AI: Add AI Tokens to US vs China AI metrics race. RTZ #1041

AI: Add AI Tokens to US vs China AI metrics race. RTZ #1041

I recently discussed the increasing importance of measuring AI usage at scale around the world in terms of “AI intelligence Tokens” in this AI Tech Wave going forward.

This is in addition to metrics like the hundreds of billions expended per year on AI Data Center, AI chips, AI Power, AI Talent and the rest. And in addition to closed vs open AI models. Also on top of the frenzy of OpenAI ‘OpenClaw’ AI Agents being deployed by developers for their users here, in China, and the rest of the world.

It’s a metric that is now also useful to track in terms of the unfortunate current obsession of measuring AI here in the US vs China. And much like China, though behind the US in terms of AI GPU and related chips, and being ahead of the US in terms of AI Applications, AI supply chain manufacturing, AI Power, and AI Talent, also seems to be ahead vs the US on AI token production.

The FT discusses and illustrates this in “The rise of China’s hottest new commodity: AI tokens”:

“Chinese AI models made by groups such as DeepSeek and MiniMax have overtaken US rivals in token consumption.”

“China is gaining ground in the global AI industry’s hottest commodity: tokens. Since February, Chinese AI models made by groups such as DeepSeek and MiniMax have overtaken US rivals in token consumption, according to OpenRouter data, which tracks these units of text, code or data processed by large language models.”

This is particularly important as the AI industry shifts in emphasis from AI chatbots to AI Reasoning and AI Agents. All on the way to ‘AGI’, artificial general intelligence, however defined.

“As AI agents, such as those built on the open-source platform OpenClaw, consume vastly more tokens than earlier chatbots, the ability to cheaply produce tokens is reshaping global competition — and giving China a new edge. “If your agent is burning through millions of tokens a day, even a small per-token price difference becomes a significant line item,” said Will Liang, chief executive of Amplify AI Group, a Sydney-based technology consulting firm.”

“That’s a structural tailwind for Chinese labs, and it only grows as agentic adoption scales.” Chinese AI groups’ cost advantage stems from cheaper energy and more efficient models, allowing companies such as MiniMax and Moonshot to charge $2 to $3 per million output tokens, compared with about $15 for Anthropic’s Claude Sonnet 4.5 — a near sixfold gap.”


”The difference becomes pronounced with AI agents, which consume far more tokens than chatbots. Summarising Shakespeare’s Hamlet might take about 30,000 tokens for a chatbot, but an AI agent can require up to 20mn on a minor coding task.”

“That is changing how AI developers choose to spend their money. Terry Zhang, a Hong Kong-based developer, said he now spends about $50 a day using Moonshot’s Kimi model for roughly 80 per cent of his work, reserving Anthropic’s Claude for more complex tasks. “I used to call only Claude but now with an increasing amount of workload, using just Claude would cost me about $900 a day,” he said.”

These performance and pricing differentials are important to internalize. Especially in terms of the open and closed source dimensions. It’s not either/or for users globally. But BOTH.

“It’s too much and the mixed use of Kimi and Claude works well for me.” The trend is feeding through to revenues. MiniMax, whose M2.5 model is now ranked among the most used globally by token consumption, has seen token usage rise 476 per cent from a month ago as of March 20, according to OpenRouter. While OpenRouter accounts for only a fraction of the global model consumption, it is widely used as an industry indicator, as such data is scarce elsewhere.”

“US groups are still growing rapidly as the overall market expands, with OpenAI, Anthropic and Google all reporting strong revenue growth and adoption. But lower-cost Chinese models have obtained an opening to gain ground among users around the world. China’s token pricing advantage stems partly from the country’s vast investment in renewable energy.”

China of course, run by technocratic engineers vs lawyers in the US, is focused on all this top down and bottom up in a meticulous, detailed way (Dan Wang book ‘Breakneck’ highly recommended).

“The Chinese government this month designated “computing-electricity synergy” a national priority in its 2026 work report, explicitly linking energy policy with AI competitiveness. On the software side, Chinese groups have embraced efficient AI architectures, such as a “mixture-of-experts” designs that reduce computational demand, sometimes at the expense of accuracy. This push for computing efficiency has been driven by a shortage of advanced chips in China due to US export controls.”

China’s tech and AI companies like Alibaba, Tencent and others, are also laser focused on the AI token dynamics:

“China’s tech giants have moved quickly to press their advantage. Earlier this month Alibaba announced the creation of Alibaba Token Hub, a new business group that will be led by chief executive Eddie Wu. The unit signals Alibaba’s view that token economics will define the next phase of AI competition.”

“We are standing at the threshold of an AGI inflection point,” Wu wrote in an internal memo last week. “Billions of AI agents are poised to take on an ever-greater share of digital work, each powered by tokens generated by models, and these agents will increasingly become the primary interface between people and the digital world.”

It’s early days, but China’s focus on tokens is important to track.

“Whether China’s token advantage can persist remains unclear, especially as some companies remain wary of relying on models run on Chinese data centres. “The geopolitical headwinds are significant, particularly for governments and regulated industries,” said Amplify’s Liang. “Regulators are asking harder questions about where data is processed and under whose jurisdiction it falls.”

The whole piece is worth a full read for the charts and additional details.

But the broader point is we need to add ‘AI Tokens’ to our AI metrics awareness in this AI Tech Wave going forward. Stay tuned.

(NOTE: The discussions here are for information purposes only, and not meant as investment advice at any time. Thanks for joining us here)





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