Menlo Ventures estimates that enterprise AI deployment spending reached $176.8 billion by September 2026, up 5.4x from $33 billion a year earlier, in 2026: The State of Enterprise AI, published on 3 November. By our arithmetic, one category alone, foundation models and inference at $94.2 billion, is now about 2.9 times the size of the entire 2025 market.
Menlo says the report draws on a survey of nearly 400 U.S. enterprise decision-makers responsible for more than $4 billion in AI spending, fielded by Morning Consult from September 8 to 26, 2026. Menlo Ventures is an investor in Anthropic, and the report marks its other portfolio companies with an asterisk.
Business Pill · Jevons Paradox
A short explainer on the Jevons paradox: why a cheaper input can lead to a bigger total bill. It teaches the general idea only and does not discuss Menlo’s report.
The key insight: By our arithmetic, foundation models and inference, at $94.2 billion in Menlo Ventures’ estimate, are about 2.9 times the $33.0 billion Menlo gives for the whole 2025 enterprise AI deployment market.
The report opens its sizing section with: “In 2026, AI became an economy-scale force. Enterprise AI deployment spending surged from $33 billion to $177 billion in just one year. That’s over $140 billion in new annual spending, more than four times the size of the entire market a year ago”.
Menlo’s series runs from $1.7 billion in 2023 to $8.5 billion in 2024 (5.0x) and $33 billion in 2025 (3.9x). Its key findings put it this way: “Growth is accelerating: 5.4x vs. 3.9x a year ago”.
Menlo splits deployment spending into two layers. Infrastructure, “the stack that runs models and the agents built on them”, comes to $99.5 billion. Applications, the AI products enterprises use, come to $77.3 billion.
Inside infrastructure, Menlo’s chart shows foundation models and inference rising from $12.5 billion to $94.2 billion (7.5x) and AI infrastructure from $1.5 billion to $5.3 billion. Inside applications, coding rose from $4.4 billion to $34.0 billion (7.7x), AI assistants and productivity from $8.4 billion to $25.0 billion, and other apps from $6.1 billion to $18.3 billion.
Model development is counted separately and is not part of the $176.8 billion: Menlo estimates $87.2 billion for training compute and $15.3 billion for training data and services, $102.5 billion in all.
By our arithmetic, foundation models and inference make up about 53% of deployment spending, and at $94.2 billion the category is about 2.9 times Menlo’s $33.0 billion figure for the whole 2025 deployment market.
Menlo’s December 2025 report put 2025 spending at $37 billion and said it was “capturing 6% of the global SaaS market”. The 2026 report gives $33 billion for 2025 because, it says, “Figures for prior years were reclassified to reflect this year’s taxonomy, which moved data labeling vendors like Mercor from deployment to development”.
The 2025 methodology also excluded “inference and model serving (e.g., AWS, GCP, Azure, Fireworks)”. The 2026 report’s infrastructure layer names inference platforms such as Fireworks, Baseten and Modal. Menlo adds that “Revenue is shown as annualized run rate”, and the 2026 figure is as of September.
The Structural Read
As we read Menlo’s chart, the growth sits in the two lines closest to models doing work: foundation models and inference rose 7.5x and coding apps 7.7x, while AI assistants and other apps each rose 3.0x.
As we read the report, building and using now run at a similar pace: Menlo puts deployment spending up 5.4x and model development spending up 5.3x, with deployment the larger of the two at $176.8 billion against $102.5 billion.
Menlo Ventures, 2026: The State of Enterprise AI
“In 2026, AI became an economy-scale force. Enterprise AI deployment spending surged from $33 billion to $177 billion in just one year.”
Three Implications
FOR THE MODEL LABS Menlo estimates Anthropic, OpenAI and Google command $122.7B, or 69% of all deployment dollars, up from 50% last year.
FOR APPLICATION COMPANIES Menlo puts the application layer at $77.3 billion, with coding at $34.0 billion and AI assistants and productivity at $25.0 billion.
FOR READERS OF THE SERIES Menlo reclassified prior years, so its 2025 figure is $33 billion in this report against $37 billion in the December 2025 report.
The framework argues: “Training was a cost center: episodic, concentrated among roughly twenty frontier model labs, amortizable across trillions of tokens. Inference is a revenue engine: continuous, distributed across millions of applications and agents, incurring marginal cost with every query.”
As we read it, Menlo’s largest deployment line, foundation models and inference at $94.2 billion, is that revenue engine measured in enterprise dollars. The framework is the owner’s argument and does not discuss Menlo’s report.
The dollar figures are Menlo’s estimates from “a bottoms-up model informed by survey responses, public filings, and third-party reports”, not company-reported revenue. The chart is titled global while the survey covers U.S. enterprises.
The restated 2025 figure for foundation models and inference, $12.5 billion, is the same number the 2025 report gave for foundation model APIs; the report does not say whether inference platforms were added to the earlier year, so part of the 7.5x may reflect scope. Menlo Ventures is an investor in Anthropic, and the report marks its other portfolio companies with an asterisk. We did not contact Menlo.
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Menlo Ventures estimates enterprise AI deployment spending at $176.8 billion by September 2026, up 5.4x in a year, with $99.5 billion in infrastructure and $77.3 billion in applications. By our arithmetic, foundation models and inference alone are about 2.9 times the whole 2025 market.
A note on sourcing. The spending figures, chart values, survey details and quotes come from Menlo Ventures’ 2026 State of Enterprise AI report, published on 3 November 2026, and its 2025 report. The two charts are Menlo’s own. The 2.9x and 53% figures are our arithmetic on Menlo’s numbers. Menlo Ventures is an investor in Anthropic and in other companies named in the report. We did not contact Menlo.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
