Goldman Sachs CEO David Solomon recently addressed concerns about the potential impact of artificial intelligence on employment, refuting claims that AI will lead to widespread job losses. In a guest article published in the New York Times on May 22, 2026, Solomon emphasized his belief in the adaptability of the US workforce to the rapidly advancing technology.
While acknowledging the forthcoming disruptions, Solomon highlighted estimates from Goldman Sachs research indicating that AI could automate approximately 25% of current working hours over the next decade. He pointed out that professions like accounting, banking, and law may witness the automation of routine tasks by machines, particularly in white-collar sectors, though the effects on blue-collar roles remain uncertain.
In outlining his optimism for the economy’s resilience, Solomon presented three key reasons. He suggested that AI would enable workers to focus on more complex duties, enhance standards within existing professions, and introduce new positions for individuals managing AI systems within organizations. However, he conceded that if significant job displacement occurs, a collaborative effort between the public and private sectors would be necessary to assist affected workers and institutions.
Drawing on the insights of economist John Maynard Keynes, who envisioned a future where people would work only 15 hours a week by the year 2000, Solomon emphasized the historical tendency to underestimate technology’s potential to drive productivity and economic growth.
Furthermore, Solomon’s views align with internal analyses at Goldman Sachs, distinguishing between jobs at high risk of being replaced entirely, such as telephone operators and insurance claims representatives, and those likely to be enhanced by AI, such as education administrators, physicians, surgeons, construction managers, and chief executives. Additionally, a 2025 survey by McKinsey revealed that 51% of organizations acknowledged the impact of generative AI in reducing their reliance on entry-level hires.
