AI startup valuation requires more than applying a software multiple to headline revenue. Model and media costs can change gross margin, while launch novelty can temporarily inflate growth. Investors need to understand the quality of the underlying economics.

Start with revenue quality

Separate recurring subscriptions, usage revenue, services and one-time contracts. Revenue tied to durable workflows generally deserves different treatment from experimental pilots.

Read growth with retention

Fast acquisition is valuable only if cohorts remain active. Our AI startup retention guide explains how to distinguish novelty from product-market fit.

Normalize gross margin

Include inference, image or video generation, API fees and human review when they scale with usage. See our gross-margin framework.

Evaluate defensibility

Model access alone is rarely enough. Distribution, proprietary data, workflow integration and identity systems may create stronger moats.

Consider capital intensity

Companies training models or owning infrastructure may need more capital than application businesses. Compare the expected advantage with the financing required to sustain it.

Use scenarios, not one multiple

Model downside, base and upside cases for growth, margin and retention. Valuation should reflect uncertainty rather than hide it behind a single optimistic forecast.