AI Software Valuation

Windsor Drake’s AI Software Valuation Report distills how the market is pricing AI-native software as multiples diverge by subsector and operating quality. It frames inference efficiency as the new gross-margin lever, linking compute cost per request to margin durability and valuation support. The report details premium drivers—proprietary data and defensible IP, clean data rights and provenance, and software-grade unit economics (e.g., >70% gross margins)—and contrasts them with common drags such as services-heavy revenue mix and unclear training-data exposure. It also breaks valuation dynamics by stage: early rounds can price scarcity and platform optionality (LLM assets cited at roughly 25–40x revenue, versus 8–12x for enterprise apps), while mid-stage outcomes increasingly hinge on GTM efficiency, cohort net revenue retention, CAC payback, and Rule of 40 performance. At scale, the framework shifts toward profitability trajectory and EBITDA-based benchmarks, with diligence focused on data-chain documentation, compute COGS roadmaps, customer concentration, and retention by segment. It clarifies when EV/Revenue remains appropriate for growth assets and when valuation anchors migrate to EBITDA as margins mature, emphasizing repeatable adoption signals over narratives and outlines what buyers probe first.