US/China Tech Race

Speculating about this question has become almost an obsession for leaders and analysts from both China and the United States, wrote George Washington University’s Jeffrey Ding. They believe that the global balance of economic power tips toward the states that pioneer the most important innovations. On this basis, some argue that China has a better chance than the United States of triumphing.

Diffusion versus innovation

But innovation only gets you so far, according to Ding. Without the humbler undertaking of diffusion—how innovations spread and are adopted—even the most extraordinary advances will not matter. A country’s ability to embrace technologies at scale is especially important for technologies such as electricity and AI, foundational advances that boost productivity only after many sectors of the economy begin to use them.

A focus on the diffusion of technology points toward an alternative explanation for how technological revolutions change geopolitics: it matters less which country first introduces a major innovation and more which countries adopt and spread those innovations.

UK and First Industrial Revolution

In making his case, Ding examines the United Kingdom’s rise in the wake of the First Industrial Revolution, which lasted from roughly 1780 to 1840. He argues that the adoption of iron machinery across a wide range of economic activities proved more central to its economic rise than the pioneering of new technologies in textiles, for instance.

Although its industrial rivals boasted superior systems of higher technical education for training expert scientists and engineers, the United Kingdom benefited from mechanics’ institutes, educational centers such as the Manchester College of Arts and Sciences, and other associations that expanded access to technical literacy and applied mechanics knowledge to a broader segment of society.

The US and the Second Industrial Revolution

The diffusion of technology also defined how countries benefited from the Second Industrial Revolution, which began around 1870 and ended around 1914, writes Ding. It was spurred by inventions in machine tools—the industrial production of interchangeable parts.

During this period, the United States did not produce the world’s most sophisticated machinery, but it surpassed the United Kingdom in productivity by adapting machine tools across almost all branches of industry. In 1907, machine intensity (which measures the horsepower of installed machines per manufacturing worker) in the United States was more than double that of the United Kingdom and Germany.

As in the earlier British example, education and public policy played a major role in securing the U.S. advantage. The United States had a wide pool of mechanical engineering expertise, supported by land-grant schools, technical institutes, and standardization efforts in screw threads and other machine components. These institutions broadened the base of expertise, creating more competent engineers and not simply producing a narrow technical elite.

Similar dynamics prevailed in chemical engineering, where U.S. institutions of higher education helped cultivate a common language and a professional community of chemical engineers who could help speed productivity in a wide range of industries, including ceramics, food processing, glass, metallurgy, and petroleum refining.

So who will lead the way in the Fourth Industrial Revolution?

Ding laments that leaders and analysts neglect the real determining factor in the AI/tech race. In this competition: a country’s capacity to diffuse AI advances across a wide range of industries, in a gradual process that will likely play out over decades.

Ding argues that when great-power competition over AI is reframed in this way, the United States seems well positioned to maintain its technological edge. American businesses have been much quicker to embrace other information and communication technologies, such as cloud computing, smart sensors, and key industrial software.

On one influential index, China ranks 83rd in the world in terms of access to these technologies, trailing the United States by 67 places. When it comes to AI, China has only 29 universities that employ at least one researcher who has published at least one paper in a leading AI conference publication (a rough gauge for whether a university can train AI engineers); the United States is home to 159. The United States has also built tight links between academia and industry that help disseminate AI advances across the entire economy—far more than China has.

The United States should prioritize improving and sustaining the rate at which AI becomes embedded in a wide range of productive processes. To be clear, understanding the importance of diffusion does not exclude supporting the exciting research in a country’s leading labs and universities.

Undoubtedly, more R & D spending and better facilities for elite scientists will also indirectly contribute to more widespread adoption of AI. All too often, however, increased R & D spending becomes the boilerplate recommendation for any strategic technology. AI demands a different toolkit.

Conclusion

Ding’s article, and the book on which it is based, were written and published during the Biden administration, and before the disturbances of Trump 2.0. I fear that Ding’s measured, empirically-based analysis would not find much sympathy in Trump’s world.