🔍 Read the full analysis: An Economist Looks At AI Spending And The Returns So Far on ThorstenMeyerAI.com
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TL;DR
Apollo chief economist Torsten Slok says aggregate data show no clear financial payoff from AI outside the technology sector so far. Meanwhile, Meta has disclosed roughly $825 billion in future commitments, and companies are still spending heavily; studies do show gains in some specific workflows, but broad productivity effects remain uncertain.
Apollo Global Management chief economist Torsten Slok says available aggregate data show no clear financial payoff from AI outside the technology sector, even as companies commit hundreds of billions of dollars to computing infrastructure. His October 24 note contrasts rising revenue per worker and profit margins at seven large technology companies with flat measures among the other 493 S&P 500 firms; it does not establish that AI itself caused those trends.
Apollo’s analysis compares inflation-adjusted revenue per worker and profit margins for the “Magnificent 7” — 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, while the other 493 were around $525,000 and had remained in a similar range since 2021. The seven’s margins reached roughly 30%, compared with about 10% for the rest, according to the note.
Those comparisons describe company-wide outcomes, not a measured return attributable to AI. Apollo also cited San Francisco Federal Reserve productivity measures that, in its reading, show no current signs of an AI-driven productivity lift. A separate Ramp dataset, based on the corporate card and spending platform’s more than 85,000 U.S. business customers, found AI-related spending concentrated among a small share of firms: the top 10% accounted for 99.5% of model-serving and inference spending. The figures describe Ramp’s customers, not all U.S. companies.
The investment side is visible in company filings. Meta reported $34.91 billion in capital expenditures in the third quarter, against $32.79 billion in operating cash flow, leaving free cash flow at negative $2.12 billion. It forecast 2026 capital spending of $135 billion to $145 billion. Its October 22 filing listed about $355.73 billion in leases not yet started and $454.52 billion in non-cancelable contractual commitments, mostly related to cloud capacity, servers and network equipment, plus up to $14.72 billion in contingent cloud purchases. Together, those figures total roughly $825 billion; the commitments are not all 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 Costs and Returns
The contrast matters because companies are making long-term infrastructure and computing commitments before broad returns are evident in standard productivity and financial measures. For investors, the gap raises questions about whether future revenue growth will justify the cost of data centers, chips, cloud capacity and financing. For customers and workers, it is a reminder that adoption does not automatically mean a measurable improvement in output or lower costs.
The aggregate picture is not the whole story. The source material cites customer-support experiments showing 14% more issues resolved per hour, with a 34% gain for less-experienced workers, and retail experiments ranging from no sales increase to a 16.3% rise depending on the workflow. It also reports that 83% of nearly 9,000 executives surveyed saw no productivity impact so far, while the average reported effect was 0.2% a year. These results concern different measures and settings; they do not settle the economy-wide return question.
Financing adds another pressure point. The supplied report says SpaceX borrowed $40 billion to buy AI chips and notes rising insurance costs for debt issued by Meta and CoreWeave. Large commitments can expose firms to higher borrowing costs or weaker demand if anticipated returns do not arrive on schedule. The source also describes a prediction that many “neocloud” providers will consolidate, but that is a forecast, not an established outcome.
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What Apollo’s Comparison Captures
Slok’s October 24 “Daily Spark” note, titled “No Signs of AI Payoff Outside the Tech Sector,” was reported by outlets including TechCrunch, PYMNTS and Bloomberg the following day. Its headline conclusion comes from comparing trends across large groups of companies. It is not a controlled test of AI adoption, nor does it isolate AI’s contribution from other influences such as labor costs, interest rates or tariffs.
The spending totals also need careful reading. Meta’s approximately $825 billion in listed commitments combine leases, contractual obligations and contingent purchases extending over multiple years; they are not a bill due immediately. Some obligations cover infrastructure or products beyond AI, including Reality Labs hardware. Separately, estimates that five major technology companies could spend nearly $780 billion in 2026 and more than $1 trillion annually from 2027 through 2030 are forecasts cited in the source material, not corporate commitments.
That distinction helps explain how two observations can both be true: capital spending and future obligations are large and documented, while broad financial returns outside technology remain difficult to identify in current aggregate data. Returns may appear first in particular tasks or firms, and company-level averages can obscure gains that are offset by costs or weaker performance elsewhere.
“AI has delivered zero financial payoff to corporate America outside of tech.”
— Torsten Slok, Apollo chief economist, in an October 24 Daily Spark note
What the Aggregate Data Cannot Show
The evidence does not establish that AI caused the differences in revenue per worker or margins between the seven large technology companies and other S&P 500 firms. A flat average across 493 companies could conceal individual gains and losses, and the available comparisons do not separate AI effects from other economic factors. The Ramp spending figures cover its customers, while the executive survey and workflow experiments use different samples and definitions of productivity.
It is also unclear how quickly current investment will translate into durable earnings or economy-wide productivity growth. Meta’s commitments span years and include non-AI items; they should not be treated as immediate spending or as an AI-only total. The supplied material gives no final outcome for the proposed consolidation of neocloud providers, and the financing conditions for companies in that market may change.
Evidence to Watch in Coming Results
The next evidence will come from company earnings, cash-flow reports and productivity data showing whether AI-related spending is followed by sustained revenue, lower operating costs or higher output per worker. Investors will also watch whether Meta’s capital spending stays within its forecast range and how the company funds its infrastructure obligations. These indicators may take time to reflect returns because many commitments extend over multiple years.
For now, Apollo’s conclusion is best read as a statement about what broad measures show so far, rather than a final verdict on AI’s value. More detailed company disclosures and comparable studies of specific AI deployments will be needed to distinguish productive investment from spending that has yet to produce measurable financial gains.
Key Questions
What did Apollo’s economist conclude about AI returns?
Torsten Slok said aggregate financial indicators show no clear payoff outside the technology sector so far. That is his interpretation of the data, not proof that no individual firm has gained from AI.
What does Meta’s roughly $825 billion figure represent?
It combines leases not yet started, contractual commitments and possible contingent cloud purchases disclosed in Meta’s filing. The commitments stretch across multiple years and are not all AI-related or due immediately.
Is there evidence that AI helps specific tasks?
Yes. The source material cites customer-support experiments with a 14% increase in issues resolved per hour, including a larger gain for novice workers, and retail results that ranged from no sales increase to 16.3%. Outcomes varied by workflow.
Does the data prove AI has not increased productivity?
No. Apollo cited productivity measures that show no clear AI-driven lift yet, but the figures do not isolate AI’s effects or rule out gains at particular companies and tasks. The broader economic impact remains uncertain.
Source: ThorstenMeyerAI.com
