Jeff Ding on US vs China AI and Lessons from Past Industrial Revolutions

Jeff Ding is the leading US scholar on China and AI and author of one of the earliest China-focused Substacks, ChinAI.
He recently published a fire paper called, “The diffusion deficit in scientific and technological power: re-assessing China’s rise.” It makes the argument that diffusion capacity (not just innovation capacity) is critical to economic growth — and China actually fares much worse in diffusion capacity than mainstream narratives imply.
In particular, “In cases when the emerging power has a strong innovation capacity but weak diffusion capacity (diffusion deficit), it is less likely to sustain its rise than innovation-centric assessments depict. Conversely, when the emerging power possesses a strong diffusion capacity but weak innovation capacity (diffusion surplus), it is more likely to sustain its rise than innovation-centric assessments portray.”
Mainstream narratives, meanwhile, “only compare the U.S. and China’s ability to produce new innovations, neglecting their ability to effectively use and adopt emerging technologies. By revealing the gap between China’s innovation capacity and diffusion capacity, this paper argues that innovation-centric assessments mistakenly inflate China’s S&T power.”
NYC ChinaTalk Meetup: https://partiful.com/e/taNb35oaCKjglbHHdEA1
Cohosting is Teddy Collins, formerly of the White House Office of Science and Technology Policy and DeepMind.
Outtro music: https://www.youtube.com/watch?v=17Y7-gm8STI
midjourney prompt: "frank quietly industrial revolution"
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