The artificial intelligence (AI) startup is on pace to generate annualized revenue of almost $70 billion, Axios reported Tuesday (Sept. 29), citing sources familiar with the matter. That figure represents a 70% increase since the start of the third quarter.
Sources said OpenAI’s business-to-business revenue has increased more than 100% over the same period, while OpenAI added more consumer revenue during the third quarter than it added during all of last year.
Details about OpenAI’s expenses were not immediately clear, Axios added, noting this is crucial in relation to discussions of the company’s growth.
Bloomberg News had reported in August that the company was on track to take in annualized revenue of more than $40 billion, around double its run rate from the end of 2025. Annualized revenue is a projection of a company’s full-year revenue based on its earnings over a shorter period, such as one month or one quarter.
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“For the banking, payments and FinTech firms watching how AI vendors turn usage into money, the number signals that enterprise demand for AI tools keeps climbing even as the cost of building them stays high,” PYMNTS wrote at the time.
This uptick in revenue is happening as OpenAI and rival Anthropic are both preparing for the initial public offerings (IPOs).
Both companies filed confidential paperwork to go public, with Anthropic expected to list this fall, possibly after the midterm elections in November. OpenAI has said it would not go public this year, saying it wanted to deal with safety concerns around its technology.
The news about OpenAI’s revenue run rate comes one day after Reuters published insights from Anthropic’s IPO prospectus. It showed the company recorded a $42 billion net loss last year, while anticipating $518 billion in spending on cloud, computing and infrastructure needs in the years ahead.
Also Monday (Sept. 28), the Wall Street Journal (WSJ) reported that OpenAI was halting the launch of its next AI model due to safety concerns.
Saachi Jain, the company’s head of safety, said the new GPT-6.1 Astra model had regressed compared to its predecessor, doing badly on tests to gauge alignment, a term for how well a model adheres to what humans want it to do.
“For anything regarding safety and alignment, there’s a tradeoff,” Jain said. “You really do need to find what’s the right line between staying within scope, but also avoiding laziness in terms of how the model actually pursues tasks even when it hits friction.”
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