Unlike traditional SaaS, AI companies have significant variable costs for compute and tokens. This makes revenue a poor proxy for profitability, as their gross margins are fundamentally different from high-margin software businesses—a fact many investors miss.

Unlike in traditional SaaS, low gross margins in an AI company can be a positive indicator. They often reflect high inference costs, which directly correlates with strong user engagement with core AI features. High margins might suggest the AI is not the main product driver.

An AI lab's P&L contains two distinct businesses. The first is training models—a high upfront investment creating a depreciating asset. The second is the 'inference factory,' a profitable manufacturing business with positive margins. This duality explains their massive losses despite high revenue.

Established metrics for evaluating software (high gross margins, capital-light) are obsolete in the AI paradigm. Top AI companies often exhibit opposite traits, like low margins due to inference costs, signaling the "death of spreadsheet investing."

During major technology shifts like the move to cloud or AI, the best companies (e.g., hyperscalers, Snowflake) often have terrible early margins. In AI, inference costs are falling so rapidly that a company's margin profile can improve dramatically. Judging an early AI company on SaaS-era margin expectations is a mistake.

The burn multiple, a classic SaaS efficiency metric, is losing its reliability. Its underlying assumptions (stable margins, low churn, no CapEx) don't hold for today's fast-growing AI companies, which have variable token costs and massive capital expenditures, potentially hiding major business risks.

Software has long commanded premium valuations due to near-zero marginal distribution costs. AI breaks this model. The significant, variable cost of inference means expenses scale with usage, fundamentally altering software's economic profile and forcing valuations down toward those of traditional industries.

The traditional SaaS model—high R&D/sales costs, low COGS—is being inverted. AI makes building software cheap but running it expensive due to high inference costs (COGS). This threatens profitability, as companies now face high customer acquisition costs AND high costs of goods sold.

The perception of SaaS businesses as predictable, annuity-like investments is dead. AI introduces fundamental unknowns about growth, pricing, and market structure, breaking the old valuation models based on ARR and Net Dollar Retention.