🔍 Read the full analysis: A Closer Look At AI’s Payoff Beyond Technology on ThorstenMeyerAI.com
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TL;DR
Apollo economist Torsten Slok’s October 24 note found that revenue per worker and profit margins rose for major technology companies but were broadly flat across the other 493 S&P 500 companies. That does not prove AI has produced no returns: aggregate measures can conceal differences among firms, while studies cited in the source report gains in some specific tasks. Meanwhile, infrastructure commitments and spending are growing, sharpening questions about financing and returns.
Apollo’s note compared the Magnificent Seven — Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla — with the other 493 companies in the S&P 500. The charts show inflation-adjusted revenue per worker and profit margins rising for the seven technology companies, while the figures for the remaining companies were broadly flat. The source article estimates revenue per employee in the third quarter at about $780,000 for the seven and $525,000 for the other 493, with margins near 30% and 10%, respectively.
The note also points to productivity measures from the San Francisco Federal Reserve, which it says show no visible boost to productivity growth from AI so far. Data from Ramp, a corporate spending platform with more than 85,000 U.S. business customers, show AI-related spending concentrated among a small share of firms. The top 10% of firms accounted for 99.5% of spending on model serving and inference, 99% of neocloud spending and 95.6% of spending on OpenAI and Anthropic, according to the figures cited in the source.
The investment is visible in company filings. Meta reported $34.91 billion in capital expenditures in the third quarter and narrowed its 2026 spending outlook to $135 billion to $145 billion. Its filing listed about $355.73 billion in leases not yet started and $454.52 billion in non-cancelable contractual commitments, mostly for cloud capacity, servers and network equipment, plus up to $14.72 billion in contingent cloud purchases. The source calculates these items at roughly $825 billion; the total includes commitments not all attributable to AI.
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 spending and returns
The finding matters because companies, investors and workers are trying to judge whether the rapid expansion of AI infrastructure will translate into durable business gains. Spending is measurable; broad-based returns remain difficult to identify in the indicators Apollo examined. That gap could affect company investment plans, financing costs and decisions about whether to expand AI use.
But the aggregate figures are not a verdict on every AI project. The source cites studies reporting that customer-support workers resolved 14% more issues per hour, with a 34% gain for novice workers, and retail experiments ranging from no sales increase to a 16.3% rise depending on the workflow. These results suggest that benefits may be concentrated in particular applications rather than appearing evenly across entire industries.
The source also reports that 83% of nearly 9,000 executives surveyed said AI had not yet affected productivity, while the average reported effect was 0.2% a year. Those figures are attributed to the source material; the survey’s full methodology and timing are not provided here. Together, the findings point to a practical distinction: investment in AI capacity is not the same as demonstrated productivity or profit growth.
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What Apollo’s charts measure
Slok’s October 24 note, titled “No Signs of AI Payoff Outside the Tech Sector,” brought together comparisons of revenue per worker, margins, productivity and AI spending. The source says the note was subsequently covered by TechCrunch, PYMNTS and Bloomberg. Its central comparison is between the seven large technology companies and the remaining S&P 500 companies; it does not claim to measure every business or every AI deployment.
That distinction is central to interpreting the word “zero” in the note’s conclusion. A flat average across 493 companies can mask firms that improved and firms that did not, while the figures do not isolate AI from other forces such as labor costs, tariffs or interest rates. Ramp’s spending data describe its own customers, not the full S&P 500. The evidence supports a narrower statement: the cited aggregate measures have not shown a broad payoff outside tech.
Meta’s figures illustrate the scale and timing of the investment side. Its third-quarter revenue was $64.60 billion, up 26% from a year earlier, while operating cash flow was $32.79 billion. After capital expenditures, free cash flow was negative $2.12 billion, compared with positive $10.6 billion a year earlier, according to the source. Those results cover the company as a whole and do not establish that AI investment alone caused the change.
““starting to view token spending not as a source of growth but as a cost center that weighs on margins.””
— Torsten Slok, in Apollo’s October 24 note
What the aggregate data cannot show
The available figures do not establish how much of the technology companies’ rising revenue per worker or margins is due to AI. Nor do they show whether gains at some companies or in particular teams are being offset by weak results elsewhere. The source’s description of Apollo’s charts is not a firm-by-firm accounting of AI costs and revenue.
It is also unclear how much of Meta’s roughly $825 billion in future commitments will ultimately be spent, when those obligations will fall due, and what share directly supports AI rather than other operations. The figure combines leases and contractual commitments across multiple years, and the source says some items are not AI-related. A cited forecast of large capital expenditures by major technology companies is a projection, not a record of spending already completed.
The source raises concerns about financing, including reports that insurance costs on some technology debt have increased, but it does not provide enough detail here to establish the scale or cause of that change. The economics may also differ by task, model and company. Whether specific productivity gains can offset infrastructure and operating costs remains unresolved.
Evidence to watch in coming quarters
The next useful evidence will come from company earnings and filings showing whether AI-related costs are accompanied by measurable revenue growth, improved margins or lower labor and operating costs. Investors will also be watching whether planned infrastructure spending proceeds as announced and how companies fund it, particularly as data-center, server and cloud commitments grow.
For businesses adopting AI, task-level results may offer a clearer test than economy-wide averages: whether a tool reduces time per case, increases output or improves sales after accounting for its operating costs. The studies cited in the source show that outcomes vary by workflow, so reported gains should be assessed alongside the comparison group, time period and costs.
Until more results are available, Apollo’s conclusion is best read as a snapshot of limited aggregate evidence outside the technology sector, not proof that AI has no value. The main question is whether gains now visible in selected applications become broad and durable enough to justify continued spending.
Key Questions
What did Apollo mean by “zero financial payoff”?
Apollo economist Torsten Slok used the phrase to describe aggregate trends in which revenue per worker and profit margins rose for seven major technology companies but were broadly flat for the other 493 S&P 500 firms. It is an interpretation of those indicators, not proof that no company outside tech has benefited from AI.
Does the analysis prove AI has not improved productivity?
No. The source says productivity measures cited by Apollo show no clear economy-wide AI boost so far. Broad averages can conceal gains in particular firms, roles or tasks, and the cited data do not isolate AI from other economic factors.
What does Meta’s roughly $825 billion figure include?
The source adds together 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 span multiple years and are not all attributable to AI.
Are there examples of AI delivering measurable gains?
Yes. The source cites customer-support research reporting 14% more issues resolved per hour, including a 34% gain for novice workers, and retail experiments with sales results ranging from zero to a 16.3% increase depending on the workflow. Those results do not establish that the same gains apply across other tasks or businesses.
Source: ThorstenMeyerAI.com
