https://aifaultline.com/authors/ceyhun-elgin/New papers by Ceyhun Elgin in the AI Fault Line guide, and news naming them. https://aifaultline.com/research/p/doi-10-4324-9781042011162-4/https://aifaultline.com/research/p/doi-10-4324-9781042011162-4/Tue, 20 Oct 2026 00:00:00 +0000A book chapter on how AI changes labour markets and income distribution, moving from skill- and routine-biased technical change to a task- and workflow-based view of generative AI. It argues exposure is not unemployment and that distributional outcomes depend on adoption costs, complementary investment, institutions, bargaining power and how productivity rents are shared. https://aifaultline.com/research/p/doi-10-4324-9781042011162-3/https://aifaultline.com/research/p/doi-10-4324-9781042011162-3/Tue, 20 Oct 2026 00:00:00 +0000A book chapter on AI, productivity and long-run growth that explains why task-level efficiency gains may not show up quickly in aggregate statistics, covering the productivity paradox, intangible investment and the J-curve. It contrasts singularity-style scenarios with Baumol-style bottlenecks and argues AI's growth impact depends more on diffusion, reorganisation, competition and institutions than on model capability. https://aifaultline.com/research/p/doi-10-4324-9781042011162/https://aifaultline.com/research/p/doi-10-4324-9781042011162/Tue, 20 Oct 2026 00:00:00 +0000Ceyhun Elgin's book sets out an economic framework for AI as a general-purpose technology that reallocates tasks between people and machines. It covers why productivity gains may be delayed or uneven and how wages, inequality, market power, public finance and development could change. https://aifaultline.com/research/p/doi-10-4324-9781042011162-9/https://aifaultline.com/research/p/doi-10-4324-9781042011162-9/Tue, 20 Oct 2026 00:00:00 +0000This book chapter argues that AI should be treated as a cross-cutting transformation rather than a single policy domain, calling for a coordinated package of skills and labour-market institutions, competition and data governance, public research and compute investment, risk-based regulation, procurement, fiscal adaptation and democratic accountability. It holds that policy should shape the direction of AI rather than only compensate for its consequences.