AI delivers ‘zero financial payoff’ to most companies, Apollo economist says

Introduction

A recent note from Torsten Slok, chief economist at Apollo Global Management, challenges the prevailing narrative that artificial intelligence is a universal driver of corporate profit. By examining the so‑called S&P 493—the S&P 500 stripped of the dominant tech players (Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla)—Slok finds that profit margins and revenue per employee have remained essentially flat, suggesting that AI has delivered zero financial payoff for most companies outside the technology sector.

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The S&P 493 analysis

Slok’s methodology focuses on the 493 companies that make up the bulk of the U.S. economy but lack the deep AI integration seen at the tech giants. The key findings are:

• Revenue per worker: No statistically significant increase since the surge in AI spending began.
• Profit margins: Remain at pre‑AI levels, indicating that cost savings or revenue lifts have not materialized.
• Investment vs. return: Companies have poured billions into AI tools, talent, and infrastructure, yet the balance sheet shows no corresponding uplift.

These results contrast sharply with the performance of the excluded tech firms, whose AI‑driven products and services have clearly boosted top‑line growth and margins.

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Why AI may fall short for non‑tech firms

Slok outlines several structural reasons why AI’s promise is harder to realize outside the tech sector:

1. Judgment over calculation – AI excels at processing data, but many business decisions hinge on nuanced judgment, cultural understanding, and ethical considerations that current models cannot replicate.
2. Accountability and trust – Non‑tech companies operate in heavily regulated environments where accountability and reputational risk are paramount. AI’s lack of intrinsic accountability can limit its adoption in critical processes.
3. Legacy systems – Older enterprises often grapple with fragmented IT landscapes, making AI integration costly and less effective.
4. Talent gap – Deploying AI at scale requires data scientists and engineers, resources that many traditional firms struggle to attract and retain.

Together, these factors suggest that AI may act more as a sophisticated calculator than a strategic partner for many businesses.

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The road ahead: realistic expectations

While the current data paints a sobering picture, Slok does not dismiss AI entirely. He emphasizes that:

• Incremental gains are possible when AI is applied to well‑defined, high‑volume tasks such as invoice processing, demand forecasting, or supply‑chain optimization.
• Industry‑specific solutions may emerge as AI models become more specialized, reducing the need for massive, generic deployments.
• Cultural shift is essential; firms must align AI initiatives with clear accountability frameworks and ethical guidelines to unlock value.

For executives, the takeaway is to temper hype with disciplined pilots, measurable KPIs, and a focus on areas where AI can complement—rather than replace—human judgment.

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Conclusion

Apollo’s economist highlights a critical reality check: AI’s financial impact is still concentrated in a handful of tech powerhouses, while the broader corporate landscape sees little measurable benefit. Companies looking to justify AI spend should prioritize clear use cases, integrate robust governance, and set realistic performance targets. Only then can the technology move from a buzzword to a genuine engine of productivity and profit for the majority of the economy.