Faced with less compute and fewer tokens, Chinese AI labs are tightening the gap with the U.S. by just being more efficient 

The race between the world’s two biggest economies to dominate AI recently escalated with new accusations and raised the question of how China came to rival the U.S.’s AI capabilities. 

Six Chinese AI companies, U.S. officials alleged earlier this week, found a shortcut to closing the gap with American AI labs by buying bulk subscriptions to their American rivals and then training on the outputs.

The FBI, the National Security Agency, and the Cybersecurity and Infrastructure Security Agency said Tuesday that DeepSeek, Moonshot, and four others extracted “capabilities worth billions” in this way since 2024, a method the agencies claimed let DeepSeek understate its $5.6 million training cost.

China’s foreign affairs ministry said the country’s AI development “is a result of high-level scientific and technological self-reliance” and called the accusations “groundless.”

The allegations are one potential explanation for why Chinese AI models are estimated to be neck-and-neck with their U.S. counterparts, with a report from Stanford putting Anthropic’s top model ahead of DeepSeek’s by just 2.7% earlier this year. 

But analysts say there’s a different advantage Chinese labs developed to compete: squeezing out more value from limited resources. 

More value for fewer tokens

There’s a technique Chinese labs perfected that stems from “attention,” the mechanism Google researchers introduced in a landmark 2017 paper that underlies every large language model.

Attention is........

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