OpenAI Told Investors Revenue Is $20 Billion Below Earlier Figure
OpenAI told investors annualized revenue is approaching $50 billion, about $20 billion below the $70 billion figure reported a week earlier, per the FT.

Updated
Why it matters
- OpenAI told investors annualized revenue is approaching $50 billion, roughly $20 billion below the previously reported $70 billion figure.
- The $70 billion number came from investors' attempts to compare OpenAI directly with Anthropic's annualized revenues, per the FT.
- Anthropic counts sales by its cloud partners in its revenue figure; OpenAI does not.
- OpenAI raised $122 billion in a March funding round; leaked 2025 financials showed about $13 billion in revenue.
- OpenAI's IPO, once rumored for this year, has been pushed to early 2027.
OpenAI has told investors that its annualized revenue (the revenue run rate over a trailing twelve-month period) is approaching $50 billion — roughly $20 billion below a figure that circulated widely just over a week ago, according to The Financial Times.
The gap matters. The earlier $70 billion number, first reported by news outlets in early reports, had positioned OpenAI as competitive with the run rate of rival AI lab Anthropic. It now appears that comparison was flawed at its source.
Where did the $70 billion figure come from?
The FT reports that the $70 billion figure was based on information previously shared with OpenAI's investors and relayed through the press. According to the outlet, that number was produced through "attempts by OpenAI's own investors to produce a direct comparison with Anthropic's annualised revenues."
That is a critical detail. The number was not an official OpenAI disclosure. It was a construction — an investor-side estimate built specifically to benchmark OpenAI against its closest competitor.
TechCrunch, which reported the new figure, reached out to OpenAI for comment.
Do OpenAI and Anthropic count revenue the same way?
No. And the difference explains much of the apparent gap.
- OpenAI and Anthropic calculate annualized revenue differently, according to the FT.
- Anthropic counts sales made by its cloud partners in its figure.
- OpenAI does not include such partner sales in its calculation.
The result: a side-by-side comparison of the two companies' run rates, built on inconsistent definitions, overstated OpenAI's competitive position. The "direct comparison" that investors attempted was not, in fact, direct.
Why does a $20 billion gap matter?
Revenue is the number OpenAI must use to justify the enormous capital flowing into the company.
The stakes are concrete. OpenAI raised $122 billion during a March funding round alone — a sum that investors expect to be anchored in accelerating commercial performance. Leaked 2025 financials, reported earlier this year, showed the company made about $13 billion while spending significantly more than that.
The revenue question has troubled the company as it attempts to justify gargantuan investments made on its behalf, as TechCrunch notes. A run rate approaching $50 billion still makes OpenAI one of the fastest-growing software businesses in history. But a $20 billion downward revision in reported expectations sharpens scrutiny of the gap between what OpenAI earns and what it spends and raises.
What happens to the IPO?
OpenAI's IPO, previously rumored to be materializing this year, has been pushed off until early 2027, according to the report.
That timeline gives the company additional runway to close the distance between its roughly $50 billion annualized revenue and its cost structure. It also means public-market investors will not get an independent look at OpenAI's books until 2027 at the earliest — leaving analysts dependent on investor briefings and leaks of the kind that produced both the $70 billion figure and its correction.
For now, the corrected number stands: approaching $50 billion, per the company's own message to investors, as reported by The Financial Times.
Original: axios.com
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Senior reporter covering companies, deals and strategy in artificial intelligence and machine learning at AI In Context.
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