Qwen boss says Chinese AI models have ‘less than 20%’ chance of leapfrogging Western counterparts — despite China’s $1 billion AI IPO week, capital can’t close

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Qwen boss says Chinese AI models have 'less than 20%' chance of leapfrogging Western counterparts — despite China's $1 billion AI IPO week, capital can't close

The Senate's new SAFE bill is set to curb access to advanced chips to China, but that won't slow down the AI war

Nvidia's China presence hits zero, says CEO Jensen Huang, and companies are already working around it

U.S. export controls have cut China off from Nvidia’s most capable data center GPUs and the advanced manufacturing tools needed to produce equivalents at scale. Domestic alternatives such as Huawei’s Ascend series have improved rapidly, but even optimistic assessments place them behind current-generation U.S. hardware in raw performance and ecosystem support. More importantly, they are produced in far smaller volumes.

As a result, Chinese AI developers face a tradeoff that their U.S. counterparts largely do not. They can train more models, or they can train larger models, but doing both simultaneously strains available infrastructure. Several firms have responded by shifting emphasis away from general-purpose foundation models toward narrower, application-specific systems that can be trained and deployed with fewer resources.

We have been debating talent pipelines and research output when it comes to the U.S. and China for much of the past decade, but today, the differentiator is the fact that the U.S. controls the bulk of the world’s advanced AI compute.

U.S. hyperscalers operate GPU clusters measured in the tens of thousands of accelerators, with software stacks tuned over years of production use. Private investment in U.S. AI companies continues to dwarf that in China, even as Chinese firms turn to public markets. Just as important, U.S. companies can deploy capital directly into hardware procurement at a global scale, something Chinese firms cannot match under current geopolitical dynamics.

Chinese execs have begun acknowledging this imbalance publicly, warning that U.S. AI infrastructure may be an order of magnitude larger than China’s in effective capacity. That gap compounds over time and, unfortunately for China, more compute enables larger models, which attract more users, data, and revenue, which in turn fund even larger deployments.

So, while a $1 billion IPO week is impressive on the face of it, it still leaves China well behind the U.S. in all the areas that matter. Yes, it ensures that China’s AI firms remain viable and competitive domestically, but it does not, in its own right, alter the global AI race.

Public listings also impose discipline and transparency, in theory, and lock firms more tightly into national industrial policy. Over the next few years, that’s likely to produce a bifurcated outcome, with China’s AI ecosystem advancing quickly in areas where scale isn’t quite so important, such as consumer and industrial platforms and applied AI.

Meanwhile, the cutting edge of general-purpose AI remains anchored in environments that have access to abundant compute. Capital can sustain progress, sure, but compute ultimately determines whether that progress will have any measurable impact outside of China.

Luke James Social Links Navigation Contributor Luke James is a freelance writer and journalist. Although his background is in legal, he has a personal interest in all things tech, especially hardware and microelectronics, and anything regulatory.

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