When Intelligence Gets Cheap

The United States keeps describing its competition with China as an AI race, a rerun of the space race it once ran with the Soviet Union. The framing assumes the prize is intelligence itself, that whoever builds the smartest models wins. China is not really running that race. It is making a different bet, that intelligence will become abundant, and that power will flow instead to whoever can reliably turn intelligence into economic value.

It is not much of a race if only one side is running it.

The American version is organized around who builds the best models, as if superior reasoning alone decides the future. China's strategy reveals a different reading of the game. Its most consequential bet is on coordination, and a very specific kind: the ability to reliably turn intelligence into energy backed execution at scale.

Four factors, one binding constraint

Economic value gets 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 toward larger system level outcomes.

None of these substitutes for the others. Power accrues to whoever controls the binding constraint among them.

The US is betting that intelligence is that constraint. The dominant assumption is that 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. That is why American AI strategy gravitates toward closed weight models and proprietary control at the intelligence layer.

China is making a deeper bet. It assumes intelligence is not scarce, and treats coordination as the true locus of advantage. Open weight models are not a concession to hardware limits. They are a deliberate move to commoditize intelligence so that value shifts to the layers China already controls: energy infrastructure, execution capacity, and the ability to coordinate. For years it has been building dominance across the layers that turn energy into coordination, including batteries, motors, power electronics, embedded compute, and algorithmic control.

Why coordination compounds

Those components 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 the components are reused across many products, learning compounds across domains rather than staying confined to a single industry.

Firms like BYD, DJI, and Huawei look like manufacturers, but they are really system integrators. They 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, drops below that of the incumbent technology. Once that threshold is crossed, adoption accelerates, because the electric alternative is cheaper, better, or both. Each adoption increases scale, which pushes costs down further, pulling even more products across the line. Reversal becomes unlikely, because further learning keeps improving the economics.

Commoditizing intelligence on purpose

China's open weight push is best understood as a deliberate strategic commoditization play, designed to force value to migrate away from the intelligence layer and into the coordination heavy physical systems where China holds an advantage.

Strategic commoditization means 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, which speeds the absorption of AI into products and operations. The point is to prevent models from becoming a choke point for anyone at all.

The logic holds because intelligence does not create economic value at the moment of prediction or reasoning. It creates value only when tied to the ability to allocate resources, coordinate activity, and execute at scale. That requires three complements: energy in the form of cheap, reliable power; execution, the ability to build and operate machines; and coordination, the institutional and industrial capacity to synchronize supply chains, standards, manufacturing, deployment, and learning loops.

If you already dominate those complements, abundant intelligence acts 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 and software systems, manufacturing scale, supply chain orchestration, and reliable physical throughput. A robot with a commoditized brain will not yield durable profits to the brain supplier. Profits accrue to whoever controls the motors, batteries, power electronics, integration, and deployment networks that make the robot economically valuable.

Why the US stays stuck on intelligence

The quest to win AI assumes intelligence stays scarce and that coordination can be handled frictionlessly by markets. It treats execution and energy as modular, interchangeable, and cheaply available. That belief was economically rational for a specific technological regime, the one we have lived in since the mid 1990s. It becomes a fallacy when the regime changes.

Since the rise of the commercial internet, digital technologies pushed marginal costs toward zero across much of the economy. Compute, bandwidth, and distribution became cheap and abundant. In that environment, intelligence, expressed through algorithms, product design, and user experience, stayed scarce and rent bearing. If you controlled the user experience and captured user attention, you controlled the most important control point. Ben Thompson's Aggregation Theory describes that world, where value accrues to intermediaries that aggregate attention and demand by owning the interface and informing user choice.

Those assumptions break down today. In capital intensive, tightly coupled physical systems, factories, grids, vehicles, robots, supply chains, markets alone cannot absorb coordination complexity. Integration knowledge, how the value creating components fit together, becomes strategic. China gets this. The US is still operating with an Aggregation Theory mindset, which is why open models look irrational if intelligence is assumed to be the choke point, and brilliant once you bet on its commoditization.

Where the real AI bubble is

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 stays 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 and a weak capacity to realize any of them.

The fallacy is not overvaluing intelligence. It is assuming intelligence will remain the scarce layer once a technology arrives whose primary effect is to make intelligence abundant. The real AI bubble is not that intelligence is overvalued, but that its ability to sustain rents is overestimated.

When a technology lowers the cost of intelligence, power migrates to those who can coordinate its application at scale.

A lesson from 1900

In the late 19th century, Britain and parts of Europe were the unquestioned leaders in invention. They produced the foundational technologies of the age: steelmaking, the internal combustion engine, chemical synthesis, electrical generation and transmission. Their universities and engineering culture were the envy of the world. They believed that if you invent the core technologies, industrial dominance follows.

The United States made a different bet. It did not lead in invention. It focused on coordination. American firms invested in standardized, interchangeable parts and production processes that could scale across geography. They built large factories organized around new coordination mechanisms rather than craft. They developed managerial hierarchies, accounting systems, and planning routines to coordinate thousands of workers and suppliers. Most important, they built railroads as a coordination infrastructure that synchronized production, distribution, and demand across a 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, but the coordination systems, the factories, logistics networks, managerial routines, and institutional know how, could not cross borders the other way.

The US today resembles Britain in 1880. It leads in model architecture and training, and is pouring money into intelligence and the infrastructure to support it, on the belief that execution, energy, and coordination can be bought or outsourced. China today resembles the US in 1900. It is not trying to monopolize invention at the intelligence layer. It is embedding AI into factories, vehicles, robots, and grids, and investing in coordinating supply chains, standards, hardware, software, and energy. Open weight models play the role standardized parts played a century ago: they lower friction, encourage adoption, and ensure intelligence diffuses rapidly into downstream systems.

Britain did not fail for lack of intelligence. It failed because it assumed intelligence would remain the binding constraint. The US won because it recognized that once technologies mature, coordination dominates value creation. 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 works for China

China's openness in AI reflects an expectation that intelligence will commoditize, and a strategy to make sure that commoditization works in its favor. By making intelligence ubiquitous, it increases the rate at which its factories, vehicles, robots, grids, and logistics systems absorb AI, compounding learning where it already has an advantage. Open models lower adoption friction, accelerate experimentation, and embed AI deeper into the physical systems whose execution and energy layers China already governs. Intelligence becomes an accelerant, not a control point.

The strategic objective of openness is not to win an AI race, and the space race analogies miss the point. It is to prevent intelligence from becoming someone else's control point while profit and power migrate to coordinated execution. That is the bet. Whether it pays off is the open question, and it is the one most of the current race coverage is not asking.