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.

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.

Companies like Atlassian and Figma, which spend a far greater share of revenue on R&D than peers, are making a dual-purpose bet. It's an offensive move to create new AI-native products and capture market share. Simultaneously, it's a defensive measure to protect their existing product moats from being eroded by disruptive AI agents.

ARM, known for its high-margin IP licensing, is now manufacturing its own chips. While this drastically lowers gross margins from 97% to ~50%, it's a strategic move to capture a much larger revenue opportunity created by the CPU demand from AI agents.

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.

While AI companies are structurally lower gross margin due to cloud and LLM costs, this may be offset by significantly lower operating expenses. AI tools can make engineering, sales, and legal teams more efficient, potentially leading to a higher terminal operating margin than traditional SaaS businesses, which is what ultimately matters.

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.

For companies in a generational platform shift like AI, fiscal prudence takes a backseat to absolute victory. Citing the example of WWII, the argument is that history only remembers who won, not whether they came in on budget. This mindset justifies seemingly excessive spending on talent and R&D to secure market dominance.

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.