The Yale Budget Lab examines the widespread expectation that artificial intelligence will drive a major productivity boom, urging caution about reading too much into current productivity data. The analysis highlights the gap between AI's theoretical potential and what official economic statistics actually show so far, noting that historical precedents with transformative technologies suggest productivity gains often take longer to materialize than anticipated and can be difficult to measure accurately in real time.

The piece argues that while AI tools are rapidly being adopted across industries, the macroeconomic productivity data has not yet reflected a clear, sustained acceleration. Measurement challenges — including how to capture quality improvements, new product categories, and shifts in how work is performed — complicate the picture further. The authors caution policymakers and analysts against prematurely assuming that AI-driven productivity growth will resolve fiscal or economic challenges, emphasizing that counting on uncertain future gains to inform present-day decisions carries significant risks.

Goldman Sachs Research finds that AI's impact on the labor market is a two-sided story: while it displaces workers in some roles, it simultaneously boosts employment in occupations where it augments human capabilities. The analysis estimates AI has reduced US monthly payroll growth by roughly 16,000 jobs over the past year and raised the unemployment rate by 0.1 percentage point, though the true aggregate impact is likely smaller when accounting for data center construction hiring and AI-driven productivity gains.

The research distinguishes between AI substitution (where AI replaces human labor) and AI augmentation (where AI complements human workers). Jobs with high augmentation potential—such as education workers, judges, and construction managers—have actually seen increased employment, adding about 9,000 jobs per month. This reflects Jevons paradox: when AI makes workers more productive, it lowers the cost per unit of output, which can increase demand enough to create net job gains. Meanwhile, roles like telephone operators, insurance claims clerks, and bill collectors face the highest substitution risk.

Notably, the negative effects of AI on job creation appear to fall disproportionately on younger, less-experienced workers. The framework combining AI displacement scores with an IMF-developed complementarity index offers a more nuanced view than earlier research, helping to clarify which professions stand to benefit from AI adoption and which are most vulnerable to disruption.