China wants to industrialize the token.

In March, China's National Data Administration fixed the official Chinese term for token as 词元, cí yuán, and called it a settlement unit linking technical supply to commercial demand. Beijing now tracks daily token volume and reports it at policy events the way it reports steel output.

The headline number is a thousandfold rise in two years. About 100 billion daily calls in early 2024, more than 140 trillion by March 2026.

Treat that with some care. It is state-reported, nobody audits it, and it counts consumption instead of value. That is not a China problem specifically. No government or vendor anywhere publishes an audited token number. An agent stuck in a bad loop overnight burns an absurd quantity and produces nothing.

In April, Shantou in Guangdong closed a loop under a policy called 来数加工, inbound data processing. Overseas data enters a digital bonded zone. Domestic compute runs the inference. The answer goes back out by API. Power, compute and revenue all stay in China.

Chinese state media summed it up with a line that traveled widely: the salt can travel the world, but the salt fields must stay home. Good line. I have caught myself repeating it. It is also promotional framing from the same system that publishes the consumption figures.

What has not been published is volume. One city, one pilot, no disclosed revenue. Shantou was picked for reasons that predate AI entirely. It holds one of three mainland submarine cable landing stations, and latency to Singapore runs about 32.7 milliseconds. Most of the country has neither. That part tends to get left out.

Nobody argues with this one. It is the strongest leg the strategy has.

DeepSeek V4 Flash costs $0.14 per million input tokens. GPT-5.5 costs $5.00. Open-weight Chinese models run 60 to 90 percent below leading Western pricing. On OpenRouter, which routes calls to whichever provider wins on price, Chinese models went from under 10 percent of usage in early 2025 to a record 58 percent of tokens processed by US firms in July.

In agent workloads the gap compounds, because one task can fan out across dozens of parallel sub-agent calls.

This is the gap most coverage understates, and it is the one that bothers me. You cannot industrialize output on equipment you cannot get enough of.

Huawei's 910C delivers roughly 60 percent of H100 inference performance, by DeepSeek's own published assessment. The Council on Foreign Relations puts the best US chips at about five times more powerful than Huawei's best today, widening toward 2027. Then there is the SemiAnalysis detail, which matters more than any chip spec: most of DeepSeek's tokens are still inferenced on Western hardware. The flagship of Chinese open-weight AI is not running the Chinese stack at scale.

The flagship of Chinese open-weight AI is not running the Chinese stack at scale.

The energy argument, which usually gets made next, has the same problem. China generates more than twice the electricity of the United States and added 543 gigawatts in 2024 alone. But a four-fold power penalty on domestic accelerators eats much of a two-fold price advantage. Cheap power ends up subsidizing inefficient silicon rather than producing cheaper tokens.

Chinese firms are not sitting still. Zhipu trains and serves on domestic accelerators. Alibaba is building data centers across South Korea, Malaysia, Thailand and Mexico, renting foreign capacity to sidestep the silicon problem. Both are rational moves, and neither closes the gap this year.

The factory framing implies the plants are running. Chinese domestic reporting suggests many are not.

Tencent Cloud puts average GPU utilization at China's intelligent computing centers below 30 percent. CAICT data cited by Huxiu shows an effective usage rate of 36.8 percent in 2025, meaning roughly 70 yuan of every 100 invested is idling. Securities Times reported rack utilization of 20 to 30 percent at many centers.

Calling this oversupply misses it. The market has split, and I have watched the same pattern in Chinese logistics: healthy national totals sitting on stranded regional capacity. Leading platforms run at 90 to 95 percent with order books into 2028. The high-quality compute is genuinely short, while the low-quality stuff cannot find a customer at any price. That is a coordination problem, and those take years to clear.

If you read one number here, make it this one. Anthropic holds around 12 percent of token volume on OpenRouter and captures close to 46 percent of the revenue.

The market is splitting into a commodity lane and a premium lane. China owns the commodity lane. The profit sits in the other one.

The economics are moving the wrong way for everyone. Huxiu documents Alibaba Cloud and Baidu AI Cloud raising prices by up to 34 percent this year. Agents consume 100 to 1,000 times what a chatbot does. Unit prices keep falling while invoices keep growing, and nobody has an accepted way to link tokens consumed to work completed.

Under China's 2017 National Intelligence Law, companies must cooperate with state intelligence work. That is a jurisdictional fact. It says nothing about how any particular company behaves, and it is still enough to keep regulated finance, healthcare and government workloads with Western providers.

Washington is escalating separately. The Treasury Secretary raised the prospect of sanctions in July over alleged distillation of US models, days ahead of a September bilateral AI dialogue. If part of the Chinese cost advantage comes from not paying frontier training costs, it is less durable than it looks. Nobody has proven that case. It is not trivial either.

I am not predicting failure. The export story is running ahead of the evidence, which is different. Three signals would move me: Shantou publishing real export volume and revenue, Chinese computing center utilization crossing 50 percent, and a regulated Western enterprise putting a production workload on a China-hosted endpoint.

Any of those would tell you more than another month of routing charts. If all three move, I have this wrong, and I would want to know.

Watching signals is for analysts. If you run a budget, the question is operational. Almost nobody hassorted their workloads by whether they need frontier capability. It gets discussed, then it sits on the list.

A rough method: pull last month's token spend by application. Flag anything doing classification, extraction, summarization, translation, first drafts or routine code completion. That bucket is usually 60 to 80 percent of volume and rarely needs a frontier model. Run a week of parallel traffic against a cheap open model, measure task completion rather than benchmark scores, and keep the expensive model for what actually fails.

At a 35-to-1 input price gap the arithmetic is blunt. A team spending $40,000 a month with 70 percent of volume in that bucket carries about $28,000 where the cheap option runs closer to $1,000. Allow for higher retry rates and some workloads bouncing back, and the saving is still not marginal.

Coding tool vendors, support platforms and translation shops moved first, because their margins are directly exposed to inference cost. Regulated industries have not, and probably will not.

One thing to know before you route anything: because the weights are open, Western hosts serve the same Chinese models with no data touching China, at roughly double first-party pricing and still far below frontier rates. For most companies that is the sensible version of this trade.