AI's Economic Payoff Still Confined to Tech Sector, Data Shows

AI's Economic Payoff Still Confined to Tech Sector, Data Shows

Despite the relentless hype and massive capital deployment around artificial intelligence, the technology has yet to deliver any measurable financial benefit to corporate America outside the technology sector. That's the stark conclusion from Apollo chief economist Torsten Slok in his latest Daily Spark note, published October 24, 2026. The analysis reveals a widening chasm between the tech giants and the rest of the S&P 500, with profound implications for the broader economy and AI investment thesis.

Slok's report, titled "No Signs of AI Payoff Outside the Tech Sector," directly challenges the narrative that AI is a general-purpose technology poised to transform every industry. Instead, the data suggests that the benefits of AI are accruing almost exclusively to a small group of technology leaders, while mainstream companies are beginning to treat AI spending as a drain on profitability rather than a catalyst for growth.

The Magnificent Seven vs. The S&P 493

The core of Slok's analysis hinges on a direct comparison between the Magnificent Seven (Mag 7) tech giants and the remaining 493 companies in the S&P 500. The findings are unambiguous. Revenue per worker is rising for the Mag 7, but remains flat for the S&P 493. Similarly, profit margins are expanding for the tech leaders while staying stagnant for the rest of the index.

This divergence is not a temporary blip but a structural trend. Slok's charts, sourced from Bloomberg and Macrobond, show that the productivity gains and financial rewards from AI are highly concentrated. "The AI story will be decided in the S&P 493, not the Mag 7," Slok writes, "and so far, there is no evidence that revenue growth or margins are improving outside the tech sector."

Productivity Data Shows No AI Boost

Perhaps the most damning evidence comes from productivity statistics. Slok points to Total Factor Productivity (TFP) data, adjusted for cyclical variation in factor utilization by the San Francisco Fed. The data shows no signs of AI boosting productivity growth across the economy. This is a critical finding because the entire investment thesis for AI rests on its potential to fundamentally improve efficiency and output.

If AI were truly transformative, we would expect to see early signs in productivity metrics. Instead, the data remains flat. This suggests that the technology, in its current form, is not being effectively deployed to enhance operational efficiency in mainstream businesses. The gap between technological potential and actual economic impact remains wide.

Token Spending: A Cost Center, Not a Growth Engine

As the financial payoff fails to materialize, corporate sentiment is shifting. Slok notes that mainstream corporate America is "pulling back and starting to view token spending not as a source of growth but as a cost center that weighs on margins." This is a significant psychological shift from the initial AI gold rush mentality, where companies felt compelled to invest for fear of being left behind.

The spending data supports this view. According to charts sourced from Ramp and Apollo, AI spending is extremely concentrated. The top 10% of firms account for 99.5% of model-serving and inference spend. Even more telling, 80.4% of the top 1% of AI spenders are venture capital-backed companies. This indicates that the AI boom is being fueled by a small cohort of well-funded startups, not by broad-based corporate adoption.

This concentration creates a circularity problem. VC-backed companies are spending heavily on tokens, which in turn generates revenue for a few AI infrastructure providers, but this cycle has little to no spillover effect on the broader economy. Slok argues that as long as agentic AI and token spending are confined to a small part of the economy, the macroeconomic effects of AI will remain very limited.

The Judgment Problem

Looking ahead, Slok raises a fundamental question: Can mainstream corporate America turn increasingly cheap intelligence into higher revenue growth and profit margins? His answer suggests this may be harder than it sounds. The core issue lies in the nature of business intelligence itself.

"AI may be a great calculator, but in business, intelligence is mostly judgment," Slok writes. "Judgment depends on context, and the answer varies with which human or which model you ask." This is a crucial insight that gets lost in the AI enthusiasm. Business decisions are not purely analytical; they involve navigating accountability, trust, culture, unwritten rules, ethical trade-offs, and fairness. These elements shape reputational risk, which ultimately drives revenue and margins.

AI, in its current form, cannot understand or replicate these human nuances. It lacks the ability to assess the social and ethical dimensions of business decisions. This limitation may prove to be the fundamental barrier to AI's widespread economic impact.

The Road Ahead

The bottom line from Slok's analysis is clear: the AI payoff is not materializing for the vast majority of corporate America. The technology has delivered impressive returns for the Mag 7 and the VC ecosystem, but the S&P 493 has seen no improvement in revenue growth or margins. As token spending becomes viewed as a cost rather than an investment, we may see a slowdown in AI adoption outside the tech sector.

The real test of AI's economic value will come from the S&P 493. If these companies cannot find ways to leverage AI to improve their bottom lines, the AI investment boom may face a reckoning. The data suggests that the path to AI-driven economic transformation is far more complex and uncertain than the hype suggests. For now, the evidence points to a technology that is powerful but not yet delivering on its broad economic promise.