SaaS companies are trying to preserve high gross margins, but this is impossible if they want to succeed in AI, which is compute-intensive. Lower margins should be reframed as a positive signal of user adoption and AI integration, much like the successful transition from on-prem to cloud.

a16z isn't deterred by AI companies' 0-50% gross margins, a stark contrast to the usual 70% software benchmark. They accept these margins if they stem from LLM costs, focusing instead on whether the company is building defensible value through unique data, workflows, and integrations.

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.

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.

AI application-layer companies are knowingly accepting negative gross margins by reselling expensive model inference. Their strategy is to first lock in users with a superior UX, then solve the cost problem later through vertical integration or cheaper models.

Legal AI firm Harvey's gross margins fell to -50% due to increased agentic AI usage on fixed seat-based pricing. The founder framed this as a deliberate choice to absorb costs and maintain customer trust during a transition period, rather than immediately forcing a new pricing model on them.

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.

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.