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Application layer's share of enterprise generative-AI spend (Menlo)

Enterprise spend on generative-AI applications as a share of total enterprise generative-AI spend in the US, per Menlo Ventures' annual buyer survey (derived metric enterprise_app_spend_share).

Reason through this

The reading

Applications took a third of enterprise generative-AI dollars in 2024 and half in 2025; the money is moving up the stack even as chips keep the margin.

Direction over the last 2 readingseach dot links to its record
33.3% · 2024-12-31 · reported51.4% · 2025-12-31 · reported
in the rule's windowmoves within ±5.0 pts read as stable, and a move counts only if more than half the steps go its way; this window moved +18.0 pts and 1 of 1 steps rose; higher reads concentrating
reported

2 readings drawn from the derived metric enterprise_app_spend_share; 4 observations in the evidence.

Derived (enterprise_app_spend_share) from: Menlo Ventures, State of Generative AI in the Enterprise. Menlo Ventures, '2025: The State of Generative AI in the Enterprise'.

The numbers
as ofreadingvaluerecord
2025-12-31—51.4%reported
2024-12-31—33.3%reported

Why it matters

The only published layer split of enterprise dollars. A rising application share means buyers' money is landing above the model layer, where the model-tax question starts.

What cuts against it

Survey of a few hundred buyers, not filings; scope excludes chips, standalone inference and embedded features, so the infrastructure denominator is understated; two waves only.

How it is measured
  • series menlo.us_enterprise.genai_spend_usd.fy
  • series menlo.us_enterprise.genai_app_spend_usd.fy
  • derived metric enterprise_app_spend_share (formula in the semantic layer)
  • source Menlo Ventures, State of Generative AI in the Enterprise · default tier 6 · Report
  • proxy capital_flow · cadence annual

Two annual waves, so the trend is one step; a move under five points is within survey error. Rising application share = value concentrating at the application layer.

Applied to metric:enterprise_app_spend_share.

Derived rows (2)
as ofdimsvalueinputs
2025-12-3151.4%sources12
2024-12-3133.3%sources12
Latest readings
  • 2025-12-31 · 51.4%as of 2025-12-31(2 obs)
  • 2024-12-31 · 33.3%as of 2024-12-31(2 obs)
Why this status, and its history

Menlo Ventures' buyer surveys: applications took $4.6B of $13.8B enterprise generative-AI spend in 2024 (33%) and $19B of $37B in 2025 (51%), an 18-point rise against a five-point dead band, so the direction reads concentrating at the application layer. Capped at emerging because Menlo is the only source and a survey is tier 6; a second, independent layer split would lift the cap. Initial seed.

  1. 2026-09-10unmeasured to emergingconf — → 55 · the evaluator (automatic)

    Menlo Ventures' buyer surveys: applications took $4.6B of $13.8B enterprise generative-AI spend in 2024 (33%) and $19B of $37B in 2025 (51%), an 18-point rise against a five-point dead band, so the direction reads concentrating at the application layer. Capped at emerging because Menlo is the only source and a survey is tier 6; a second, independent layer split would lift the cap. Initial seed.

    sources12
Why this confidence

Rests on 4 observations from Menlo Ventures, State of Generative AI in the Enterprise; best evidence tier 6, grade B; current.

Confidence is independent of status: 90–95 multiple strong independent sources; 70–89 good evidence, some ambiguity; 50–69 mixed or hard to operationalise; 0–49 limited or vague evidence.