Key Takeaways
- Goldman Sachs has pinpointed 20 Russell 1000 companies positioned to capitalize on AI-enabled workforce cost savings
- Just 2% of S&P 500 firms provided specific numbers on AI’s earnings contribution during Q2 2026, matching Q1 levels
- Approximately half of this year’s S&P 500 earnings per share expansion has come from AI infrastructure players
- Current AI inference costs represent under 0.5% of S&P 500 total revenues, though Goldman notes rapid spending growth
- Goldman references academic research demonstrating 20-30% workforce efficiency gains in organizations deploying generative AI tools
The AI earnings boom remains concentrated among infrastructure providers rather than spreading across the broader market, according to [[LINK_START_0]]Goldman Sachs[[LINK_END_0]], though analysts believe this dynamic is approaching a turning point.
A team headed by strategist Ben Snider discovered that when stripping out “other income” from private equity holdings, second-quarter 2026 earnings per share climbed 31% annually. Nearly half of this expansion came from AI infrastructure businesses. The typical S&P 500 member posted 14% EPS growth.
The numbers look impressive, but Goldman notes AI’s earnings influence remains concentrated. Just 11% of S&P 500 members quantified productivity improvements linked to specific AI applications. A mere 2% identified measurable earnings impact from AIāessentially unchanged from the first quarter of 2026.
According to Goldman’s analysis, Q2 earnings data revealed no statistically significant performance gap between firms reporting AI productivity benefits and those that didn’t.
However, the investment bank anticipates a transition ahead. Corporate AI expenditures have surged in recent periods. While Goldman calculates AI inference expenses currently account for less than 0.5% of S&P 500 aggregate revenues, the firm highlights accelerating outlays based on the Ramp AI Index tracking monthly spending per worker.
Goldman’s Stock Selection Methodology
The firm’s analysts screened Russell 1000 constituents using two primary criteria: workforce expenses as a percentage of total revenue, and the proportion of each organization’s payroll vulnerable to AI-driven automation, leveraging occupation-specific information from workforce intelligence provider Revelio Labs.
Qualifying companies needed to score in the upper 50% of their respective sectors on both metrics and had to reference AI regarding productivity or operational efficiency during Q2 or Q1 earnings discussions. Goldman filtered out any names already included in its AI infrastructure or AI disruption risk portfolios.
The 20 highest-ranked stocks by composite score 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.
Analysis Highlights
CoStar Group earned the top position overall, featuring 37% of its compensation expenses exposed to AI automation potential while labor represents 31% of revenues. eBay and Dollar Tree secured positions near the list’s summit.
Goldman’s economic research team cites scholarly studies indicating 20-30% efficiency improvements in functions where generative AI has been implemented. Sectors with elevated AI integration are beginning to exhibit accelerated productivity expansion in official U.S. government statistics.
Market participants currently favor AI infrastructure companies. Goldman suggests this preference may rotate as AI implementation broadens and efficiency gains become visible in financial results during upcoming reporting periods.
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