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.

A stark disconnect exists between the private and public AI markets. Over a recent six-week period, top private AI companies like OpenAI and Anthropic saw their best growth ever, while public AI and semiconductor stocks had their worst performance, pointing to a lag or divergence in market sentiment.

A significant disconnect exists between private and public AI markets. While private AI labs like OpenAI and Anthropic are reporting their best growth ever, public semiconductor and AI stocks are falling. This suggests public markets have priced in perfection and are now correcting, while private sentiment remains extremely bullish.

In a market downturn, public AI companies face mark-to-market stock pressure and employee anxiety. As a private player whose public partners' valuations depend on it, OpenAI could be insulated from this volatility, giving it a stability advantage.

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.

An a16z partner highlights a major disconnect where fewer than five public software companies are growing over 30%, while private AI giants like OpenAI and Anthropic are adding massive revenue, shifting the growth focus to private ventures.

The current market is unique in that a handful of private AI companies like OpenAI have an outsized, direct impact on the valuations of many public companies. This makes it essential for public market investors to deeply understand private market developments to make informed decisions.

The IPOs of AI leaders like OpenAI will expose their core financial metrics to the public. This transparency will create concrete valuation benchmarks, forcing private market investors to move beyond qualitative hype and apply more disciplined, fundamentals-based analysis to earlier-stage AI startups.

Contrary to fueling hype, public offerings from companies like OpenAI would introduce real financial data into the market. This transparency could ground the "AI bubble" conversation in actual performance metrics, clarifying the significant information gap that currently exists for investors.