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This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
As AI moves beyond language into systems that can perceive, understand, and act in the physical world, a new frontier is emerging: world models—AI systems that build and maintain working representations of real environments to predict how they change in response to action.
As AI moves beyond language into systems that can perceive, understand, and act in the physical world, a new frontier is emerging: world models—AI systems that build and maintain working representations of real environments to predict how they change in response to action.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
How do technological revolutions affect the rise and fall of great powers? Scholars have long observed that major technological breakthroughs disrupt the economic balance of power, bringing about a power transition. However, there has been surprisingly limited investigation into how this process occurs. Existing studies establish that a nation’s success in adapting to revolutionary technologies is determined by the fit between its institutions and the demands of these technologies. The standard explanation emphasizes institutional factors best suited for monopolizing innovation in new, fast-growing industries (leading sectors). Jeff proposes an alternative mechanism based on the diffusion of general-purpose technologies (GPTs), which presents a different trajectory for countries to leapfrog the industrial leader.
Characterized by their potential for continuous improvement, pervasiveness, and synergies with complementary innovations, GPTs only make an economy-wide impact after a drawn-out process of diffusion across many sectors. The demands of GPT diffusion shape the institutional adaptations crucial to success in technological revolutions. Specifically, Jeff emphasizes the role of education systems and technical associations that broaden the base of engineering skills associated with a GPT. To test this argument, Jeff sets the leading-sector mechanism against the GPT diffusion mechanism across three historical case studies, which correspond to past industrial revolutions: Britain’s rise to preeminence in the early 19th century; the U.S.’s overtaking of Britain before World War I; Japan’s challenge to U.S. technological dominance in the late 20th century. Evidence from these case studies support the GPT diffusion explanation, shedding new insights into how emerging technologies like AI, which some regard as driving a fourth industrial revolution, will affect a possible U.S.-China power transition.
Jeffrey DingHAI-CISAC Postdoctoral Fellow
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