AI companies with the foresight to sign long-term, multi-year compute contracts gain a significant margin advantage. They lock in prices based on past valuations, while competitors are forced to buy capacity at much higher current market rates driven up by the increasing value of new AI models.

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.

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.

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.

Despite being open-source, leading Chinese AI firms are profitable. They generate hundreds of millions in revenue by selling managed services and API access, saving customers the complexity of self-hosting, GPU management, security, and deployment.

To profitably handle over 100 billion weekly LLM calls, Superhuman primarily uses its own fine-tuned versions of open-source models like Llama and Gemma. This in-house infrastructure allows it to operate at an 85%+ gross margin, a stark contrast to companies reliant on costly third-party APIs.

The paradoxical financial state of AI labs: individual models can generate healthy gross margins from inference, but the parent company operates at a loss. This is due to the massive, exponentially increasing R&D costs required to train the next, more powerful model.

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.

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.