Transcript: Is China Gaining Ground in Technology Diffusion? A Conversation with Jeffrey Ding

Below is a complete transcript of the episode. Thanks to CadreScripts for their great work, to Lili Shoup for checking and formatting, and to Zhou Keya for the image! Listen in the embedded player above.

Kaiser Kuo: Welcome to the Sinica Podcast, a weekly discussion of current affairs in China. In this program, we’ll look at books, ideas, new research, intellectual currents, and cultural trends that can help us better understand what’s happening in China’s politics, foreign relations, economics, and society. Join me each week for in-depth conversations that shed more light and bring less heat to how we think and talk about China. I’m Kaiser Kuo, coming to you from Beijing, where I am in town, all too briefly.

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A few weeks ago on the show, in the Paying It Forward section of the pod, one of my guests, the estimable Jessica Chen Weiss, name-checked Jeffrey Ding, who was on the AI panel she convened at Hopkins SAIS earlier this year on “Getting China Right.” I, naturally, concurred with that endorsement having seen that panel. I’ve been reading Jeff for quite some time, and he has quickly emerged as perhaps the leading voice writing in English about the development of AI in China. His book, Technology and the Rise of Great Powers: How Diffusion Shapes Economic Competition has been recommended before on the show, I think more than once, and I am delighted, at last, to have Jeff on the program today. Jeff is now Assistant Professor of Political Science at George Washington University. His PhD is from Oxford, where he was a Rhodes Scholar. Jeff Ding, welcome at long last to Sinica. This is long overdue.

Jeff Ding: Thanks so much for having me, Kaiser. Really excited to be here.

Kaiser: Great. Well, you have talked about your book in many different places. So, in the time that we have, I don’t actually want us focus too much on what you’ve written, but about some of what’s happened since you published and some of the other factors that, for very good reasons, were not the focus of your book, but which I, nonetheless, would be very curious to hear your thoughts on. But we do want to talk about the book a bit here. You have produced a body of work — some working papers that are freely available online, and I’ll put up links, your excellent book, of course, your outstanding newsletter, the ChinAI newsletter, which I’ve been reading for basically since you started, that really challenges the conventional way of assessing how technology contributes to national strength. If you were to summarize that, and maybe correct me if I’m oversimplifying here or if I’ve otherwise mischaracterized it, well, the conventional notion is that countries that benefit the most from technology and especially from what you call general purpose technologies, or GPTs handily, are the ones that made the sort of big zero to one breakthroughs, the eureka moments, the quantum leaps. And they’re able to do so because, the traditional argument goes, the institutions in those countries — and we’ll get into what you mean exactly by institutions in a bit — but they’re able to do so because their institutions are conducive to this kind of innovation.

But you argue something very different that if you look historically at the different industrial revolutions, it’s not ultimately the nations that have been successful in inventing new technologies, but the ones that excel at diffusing technology, seeing it widely adopted across different industry sectors, at channeling the GPTs into practical, useful, and ultimately profitable applications. These are the countries that see that the overall national strength benefit from this. So, Jeff, can you flesh out that argument a bit and perhaps offer a summary of the historical cases that you look at without getting too much into the weeds with your methodology, which takes up a good part of the book? Maybe give the listeners a sense of how you went about comparing tax contribution and national power, how you compared institutional fit for tech diffusion, and how you measured diffusion deficit versus diffusion surplus.

Jeff: Yeah, so I think the one-line summary of the book’s thesis is technological leadership in industrial revolutions is less about who can create the most brilliant Silicon Valleys, but about who can create the most robust diffusion channels between their Silicon Valleys and their Iowa Cities, which is where I grew up in the U.S. And so through these historical cases that you mentioned like the First Industrial Revolution, the Second Industrial Revolution, when the U.S. became the preeminent economic power, and U.S.-Japan competition in the information revolution, in all of these cases, leadership over creating and developing foundational breakthroughs was contested among the most advanced economies. And the real separating factor was which country could sustain and diffuse these general-purpose technologies throughout their entire economy pervasively across all these different application sectors. And the institutional component of that is the set of education and training institutions that widen the base of engineering skills connected to a general-purpose technology as the facilitating factor for widespread diffusion of a GPT.