ChinAI #259: Year 6 of ChinAI (The Rise and Fall of Technological Leadership)

Greetings from a world where…

ChinAI is now six years old, which means it should be beginning to speak in simple but complete sentences, tell time, and develop a sense of humor, but let’s not put too much pressure on it to hit these milestones. After all, all newsletters develop at their own pace.

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The Rise and Fall of Technological Leadership: General-Purpose Technology Diffusion and Economic Power Transitions

How do technological revolutions produce economic power transitions? Last week, I tackled this question in an International Studies Quarterly article that draws directly from my dissertation and previews some of the main arguments in my forthcoming book. This year in review post distills some of the key findings form the article. *Note: for those with the full manuscript (without typesetting and copyedits) is available here on my personal site.

We begin at the beginning, with the first sentence: “Policymakers and scholars increasingly frame today’s U.S.-China rivalry as a contest for technological leadership in the Fourth Industrial Revolution.” In these analyses of how technological breakthroughs affect the rise and fall of great powers, the conventional wisdom focuses on the country that dominates innovations in leading sectors: By exploiting a brief window to monopolize profits in these new, fast-growing industries, this country rises to become the world’s most productive economy, which then translates into geopolitical and military influence.

In this article, I challenge leading-sector theory of technology-driven power transitions by developing an alternative explanation centered on general-purpose technologies (GPTs) — fundamental breakthroughs distinguished by their scope for continuous improvement, pervasiveness in terms of economic applications, and synergies with complementary innovations. My account, GPT diffusion theory, argues that GPTs affect economic power transitions in a very different pathway than the standard leading-sector story (see table 1 below).

In other words, competition over technological leadership in electricity (GPT) is very different than competition over technological leadership in automobiles (a leading sector that became one of the fastest-growing and largest sectors but does not qualify as a GPT). Here’s how I articulate the differences between the two technological trajectories:

First, whereas the LS explanation emphasizes the impact of technological innovations in the early stages of their life cycle, the greatest boosts to productivity come late in a GPT’s development. Second, the GPT mechanism places more weight on diffusion. No one country dominates innovations in GPTs; rather, national success is determined by a state’s effectiveness in adopting GPTs across a wide range of economic sectors. Finally, in contrast to the LS account’s focus on the contributions of a few key industries to economic growth, GPT-fueled productivity growth is spread across a broad range of industries.

Here’s the key part of my argument. Getting the technological pathway right also informs the institutional factors most crucial to which country attains economic leadership amidst technological revolutions. The article proposes:

If the LS mechanism is operative, then the key institutional adaptations allow states to seize the market in new industries, such as scientific research investments that pioneer new technological paradigms and industry structures that monopolize LS innovation. Alternatively, GPT diffusion theory highlights institutions that facilitate widespread diffusion of GPTs, including education systems and technical associations that broaden the base of relevant engineering skills.

The meat of the article tests GPT diffusion theory against the LS explanation across three historical cases of technological revolutions and shifts in economic leadership: 1) the first industrial revolution and Britain’s rise to preeminence; 2) the U.S.’s overtaking of Britain in the second industrial revolution (1870-1914); 3) Japan’s challenge to U.S. technological dominance in the late 20th century.

Let me give you a very abridged preview of the empirical evidence from the second industrial revolution case, which many hold up as a classic example of the LS mechanism at work. According to this view, the U.S. and Germany overtook Britain because they were “the first to introduce the most important innovations” in key sectors such as electricity and chemicals (Akaev and Pantin 2014). However, carefully tracing technological trajectories challenges this conventional narrative:

Spurred by inventions in machine tools, the industrial production of interchangeable parts, known as the “American system of manufacturing,” embodied the key GPT trajectory. The U.S. did not lead the world in producing the most advanced machinery; rather, it had an advantage over Britain in adapting machine tools across almost all branches of industry…Since a nation’s success in adapting to technological revolutions is determined by how well its institutions complement the demands of emerging technologies, the GPT-based explanation of the IR-2 highlights institutional factors that differ from those featured in standard accounts. LS-based theories tend to emphasize Germany’s institutional advantages in scientific education and industrial R&D. In contrast, the IR-2 case analysis points toward the U.S.’s edge in education and training systems that widened the skill base and standardized best practices in mechanical engineering.

Okay, we get it, you like reading books about the history of technology. So, what does all of this have to do with the purported theme of your newsletter (China and AI)? I’m glad you asked! Even the briefest of scans of news articles or policy papers about today’s U.S.-China technological competition will reveal the enduring influence of the leading-sector account. I argue that policymakers in both countries have learned the wrong lessons from the past three technological revolutions:

Thanks for reading ChinAI in year six. If you gained something from the newsletter this past year, please take 20 seconds and do me a favor: help signal boost the article by posting the link and sharing my Twitter thread. Again, for those without access to the journal, the full manuscript without typesetting and copyedits is available here on my personal site.

ChinAI Links (Four to Forward)

My four favorite issues of ChinAI from this past year:

• ChinAI #225: AI Birdwatching in China: This translation of a longform piece about the use of AI to track migratory birds probably received the most positive feedback of any issue from the past year.

• ChinAI 234: The (Privacy) Cost of Being Fabulous? Ben Jiang translated and analyzed a story about the “Fabulous Duck” AI portrait generator that became one of China’s hottest apps

• ChinAI #241: The Long Road to Speech AI: a deep history of John Hopkins University’s Center for Language and Speech Processing, which traces the center as starting point for the journeys of many Chinese AI rising stars.

• ChinAI #255: Panic buying, speculative booms, and whack-a-mole: What lengths will Chinese companies go to get an NVIDIA A100 chip?

Thank you for reading and engaging.

These are Jeff Ding's (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is an Assistant Professor of Political Science at George Washington University.

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Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99