OpenAI Is Testing Features by Throwing ‘a Lot of Compute’ at Them

Sam Altman is bracing OpenAI users for pricier, compute-heavy products — and hinting at what’s next.

The OpenAI CEO said in a post on X on Monday that the company is launching “new compute-intensive offerings” over the next few weeks.

Altman said because of the costs involved, some features will initially be limited to Pro subscribers, while certain new products will have extra fees.

Altman framed the push as an experiment in stretching AI infrastructure to its limits: “We also want to learn what’s possible when we throw a lot of compute, at today’s model costs, at interesting new ideas,” he wrote.

The announcement adds on to OpenAI’s balancing act: making advanced AI accessible while covering the steep costs of compute. Altman said the company’s intention “remains to drive the cost of intelligence down as aggressively as we can and make our services widely available.”

“We are confident we will get there over time,” he added.

Altman and OpenAI did not respond to a request for comment from Business Insider.

The race for GPUs

OpenAI has been vocal about its insatiable demand for computing power.

“Every time we get more GPUs, they immediately get used,” OpenAI’s chief product officer, Kevin Weil, said on an episode of the “Moonshot” podcast published last month.

Weil said the need for compute is simple: “The more GPUs we get, the more AI we’ll all use.” He highlighted that adding bandwidth made the explosion of video possible.

Altman said in July that the company will bring on more than 1 million GPUs by the end of the year.

“Very proud of the team but now they better get to work figuring out how to 100x that lol,” Altman wrote on X in July.

For comparison, Elon Musk’s xAI disclosed that it used a supercluster of over 200,000 GPUs called Colossus to help train Grok4.

Other tech giants have also been blunt about their appetite for GPUs.

Mark Zuckerberg said on an episode of the “Access” podcast published Thursday that Meta is making “compute per researcher” a competitive advantage and is outspending rivals on GPUs and the custom infrastructure needed to power them.