For a true AI-native product, extremely high margins might indicate it isn't using enough AI, as inference has real costs. Founders should price for adoption, believing model costs will fall, and plan to build strong margins later through sophisticated, usage-based pricing tiers rather than optimizing prematurely.

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.

On a $2M ARR, Dresma’s largest customer pays $500,000 annually, representing 25% of their total revenue. This validates that a pure usage-based pricing model, without seat-based or feature-gated upsells, can successfully land and expand large enterprise accounts, demonstrating a clear path to significant customer lifetime value.

Many AI startups are "wrappers" whose service cost is tied to an upstream LLM. Since LLM prices fluctuate, these startups risk underwater unit economics. Stripe's token billing API allows them to track and price their service based on real-time inference costs, protecting their margins from volatility.

Unlike high-margin SaaS, AI agents operate on thin 30-40% gross margins. This financial reality makes traditional seat-based pricing obsolete. To build a viable business, companies must create new systems to capture more revenue and manage agent costs effectively, ensuring profitability and growth from day one.

Counterintuitively, very high gross margins in a company pitching itself as "AI" can be a warning sign. It may indicate that users aren't engaging with the core, computationally expensive AI features. Lower margins can signal genuine, heavy usage of the core AI product.

Google's strategy with the Gemini API is not direct profit but customer acquisition for its broader cloud ecosystem. Internally, they calculate a multiplier effect where API calls lead to much larger spending on services like storage and databases, justifying early negative profit margins on the API itself to win platform loyalty.

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.

Contrary to traditional software evaluation, Andreessen Horowitz now questions AI companies that present high, SaaS-like gross margins. This often indicates a critical flaw: customers are not engaging with the costly, core AI features. Low margins, in this context, can be a positive signal of genuine product usage and value delivery.