Who's Afraid of Chinese Models? The Return of Marginal Costs in AI

The 2 things people need to remember:

1) China can (and does) use the models to influence the west. They train in false information about Taiwan and Hong Kong. Or pretend like history is in favor of China.

2) Ignoring the models containing false information, they are incredible. But you should be scared of running inference via the model creators directly. If you think your data is safe compared to running it via model providers in the US ( either frontier or model hosts like fireworks.ai ) then please let me know your bank details so I can poke around.

People seem to conflate "made in China" with "can't be trusted." id argue the bigger distinction is open vs. closed. An open model can be audited, fine-tuned, and technically run entirely on your own hardware. A closed model is basically "trust us."

I am afraid — if Chinese models go mainstream it has a clear way of pushing its narrative way beyond its otherwise borders. More like a Trojan horse it is for the Chinese.

Here is a quick example of how Chinese deepseeks agent works kn its underlying model) when asked a tough question

https://x.com/jinen83/status/2079406993979383902?s=46&t=D7hQ...

> distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? ... The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service that forbid distillation

Sounds great to me; live by the sword, die by the sword.

The article makes a point about agent harnesses being sticky (the supposed moat). I have been building my own agent harness for a while, and I can tell with confidence that the harness almost does not matter, the entirety of the AI magic is the model itself. The harness can be almost barebones (like, for example, mini-swe-agent used for benchmarks), and yet the model still does the task just fine.

So from my perspective, it's doubtful that this is the moat. Besides, for example, Claude Code in particular is so buggy (and always has been).

> The defining characteristic of a commodity is that it is fungible: a gallon of oil is a gallon of oil; a ton of copper is a ton of copper; a bushel of wheat is a bushel of wheat.

The concept of “commodity” as defined above is a model, a simplified abstract representation of reality, but that does not match the reality perfectly (the map != the territory).

The author claims that a token isn't literally an ideal commodity, but neither is oil or wheat, many factors influence their real value (intrinsic properties, location, available storage at production, expected delivery date, etc.) so that no two gallons of oil in different contracts have the same price.

Is treating “tokens” as a commodity a worse model than treating oil this way? It depends who you ask! I'm pretty sure that a chemist working at a refinery would be more happy to see tokens being felt with like a commodity by his company than if they started viewing crude oil like one.

(Overall, there's way too much economism in that post, and way too few facts, and as a result the argument makes very little sense, the author basically wrote that both OpenAI and Anthropic are drowning in cash right now because compute scarcity means the price must be significantly higher than the marginal cost…)

Lets do "who's afraid of US models" version:

* Me, as an individual, because I might not be able to pay price hikes, because my revenue (salary) is much lower than what they want and I can't support my expenses via huge bank loans.

* Again, me as a new entrant to the industry, LLMs are basically pay-to-play games, again related to price hikes, new entrants might not be able to afford paying those prices 24/7 - which you need when learning new things.

* Any non-US company, US can block the models which can disrupt the whole business.

* Even some US companies, for example if you operate in EU and EU somewhat changes their mind and follow the ICC and require you to stop working with Netanyahu (war criminal as per ICC), then following laws in EU, might create trouble to your whole business.

Releasing open weights that can approach frontier level intelligence (irrespective of number of tokens burned) is just a way of telling the world that anyone, even China, can serve frontier level inference if they have the chips and warm shells to do so.

What is stopping China from gaining a majority market share, then, in terms of serving inference?

AI Sovereignty -- yes

Cybersecurity concerns -- yes

Latency -- no, unlike previous emerging IT workload types , inference does not have strong latency requirements. eg 1s of additional network latency doesn't matter to a 15 min, 10-turn agent session.

Cost -- ultimately this comes down to a nations ability to plug chips into warm shells. which forks into geopolitical / trade on the chips side and energy scalability and modularity on the warm-shell side. Even if you call geopolitical / trade a toss-up, China has the US beat HANDILY on the energy front, yearly they are deploying 10x power to their grid relative to the US, which is shooting itself in the foot at every possible moment.

IMHO chip tech will travel across borders, absent a breakthrough in analog inference, energy scalability will ultimately dominate.

The people who are most afraid of Chinese models are the VCs who poured into Anthropic and OpenAI at astronomically high valuations. Anthropic is valued at $1.2T and OpenAI is targeting $850B. These astronomical valuations were built on the premise that these labs would generate massive profits from premium API pricing, but the Chinese labs are completely undercutting this strategy by releasing excellent open models for free. If the frontier labs are forced to cut prices and join the race to the bottom in token prices, these valuations are unjustified, and VCs will face enormous (paper) losses.