Lines of conflict drawn in the battle for AI dominance

China might have become the manufacturing floor for the global economy. But the West has taken comfort from the assessment that the United States retains the lead when it comes to the quest for artificial intelligence or AI. 

Yet that might depend on how one defines the competition. The US tends to define it in terms of the race toward artificial general intelligence, or AGI, that is, self-improving and surpasses the cognitive power of human beings.

It would be capable of executing real-world knowledge work tasks. Trump’s AI czar David Sacks’ estimates, “China is not years and years behind us in AI. Maybe they’re three to six months.” 

But no one can really be certain – what that means, whether that is true, and whether it really matters. For one, how will we know when AGI has been achieved? The point at which AI crosses over into AGI. 

Second, does it matter? If both the US and China are going to achieve AGI, maybe as little as six months apart, does it matter who gets there first – other than to feed the vanity of the entrepreneur who achieves that milestone? 

Cybersecurity screenings

What is going to happen in that period to position the winning country in a categorically different situation than otherwise might be the case? 

And that leads to the third question: Are we, the United States, racing toward the wrong finish line? Indeed, we assume we are in a neck-to-neck race with China, but they might be racing on an entirely different course. 

If success is building the biggest, most beautiful model, the US is doing quite well. 

As American firms invest hundreds of billions of dollars into the latest models, chips, and AI infrastructure, I was comforted to read the National Institute for Standards and Technology’s new AI benchmarking report.

It found that the best US model outperformed the best Chinese model, DeepSeek V3.1, across almost every benchmark. That included a 20% margin in software engineering tasks, a 35% margin in general operating costs, as well as cybersecurity screenings. 

But chatbots might not be the be-all and end-all when it comes to thinking machines and the strategic competition between Washington and Beijing. 

There is a growing – if self-serving – argument among China’s leading technologists, officials, and researchers that the large language models captivating Silicon Valley do not represent the most strategic path to an AI-enabled future. 

Or, to paraphrase Chinese experts on Weibo, “ChatGPT outputs are capitalist drivel.” 

While Beijing is certainly working to improve its large language models too, it is pursuing a somewhat different strategy – by choice or by necessity. 

China is less focused on large frontier models, such as ChatGPT-5, and more focused instead on wiring intelligence into the physical economy at scale. 

Autonomous vehicles

As Fareed Zakaria and Eric Schmidt have noted, Chinese President Xi Jinping often frames AI as “application-oriented,” and Beijing’s policies and procurement decisions reflect this vision

That is illustrated by projects such as the “city brain” pilots in Wuhan, which fuse traffic cameras, internet-of-things sensors, and other devices with autonomous vehicles. 

The real action is in manufacturing, where China is surging ahead in “embodied AI.” 

It operates roughly two million industrial robots and installed about 295,000 more in 2024 alone – more than the rest of the world combined – with a majority now made domestically in the country. 

By contrast, US factories installed about 34,000. These robots will all be powered or augmented by smaller-scale Chinese AI applications that don’t require the immense training compute or inference infrastructure of increasingly powerful Western chatbots.

China’s Ministry of Industry and Information Technology estimates that by the end of 2025, over 60% of large Chinese manufacturers will have adopted some form of “AI + Manufacturing” integration.

Thousands of “AI-empowered” factories have already been certified nationwide. The country’s 14th Five-Year Plan also calls for “comprehensive intelligent transformation” of industrial production, with AI embedded across 70% of key sectors by 2027.

That would rise to 90% by 2030, and 100% by 2035. 

This diffusion is already measurable on the ground. Nearly half of all new Chinese manufacturing equipment sold last year incorporated machine vision, predictive maintenance, or autonomous-control functions.

Clear evidence that AI is no longer confined to pilot projects and that it is now becoming a default layer of the industrial economy. 

AI advances

The United States, obviously, has no such plan or benchmarks, but it is not hard to imagine armies of entrepreneurs across the United States developing new applications to deploy across the economy as AI advances. 

The US is wagering on hundreds of billions of dollars of compute, hyper-scale superclusters, and ever-larger language models in pursuit of AGI – systems so capable and creative that they might unleash an epoch of explosive economic growth and scientific discovery. 

But that is quite different from China’s approach of having a plan, backed up by a set of incentives and sanctions, to ensure the rapid diffusion and integration of AI across the whole of the industrial sector. 

As Charlie Munger of Berkshire Hathaway once said, “Show me the incentive, and I’ll show you the outcome.” 

In the US, AI has probably taken the form of consumer apps and enterprise software because that is where the incentives – that is, the near-term profits – lie. 

China’s approach, by contrast, revolves around smaller-scale AI applications as an input to production rather than a product itself. 

The strategic question is whether, over time, intelligence diffused through the physical economy proves more transformative than the wisdom of a future “ChatGPT-15.” 

There are certainly merits to both strategies, and we shouldn’t dismiss the transformative potential of large language models, particularly to advance basic research and scientific innovation, in addition to knowledge work. 

But we should probably be less confident and less complacent about the significance of our lead in model development.

The differences in AI strategy between Washington and Beijing are also reflected in each country’s policy response, including their approach to export controls. 

New chokepoints

Last week, China’s Ministry of Commerce turned the United States’ “small yard, high fence” doctrine on its head and unveiled what can only be described as a “big square, great wall” policy. 

The new measures build atop earlier semiconductor-focused curbs and now extend Beijing’s reach well beyond its borders. 

Any product containing as little as 0.1% of Chinese heavy-rare-earth content, or manufactured using Chinese mining, separation, or magnet-making technology, now falls under Beijing’s jurisdiction. 

At the same time, the new measures clamp down on technology transfer itself, restricting the export of rare-earth and magnet-production expertise and Chinese participation in related overseas projects. 

Taken together, the rules layer new chokepoints atop old ones, transforming not just the raw materials of modern computing but the tools and know-how required to reproduce them into instruments of strategic leverage.

According to the International Energy Agency and the US Geological Survey, China has a dominant global market position in nickel, cobalt, graphite, gallium, and germanium refining – materials essential to advanced chipmaking, sensors, and batteries. 

So too in manufacturing specialized, synthetic industrial diamonds, and processing heavy rare earths – areas in which they also own and control the intellectual property for processing techniques and equipment. 

In other words, the US can cut China off from the chips of today, but China can make it vastly harder to build the chips and other advanced technologies of tomorrow. 

If Beijing enforces these controls, even selectively, it could send shockwaves through the global supply chain for advanced computing, electric vehicles, and renewable energy systems. It also reframes the AI competition itself. 

For all of America’s prowess in software and design, the uncomfortable truth is that in a world where China can twist the spigot on raw materials essential to chipmaking, its applied, industrial AI strategy cannot be dismissed.

Xi’s decision to escalate the dispute came ahead of a planned meeting with Trump on the margins of the upcoming APEC summit in South Korea. Is it to seriously damage or retard the United States’ prospects for winning the AI race? 

Trump administration

Is it to have something to trade away in exchange for concessions from Washington? Or is it to show that China has learned from the Trump administration that export controls can confer real leverage in negotiations in unrelated issues, such as Taiwan. 

In reaction to the export control announcement, Trump threatened to cancel his upcoming meeting with Xi and impose new tariffs, posting on Truth Social: 

For every Element that they have been able to monopolize, we have two. I never thought it would come to this but perhaps, as with all things, the time has come. 

When Trump first imposed tariffs on China, many of us noted that Beijing might retaliate by exploiting the chokepoints over certain products it controlled. Now we’re competing in the use of economic leverage as well. 

Time will tell who, if either, will prove “the winner” of that competition.

Michael Froman is president of the Council on Foreign Relations.

This edited article was published by the Council on Foreign Relations under a Creative Commons license. Read the original here.

The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy of China Factor.