Apollo sees no broad AI payoff outside Big Tech—but its charts cannot isolate AI's effect
Revenue per worker and profit margins have diverged between the largest technology companies and the rest of the S&P 500. The aggregate comparison is a useful warning against easy productivity claims, not causal evidence about AI.
The short version
- What happened
- Apollo compares revenue per worker and margins for the Magnificent Seven with the other 493 S&P 500 companies and argues that gains remain concentrated in Big Tech.
- Who it affects
- Executives and workers should not treat aggregate market charts as evidence that a particular AI deployment will raise productivity or justify staffing changes.
- What remains unproven
- No firm-level AI-adoption measure
Keep in mind
No firm-level AI-adoption measure
No causal identification strategy
No uncertainty intervals
Commercial market commentary rather than peer-reviewed research
Source analysis · 1 linked source. Primary analysed source date: 24 October 2026.
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The divergence is real in the displayed aggregates
Apollo Global Management's 24 October commentary contrasts the Magnificent Seven technology companies with the remaining 493 companies in the S&P 500. Its Bloomberg and Macrobond charts show a widening gap in revenue per employee and profit margins, while separate San Francisco Fed and US labour data show no obvious economy-wide productivity acceleration. A Ramp spending series is used to illustrate corporate AI expenditure.
That combination supports a limited observation: the most visible operating gains in these aggregates remain concentrated among the largest technology firms. It also challenges the stronger claim that AI investment has already produced a broad, measurable productivity boom across listed US companies.[1]
Aggregate charts cannot tell us what AI caused
The comparison does not measure firm-level AI adoption, exposure or implementation quality. Big Tech differs from the rest of the index in business model, capital intensity, demand and pricing power, so its performance cannot be attributed to AI alone. The page reports no causal method, uncertainty intervals or denominator linking AI spending to output.
For workers and managers, the practical message is restraint: rising software spend is not proof of a productivity return. The assessment would change with pre-registered firm-level studies that connect verified adoption to comparable output, quality, wages and headcount over time. Apollo labels the article opinion, and its position as a global asset manager is a relevant commercial interest.[1]
The distinction that matters
What the charts can and cannot establish
The series describe groups and trends; they do not identify an AI treatment effect.
Observed
Compare Big Tech with S&P 493
Revenue-worker and margin gap
Not observed
Firm-level AI adoption
No adoption denominator
Not established
Causal attribution
Other differences remain
Watch the background, then check the evidence
Illustrative photo · not a video frame
UNESCO · 11 min 27 sec · Background explainer
UNESCO Talks: Interview with 2024 Nobel laureate Professor Daron Acemoglu
YouTube loads only when you choose; playback starts when you press play.
Economist Daron Acemoglu discusses how choices about AI affect workers, inequality and society. Pair the interview with our comparison of task exposure, hiring and skills evidence.
Evidence check: An expert interview from 2024, offering a perspective on policy and work rather than a measured forecast of job losses.
Published 2024-10-14. Source checked 14 October 2026. Check captions on the publisher’s video page.
Read the AI and jobs guidePhoto: Dylan Gillis / Unsplash · Licence
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Read next
Continue exploring ai investment: compare the finding and its evidence limits.
Is the AI boom propping up global growth—or concentrating it?
The IMF says AI investment and its hardware supply chain are helping activity withstand other shocks, but it does not assume an extra AI productivity gain in its baseline. Its 13 October outlook projects 3.1% global growth in 2026 while warning that the benefit is concentrated among economies connected to the technology cycle.
Read the next reportExplore the full source and evidence record
What this means for people
- Executives and workers should not treat aggregate market charts as evidence that a particular AI deployment will raise productivity or justify staffing changes.
Global context
The comparison centres on large US-listed companies. Smaller firms, public services and economies with different labour and technology markets may show different patterns.
What the evidence does not yet show
- No firm-level AI-adoption measure
- No causal identification strategy
- No uncertainty intervals
- Commercial market commentary rather than peer-reviewed research
What to watch next
- Firm-level adoption panels
- Quality-adjusted output measures
- Wage and task changes
- Replication outside large US companies
Living evidence record
Impact record IAI-0O4XHQC
Evidence stage
Announced
Confidence
Developing
Reporting basis
Source analysis
Independent or research support
Not yet
Record status
Monitoring
Last checked
25 October 2026
Source trail
1 direct source across 1 source type.
People impact
Documented in this record.
Uncertainty
Limits and next checks are explicit.
Stages describe the evidence available—not whether a technology is good or bad. See the public method.
Single-source reporting disclosure
This record analyses one direct source. It can establish what Apollo Global Management published or reported, but it is not independent corroboration of every performance claim or predicted outcome. The confidence label will change only when broader evidence is added.
Evidence trail
Sources used for this report
Links checked 25 October 2026
Research question, topics and regions
Topics: AI investment · Productivity · Corporate margins · Big Tech
Regions: United States · Global
This report is labelled source analysis. We summarise and analyse source material in our own words; company statements remain attributed claims until independently supported. Translated summaries preserve the meaning of the original source and link back to it. Read our editorial standards.
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