The S&P 493 Reality Check
Artificial intelligence has delivered zero measurable financial payoff to corporate America outside of a handful of major technology firms, according to an economic note published in late October 2026 by Torsten Slok, chief economist at TechCrunch. Slok examined the financial performance of what he termed the S&P 493 — the S&P 500 stock index excluding the seven dominant tech giants: Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla. While those major firms have capitalized heavily on the ongoing technology boom, the economic data indicates that non-tech enterprises are experiencing flat profit margins and stagnant productivity growth despite substantial capital expenditures on artificial intelligence infrastructure and software integrations.
According to research highlighted by Business Insider, revenue per employee for major technology corporations has climbed sharply from roughly $550,000 to nearly $800,000 since 2022. By contrast, that same metric has remained essentially flat at approximately $550,000 for the remaining companies in the S&P 500, where profit margins have also stagnated around 10%. Slok noted that mainstream corporate enterprises are increasingly viewing spending on artificial intelligence tokens not as a growth catalyst, but rather as an emerging cost center that weighs on operating margins.
Evaluating the Spending and Productivity Gap
The stark disparity between the handful of infrastructure providers and the broader market has sharpened financial scrutiny surrounding enterprise technology budgets. As Startup Fortune reported, the top-tier technology companies continue to accelerate capital spending, with hyperscalers projected to invest hundreds of billions of dollars. However, economists and market analysts point out that these investments have yet to translate into comparable productivity gains or accelerated revenue growth for the commercial clients purchasing those AI tools.
Slok argued that business intelligence differs fundamentally from computational capacity, emphasizing that artificial intelligence models do not inherently account for organizational culture, trust, accountability, or reputational risk. As enterprises head deeper into the corporate earnings season, management teams face increasing pressure from investors to justify ongoing artificial intelligence expenditures by pointing to concrete efficiency gains or measurable top-line revenue lift rather than broad operational assumptions.