Slok’s analysis, published on October 24, compares the seven largest technology-oriented stocks with the remaining 493 companies in the S&P 500. He found that revenue per worker and profit margins have risen among the seven but remained broadly flat across the other companies.
The seven, collectively known as the Magnificent Seven, are Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla. Slok calls the remainder the S&P 493, using the division to assess whether enthusiasm for AI is translating into measurable returns beyond the dominant technology businesses.
“Despite massive investments and widespread hype around artificial intelligence, the technology has delivered zero financial payoff to corporate America outside of tech,” Slok wrote in his Daily Spark commentary.
His findings suggest that AI’s economic benefits remain concentrated among its biggest suppliers and technology platforms, rather than being widely reflected in the financial results of businesses adopting the tools. However, flat aggregate margins do not establish that every company outside the seven has failed to secure returns from AI, or that gains in particular activities are absent.
Slok also pointed to the absence of a discernible acceleration in broader productivity growth attributable to AI. His comparison draws on inflation-adjusted revenue per worker, corporate profit margins and productivity indicators, rather than a company-by-company assessment of individual AI projects.
The distinction is significant as businesses weigh the costs of software subscriptions, computing capacity and specialised systems against promised improvements in efficiency. An individual company can save employees time through automation without immediately recording a corresponding improvement in revenues or margins, particularly where deployment and integration carry additional expenses.
Slok said spending on the computing tokens used to operate AI models was increasingly being treated as an expense rather than a driver of growth. “With no impact on the bottom line, mainstream corporate America is pulling back,” he wrote, describing concern that spending could weigh on margins.
He contrasted that caution with spending by venture-backed companies, which he said continued to increase their consumption of AI tokens. Businesses outside that group, he argued, often do not require the most advanced models and are unwilling to pay premium prices for their capabilities.
The economist warned that the macroeconomic effects of AI could remain limited if autonomous software agents and intensive model usage stayed concentrated within a narrow segment of the economy. The wider financial test, he said, would be whether cheaper computing intelligence eventually translated into stronger sales and profitability across ordinary businesses.
That challenge extends beyond the technical capabilities of models. Slok argued that commercial decisions require judgement shaped by organisational context, trust, accountability, corporate culture and reputational considerations, qualities that cannot automatically be replaced by cheaper automated reasoning.
His October assessment builds on an August Apollo analysis showing that profit margins outside the seven technology leaders had not improved despite substantial AI expenditure. That earlier review described mixed trends across sectors, including little movement in consumer industries and weaker margins in some commodity-related businesses.
The latest findings arrive as investors scrutinise the divide between companies investing heavily to develop and supply AI infrastructure and customers expected to generate economic returns from using it. Financial markets have rewarded several leading technology groups for their exposure to the expansion of computing infrastructure, even as questions persist about how broadly the benefits will spread.
The results also require careful interpretation because revenue per worker can be influenced by prices, business mix and employment changes, while profit margins respond to borrowing costs, wages and demand. Neither measure independently isolates the causal contribution of artificial intelligence to corporate performance.
The analysis therefore presents a snapshot of reported financial outcomes, not a definitive verdict on the usefulness of individual applications. Benefits may also take time to reach corporate financial accounts after implementation and staff training.
Apollo’s commentary draws on market and economic datasets, including charts of revenue per employee and margins for the two groups, alongside productivity and AI expenditure measures. Slok framed the unresolved issue as whether companies outside the dominant technology stocks can translate access to increasingly inexpensive AI capabilities into sustained improvements in commercial results.
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