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.

Reported ARR for AI labs varies greatly depending on whether cloud reseller margins are subtracted or recognized gross. Anthropic includes total partner sales while OpenAI subtracts cloud revenue cuts, causing investor confusion when comparing top-line performance metrics across companies.

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 narrative of AI labs burning cash is misleading. Their core business of selling API access for inference is highly profitable, with margins like Anthropic's reported 80%. Unprofitability stems from the massive, discretionary R&D cost of training next-generation frontier models, not poor unit economics.

While a megawatt of compute costs ~$15M, leading labs like Anthropic can generate up to $50M in revenue from it. This massive 3x+ profit margin creates a powerful flywheel, allowing them to reinvest heavily in training the next generation of models and accelerate their lead.

While critics view AI models as low-margin commodity businesses, Dylan Patel notes that Anthropic generates gross margins exceeding 75% on frontier model inference, rivaling mature software giants like Salesforce. Unlike SaaS companies burdened by customer acquisition costs reaching 30%, frontier AI labs face virtually zero customer acquisition costs. High workload stickiness and slower R&D growth relative to revenue have already pushed leading labs toward operating profitability.

Anthropic's claims of positive operating income are based on adjusted figures that strip out major expenses like stock-based compensation and model training. This accounting practice masks the company's true financial health, which will be revealed for the first time in unadjusted IPO documents, posing a potential risk for investors.

Anthropic’s cloud partnerships, like its one with Amazon, are structured as a 50% gross profit share, meaning costs like inference are deducted before sharing. This contrasts sharply with OpenAI's simpler 20% total revenue share with Microsoft, revealing different economic models for AI platform distribution.

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.