open source Chinese models! We'll do even better once we raise money to be able to afford frontier models!"

The way the author presents this quote makes me think he had a preferred narrative and found quotes to back it up. Or he's just a very uncareful reader.

If you saw the engineers you’d see 80% Macs and 20% Linux laptops.

The statistic would technically be true.

I use Chinese open weight models a lot, but they’re not what I reach for when I’m doing important coding work.

The ai libraries we use let us switch models with just a configuration change.

I put my foot in the mobile comparison the other day, and will again. If you were to go back and be a mobile dev in 2010 by all means specialize on one platform, but play with both as a professional interest to stay realistic. Here it's important people have access to US/Chinese/Other, open/closed, local/cloud and that this remains. Don't become a blind Claude guy or a open weights fanatic: that way lies disappointment.

But the big price is AGI and who gets there first, right?

If you're putting a lot of your money and time into a business, do you really want it built on a service only hosted by one company that will turn it off eventually and you have no recourse?

If you build something against an open model you can take that and run it anywhere. If your favorite model provider stops hosting it, you can go elsewhere, you can go rent GPU instances, you can even shell out and buy hardware to run it yourself if you've got the capital and it makes economic sense. Change some API keys, update a URL in your config, and you move on.

If the government decides that proprietary model is too good and so it gets shut off, what do you do? If a proprietary provider decides it's not worth it for them to continue hosting that model, what do you do? If that provider silently updates the proprietary model and it makes your app broken, what do you do?

Model ai startups start from OSS models, and use them extensively for different purposes as their work would usually be banned by proprietary labs.

Application ai startups don’t want to fight the model game, so they either pick the best or let the user control it.

On the other hand, when developers are developing, they mainly use US AI because the quality is better.

When developing AI related services, they prioritize Chinese models due to lower API costs.

It seems like the article didn't make this distinction.

So the claim that Chinese AI is the top choice for service level AI isn't entirely wrong.

All of a sudden in almost all social media channels I'm seeing this type of content and then its usually upvoted to the top.

Non-gatekept forums like this are exceptionally easy to astroturf.

The model I use to vibe code with I am just going back and forth with. Since Im building it as I go using an agent doesn't make sense but I guess that's where all the token usage comes from? Pardon ramping up my skills via vibe coding this one idea for about a month and have never hit any quota and or have gotten anywhere near my limit.

They care about control. I see many of my enterprise (or just-below-enterprise) clients very annoyed at OpenAI and Google after 2-3 years of model toil, where they had to constantly re-calibrate onto new models, on tight externally mandated deadlines, with little certainity. Now they are reaching for open weight models instead, that they currently host with the same inference providers, but have the option to in-house if push comes to shove.

In this context, open source/self-host means do it yourself, closed source means you trust someone else to do it for you.

In the long run open source always wins because of the community effort, customization, network effects and price.

Internet protocols are open, anyone can setup a website, host their own email server..., but most people don't do that, they rely on someone else to do it for them.

People still pay for Windows rather than use Linux because most people and companies have better things to do.

The main threat to American AI companies is not that consumers will self-host, but it's that new hosting companies will appear that will host open source models and offer them (cheaper) to consumers. It'll be like the web hosting market before the era of cloud computing.

The llama drama will be a netflix show of it's own in 5 years.

This resulted almost every time in "screw off we'll train our own models or use refined open source ones instead" leading to a lot of anger at Anthropic by CEOs these days.

We all want an alternative and Anthropic and OpenAI need to charge more than they are worth to pay back their investors and everyones stuck now.

This is also a strange way of framing it, though. Llama was released as a research project, it was never intended to create some vast ARR revenue stream or reframe the way people look at AI. If Meta wanted to exploit it for personal success then they had lots of opportunities to do so.

With OpenAI and Anthropic's profitability under question, it is up in the air whether or not America's stance towards AI will work. If they can't convince the world that they're a proper software business, then China's philosophy will win by-default.

The open source character of the models is irrelevant if you need a nuclear powered data center for inference. In the end it is just another internet service.

I’m not an American so I don’t particularly like the idea of giving an American cartel of AI companies having so much power over this technology.

I don’t necessarily buy into the “China bad”, “they are communists” and all that BS either.

I will call a spade a spade and say that in this instance, what China is doing is a net good for the world, ideology be damned.

I'd be happy to pay a small monthly fee to license the model to run locally. I'm already paying for Claude, GPT, Gemini,etc.

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.

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.

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.

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.

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.

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.

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.

You have to be careful with the inference provider though, Chinese providers are subject to laws that mandate data sharing with their government.

Most popular models on OpenRouter right now: https://openrouter.ai/models?categories=programming&order=mo...

Top 7 are all open models.

Assembling a new model from scratch requires a ton of resources and knowledge bases... there's been a lot of sketchy activity just in training. You also have weighting, distillation and other approaches to create more portable options that can run on lesser hardware. But, K3 as an example takes massive compute resources to run.. and this isn't going to get to a portable device any time soon... as Moore's law is effectively dead, you may get newer/better tooling around the LLMs, or you may get an entirely new/unique approach to AI... but current trends aren't going to put a leading model on your own hardware anytime soon for most people.

I.e. possibly the same thing USA is also doing, but USA is an ally in some sense so it's not quite as bad.

This should be done regardless of which model was used - American or otherwise.

A hosted model is different because you could prompt inject specific customers, but I assume from this question you mean a malicious open source model being hosted by an honest provider.

I tried Kimi K3, Qwen3.6 35B A3B, GLM 5.2 and Qwen3.7 Plus, chosen arbitrarily from Chinese models I could access quickly. I used your prompt exactly, and all 4 managed to produce correct functions all with the correct name. Interestingly, Kimi K3 wrote one in both C and Python, Qwen 3.6 chose Python, GLM 5.2 also chose Python, and Qwen3.7 decided to be an over-achiever and wrote functions in Python, C++, Java, and TypeScript. All correct and with the correct names.

1. Can you give me some examples?

2. Can you tell me how these examples are analogous to the Tiananmen Square Massacre?

Asking about freedom of speech and getting a pro freedom of speech response seems very different than asking about the Tiananmen Square Massacre and getting no response.

The failed rebellion against Sam Altman at OpenAI pretty much proved that.

School shootings happen every year in the US, yet guns aren't banned.

Besides, banning open models would put ordinary US businesses at a disadvantage compared to the rest of the world.