Altman says AI benefits outweigh some harms, while rejecting catastrophic risks.
OpenAI says ChatGPT now reaches more than 1.2 billion people each week, while its annualized revenue rate nears $70 billion amid enterprise growth, Codex adoption, and pricing pressure in the AI market.

OpenAI says ChatGPT now reaches more than 1.2 billion weekly users. At DevDay, the company also cited more than 35 million weekly ChatGPT Work and Codex users, plus 2.5 million businesses using OpenAI products. The Decoder reports that OpenAI is nearing a $70 billion annualized revenue rate, up about 70 percent since the start of Q3.
The reported growth is tied to enterprise sales, the Codex coding assistant, and aggressive pricing competition against Claude and Chinese models. The company has also leaned into its GPT-6 model family, including the just-launched GPT-6.1-Sol. For readers, the key takeaway is that OpenAI’s business is expanding beyond consumer ChatGPT usage into workplace and developer adoption.
The Decoder reports that Anthropic’s annualized revenue rate passed $65 billion in July and may now match or exceed OpenAI’s. Anthropic is also preparing an IPO as early as November, according to the article. That makes the OpenAI-Anthropic race a central benchmark for the commercial AI market, especially in enterprise tools and coding assistants.
The annualized revenue rate projects current monthly revenue over a full year, making it a momentum signal rather than a complete picture of long-term profitability. The Decoder frames the major question as whether revenue can grow fast enough to cover massive data center bills. For businesses watching the sector, usage growth matters, but infrastructure costs remain the pressure point.
Altman says AI benefits outweigh some harms, while rejecting catastrophic risks.
A former OpenAI safety leader says the company is not being careful enough.

A departing OpenAI safety employee says the company’s culture is broken and calls for stronger safeguards.

A former OpenAI safety researcher says AI companies need deeper safeguards, not trial-and-error risk management.