Name: US Generative AI Applications Market Study 2026-2031 — Consumer & B2B
Brand: eFinancialModels
SKU: 656533
Availability: InStock

USA Mobile Apps Internet Platforms Market Study 2026-2031 | eFinancialModels

SaaS

Financial Model, Financial Modeling, Free Financial Model Templates, Market Studies, PDF

Why this study

US enterprises spent roughly US$37 billion on AI in 2025 and, for the first time, more than half went to applications rather than models or infrastructure. ChatGPT reached hundreds of millions of weekly users; Cursor hit about US$2 billion ARR; Harvey reached about US$300 million in legal. This study analyzes the layer where that money meets paying customers — the assistants, coding tools, copilots, vertical AI and agents — and decomposes the one thing a generic SaaS model gets wrong: the inference cost line that holds gen-AI gross margins below software norms.

What you get

A 34-page Word and PDF market study with 12 EFM-branded charts and 13 data tables.
The application-layer revenue build for both engines — consumer (reach, conversion) and B2B (seats, usage, outcomes).
The “token tax” decomposed — why gen-AI gross margins sit near 52 percent versus 78 to 80 percent for SaaS, and where they go.
A modeler’s base-case assumption set: gross-margin path, inference cost decline, retention and expansion, scenario envelope.
Three scenarios to 2031 with explicit application-layer spend and gross-margin outcomes.
A source-verification workbook with every figure cited and checked.

Key findings

Applications are now the majority of US enterprise AI spend — more than half of the ~US$37 billion 2025 enterprise AI spend went to apps, led by copilots (~US$8.4bn), coding (~US$7.3bn) and vertical AI (~US$3.5bn).
Adoption is real and compresses the sales cycle — AI buyers convert at ~47 percent versus ~25 percent for SaaS, and ~27 percent of app spend arrives through product-led growth.
The token tax defines the economics — inference is ~23 percent of revenue, holding blended gross margins near 52 percent in 2026, ~25 to 30 points below the SaaS norm, but rising as inference costs fall ~10x a year.
Startups are winning the application layer — AI-native startups captured ~63 percent of the app market overall, and 71 to 91 percent in coding, sales and finance, though incumbents retain bundling power.
Vertical AI is the most defensible — proprietary data and workflow lock-in (Harvey in legal, Abridge in healthcare) build moats a general assistant cannot replicate.

Who it’s for

Financial-modeling professionals and analysts (primary); VC, growth and PE investors; founders and operators; enterprise AI buyers and corporate strategy.

Methodology

Built from Menlo Ventures, Andreessen Horowitz, Gartner, IDC and McKinsey, ICONIQ, Sacra, Statista, Grand View Research and Mordor Intelligence, plus White House primary sources and law-firm regulatory trackers (Baker Botts, White & Case, DLA Piper, King & Spalding, McGuireWoods). Every claim, statistic, source, and chart was reviewed and verified by the eFinancialModels editorial team. Forward-looking figures for 2026 to 2031 are eFinancialModels Base Case projections and are labeled as such; company run-rates are fast-moving and cited as approximate; market sizings diverge by definition and are cited as ranges. eFinancialModels uses AI-assisted research and drafting tools alongside human research and editorial review; we do not publish unverified content.

Pair it with a template

Translate the findings into a company-level model with the eFinancialModels SaaS and technology financial model templates (subscription and usage revenue build with an explicit inference cost-of-revenue line, retention and expansion, and a valuation tied to margin and retention): https://www.efinancialmodels.com/financial-model-templates-by-industry/saas-financial-model/

Disclaimer

For informational and educational purposes only; not investment, financial, legal, or tax advice. Forward-looking statements are subject to material uncertainty. Conduct your own due diligence.

You must log in to submit a review.