Key Highlights
- Goldman Sachs has pinpointed 20 Russell 1000 companies positioned to gain the most from AI-powered labor cost savings
- Just 2% of S&P 500 firms provided concrete numbers on AI’s earnings impact during Q2 2026, consistent with the previous quarter
- Companies building AI infrastructure have contributed approximately 50% of S&P 500 earnings per share expansion this year
- Current AI inference costs represent under 0.5% of S&P 500 total revenues, though Goldman notes spending is ramping up quickly
- Research studies referenced by Goldman indicate 20-30% productivity improvements in workplaces that have implemented generative AI tools
The profit surge from artificial intelligence continues to concentrate heavily in infrastructure providers rather than spreading across the broader corporate landscape, according to [[LINK_START_0]]Goldman Sachs[[LINK_END_0]]. However, analysts believe this dynamic is approaching an inflection point.
A research team headed by Ben Snider discovered that when stripping out “other income” generated from private equity holdings, second quarter 2026 earnings per share climbed 31% compared to the prior year. Companies focused on AI infrastructure were responsible for approximately half of this expansion, while the typical S&P 500 member posted 14% EPS growth.
The robust performance masks an important reality: AI’s measurable effect on corporate profitability remains limited in scope. Goldman found that only 11% of S&P 500 firms provided specific examples of productivity improvements tied to particular AI applications. A mere 2% reported AI delivering concrete earnings benefitsāessentially unchanged from the first quarter of 2026.
According to Goldman’s analysis, Q2 earnings data revealed no statistically significant performance gap between companies claiming AI productivity wins and those making no such assertions.
Despite this, the investment bank anticipates a transition. Business investment in enterprise AI solutions has surged considerably in recent months. While Goldman calculates that AI inference expenses currently account for less than half a percent of S&P 500 aggregate revenues, the firm points to acceleration in the Ramp AI Index tracking monthly expenditure per worker.
Goldman’s Screening Methodology
Goldman’s approach to identifying potential winners involved analyzing Russell 1000 members across two key dimensions: the proportion of revenue consumed by labor expenses, and what percentage of each firm’s payroll faces potential AI automation based on job-level analysis from workforce data provider Revelio Labs.
To qualify for the list, companies needed to rank in the upper 50% within their respective industries on both metrics and had to reference AI in relation to productivity enhancements or operational efficiency during their Q1 or Q2 earnings discussions. Goldman filtered out any firms already featured in its AI infrastructure or AI disruption vulnerability categories.
The 20 highest-ranked companies based on composite scoring are 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.
Breaking Down the Numbers
CoStar Group secured the top position with 37% of its total compensation exposed to potential AI automation while labor represents 31% of total revenue. [[LINK_START_1]]eBay[[LINK_END_1]] and Dollar Tree also placed prominently in the rankings.
Goldman’s economics division references scholarly studies documenting productivity gains ranging from 20% to 30% in functions where generative AI technology has been successfully implemented. Sectors with elevated AI adoption rates are beginning to demonstrate accelerated productivity growth in official government economic statistics.
Market participants remain primarily focused on AI infrastructure providers at present. Goldman suggests this attention may broaden as AI deployment expands throughout the economy and efficiency improvements begin materializing in quarterly financial results in the months ahead.
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