US vs China: How to win the wrong AI race
The US is trying to win the AI race. China is playing an entirely different game.
TL;DR:
The US is betting on intelligence. China is betting somewhere else!
The US frequently frames its competition with China as an AI race, similar to the space race it ran with the USSR. The idea of a race was triggered around a year ago with the launch of DeepSeek. Ever since, much of the US media has been fascinated with the idea of the US winning the AI race against China.
Ironically, it isn’t much of a race if the US is the only one running it.
The American ‘AI race’ is framed around who will build the smartest models, as if superior intelligence alone decides the future.
China’s strategy reveals a different understanding of the game altogether.
It is not trying to win AI at all. It is betting that intelligence will become abundant, and that power will flow instead to whoever can reliably turn intelligence into economic value.
Great! Let’s dig in…
The US is building AI, China is preparing for the Reshuffle
The most consequential strategic bet China is making in the AI era is on coordination - and a very specific type of coordination -
The ability to reliably turn intelligence into energy-backed execution at scale.
China’s thesis is best understood as the following:
Economic value is created through the complementary effects of four factors:
Energy creates the capacity to perform work.
Intelligence creates the capacity to decide what work should be done, when, and how.
Execution creates the capacity to perform task-level work.
Coordination creates the capacity to align many task-level executions across time, space, and actors towards larger system-level outcomes.
None of these substitutes for the others.
Power accrues to whoever controls the binding constraint among them.
A tale of two bets
The US is betting that intelligence is that constraint. Their dominant assumption is that superior AI models - better prediction, reasoning, and abstraction - will sit atop a global system where energy, execution, and coordination can be purchased through markets.
Intelligence is treated as scarce and rent-bearing; everything else is assumed to be modular, interchangeable, and cheap.
This is why American AI strategy gravitates toward closed-weight models and proprietary control at the intelligence layer.
China is making a different, deeper bet.
It assumes that intelligence isn’t scarce, and treats coordination as the true locus of advantage.
Open-weight models are not a concession to hardware constraints; they are a deliberate move to commoditize intelligence so that value shifts to the layers that China controls today - energy infrastructure, execution capacity, and the ability to coordinate.
China has been steadily building dominance across these layers that turn energy into coordination - batteries, motors, power electronics, embedded compute, and algorithmic control.
China’s natural advantage in coordination
Together, these components mentioned above form a tightly coupled production system whose costs decline through cumulative output, learning-by-doing, and cross-layer complementarities. When each layer improves, it raises the returns to improvement in the others.
Because these components are reused across many products, learning compounds across domains rather than being confined to a single industry.
Firms like BYD, DJI, and Huawei could be misunderstood as manufacturers, but they are really system integrators that learn continuously through production, refine components through scale, and feed those improvements back into adjacent products. Coordination turns execution into a flywheel.
For any product, electrification becomes attractive only once the total cost of ownership, across capital cost, operating cost, maintenance, reliability, and performance, is lower than that of incumbent technologies. Once the threshold is crossed, adoption accelerates because the electric alternative is cheaper, better, or both.
When electric systems offer lower lifetime cost and superior performance, firms adopt them to remain competitive. Those adoptions increase scale, which pushes costs down further, pulling even more products across the threshold.
Once an application crosses the threshold, reversal is unlikely, because further learning and scale continue to improve the economics.
The strategic commoditization of AI
China’s open-weight AI push is best understood as a deliberate strategic commoditization play designed to force value migration away from the intelligence layer and into the coordination-heavy physical systems where China holds an advantage.
Strategic commoditization involves
removing scarcity from a layer you do not want as the profit center,
so that rents shift to adjacent layers you do control.
Open weights do exactly that: they reduce friction in adoption and accelerate experimentation. This speeds adoption of AI into products and operations. The point of open weights is to prevent models from becoming a choke point for anyone at all.
China’s bet makes sense. Intelligence does not create economic value at the moment of prediction or reasoning. It creates economic value only when tied to the ability to allocate resources, coordinate activity, and execute at scale.
This requires three complements:
energy (cheap, reliable electrons),
execution (the ability to build and operate machines), and, most critically,
coordination (the institutional and industrial capacity to synchronize supply chains, standards, manufacturing, deployment, and learning loops).
If you already dominate these complements, abundant intelligence functions as an accelerant that raises utilization, throughput, quality, and the speed of iteration across systems you govern.
If intelligence becomes a commodity, rents migrate downstream to electrified products, integrated hardware-software systems, manufacturing scale, supply chain orchestration, and reliability in physical throughput.
A robot with a commoditized brain will not yield durable profits to the brain supplier; profits will accrue to whoever controls the motors, batteries, power electronics, integration, and deployment networks that make the robot economically valuable.
Understand China’s Reshuffle
The ideas in this post are based on the thesis originally proposed in my book Reshuffle.
Why the US remains stuck in the intelligence distraction
The US’s quest to win AI assumes intelligence remains scarce and that coordination can be handled frictionlessly by markets. It treats execution and energy as modular, interchangeable, and cheaply purchasable through markets.
This belief was economically rational for a specific technological regime that we’ve lived through since the mid-1990s. It, however, becomes a fallacy when that regime changes.
Since the rise of the commercial internet, digital technologies have pushed marginal costs toward zero across much of the economy. Compute, bandwidth, and distribution became cheap and abundant over the past 30 years. In that environment, intelligence, expressed through algorithms, product design, and user experience, remained scarce and rent-bearing. If you controlled the user experience and harnessed user attention, you controlled the most important control point.
Ben Thompson’s Aggregation Theory describes this world. In this logic, value accrues to intermediaries that aggregate attention and demand by controlling user interfaces and informing user choice. Aggregation Theory assumes intelligence would remain scarce, coordination across the rest of the value chain would remain cheap, and the physical world would not reassert itself as a bottleneck.
These assumptions break down today. In capital-intensive, tightly coupled intelligent physical systems - factories, grids, vehicles, robots, supply chains - markets alone cannot absorb coordination complexity. Integration knowledge - how various value-creating components across the value chain fit together - becomes strategic.
China gets it! The US is still stuck in the Aggregation Theory mindset.
China’s strategy appears puzzling only if one remains trapped in the old regime. Open models look irrational if intelligence is assumed to be the choke point. They look brilliant once you make a bet on the commoditization of intelligence. By making intelligence abundant, China accelerates its absorption into coordinated physical systems - factories, vehicles, robots, grids - where learning compounds through execution.
In that sense, China doesn’t aggregate attention or users anymore; it coordinates across execution capacity, supply chains, manufacturing learning, and energy infrastructure .
Will the real AI bubble please stand up?
Aggregation dominated when intelligence was scarce and execution was cheap.
AI reverses that condition. When intelligence becomes cheap, it stops being the bottleneck and the rent-bearing layer.
What remains scarce, and therefore power-conferring, is the ability to coordinate complex systems and execute reliably at scale. Intelligence without coordination simply generates plenty of possible actions, but weak capacity to consistently realize them.
The fallacy, then, is not overvaluing intelligence; it is assuming intelligence will remain the scarce layer once a technology emerges whose primary economic effect is to make intelligence abundant.
When intelligence gets cheaper, power migrates to those who control the systems that turn intelligence into coordination at scale.
The real AI bubble is not that intelligence is overvalued, but that its ability to sustain rents is overestimated.
AI’s dominant long-run economic effect is not to concentrate intelligence, but to commoditize it, expanding the supply of intelligence faster than demand for proprietary control, and shifting durable rents to the coordination and execution layers that determine where intelligence can act at scale.
As intelligence becomes abundant, durable advantage shifts away from model ownership toward the coordination, execution, and energy systems that determine where and how intelligence can act at scale.
This is China’s play! This is where rents will eventually accrue.
Make America Great Again?
In the late 19th century, Britain and parts of Europe were the unquestioned leaders in “intelligence”.
They produced the foundational inventions of the age: steelmaking processes, internal combustion engines, chemical synthesis, electrical generation and transmission. Their universities, labs, and engineering culture were the envy of the world. They believed that if you invent the core technologies, industrial dominance will follow.
The United States made a different bet. It did not lead in invention. Instead, it focused on coordination.
American firms invested in standardized parts, interchangeable components, and production processes that could scale across geography. They built large factories organized around new coordination mechanisms rather than traditional craft. They developed managerial hierarchies, accounting systems, and planning routines to coordinate thousands of workers and suppliers. Most importantly, they built railroads as a coordination infrastructure that synchronized production, distribution, and demand across an entire continent.
British firms optimized for local performance. American firms optimized for system-level performance.
Eventually, engineering intelligence got commoditized as techniques and skilled workers crossed borders from the UK to the US, but there was no way for the coordination systems - the factories, the logistics networks, the managerial routines, and the institutional know-how required to run production at scale - to cross borders in the other direction.
The US today resembles the UK in 1880.
It leads in model architecture and training techniques and is pumping money into intelligence and the infrastructure to support it.
The implicit belief is that execution, energy, and coordination can be bought, outsourced, or handled by markets.
China today resembles the US in 1900.
It is not trying to monopolize invention at the intelligence layer. Instead, it is focused on embedding AI into factories, vehicles, robots, and grids. It’s investing in coordinating supply chains and standards and in integrating hardware, software, and energy.
Open-weight models play the same role that standardized parts played a century ago. They lower friction, encourage adoption, and ensure that intelligence diffuses rapidly into downstream systems. The goal is not to win at the frontier of invention, but to own the coordination system where intelligence creates physical capability.
Britain did not fail because it lacked intelligence. It failed because it assumed intelligence would remain the binding constraint. The United States won because it recognized that once technologies matured, coordination would dominate value creation.
When a technology lowers the cost of intelligence, power migrates to those who can coordinate its application at scale.
A century ago, that shift favored the United States over Britain.
Today, the same logic may favor China over the US, unless the US relearns the lesson it once taught the world.
Why Open China Works!
China’s openness in AI reflects an expectation that intelligence will commoditize, and a strategy to ensure that commoditization benefits China rather than undermines it.
By making intelligence ubiquitous, China increases the rate at which its factories, vehicles, robots, grids, and logistics systems absorb AI, and compounds learning where it already has advantage.
In this context, making intelligence abundant strengthens China’s position. Open models lower adoption friction, accelerate experimentation, and embed AI deeper into physical systems whose execution and energy layers China already governs. Intelligence becomes an accelerant, but not a control point.
The strategic objective of openness is not to get into some kind of AI race (never mind the pointless space race analogies) but to prevent intelligence from becoming someone else’s control point while profits and power migrate to coordinated execution.
This resonates strongly with what I wrote recently about China and the West not even looking at the same scoreboard.
In my China observations (check my page for a series of paper on this topic), what stood out wasn’t a singular obsession with “beating the US to AGI”, but how normalised, embedded, and utilitarian AI already is: in drones, robotics, logistics, manufacturing, public services, consumer apps. The metric isn’t “who has the smartest model”, it’s “how much of the real economy and daily life is already AI-augmented.”
The Western narrative frames competition as a frontier-model race. The Chinese narrative — and policy reality — looks much closer to what you describe here: system-level capability, deployment at scale, resilience, and integration into industrial and social infrastructure.
So when we say “China is behind” or “catching up,” we’re implicitly projecting a US-style scoreboard: parameter count, benchmarks, AGI timelines. But if the scoreboard is AI-as-general-purpose infrastructure, not AI-as-mythical intelligence breakthrough, then the relative positions look very different.
In that sense, this piece is right: it’s not a single race. It’s two civilisational strategies optimising for different futures — and misunderstanding that may be the West’s bigger strategic blind spot than any single model gap.
Great way to boil down the US-China challenge today with the analogy to the US-UK history in the early part of the last century.