The stagnation of the S&P 493
The lack of a measurable AI financial payoff is most evident when looking at the broader market rather than the tech-heavy indices. According to a note published by Apollo Global Management in 2026, the technology has failed to benefit the bottom line of large corporations outside of the technology sector.
To isolate the impact of AI, chief economist Torsten Slok focused his research on what he terms the "S&P 493." This specific group consists of the S&P 500 constituents excluding the massive influence of Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla. By removing these tech giants, the data reveals a different reality for the rest of corporate America.
The findings indicate that profit margins and revenue per worker have remained largely flat. For many organizations, there is currently no empirical evidence to suggest that AI integration is driving the widespread productivity improvements that many analysts had predicted.
The challenge of converting intelligence to revenue
Transforming raw computational power into economic value presents a significant hurdle for traditional industries. Slok expressed skepticism regarding the ability of non-tech companies to bridge this gap, noting the difficulty in turning increasingly cheap intelligence into higher revenue growth and improved profit margins.
While the cost of processing information may be decreasing, the ability to leverage that information for commercial success is not guaranteed. The economist suggests that the current era of massive investment and widespread hype has yet to translate into the fundamental shifts in corporate efficiency that the market anticipated.
The distinction between calculation and judgment
A critical component of this disconnect lies in the nature of what AI actually provides. While AI excels at processing data, it lacks the essential human elements required for high-level business success.
The limitations of AI in complex business environments
The fundamental reason for the missing AI financial payoff may be rooted in the distinction between information processing and decision-making. According to Torsten Slok, while AI might function as an exceptional calculator, true business success relies heavily on judgment.
Effective business management requires navigating nuances that algorithms are currently unable to grasp. Slok noted that AI lacks an understanding of several key pillars of corporate stability and growth, including:
- Accountability and trust
- Corporate culture and unwritten rules
- Ethical trade-offs and fairness
These qualitative factors are not merely administrative details; they are the elements that shape reputational risk. Ultimately, it is this management of risk and human complexity that drives long-term revenue growth and sustains profit margins, areas where current AI models remain limited.
FAQ
Why is the S&P 493 significant in this analysis?
The S&P 493 is a subset of the S&P 500 that excludes the dominant tech companies like Nvidia, Microsoft, and Apple. By analyzing this group, economists can see if AI is actually helping the general economy or if the gains are strictly limited to the tech sector.
What is the main reason for the lack of AI productivity?
The primary reason cited is that AI lacks the capacity for human judgment. While it can calculate and process data quickly, it cannot navigate the ethical trade-offs, corporate culture, or accountability issues that are essential for driving real revenue and managing reputational risk.
Has AI helped any companies financially?
Yes, but primarily within the technology industry. According to the Apollo economist, the financial benefits of AI have been concentrated among the tech giants, leaving the rest of corporate America with flat profit margins and no significant evidence of increased revenue per worker.
Conclusion
The current economic landscape suggests a widening gap between AI hype and actual corporate performance. While the technology offers unprecedented calculating power, the transition from
