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TL;DR
Apollo economist Torsten Slok says recent productivity and financial data show no visible AI payoff outside the technology sector, though the finding describes aggregate trends rather than proving AI has produced no benefits. At the same time, major companies are committing vast sums to computing infrastructure, while studies report measurable gains in some customer service and retail tasks.
Companies are spending heavily on AI infrastructure, but broad financial returns remain difficult to see, according to an October 24 note from Apollo Global Management chief economist Torsten Slok. His analysis found that revenue per worker and profit margins have risen among seven major technology companies while remaining broadly flat among the other 493 S&P 500 firms; it does not establish that AI itself caused the difference or that no individual companies are benefiting.
The note, titled “No Signs of AI Payoff Outside the Tech Sector,” compares revenue per worker and profit margins for the so-called Magnificent Seven—Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla—with the rest of the S&P 500. Apollo’s charts put the seven near $780,000 in revenue per employee in the third quarter of 2026, compared with roughly $525,000 for the other companies. The seven’s margins rose to about 30%, while the broader group stayed near 10%, according to the note.
Those figures are aggregate comparisons, not a direct measure of AI’s effect. Slok also cited productivity data from the San Francisco Federal Reserve that he said showed no signs of an AI-driven increase in productivity growth. Apollo’s conclusion is that a broad payoff is not yet apparent in the data; it does not show that AI caused the technology companies’ stronger performance.
Spending disclosures illustrate the scale of investment. Meta’s third-quarter results showed $34.91 billion in capital expenditures and negative free cash flow of $2.12 billion, compared with positive free cash flow of $10.6 billion a year earlier. The company forecast $135 billion to $145 billion in capital spending for 2026. Its October 22 filing listed roughly $355.73 billion in leases not yet started and $454.52 billion in non-cancelable contractual commitments, along with up to $14.72 billion in contingent cloud purchases. The categories include data centers, cloud capacity, servers and other infrastructure; not all the commitments are AI-related.
Infographic 01 · The “zero” in numbers
What “zero financial payoff” actually measures.
Apollo’s headline rests on five charts. Here are their numbers, and what they can and cannot prove.
Who pays for AI? Share of spending by the top 10% of firms
Ramp customer data via Apollo · non-AI categories in grey for comparison
Spending is concentrated in every category, not only AI; AI is the most extreme.
What the five charts do and do not show
shown not shown
- Shown: revenue per worker and margins rising for the 7 tech giants, flat for the other 493
- Shown: no AI lift yet in US productivity statistics (San Francisco Fed TFP, output per hour)
- Shown: AI spending sits in few, mostly venture-funded firms (Ramp, 85,000+ US businesses)
- Not shown: that AI caused the gap; rates, tariffs and wages move the same lines
- Not shown: gains inside single functions, which can cancel out in an average
- Not shown: the S&P 493 itself in the Ramp data; those are Ramp’s customers
“The AI story will be decided in the S&P 493, not the Mag 7.”Torsten Slok, Apollo chief economist, 24 Oct 2026 (opinion)
Sources: Apollo Daily Spark (24 Oct 2026) with charts by Bloomberg, Macrobond, SF Fed, BLS and Ramp; Apollo (20 Oct 2026); Ramp AI Index. Values with ≈ are read off Apollo’s charts. Assessment: Thorsten Meyer.
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The Gap Between AI Bills and Returns
The mismatch matters because companies are making long-term commitments to infrastructure before evidence of broad productivity gains has emerged. Meta’s commitments span years, and the company’s filing describes obligations extending through 2036. Such investments can support future services and capacity, but they also create costs that may weigh on cash flow if revenue or efficiency gains do not keep pace.
Evidence of benefits is more concrete in some specific tasks than in economy-wide statistics. Research cited in the source reports a 14% increase in customer-support issues resolved per hour, rising to 34% for novice workers. Retail experiments found sales changes ranging from zero to 16.3%, depending on the workflow. These results suggest that outcomes can differ by job and implementation; they do not establish a typical return for all businesses.
For workers and customers, the stakes are practical: AI may change how services are delivered, while spending on computing can affect corporate budgets and investment priorities. The current evidence points to a divide between targeted applications with measurable results and broad claims of economy-wide transformation. Companies and investors still have to determine whether gains in particular processes can cover the cost of tools, infrastructure and financing.
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What Apollo’s Comparison Measures
Apollo’s note was published October 24 and was subsequently covered by outlets including TechCrunch, PYMNTS and Bloomberg. It draws on comparisons of revenue per worker and margins, productivity indicators, and corporate spending data. The figures are useful for describing differences between large technology firms and other S&P 500 companies, but they are not a controlled test of AI adoption or its financial effects.
The note also cites data from Ramp, a corporate spending platform with more than 85,000 U.S. business customers. Ramp data showed the top 10% of firms accounted for 99.5% of model-serving and inference spending, 99.0% of neocloud spending and 95.6% of spending on OpenAI and Anthropic. The top 1% of AI spenders were much more likely to be venture-backed than the median spender. Those findings describe Ramp’s customers, not the entire U.S. economy.
Slok has characterized token spending as a potential cost center and raised concerns that computing costs could exceed the benefits users receive. Those are his interpretations of the data. Separately, Meta’s reported commitments and capital-spending forecast show how much infrastructure investment is underway, while projections for major technology companies’ future spending remain forecasts rather than completed expenditures.
“No Signs of AI Payoff Outside the Tech Sector”
— Torsten Slok, Apollo chief economist, in the October 24 note
What the Aggregate Data Cannot Show
The data do not establish how much of the technology sector’s stronger results comes from AI, or whether AI is producing gains that are offset by losses or costs elsewhere. A flat average for 493 companies could conceal businesses that benefit and others that do not. The comparisons also cannot isolate AI from other influences, including labor costs, interest rates and tariffs.
It is also unclear how representative early task-level studies are of routine business use. The reported customer-support and retail results vary by workflow, and the source does not provide enough detail to treat them as a general return on investment. Meta’s disclosed commitments are not all AI-related, and the source does not specify how much of the spending will ultimately produce commercial returns. Apollo’s “zero payoff” framing should be read as a claim about visible aggregate results outside technology, not proof that AI produces no financial benefit anywhere.
Evidence to Watch in Coming Results
Upcoming company results and filings will show whether major technology firms continue to raise infrastructure budgets and whether those expenses are accompanied by revenue growth, improved margins or stronger cash generation. For Meta, its capital-spending outlook and future cash-flow figures will provide updates on the cost side. Comparable disclosures from other large cloud and AI investors may help clarify the scale and pace of spending, though company forecasts are not guarantees of actual outlays.
For businesses adopting AI, the key evidence will be task-specific: changes in output, service quality, labor needs and costs compared with a clear baseline. Broader productivity measures will take time to reflect adoption across industries. Until those measures show sustained gains, the central question remains open: whether measurable improvements in particular workflows can spread widely enough to offset the expanding cost of computing.
Key Questions
Did Apollo prove that AI has produced no financial returns?
No. Apollo’s analysis found no visible payoff in aggregate data outside the technology sector. It did not directly measure AI’s effect at every company, and the findings cannot rule out benefits in particular businesses or tasks.
What does Meta’s roughly $825 billion figure represent?
It combines about $355.73 billion in leases not yet started, $454.52 billion in non-cancelable contractual commitments and up to $14.72 billion in contingent cloud purchases. The commitments cover infrastructure and other items, and not all are AI-related.
Are businesses seeing any measurable gains from AI?
Some reported studies show gains in defined tasks, including faster customer-support issue resolution and varying sales changes in retail experiments. Results depend on the workflow, and they do not establish a typical return across companies.
Why can’t the S&P 500 comparison isolate AI’s impact?
The comparison tracks broad differences in revenue per worker and margins, not a controlled comparison of AI adopters and non-adopters. Other factors, such as labor costs, interest rates and tariffs, may also affect company results.
Source: ThorstenMeyerAI.com
