Goldman Finds AI Exposure Pressures Entry-Level Hiring

According to CNBC and PYMNTS, Goldman identified information and communication services as among the industries most exposed to AI and found that employment growth in those sectors had slowed across nearly all major developed economies since 2022. Outside the United States, however, employment in those industries remained near or above its long-run trend, CNBC reported.

Goldman's analysis found employment in several highly exposed sectors below historical trend across developed markets:

• •Call centers: 39% below trend in the United States, 33% below trend in Canada, and 27% below trend in Germany.
• •Software publishing: Employment has fallen sharply below trend, according to Goldman, as reported by CNBC and PYMNTS.
• •Advertising services and management consulting: Both sectors also showed employment below historical trend in the analysis.

Goldman's analysis described the cross-industry relationship as an association. The report stated that labor-automating tools were already available in the industries where it observed employment headwinds, PYMNTS reported.

Goldman also found that AI-related hiring headwinds were felt more strongly by entry-level workers. CNBC reported that the bank cited recent U.S. labor-market data and other evidence indicating that displacement effects could be stronger for less-experienced workers.

The report arrives as AI adoption becomes more widespread across developed economies. CNBC reported Goldman estimated adoption at roughly 15% to 20% on average. That diffusion matters because entry-level roles often include standardized, high-volume tasks that can be partially automated or augmented with generative AI and workflow tools.

For data and ML practitioners, the findings reinforce the importance of measuring workforce effects separately from technology deployment. Across comparable industry transitions, changes in vacancy postings, hiring seniority mix, task composition, and output per worker can move differently. Labor-market associations alone do not establish causation, but they can identify sectors where more granular analysis of AI use, process redesign, and job creation is warranted.