How do Chinese AI models use tokens to "export" electricity?

Author: Black Lobster, Deep Tide TechFlow

Original Title: Token Goes Global, Selling China’s Electricity to the World

In the summer of 1858, a copper-core cable crossed the Atlantic Ocean floor, connecting London and New York.

The significance of this event was never about transmission speed, but about power structures—who laid the submarine cable could siphon off the flow of information. The British Empire, through this global telegraph network, controlled intelligence from colonies, cotton prices, and war news.

The empire’s strength was not just its fleet, but also that cable.

Over 160 years later, this logic is being replayed in an unexpected way.

By 2026, China’s large models are quietly consuming the global developer market. According to the latest OpenRouter data, Chinese models account for 61% of token consumption among the top ten models on the platform, with the top three all from China. Developers in San Francisco, Berlin, and Singapore send API requests daily, crossing the Pacific via submarine cables to Chinese data centers, where computing power is consumed, electricity flows, and results are returned.

The electricity never leaves China’s power grid, but its value is delivered across borders through tokens.

AI Model Great Migration

On February 24, 2026, OpenRouter released weekly data: the top ten models on the platform consumed about 87 trillion tokens, with Chinese models dominating at 53 trillion, accounting for 61%. MiniMax M2.5 led with 24.5 trillion tokens, followed by Kimi K2.5 and Zhipu GLM-5—all from China.

Latest data as of February 26

This is no coincidence; a spark ignited everything.

Earlier this year, OpenClaw emerged—a truly open-source tool that allows AI to “do work” directly by controlling computers, executing commands, and completing complex workflows in parallel. Within weeks, it surpassed 210,000 stars on GitHub.

Financial professional John installed OpenClaw immediately, integrated it with the Anthropic API, and began automatically monitoring stock market information, providing timely trading signals. A few hours later, he stared at his account balance in disbelief: just a few dollars—gone.

This is the new reality brought by OpenClaw. Previously, chatting with AI involved just a few thousand tokens per conversation, with negligible costs. After integrating OpenClaw, AI runs multiple sub-tasks in the background, repeatedly calling context and looping iterations, causing token consumption to grow exponentially. The bill accelerates like a car with its hood open, the fuel gauge dropping—impossible to stop.

A “trick” quickly circulated among developer communities: using OAuth tokens to connect Anthropic or Google subscription accounts directly to OpenClaw, turning the monthly “unlimited” quota into free fuel for AI agents. Many developers adopted this approach.

Official countermeasures soon followed.

On February 19, Anthropic updated its terms, explicitly prohibiting the use of Claude subscription credentials for third-party tools like OpenClaw. To access Claude features, API billing must be used. Google also broadly banned subscription accounts accessing Antigravity and Gemini AI Ultra via OpenClaw.

“Enduring suffering from Qin,” Jhon then embraced domestic large models.

On OpenRouter, domestic large models like MiniMax M2.5 scored 80.2% on software engineering tasks, while Claude Opus scored 80.8%. The difference is negligible. But the prices are worlds apart: the former costs $0.3 per million tokens at input, while the latter costs $5—a roughly 17-fold difference.

Jhon switched over, workflows continued, and bills shrank by an order of magnitude. This migration is happening globally in parallel.

OpenRouter’s COO Chris Clark explained plainly: Chinese open-source models capture a large market share because they are disproportionately used in US developer workflows.

Power Goes Overseas

To understand the essence of token going abroad, one must first grasp the cost structure of a token.

It seems lightweight—one token is roughly 0.75 English words. A typical AI conversation consumes only a few thousand tokens. But when these tokens stack into trillions, the physical reality becomes heavy.

Breaking down token costs, there are two core components: computing power and electricity.

Computing power is the depreciation of GPUs. Buying an Nvidia H100 costs about $30,000, and its lifespan amortized per inference is the depreciation cost. Electricity is the fuel for data center operation. When GPUs run at full load, each consumes about 700 watts, plus cooling costs. A large AI data center’s annual electricity bill can easily exceed hundreds of millions of dollars.

Now, map this physical process.

An American developer sends an API request from San Francisco. Data travels across the Pacific via submarine cable to a Chinese data center. GPU clusters start working, electricity flows from China’s grid to the chips, inference completes, and results are sent back. The entire process may only take one or two seconds.

Electricity never leaves China’s grid, but its value is delivered across borders through tokens.

Here’s the magic that surpasses ordinary trade: tokens have no physical form, no customs, no tariffs, and are not counted in current trade statistics. China exports vast amounts of computing and electricity services, yet they are almost invisible in official trade data.

Tokens have become derivatives of electricity; token exports are fundamentally electricity exports.

This is also thanks to China’s relatively low electricity prices—about 40% lower than the US—an inherent physical cost advantage that competitors can easily replicate.

Moreover, Chinese AI large models have algorithmic and “involution” advantages.

DeepSeek V3’s MoE architecture activates only parts of the model during inference. Independent tests show its inference cost is about 36% lower than GPT-4o. MiniMax M2.5, with 229 billion total parameters, activates only 10 billion.

At the top level is involution—companies like Alibaba, ByteDance, Baidu, Tencent, Mingyue Anmian, Zhipu, MiniMax… over a dozen firms are competing fiercely on the same track, with prices already below reasonable profit margins. Loss-leading has become industry norm.

In detail, this mirrors China’s manufacturing export strategy—leveraging supply chain advantages and industry involution to push token prices down sharply.

From Bitcoin to Tokens

Before tokens, there was another form of electricity going abroad.

Around 2015, power plant managers in Sichuan, Yunnan, and Xinjiang began welcoming strange visitors.

These people rented abandoned factories, packed them with countless machines, and ran them 24/7. The machines produced nothing but kept solving a mathematical problem—occasionally deriving a Bitcoin from this endless math puzzle.

This was the first form of electricity going abroad: cheap hydro and wind power converted into globally circulating digital assets via mining hardware, then monetized on exchanges into dollars.

Electricity didn’t cross borders, but its value, carried by Bitcoin, flowed into global markets.

In those years, China accounted for over 70% of global Bitcoin mining hash power. China’s hydropower and coal power, through this circuitous route, participated in a global redistribution of capital.

In 2021, all this abruptly stopped. Regulatory crackdowns scattered miners, and hash power migrated to Kazakhstan, Texas, and Canada.

But the logic itself never disappeared—only waiting for a new shell. When ChatGPT emerged, large models became the new battleground. Former Bitcoin farms transformed into AI data centers; mining machines became GPUs; the Bitcoin mined turned into tokens. Only electricity remained unchanged.

Bitcoin’s overseas expansion and token’s overseas expansion are structurally isomorphic, but tokens now hold greater commercial value.

Mining is purely mathematical computation; the Bitcoin produced is a financial asset, its value derived from scarcity and market consensus, unrelated to “what was calculated.” Computing power itself is non-productive, more like a trust mechanism byproduct.

Large model inference is different. GPUs consume electricity to produce real cognitive services—code, analysis, translation, creativity. The value of tokens directly stems from their utility to users. This is a deeper embedding: once a developer’s workflow depends on a model, switching costs grow over time.

Of course, a key difference remains: Bitcoin mining was expelled from China, while token exports are actively chosen by developers worldwide.

Token Wars

The submarine cable laid in 1858 represented the British Empire’s sovereignty over the information highway—who owns the infrastructure can set the rules.

Token exports are similarly a war without declared combat, facing heavy resistance.

Data sovereignty is the first barrier. An API request from a US developer processed by a Chinese data center physically traverses China. For individual developers and small applications, this isn’t a problem. But for enterprise-sensitive data, financial information, or government compliance scenarios, it’s a serious issue. That’s why Chinese models have the highest penetration in developer tools and personal applications but are almost invisible in core enterprise systems.

Chip bans are the second barrier. China’s AI development faces export controls on high-end Nvidia GPUs. MoE architectures and algorithmic optimizations can partially offset this disadvantage, but a ceiling remains.

But these obstacles are only the prologue. A larger battlefield is taking shape.

Tokens and AI models have become a new strategic arena between China and the US—comparable to the semiconductor and internet wars of the 20th century, or even closer to an ancient metaphor: space race.

In 1957, the Soviet Union launched Sputnik, shocking the US, which then launched the Apollo program, investing hundreds of billions of dollars to ensure they wouldn’t fall behind in space.

The logic of AI competition is eerily similar, but the intensity will far surpass the space race. Space is physical and invisible to most people; AI penetrates the economic capillaries—every line of code, every contract, every government decision system may be running a large model from some country. Whose model becomes the default infrastructure for global developers? That country gains an invisible but structural influence over the global digital economy.

This is precisely what makes China’s token export strategy so unsettling for Washington.

When a developer’s codebase, agent workflows, and product logic revolve around a Chinese model’s API, the migration cost grows exponentially over time. Even if US legislation restricts it, developers will resist actively—just as today no programmer can abandon GitHub.

Today’s token export is perhaps just the beginning of this long game. Chinese large models have not claimed to overthrow anything; they simply deliver services at lower prices to every developer worldwide with an API key.

This time, the cables are laid by engineers coding in Hangzhou, Beijing, and Shanghai, and GPU clusters running day and night in some southern province.

There’s no countdown to this war; it’s ongoing 24/7, measured in tokens, fought on every developer’s terminal.