The Innovation Fallacy: Diffusion vs. Invention in the U.S.-China Tech Race
Author and Contextual Background
Jeffrey Ding is an Assistant Professor of Political Science at George Washington University.
He is the author of the book Technology and the Rise of Great Powers: How Diffusion Shapes Economic Competition (published by Princeton University Press).
The essay is adapted from this book and was published on August 19, 2024.
Xi Jinping and the Chinese Perspective on Innovation
In 2018, Chinese leader Xi Jinping delivered remarks highlighting the potential for "disruptive technological innovation" to alter the course of history.
Xi argued that key advancements have remade the world through a series of transformations: * The First Industrial Revolution was defined by "mechanization." * The Second Industrial Revolution was characterized by "electrification." * The Third Industrial Revolution was driven by "informatization."
Xi posited that the world is currently on the cusp of a Fourth Industrial Revolution triggered by breakthroughs in cutting-edge technologies like artificial intelligence (AI).
The prevailing view in China is that the pioneers of these new technologies will emerge as the winners of the upcoming era.
Jin Canrong, an influential Chinese international relations scholar, analyzed Xi' s remarks and argued that China possesses a better chance than the United States to triumph in the competition over the Fourth Industrial Revolution.
Official Chinese Communist Party publications have detailed historical precedents for this view: * The United Kingdom established a world-leading productivity advantage by seizing the opportunity of the first industrial revolution. * The United States seized dominance of advanced productivity from the United Kingdom following the second industrial revolution.
U.S. Policy and the National Security Commission on AI
U.S. policymakers share the belief that technological innovation is linked to national power.
President Joe Biden, in his first press conference after taking office, stated the necessity for the U.S. to "own the future" regarding competition in emerging technologies.
Biden pledged that China's goal to become "the most powerful country in the world" would not happen during his watch.
In 2018, Congress established the National Security Commission on Artificial Intelligence (NSCAI). * This body convened government officials, technology experts, and social scientists. * The commission's final report compared AI's potential impact to that of electricity. * It warned that the United States would lose its technological leadership to China if it failed to prepare for the "AI revolution."
Defining the Innovation Fallacy: The Importance of Diffusion
The "Innovation Fallacy" is the belief that the global balance of economic power tips exclusively toward states that pioneer the most important innovations in new, fast-growing industries.
Jeffrey Ding argues that both Chinese and American leaders risk overlooking the role of diffusion—how innovations spread and are adopted across an economy.
While the United Kingdom is often cited as a leader due to inventions like the spinning jenny in the textile industry, innovation alone is insufficient for geopolitical supremacy.
A country's ability to embrace technologies at scale is more critical, particularly for foundational advances like electricity or AI that only boost productivity once used across many sectors.
The strategic reality is that it matters less which country first introduces an innovation and more which country adopts and spreads it most effectively.
Historical Case Study: The First Industrial Revolution (–)
Conventional narratives attribute the United Kingdom's rise to a monopoly over innovations in cotton textiles and the institutional capacity to nurture "genius" inventors.
Economic historians using improved data challenge this, arguing that the adoption of iron machinery across a wide range of economic activities was more central to the UK's rise than textile pioneering.
While industrial rivals had superior systems of higher technical education for elite scientists, the UK benefited from institutions that expanded technical literacy to a broader segment of society, such as: * Mechanics’ institutes. * The Manchester College of Arts and Sciences. * Various associations facilitating applied mechanics knowledge.
Historical Case Study: The Second Industrial Revolution (–)
This era was spurred by inventions in machine tools, specifically the industrial production of interchangeable parts.
The United States did not necessarily produce the most sophisticated machinery but surpassed the United Kingdom in productivity by adapting machine tools across nearly all industrial branches.
In 1907, U.S. machine intensity (horsepower of installed machines per manufacturing worker) was more than double that of the United Kingdom and Germany.
U.S. advantage was secured by: * Land-grant schools and technical institutes that created a wide pool of mechanical engineering expertise. * Standardization efforts in screw threads and other machine components. * The creation of a common language and professional community in chemical engineering, which accelerated productivity in industries including ceramics, food processing, glass, metallurgy, and petroleum refining.
The Current Race: AI and Global Productivity
Current thinkers often emphasize how quickly AI will shape growth, where it is pioneered, and how a narrow range of industries will harness it.
The real determining factor is the capacity to diffuse AI advances across a wide range of industries in a process likely spanning decades.
The United States is currently well-positioned because its businesses embrace information and communication technologies (ICT) faster than Chinese firms. * These technologies include cloud computing, smart sensors, and industrial software. * On one influential index, China ranks rd in the world in access to these technologies, trailing the United States by places.
AI expertise and training metrics: * China has only universities with at least one researcher who has published in a leading AI conference publication. * The United States has such universities. * The U.S. maintains tighter links between academia and industry, facilitating the dissemination of AI advances.
Critiques and Recommendations for U.S. Policy
The U.S. is currently fixated on innovation cycles and preventing leaks to China through visa denials for graduate students or export controls on high-end chips.
History suggests no single country can monopolize foundational innovations; therefore, cutting China off entirely is unfeasible.
The U.S. should prioritize the rate at which AI becomes embedded in productive processes.
Policy Recommendations: * Provide greater backing to community colleges to train an AI-savvy workforce. * Fully implement the CHIPS and Science Act workforce initiatives for STEM fields. * Invest in applied technology centers to bridge the gap between basic research and industrial needs via testing and applied R&D. * Expand dedicated field services like the Manufacturing Extension Partnership, which helps businesses incorporate new technologies and diversify markets. * Support small and medium-sized enterprises in adopting AI techniques.
While R&D spending for elite scientists is helpful, AI requires a different toolkit focused on the population and industry's ability to embrace, rather than just invent, technology.