AI Valuations: Q2 2026
Windsor Drake's Q2 2026 market intelligence on AI-sector valuations. AI multiples remain far above the broader software market, with foundation-model labs priced at 15x to 30x revenue on strategic optionality and AI-native software anchored near an 11x EV/Revenue benchmark, but the premium is now conditional on demonstrated revenue rather than narrative. The report covers subsegment multiples, public versus private convergence, the IPO reopening led by Cerebras, record venture and private equity capital, and rising bubble-risk scrutiny.
- Sector
- AI
- Focus
- Valuations
- Published
- May 24, 2026
- Length
- 33 slides
- Reading time
- 8 minutes
Slide deck
33-slide deck. Desktop readers can page through the embedded viewer below. Mobile readers can open the direct PDF link.
Open slide deck PDF Key findings
- Windsor Drake's Q2 2026 EV/Revenue benchmark for AI-native application software sits near 11x, up from roughly 9x at the 2024 baseline.
- Foundation-model labs trade at 15x to 30x annualised revenue on strategic optionality; legacy software with no AI position has compressed to 3x to 6x.
- OpenAI's March 2026 funding round raised $122B at an $852B valuation; Anthropic is reported to be raising near a $900B valuation.
- Cerebras Systems listed on Nasdaq in May 2026, raising $5.55B at $185 per share — the largest US technology IPO since 2019 — and closed debut up 68%.
- Combined 2026 hyperscaler capital spending tracks above $650B; Bain projects roughly 200 GW of incremental AI compute demand by 2030.
- McKinsey finds 94% of enterprises had not yet seen significant value from AI spend as of end-2025, compressing multiples by an estimated 0.8x.
- Private equity entered 2026 with roughly $3.7T of dry powder; nearly half of Bain's larger 2025 deals involved AI-native targets.
- North America captured roughly 62% of global AI venture investment in Q1 2026; Europe held approximately 13% and APAC approximately 20%.
Methodology
Subsegment multiple ranges and the approximately 11x AI-native software benchmark are Windsor Drake's own synthesis of data from McKinsey & Company, Bain & Company, PitchBook, CB Insights and public SEC filings, calibrated against a proprietary index of 64 verified transactions from 2019 to 2026. Figures labelled as firm analysis or house estimates are presented as a Windsor Drake house view rather than third-party consensus.
Frequently asked questions
What EV/Revenue multiple do AI companies trade at in 2026?
Multiples range from 3x to 6x for legacy software with no AI position up to 15x to 30x for foundation-model labs. Windsor Drake's working benchmark for AI-native application software is approximately 11x EV/Revenue.
How do public and private AI valuations compare?
Public AI-exposed software averages roughly 11.5x EV/Revenue after re-rating from about 7x in 2023. Private AI companies average near 24x, a spread of approximately 12x concentrated in frontier labs priced on optionality.
Is there an AI valuation bubble in 2026?
The IMF has drawn comparisons to the dot-com peak of 2000, noting the largest technology names now represent roughly a fifth of the MSCI World Index. Windsor Drake reads the risk as a discipline on pricing rather than a reason to avoid the sector.
What is the Rule of 40 and why does it matter for AI software valuations?
The Rule of 40 measures revenue growth rate plus EBITDA margin; scores above 40 correlate with 11x to 15x multiples, while top-quartile scores above 50 support 15x and above. Only an estimated 15% to 20% of AI-native software clears the threshold.
When is the right time for an AI founder to run an M&A or IPO process?
Windsor Drake recommends starting after four to six quarters of predictable revenue and retention while demand, capital and pricing remain aligned. A full process runs 12 to 18 months end to end.
Who are the most active buyers of AI companies in 2026?
Hyperscalers and large software incumbents are acquiring capability across chips, power, data, models and talent rather than building internally. Private equity sponsors with roughly $3.7T of dry powder are concentrating on AI infrastructure and AI-native software platforms.
Companies covered
Public and private companies referenced in this report.