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.

Many AI coding agents are unprofitable because their business model is broken. They charge a fixed subscription fee but pay variable, per-token costs for model inference. This means their most engaged power users, who should be their best customers, are actually their biggest cost centers, leading to negative gross margins.

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.

Harvey's gross margin plummeted from +50% to -50% when agentic AI features caused a 20-fold spike in token usage from customers on fixed, seat-based plans. This demonstrates the extreme financial risk of predictable pricing in an era of unpredictable, resource-intensive AI consumption.

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.

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.

Companies like coding assistant Cursor reportedly face negative gross margins because their flat-rate, per-seat pricing fails to cover the high compute costs from agentic tools that generate many tokens. This creates an unsustainable business model where growth exacerbates losses.