OpenAI and Anthropic are presenting a version of profitability that excludes their largest expenses: model training and inference. Critics compare this to an airline ignoring the cost of its jets. This financial engineering aims to create a positive outlook for potential IPOs but masks their true cash burn rate.

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.

Greg Brockman simplifies OpenAI's business to its most fundamental level: buying or building massive amounts of compute and reselling it with an intelligence layer on top. This framing reveals that their primary growth vector and constraint is access to computation, making their core operation a margin-based resale of processing power.

Reports of OpenAI's massive financial 'losses' can be misleading. A significant portion is likely capital expenditure for computing infrastructure, an investment in assets. This reflects a long-term build-out rather than a fundamentally unprofitable operating model.

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 recent, successive "leaks" of escalating revenue numbers from Anthropic and OpenAI reveal a new competitive front. This public battle for financial dominance signals to investors and the market that the AI industry is rapidly maturing and moving far beyond the "no business model" critique.

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.

Sam Altman clarifies that OpenAI's large losses are a strategic investment in training. The core economic model assumes that revenue growth directly follows the expansion of their compute fleet, stating that if they had double the compute, they would have double the revenue today.

Greg Brockman demystifies OpenAI's business model as a straightforward process: acquire compute power through renting, building, or buying, and then resell that compute in the form of intelligence at a positive operating margin. Success depends on scalable demand for intelligence, which he views as unlimited.