AI Is Not Improving Productivity: Nobel Laureate Daron Acemoglu

2026-02-24 · Source: MIT Sloan Management Review · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, AI Economics & Policy · Depth: Intermediate, extended

Summary

MIT Institute Professor Dana Samoklu, a Nobel Prizewinning economist and author of "Power of Progress," discusses the future of AI on the "Me, Myself, and AI" podcast from MIT Sloan Management Review. Samoklu argues that technology's direction is not predetermined, emphasizing human agency in shaping AI's future. He highlights two divergent paths for AI development: automation, which benefits capital owners by replacing human tasks, and human-complementary technologies, which enable workers to perform new or more sophisticated tasks. Samoklu advocates for AI as an information technology that augments human capabilities, citing examples like electricians or nurses using AI to access domain-specific knowledge. He expresses concern that current economic incentives primarily drive AI towards automation and centralization, rather than pro-human, decentralized applications, and calls for proactive regulation to steer AI development towards socially beneficial directions.

Key takeaway

For AI Product Managers and entrepreneurs developing new AI solutions, recognize that current incentives favor automation and centralization. Focus your efforts on creating AI tools that augment human capabilities, facilitate new tasks, and promote decentralization. This approach, while less aligned with current big tech models, offers greater societal benefit and long-term productivity gains, potentially requiring a shift in regulatory philosophy to support such innovation.

Key insights

AI's future is shaped by human choice, not destiny, with paths towards either automation or human augmentation.

Principles

• Technology's destiny is not pre-ordained.
• Automation benefits capital, not workers.
• AI is an information technology.

Method

AI should be developed as a pro-human, pro-worker tool, focusing on new task creation and decentralization, rather than solely automation and information centralization.

In practice

• Develop AI tools for domain-specific information access.
• Prioritize AI for human task augmentation.
• Challenge the "AI as automation" narrative.

Topics

• AI Economics
• Automation
• Human-Complementary AI
• Large Language Models
• AI Regulation

Best for: AI Scientist, AI Product Manager, Product Manager, Executive, Research Scientist, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by MIT Sloan Management Review.