Why 'Winning' the AI Race Means Deployment, Not Innovation

The first-mover fallacy in AI competition

American leaders warn that falling behind China in artificial intelligence would be catastrophic. Treasury Secretary Scott Bessent recently declared "there is no day after tomorrow" if China pulls ahead. Yet this framing ignores a fundamental lesson from technological history: the first to innovate rarely becomes the ultimate winner.

In 1884, British inventor Charles Parsons revolutionized electricity generation with his steam turbine. Britain had produced pioneers like Michael Faraday, the "father of electricity." But four years after Parsons' breakthrough, Britain still lacked a single public electricity station. Meanwhile, Thomas Edison's company had built 185 stations across America, growing to over 1,000 within two years. By 1912, the United States produced five times more electricity per person than Britain and had become the world's leading industrial power.

The pattern repeated across steel, chemicals, and automobiles—American firms pioneered fewer than a third of the key innovations in these industries, yet America dominated all four sectors economically.

Why it matters

The obsession with frontier AI models—who has the most advanced system—may be strategically misguided. If economic leadership comes from widespread adoption rather than cutting-edge capabilities, the current race mentality could actually undermine American competitiveness by prioritizing raw power over reliability and trust.

Diffusion beats innovation

Jeffrey Ding, a political scientist at George Washington University, argues in his 2024 book "Technology and the Rise of Great Powers" that being first is a poor predictor of who wins. "The lead is about who can diffuse AI across the entire economy and gain a productivity boost," Ding explained to The New Yorker.

Chinese policymakers appear to understand this. Their "AI+" initiative from 2025 explicitly aims to integrate AI into factories, hospitals, and local administration—a focus on diffusion rather than frontier capabilities. Yet Ding remains bullish on America's prospects, noting that state-directed economies excel at sprints but struggle with marathons. The Soviet Union launched Sputnik, but America built an economy on satellites.

The deployment challenge

New technologies require public trust to achieve widespread adoption. The 1979 Three Mile Island meltdown derailed nuclear energy in America—since 1996, the U.S. has completed only three reactors while China broke ground on nine last year. Recent incidents, including OpenAI agents hacking Hugging Face, risk triggering similar public skepticism about AI.

Jack Shanahan, former director of the Department of Defense's Project Maven AI initiative, described the fundamental challenge: "The gears didn't match. We're trying to turn it at a thousand r.p.m. and the rest of the bureaucracy is moving at fifty r.p.m." Maven took nine years to reach widespread military use, and fully integrating current AI systems could take another fifteen years.

What winning actually requires

Ding argues America needs less investment in frontier models and more in the "broad middle"—community colleges, vocational schools, and state universities that can train an AI-literate middle class. These are the people who will actually carry the technology into local banks and city halls.

The "But China" rhetoric that tech companies deploy to justify unchecked growth—from Meta's "break up strengthens Chinese companies" to OpenAI's copyright arguments—treats the world as a theoretical construct rather than addressing practical deployment challenges.

These details were first reported by Chang Che in The New Yorker.