OpenAI CFO signals public listing by 2027

OpenAI Chief Financial Officer Sarah Friar informed staff members that the artificial intelligence firm will become a public company in 2027, or potentially sooner. Speaking during an all-hands meeting on Wednesday, 19 August, Friar indicated that a debut could occur this year if the business continues to show positive momentum. The comments were reported by CNBC, which cited unnamed sources present at the gathering.

Friar addressed concerns regarding her chief rival, Anthropic, by asserting that the company is running its own race. She noted that there is no cause for alarm if Anthropic reaches the market first. The executive also shared internal performance metrics, stating that OpenAI’s revenue run rate has increased by 35 per cent so far this quarter. Enterprise revenue run rate has risen by 50 per cent, while the company’s AI coding and work products have reached 20 million weekly active users.

Both OpenAI and Anthropic have submitted confidential paperwork to go public. Bloomberg reported on 13 August that Anthropic is expected to reach the market as soon as this autumn. OpenAI disclosed on 8 June that it had submitted a confidential S-1 registration statement to the Securities and Exchange Commission. The company stated that while it had not decided on timing, the move provides the option to go public sooner if that proves to be the best course of action.

Recent reports suggest Anthropic’s sales surpassed those of OpenAI for the first time in the second quarter. OpenAI told investors that its revenue grew by 18 per cent to reach 6.7 billion dollars, whereas Anthropic’s revenue more than doubled to 11.6 billion dollars. These results were attributed to a slowdown in ChatGPT growth, contrasted with continued success for Anthropic’s Claude Code tool. OpenAI indicated that its growth rate has accelerated in the current quarter following the launch of new AI models in July.

The company also announced a deliberate slowdown in its development process. OpenAI implemented a two-week pause in reinforcement learning training for models intended for deployment to harden research environments and expand monitoring coverage. Its largest planned frontier training run remains on hold. The firm stated that as models become more capable, the risks associated with developing and testing them internally also grow, necessitating time to meet safety standards.