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.

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.

Anthropic has surpassed OpenAI's revenue growth while maintaining training costs at a quarter of OpenAI's. This combination of accelerated growth and superior cost efficiency presents a significant competitive threat, a rare dynamic where a competitor is both faster and more efficient.

OpenAI's CFO highlights a key dynamic: the cost of raw compute inputs (power, memory) is rising, but the cost to produce a unit of intelligence is falling dramatically, citing a 97% cost reduction from GPT-4 to 5.4. This deflationary curve is central to their financial modeling, allowing them to price future capacity and value creation more aggressively.

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.

AI companies operate under the assumption that LLM prices will trend towards zero. This strategic bet means they intentionally de-prioritize heavy investment in cost optimization today, focusing instead on capturing the market and building features, confident that future, cheaper models will solve their margin problems for them.

While AI companies are structurally lower gross margin due to cloud and LLM costs, this may be offset by significantly lower operating expenses. AI tools can make engineering, sales, and legal teams more efficient, potentially leading to a higher terminal operating margin than traditional SaaS businesses, which is what ultimately matters.

By considering drastic price cuts to compete with Anthropic, OpenAI risks devaluing its position as a 'luxury' frontier model provider. This move could commoditize the market, hurting long-term profitability and making it harder to compete against lower-cost alternatives.

AI companies like OpenAI are losing money on their popular subscription plans. The computational cost (inference) to serve a user, especially a power user, often exceeds the subscription fee. This subsidized model is propped up by venture capital and is not sustainable long-term.