Contrary to the narrative of burning cash, major AI labs are likely highly profitable on the marginal cost of inference. Their massive reported losses stem from huge capital expenditures on training runs and R&D. This financial structure is more akin to an industrial manufacturer than a traditional software company, with high upfront costs and profitable unit economics.

While headlines focused on OpenAI's staggering $38.5B net loss, the underlying numbers show a profitable core business. The company generated $13B in 2025 revenue on just $7.5B in direct costs, indicating that selling tokens for inference is a high-margin activity separate from massive R&D costs.

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.

An AI lab's P&L contains two distinct businesses. The first is training models—a high upfront investment creating a depreciating asset. The second is the 'inference factory,' a profitable manufacturing business with positive margins. This duality explains their massive losses despite high revenue.

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.

During major technology shifts like the move to cloud or AI, the best companies (e.g., hyperscalers, Snowflake) often have terrible early margins. In AI, inference costs are falling so rapidly that a company's margin profile can improve dramatically. Judging an early AI company on SaaS-era margin expectations is a mistake.

The value unlocked by frontier AI models is expanding so rapidly that there isn't enough hardware to meet demand. This scarcity ensures that not just the top lab (like OpenAI), but also second and third-tier competitors, will operate at full capacity with strong margins.

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.

Traditional SaaS metrics like 80%+ gross margins are misleading for AI companies. High inference costs lower margins, but if the absolute gross profit per customer is multiples higher than a SaaS equivalent, it's a superior business. The focus should shift from margin percentages to absolute gross profit dollars and multiples.