🔍 Read the full analysis: Why AI Pays Off Almost Nowhere Outside Tech, An Economist Says on ThorstenMeyerAI.com
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TL;DR
Apollo chief economist Torsten Slok says revenue per worker and profit margins have risen among seven major technology companies but stayed broadly flat among the other 493 S&P 500 firms. The conclusion describes aggregate trends, not proof that AI has produced no benefit at any individual company; studies cited in the source report gains in some specific tasks. Meanwhile, major AI spending commitments are growing, with the financing burden and future returns still uncertain.
Apollo chief economist Torsten Slok said in an October 24 research note that available data show no clear financial payoff from AI outside the technology sector, even as large companies commit hundreds of billions of dollars to computing infrastructure. His conclusion is based on aggregate trends, not a direct measurement proving that AI has generated no gains at individual businesses.
Apollo’s note, titled No Signs of AI Payoff Outside the Tech Sector, compares the seven largest U.S. technology companies—Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla—with the other 493 S&P 500 companies. In the charts described in the source material, inflation-adjusted revenue per worker rises for the seven but remains broadly flat for the other companies. Profit margins also diverge: the seven are shown near 30%, while the rest are around 10%. Those figures describe group trends, not a controlled estimate of the effect of AI.
The note also points to San Francisco Federal Reserve measures of productivity, which Apollo says show no signs yet of AI lifting productivity growth. Separate spending data from Ramp, a corporate card and 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 account for 99.5% of spending on model serving and inference, 99.0% of neocloud spending, and 95.6% of spending on OpenAI and Anthropic, according to the figures cited by Apollo.
That concentration matters to Slok’s interpretation: much of the spending may be coming from firms investing in AI rather than businesses broadly using it to improve results. Apollo’s note says 80.4% of the top 1% of AI spenders are venture-backed, compared with 13.1% of median spenders. The source material also cites a survey of nearly 9,000 executives in which 83% reported no productivity impact so far, with an average reported effect of 0.2% a year. The survey’s scope and methodology are not detailed in the material provided.
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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Spending Is Outpacing Visible Returns
The gap between the scale of investment and the lack of a broad, visible return is central to current questions about the AI boom. Meta’s third-quarter results, released October 21, reported $34.91 billion in quarterly capital expenditures. Operating cash flow was $32.79 billion, leaving free cash flow at negative $2.12 billion, compared with positive $10.6 billion a year earlier. Meta also narrowed its 2026 capital spending outlook to $135 billion to $145 billion.
A filing puts Meta’s future obligations at roughly $825 billion, combining leases that have not yet started, contractual commitments and possible additional cloud purchases. The total includes commitments for data centers, energy infrastructure, network capacity, servers and cloud services; the source says not all of it is AI-related. The figure is a long-term total, not a single-year cash bill. It nonetheless shows how much infrastructure spending is being arranged before the return on that investment is clear.
For companies considering AI tools, the broader point is not that the technology cannot create value. It is that spending on models and computing does not, by itself, establish a financial payoff. Businesses need to know whether a particular use case improves output, revenue or costs enough to justify its expense. For investors and lenders, the same distinction bears on whether rapid infrastructure expansion can be financed and earn returns.
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Evidence Varies by Task and Company
Slok’s phrase “zero financial payoff” is a summary of group-level financial and productivity indicators, not a finding that every non-tech company has gained nothing from AI. An average can conceal companies or departments that improve while others do not. It also cannot, on its own, separate AI’s effect from other influences such as labor costs, tariffs or interest rates.
The source material cites task-level studies that point to a more mixed picture. One customer-support study found 14% more issues resolved per hour, rising to 34% for novice workers. Retail experiments reported sales effects ranging from no increase to 16.3%, depending on the workflow. These results concern specific settings and should not be treated as proof of economy-wide gains; they also do not contradict the possibility that aggregate productivity data have yet to show a broad change.
There is also a difference between spending on AI and adopting it productively. Ramp’s figures cover its own business customers, not the S&P 493 as a whole, and the concentration of spending suggests activity is not evenly distributed. Apollo’s claim is that broad corporate indicators have not yet recorded a payoff outside tech—not that no company outside tech has useful applications.
“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 available evidence does not establish how much of the reported financial or productivity trends is caused by AI. Apollo’s comparisons show differences between groups, but do not isolate AI from other business and economic changes. The Ramp figures are specific to Ramp customers, and the executive survey’s full methods are not provided in the source material.
It is also unclear how quickly task-level gains will translate into higher company-wide productivity, revenue or margins. Results vary by workflow, and examples of improved customer-support output or retail sales do not show whether the gains outweigh software, computing, integration and staffing costs. Meta’s $825 billion total includes long-term commitments and some spending that is not AI-related, so it should not be read as an AI-only bill or immediate cash expenditure.
The financing picture remains unsettled. The source material reports a $40 billion SpaceX borrowing for AI chips and says credit protection costs for Meta and CoreWeave became more expensive, but it does not provide enough detail to assess the terms, intended use of proceeds or long-term ability to service those obligations. Slok’s assessment is an interpretation of current data; future results could change as companies deploy AI and publish more evidence.
Returns Will Face Further Tests
The next test is whether businesses can show sustained gains in productivity, revenue or margins as AI tools move from trials into routine operations. More company disclosures and independent studies will be needed to distinguish short-term task improvements from returns that persist across a business. Until then, the evidence supports a narrower conclusion: AI’s payoff is not yet visible in broad corporate averages outside tech.
Investors will also watch whether large technology firms continue to raise spending plans and how those commitments affect cash flow and borrowing. Meta’s filings provide a detailed example, but the source material does not establish the final cost of its commitments or the revenue they will generate. The balance between infrastructure investment and demonstrated customer value will remain a key measure of the industry’s progress.
Key Questions
What did Apollo’s economist conclude about AI outside tech?
Torsten Slok said that aggregate data show no clear financial payoff outside the technology sector so far. His conclusion is based on group-level trends in revenue per worker, margins and productivity, not proof that every non-tech company has seen no benefit.
Does Apollo’s analysis prove AI has no value for businesses?
No. The analysis does not isolate AI’s effect for every company or task. The source material cites studies showing gains in some customer-support and retail settings, while noting that results vary by workflow.
What does Meta’s roughly $825 billion figure include?
It combines leases that have not yet started, non-cancelable contractual commitments and possible additional cloud purchases, including infrastructure and services. The total runs over multiple years, and the source says some commitments are not AI-related.
Why is AI spending concentrated among a small number of firms?
Apollo cites Ramp data showing that the top 10% of firms account for nearly all measured spending on model inference, neocloud services and OpenAI and Anthropic. The figures apply to Ramp’s customer base and do not describe all U.S. companies.
What would count as evidence of a broader AI payoff?
Evidence would include sustained, attributable improvement in measures such as output per worker, operating costs, revenue or margins. Current task-level examples do not yet establish that those gains have spread across entire companies or the wider economy.
Source: ThorstenMeyerAI.com
