The Wall Street Journal surveyed 16 leading economists, including Nobel laureate Daron Acemoglu, finding a near-unanimous agreement that AI will significantly increase labor productivity. However, opinions diverged sharply on AI's net effect on employment: eight economists predicted no net change in jobs, five anticipated net losses, and two expected job growth. This divergence highlights a key challenge, as experts note the current lack of robust data to fully assess AI's aggregate labor market impact. Some, like Jason Furman, observed little to no present impact, while others, such as MIT's David Autor, foresee significant changes within five to ten years.
Despite the mixed forecasts, AI is already being cited as a driver of job cuts. Challenger, Gray & Christmas reported 38,579 AI-attributed layoffs in May, representing 40% of all announced cuts that month and marking the highest monthly total since tracking began in 2023. In contrast, the Bureau of Labor Statistics reported a payroll growth of 172,000 in May. This discrepancy underscores the difficulty in reconciling micro-level job cuts with macro-level employment data. Economists like Jed Kolko suggest some companies may be using AI as a convenient explanation for layoffs that might stem from other issues, such as over-hiring post-pandemic.
Concerns about job displacement are particularly focused on white-collar roles. Stanford Digital Economy Lab economists, using ADP payroll data, found a 16% relative employment decline for workers aged 22-25 in AI-exposed fields like software development and customer service. Unemployment among recent college graduates stands at 5.6%, significantly higher than the overall rate. Experts like Justin Wolfers warn that AI's impact on cognitive work could mirror the challenges blue-collar workers faced in the 1970s. Nobel laureate Daron Acemoglu, predicting net job losses, drew parallels to the long-lasting displacement effects caused by Chinese imports and robotics.
The economists highlighted a critical need for better data and measurement regarding AI's economic effects. The Census Bureau indicates that fewer than one in five U.S. firms use AI. Erik Brynjolfsson noted that while hundreds of billions are invested in AI deployment, less than 1% is spent on understanding the economic transition. Policy suggestions include improved education and vocational training, as well as focused government efforts to collect more empirical economic data to inform policy decisions. Opinions were also split on AI's impact on wage inequality, with seven scholars expecting it to expand, five predicting a reduction, and two believing it would remain unchanged.
