This article accompanies the full research report, Reconciling Enterprise AI Revenue: A Methodological Crosswalk and Vendor-Level Census, 2025. The PDF contains the 68-vendor census, the six-tier framework, each sourced deduction, and the netting of structural double-counts.
Three widely cited enterprise artificial intelligence (AI) revenue figures published in 2025 differ by 40x. A fourth, the vendor run-rate sum, sits between them. None is wrong. The defensible floor for underwriting $690B of hyperscaler capital expenditure is $63.2B to $72.5B.
Menlo Ventures put US enterprise generative AI spending at $37B for 2025 in its December 2025 report. A run-rate annualizes the most recent revenue pace. A bottom-up sum of disclosed vendor run-rates gives $100-135B worldwide. IDC sized the 2025 AI Solutions market at $307B. Gartner forecast $1.478T of total worldwide AI spending for 2025.
The trade press calls all four figures “the AI market.” Menlo is not the cautious estimate, and Gartner is not the aggressive one. Each method states its scope, or perimeter, and is correct within it. The disagreement concerns what enterprise AI revenue is, not how to count it.
Why does this even matter? At the report’s May 17, 2026 cutoff, combined hyperscaler guidance approached $690B of 2026 capital expenditure (capex). Sell-side analysts projected roughly $1.5T of AI-related debt issuance for the planned infrastructure build. JPMorgan, Apollo, Blackstone, and KKR were helping structure that debt. Capex coverage is revenue divided by capital expenditure. I call revenue supported by regulatory filings and executive dollar disclosures audit-grade. Against its $63.2B narrow floor, coverage was 9.2%. The 1990s telecom buildout peaked at roughly 28% coverage. That is the inverse of 3.5x capex-to-revenue.
Four enterprise AI revenue estimates
Menlo Ventures: $37B
Menlo and an independent research firm surveyed 495 US enterprise information-technology decision-makers from November 7 to 25, 2025. They asked buyers to identify 2025 budget items that they classified as generative AI spending. The scope has three explicit limits: US only, generative AI only, and enterprise only. It is the narrowest published figure.
Vendor run-rate sum: $100-135B
This bottom-up calculation covers 68 vendors: 42 public and 26 private. A six-tier framework grades each primary source. The lower bound subtracts the maximum estimated reseller overlap and converts annual recurring revenue (ARR) claims into estimated recognized revenue. The upper bound removes duplicate silicon revenue but does not subtract reseller overlap. The fully netted midpoint is $123B. The vendor census makes every dollar reproducible.
The framework ranks sources from Tier A US Securities and Exchange Commission (SEC) filings to Tier F extrapolated estimates. Tier B contains executive dollar disclosures. Tier C contains qualitative mentions without dollar amounts, and Tier D uses third-party recurring-revenue claims. Tier E uses private-company commentary.
IDC: $307B
The IDC Worldwide AI Spending Guide estimates worldwide AI Solutions spending from vendor-share data and channel surveys. IDC includes hardware, services, and software when it tracks them as bundled enterprise AI solutions. It excludes consumer-device hardware. The single figure mixes vendor-recognized revenue with buyer spending.
Gartner: $1.478T
Gartner’s September 17, 2025 release divides worldwide AI spending into five categories. They are AI-enabled devices ($389B), AI services ($283B), AI-optimized servers ($268B), AI software ($298B), and “other” ($242B). Gartner counts the full retail value of any product sold with an AI feature. Gartner analyst John Lovelock said buyers receive the device layer with the product and do not specifically select it.
Each figure answers a different question. Menlo captures what enterprise buyers consciously buy. The vendor sum measures what suppliers recognize or report as run-rate revenue. IDC adds services and integration. Gartner counts every product sold with an AI feature.
Waterfall: from $1.478T to $37B
Each step applies the next forecaster’s published perimeter to the previous figure. You can replace any assumption and rebuild the bridge.
Step 1: $1.478T to $307B
Start with Gartner’s $389B device layer, the largest single deduction. A $1,200 phone with an AI camera filter contributes $1,200 to Gartner and zero to IDC. Next come roughly $200B of broad AI services that IDC excludes and $268B of standalone server hardware. The final $232B covers broad AI software and “other” categories outside IDC’s solutions definition. Together, the deductions total roughly $1.171T. Gartner’s own analyst said buyers do not actively select the device layer.
Step 2: $307B to $123B
The next $184B is not one thing. IDC counts the full transaction price, including channel margins, integration services, deployment labor, and government-channel premiums. The vendor run-rate sum counts only revenue that disclosing vendors recognize on their profit-and-loss statements. Channel margins contribute $40-60B. Consulting that software vendors do not recognize contributes $60-80B, and geographic pricing premiums add $20-30B. Tier C public vendors report another $15-25B of AI revenue without disclosing a dollar amount.
Step 3: $123B to $63.2B narrow or ~$72.5B broad
Below Tier B, the evidence changes. The $123B midpoint includes Tier C-F sources and private-vendor ARR claims. At the May 2026 cutoff, these included OpenAI’s $25B, according to Sacra. Anthropic added $19B based on remarks by its chief executive at Morgan Stanley’s technology, media, and telecommunications conference. Cursor added $2B, and DeepSeek’s estimate added $1.1B. A reader who accepts nothing below Tier B gets the $63.2B narrow audit-grade figure.
The broad definition credits hardware original equipment manufacturers (OEMs) after removing silicon overlap, and it includes CoreWeave’s non-Microsoft revenue. It excludes Arista because Arista disclosed an increased fiscal-year 2026 forecast rather than recognized revenue. That matches the treatment of ServiceNow’s $1.5B fiscal-year 2026 annual contract value target. The result is roughly $72.5B.
Both figures remove the $11B Microsoft-OpenAI resale overlap and $7B for the Amazon Web Services/Google Cloud Platform-Anthropic overlap. The $7B figure is this report’s analytical assumption, not vendor disclosure.
Step 4: $63.2B to $37B
The last step adjusts the scope to Menlo’s frame. Remove $15-20B of non-US hyperscaler AI revenue. Microsoft, Amazon Web Services, Alibaba, and Baidu have material non-US business at the AI run-rate level. Then remove $8-12B outside generative AI. Palantir, Salesforce, and Workday AI products include substantial predictive and classical machine learning. The small consumer share also comes out.
The combined adjustment is roughly $26B, leaving a figure near Menlo’s published $37B. Some of this convergence is coincidental. A bottom-up survey will not match a top-down deduction exactly. Still, the order of magnitude is right, and the deductions are transparent.
Audit-grade
The $63.2B narrow floor accepts two source types. Tier A consists of segment-level filings with the US Securities and Exchange Commission (SEC). Tier B consists of dollar disclosures that executives make on earnings calls. The calculation then removes structural double-counts across the stack. It is the maximum defensible figure for questions that require revenue traceable under generally accepted accounting principles (GAAP).
The ~$72.5B broad floor applies the same discipline to two more sources. It adds the hardware OEM layer and CoreWeave revenue from customers other than Microsoft. Neither addition duplicates revenue counted elsewhere.
The vendor census finds four Tier A disclosures among 68 vendors. NVIDIA’s Data Center segment reported $62.3B in the fourth quarter of fiscal year 2026, or ~$249B annualized. AMD’s Data Center segment reported $5.8B in the first quarter of 2026, or ~$23B annualized. Broadcom’s AI semiconductor disclosure was $8.4B in the first quarter of fiscal year 2026, or ~$34B annualized. CoreWeave has been publicly listed since March 2025. It reported ~$5.1B of fiscal-year 2025 revenue. CoreWeave says all its revenue comes from AI infrastructure.
Tier A gross disclosure is large, but its net contribution falls into a narrow band. Every qualifying vendor sits upstream of enterprise spending. The three silicon vendors sell into cloud and OEM layers. At the report cutoff, CoreWeave received 67% of its revenue from Microsoft. Microsoft resold that capacity as Azure AI Services.
Hardware OEMs also resell silicon to hyperscalers. The report annualized Dell AI revenue at $36B. It used $33B for SuperMicro, $4.4B for HPE, and $3.5B for Arista. After applying the report’s overlap adjustments, Tier A contributes $2B to $10B of net enterprise-facing AI revenue.
CoreWeave has the purest AI disclosure among Tier A vendors. Yet it routes two-thirds of its revenue through a hyperscaler that resells the capacity. The cleaner the disclosure, the more visible the resale problem.
The hardware OEM cohort behaves in the same way. Dell, SuperMicro, HPE, and Arista disclose roughly $77B of annualized AI-attributable revenue. Their bills of materials contain silicon already counted at NVIDIA, AMD, and Broadcom. After removing it, the cohort contributes approximately $10.75B of incremental margin and integration value. The $72.5B broad figure includes this amount; the $63.2B narrow figure does not. Honest analysts disagree about whether OEM resale margin belongs inside the enterprise-facing perimeter or upstream of it.
The Tier B floor totals approximately $63B after resale netting. Microsoft AI contributes $26B. This is its $37B gross figure less the $11B Microsoft-OpenAI overlap. Amazon Web Services AI adds $15B. Palantir Artificial Intelligence Platform (AIP), IBM watsonx, Baidu AI Cloud, and Alibaba AI products contribute another $19B.
The long tail of explicit AI product disclosures adds roughly $3B. It includes Salesforce Agentforce at $800M and Workday AI at $400M. Adobe Firefly contributes $250M, Zscaler AI $400M, and Box AI $118M. The leading hyperscaler and defense-software lines matter more than the rest of the long tail combined.
Four firms or cohorts account for more than 80% of the audit-grade total. They are Microsoft, Amazon Web Services, Palantir, and the Chinese hyperscaler cohort led by Alibaba and Baidu.
The trade press implies a broad enterprise-software AI rollout. In that story, Salesforce, ServiceNow, Workday, Adobe, Atlassian, Snowflake, and HubSpot convert their customer bases to AI products. This conversion has not appeared in disclosed revenue at scale. Perhaps it is happening inside bundled non-AI products, which creates a Tier C disclosure problem. Perhaps it is happening more slowly than the narrative suggests. The framework cannot decide between these explanations.
A bull objection remains. Accounting Standards Codification 280 (Segment Reporting) does not require a separate AI segment. So the absence of an “AI segment” at Salesforce or ServiceNow reveals disclosure obligations, not underlying demand. The audit-grade floor is the wrong denominator for total-addressable-market and growth questions. Capex coverage, however, is a GAAP cash-flow question. For that question, GAAP-traceable revenue is probably the right denominator.
Which number for which question
Capex sustainability and credit underwriting
Use $63.2B narrow or ~$72.5B broad. Lenders financing the nearly $690B of disclosed hyperscaler capex expect repayment from GAAP operating cash flow. Only audit-grade revenue offers reasonable certainty of forward GAAP recognition.
Public disclosures from private model laboratories do not settle how each company applies Accounting Standards Codification 606. Its principal-versus-agent guidance determines whether a company reports the gross transaction amount or only its net share. A credit memo that combines $25B of OpenAI ARR with $37B of Microsoft AI therefore uses two different units.
Mergers, acquisitions, and private equity valuation
Use the $100-135B disclosure-grade range. A buyer of an AI-adjacent target cares more about revenue in two years than its current audit grade. Tier D-E private-vendor claims are a leading indicator of recognized revenue. OpenAI’s $25B and Anthropic’s $19B belong here. The dispute concerns how to count them, not whether to count them.
Total addressable market and growth analysis
IDC’s $307B is closer to the right figure. It includes the integration and services labor that AI deployment requires. This includes $60-80B of consulting and deployment work that never reaches a software vendor’s profit-and-loss statement. A consultancy that sizes the AI services opportunity should start here.
Is AI changing the economy?
Gartner’s $1.478T is the right figure for this question. Willingness to pay includes additional spending on smartphones, personal computers, and services with AI features. Lovelock is right that buyers do not actively select the device layer. Still, cash-register spending is the relevant signal for economic reorganization. That remains true even when buyers do not identify AI as the reason.
What enterprise AI revenue means for the capex thesis
At the report cutoff, combined 2026 hyperscaler capex guidance from Microsoft, Alphabet, Amazon, Meta, and Oracle approached $690B. Revenue covered 9.2% of capex on the $63.2B narrow floor. Coverage rose to 10.5% on the ~$72.5B broad floor and 17.8% on the $123B reconciled midpoint. A plausible $150B ceiling gave 21.7% coverage. That ceiling included undisclosed Tier C AI revenue at Google Cloud, Meta, Oracle, and the software-as-a-service cohort.
The 1990s telecom buildout peaked at roughly 28% capex coverage. Its inverse was 3.5x capex-to-revenue. It is the closest analogue and the one Pozsar-era infrastructure bears cite. The May 2026 narrow coverage ratio was roughly one-third of that peak. Even the reconciled midpoint gave worse coverage than telecom in 1999.
Two bull-side adjustments are honest.
Adjustment 1: revenue follows capex
Capex builds capacity that earns revenue after an 18-24 month delay. A fairer comparison therefore uses 2028 revenue against 2026 capex. At the May 2026 cutoff, Microsoft and Amazon Web Services reported AI growth of 100-170% year over year. The report estimated that revenue from model application programming interfaces (APIs) was doubling every 9-12 months. If that growth holds, $63.2B could plausibly reach $250B by 2028. The narrow floor must roughly quadruple in 30 months. Under those assumptions, 5-to-6-year amortization would work at hyperscale gross margins.
Adjustment 2: not all capex is incremental to AI
Some of the $690B pays for baseline cloud refresh, networking, and growth in workloads outside AI. A reasonable AI-incremental estimate is $400-500B. This adjustment raises narrow coverage from 9.2% to 12.6-15.8%. It raises broad coverage from 10.5% to 14.5-18.1%. Those figures move closer to telecom in 1999 but remain below it. The midpoint analyst case uses $450B of AI-incremental capex against $63.2B of narrow revenue. It gives 14.0% coverage.
Even with delayed revenue and a smaller capex denominator, coverage remains worse than the closest infrastructure analogue. Sell-side estimates put AI-related debt financing at approximately $1.5T. At the May 2026 cutoff, JPMorgan, Apollo, Blackstone, and KKR were helping structure that debt. The build’s capital structure takes duration risk on a coverage ratio that requires revenue to more than quadruple in 30 months.
The reconciled range changes three theses on this site in different ways.
The SaaSpocalypse Paradox becomes sharper. Section 5 of the research report puts disclosed application-layer AI revenue at $21.2B. AI-native private vendors contribute $3.7B, and AI products at incumbent public software companies contribute $17.5B. The underlying software-as-a-service market exceeds $400B. The layer that supposedly disrupts its pricing structure is only 1% to 5% as large. Meanwhile, capex is $690B.
Either AI consumes software-as-a-service revenue quickly, or capex is too large for the revenue. The first case requires revenue share to rise from 5% to 20-30% within two to three years. Both can’t be cheap.
The AI Capex Arms Race thesis intensifies. The original essay used a rough $100B revenue estimate. The audit-grade calculation replaces it with $63.2B narrow, ~$72.5B broad, and a $123B midpoint. Narrow coverage is 9.2%, below the previous essay’s implicit 14-15%. The methodology upgrade strengthens the bear case.
AI Models Are the New Rebar gives a more interesting intermediate result. The report put the model-API layer at $46.6B gross and $39.6B after removing Amazon Web Services/Google Cloud Platform-Anthropic resale. That net figure was roughly 1.9x the $21.2B application layer. On dollar revenue, margin had not moved up the stack.
Prices fell hard from February 2023 to October 2025. Flagship input prices dropped ~96%, with a six-month half-life. Dollar revenue still grew because volume rose faster than prices fell, but that trend broke in late 2025. In April 2026, GPT-5.4 cost $2.50 per million input tokens and $15 per million output tokens. Its input price was double GPT-5’s price. At the report cutoff, Claude Haiku 3.5 cost ~3x more than Haiku 3. Higher prices help vendor revenue catch up with capex only if buyers absorb them rather than switch to open-weight models. Open-weight models make their trained weights publicly available.
At the May 2026 cutoff, OpenAI and Anthropic represented roughly 87% of disclosed private-vendor AI revenue within the $100-135B range. The unsettled principal-versus-agent question is therefore not a long-tail issue. It concerns the two firms whose run-rates carry almost the entire private layer. A later clarification of recognition methods could move the disclosure-grade range materially. Audit-grade Tier B would not change.
The disclosure tier also leads audit-grade revenue rather than lagging it. Suppose Google Cloud replaces its Tier C statement with a Tier B dollar figure. Its economics stay the same, but the underwritable floor rises. Google, Meta, Oracle, SAP, Snowflake, and Atlassian may move AI revenue from Tier C to Tier B. If they do so over the four quarters following May 2026, the $63.2B floor approaches $90-100B.
Spread Index
One ratio summarizes the disclosure gap. The Spread Index divides audit-grade enterprise AI revenue by Gartner’s umbrella figure. At version 1.0 in May 2026, $63.2B / $1.478T = 4.28% (narrow). The broad result is $72.5B / $1.478T = 4.90% (broad).
I will try to update the index with each quarterly filing cycle. It tracks two different quantities. The numerator is revenue that AI vendors support with SEC filings and earnings-call disclosures. The denominator is what the broadest published framework calls “AI spending.”
If audit-grade revenue grows faster than the umbrella, capex coverage improves on its own. If it grows more slowly, the gap widens. Vendors may bundle AI inside non-AI products. New disclosures may also consist only of private ARR claims. In either case, the underwriting case weakens without any change in the headline figures.
Four nested circles
The discourse keeps approaching one finding sideways: does AI revenue justify the build? Against $690B of 2026 hyperscaler capex, the right denominator is $63.2B narrow or ~$72.5B broad. Gartner’s $1.478T is appropriate for measuring AI economic activity, but not for capex coverage. The $100-135B vendor sum suits total-addressable-market and merger-and-acquisition analysis, but not credit underwriting. No defensible capex underwriting argument can use a denominator below the audit-grade floor.
If the Spread Index remains below 5% through the fourth quarter of 2026, disclosure alone cannot improve the underwriting case. Revenue itself must compound.