The compute-heavy nature of AI makes traditional 80%+ SaaS gross margins impossible. Companies should embrace lower margins as proof of user adoption and value delivery. This strategy mirrors the successful on-premise to cloud transition, which ultimately drove massive growth for companies like Microsoft.

Don't judge AI companies by their blended margins. The current 'subsidy' of free inference credits is a healthy form of customer acquisition that converts into high-LTV power users. This is far superior to the 2021 model of raising VC funds only to funnel them into Google and Facebook ads as 'empty calorie' growth.

Your true cost to acquire a paid customer via freemium isn't zero. Calculate it with this formula: (monthly cost to service a single free user) divided by (free-to-paid conversion rate). This reveals the model's actual financial viability.

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.

Founders often miscalculate Customer Acquisition Cost by measuring the cost to acquire a trial user, not a paying customer. This creates a dangerously optimistic view of unit economics. True CAC must account for the trial-to-paid conversion rate (e.g., if trial CAC is $130 and 1 in 3 convert, true CAC is ~$400).

New AI companies reframe their P&L by viewing inference costs not as a COGS liability but as a sales and marketing investment. By building the best possible agent, the product itself becomes the primary driver of growth, allowing them to operate with lean go-to-market teams.

In rapidly evolving AI markets, founders should prioritize user acquisition and market share over achieving positive unit economics. The core assumption is that underlying model costs will decrease exponentially, making current negative margins an acceptable short-term trade-off for long-term growth.

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.

Unlike SaaS, where high gross margins are key, an AI company with very high margins likely isn't seeing significant use of its core AI features. Low margins signal that customers are actively using compute-intensive products, a positive early indicator.