Key Takeaways Goldman Sachs pinpointed 20 Russell 1000 companies positioned to gain the most from AI-powered labor efficiency improvements During Q2 2026, merely 2% of S&P 500 firms provided
According to [[LINK_START_0]]Goldman Sachs[[LINK_END_0]], artificial intelligence’s profit impact continues to concentrate primarily within infrastructure providers rather than dispersing across the wider market. However, analysts believe this dynamic is approaching an inflection point.
Research conducted by a team headed by strategist Ben Snider revealed that when excluding “other income” generated from private equity holdings, second-quarter 2026 earnings per share climbed 31% compared to the prior year. Infrastructure companies focused on AI represent approximately half this expansion, while the typical S&P 500 constituent posted 14% EPS growth.
Notwithstanding robust performance figures, Goldman emphasizes that AI adoption’s influence on corporate profitability remains limited in scope. A mere 11% of S&P 500 members provided quantified AI productivity enhancements linked to particular applications. Just 2% reported AI generated measurable earnings contributions, virtually unchanged from the first quarter of 2026.
According to Goldman’s analysis, Q2 earnings data revealed no statistically significant variance in profit growth between firms citing AI productivity advances and companies making no such claims.
Nevertheless, the investment bank anticipates an approaching transformation. Corporate AI investments have surged dramatically during recent months. Goldman calculates that AI inference expenditures currently total less than 0.5% of combined S&P 500 revenues, while highlighting accelerating spending patterns evidenced by the Ramp AI Index tracking monthly outlays per worker.
Goldman’s selection process examined Russell 1000 constituents using two primary criteria: labor expenses expressed as a percentage of total revenue, and the portion of each firm’s payroll susceptible to AI-driven automation, leveraging occupation-specific information from workforce intelligence provider Revelio Labs.
Qualifying companies needed to place within the upper 50% of their respective sectors across both metrics and required documented AI discussion related to productivity or operational efficiency during Q2 or Q1 earnings presentations. Goldman deliberately omitted firms already featured in its AI infrastructure or AI disruption vulnerability portfolios.
The 20 highest-ranked companies by composite scoring include CoStar Group, Dollar Tree, eBay, Arthur J. Gallagher, Brown and Brown, Axon Enterprise, Trade Desk, CMS Energy, Jacobs Solutions, Edison International, Aon, Marsh and McLennan, Kimberly-Clark, Willis Towers Watson, Airbnb, Iron Mountain, CBRE Group, RTX, Boeing, and Expedia.
CoStar Group achieved the highest composite ranking, featuring 37% AI automation exposure across its employee compensation structure and labor expenditures comprising 31% of total revenues. eBay and Dollar Tree similarly secured positions near the list’s apex.
Goldman’s economics division references scholarly studies documenting 20-30% productivity enhancements within domains already implementing generative AI solutions. Sectors demonstrating elevated AI integration rates are beginning to exhibit preliminary evidence of accelerated productivity expansion in official U.S. economic statistics.
Market participants currently maintain stronger interest in AI infrastructure companies. Goldman suggests this emphasis may transition as AI technology adoption broadens and efficiency improvements become visible in quarterly earnings announcements throughout upcoming reporting periods.
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