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41. Migration or stagnation: Aging and economic growth in Korea today, the world tomorrow

Author:
Michael A. Clemens
Publication Date:
07-2024
Content Type:
Working Paper
Institution:
Peterson Institute for International Economics (PIIE)
Abstract:
South Korea faces an unprecedented economic crisis driven by rapid population aging, as it approaches a future of negative economic growth. This paper examines the full range of possible policy responses with the potential to restore dynamism to the Korean economy. Contrary to many prior analyses, I find that enhanced labor migration to Korea is necessary, sufficient, and feasible. Migration is necessary because in the best forecasts we have, no other class of policy has the quantitative potential to meaningfully offset aging. Migration is sufficient because enhanced temporary labor migration by itself would offset most of Korea’s demographic drag on growth over the next 50 years. And migration is feasible because the levels of migration and timescale of the transition would resemble that already carried out by Malaysia and Australia. Many advanced economies will follow in Korea’s demographic footsteps in decades to come, and have much to learn from the decisions that the Korean government makes now.
Topic:
Demographics, Migration, Labor Issues, Economic Growth, and Aging
Political Geography:
Asia and South Korea

42. The tension between exploding AI investment costs and slow productivity growth

Author:
Bertin Martens
Publication Date:
10-2024
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
This working paper explores the tension between rapidly increasing artificial intelligence investment costs and the slower pace of productivity growth, raising concerns about a potential ‘economic winter’ for AI. AI has shown significant technological progress, particularly with machine learning and generative AI models like ChatGPT. Investment in AI has surged. But there are concerns about whether these investments will yield proportional returns. Training costs for a single frontier AI model are increasing exponentially, , from $1,000 in 2017 to nearly $200 million in 2024, driven by constant returns to scale in AI model training data, compute capacity and model complexity. Costs could reach billions of dollars by 2030, despite a rapid fall in unit costs per computing operation over the same period. Global AI infrastructure costs in hardware could exceed $1 trillion by the mid-2030s. Amortizing these huge fixed costs requires business models that can be rolled out across a very large user market. Estimates about AI’s contribution to productivity growth vary, from a modest 0.5 percent per year to a very optimistic 10 percent per year. Research shows that productivity usually catches up slowly compared to costs. Without significant productivity gains, the current investment cost trajectory is unsustainable. We estimate that 3 percent annual productivity growth across advanced economies would be required to sustain AI model cost extrapolations to 2030. In an optimistic scenario, productivity increases would result in accelerated economic growth.
Topic:
Digital Economy, Economic Growth, Microeconomics, Artificial Intelligence, and Productivity
Political Geography:
Global Focus