A bout of AI jitters on Wall Street has revived fears of white-collar unemployment and an AI-led downturn, but three leading MIT economists argue the technology can be redirected to lift workers’ productivity and pay. In a Brookings report, Daron Acemoglu, Simon Johnson, and David Autor propose steering AI toward job enhancement—via government procurement, competition policy, and tax changes that rebalance incentives away from labor-substituting capital. They cite tools like Schneider Electric’s Electrician’s Assistant as a model, while warning that companies are also using AI to justify layoffs and intensify worker surveillance. The authors urge legal frameworks to curb “expertise theft” in AI training data, explore wage insurance for displaced workers, and consider a universal basic capital endowment to counter rising wealth concentration and a declining labor share of income. The piece concludes that absent policy intervention, AI will likely amplify inequality—yet governments and markets still have levers to shape outcomes.
Related articles:
— Generative AI at Work: The Impact of Large Language Models on Knowledge Worker Productivity
— U.S. Copyright Office: Artificial Intelligence Initiative
— AI Risk Management Framework (NIST)
— Europe’s Approach to Artificial Intelligence (including the EU AI Act)
