OpenAI CFO Sarah Friar sketched a future in which the company’s business models evolve beyond subscriptions and could include royalty streams tied to customer results.
Speaking on a recent podcast, Friar floated the possibility of “licensing models” in which OpenAI would get paid when a customer’s AI-enabled work produces measurable outcomes.
In one example, she pointed to drug discovery: if a pharma partner used OpenAI technology to help develop a breakthrough medicine, the startup could take a licensed portion of the drug’s sales. The pitch, she suggested, is alignment: OpenAI would make money when its customers do.
That idea sits inside what Friar described as a Rubik’s Cube of strategic options, a way to explain how OpenAI has evolved from a simpler early business into a fast-expanding matrix of infrastructure, products, and pricing.
“One of the things I love about a Rubik’s Cube, I’m probably not getting the number exactly right, but I think it has 43 quintillion different states it can be in,” Friar said. “It always blew my mind when I was in university. So now just think about that cube spinning.”
OpenAI started out as a “single block” in a Rubik’s Cube. It had one major cloud provider (Microsoft), one dominant chip partner (Nvidia), one flagship product (ChatGPT for consumers), and one basic subscription, she said.
Now, OpenAI works with multiple cloud providers and has partnerships with several chip providers, while expanding its product lineup beyond the consumer ChatGPT service to include Sora, business products, specialized industry offerings, and research platforms.
The business model has become similarly multi-layered. Friar said OpenAI started with a single consumer subscription for ChatGPT “because we needed a way to pay for the compute” and has since broadened to multiple price points, software-as-a-service pricing, and credit-based models for high-value use cases.
From there, she said, OpenAI is “beginning to think about things like commerce and ads” alongside longer-term licensing tied to outcomes.
In Friar’s telling, the Rubik’s Cube metaphor captures how OpenAI can mix and match technical choices with how it makes money. A low-latency chip could pair with a premium AI coding experience to justify a higher-end subscription, she said. Or the company could optimize for scale, attracting more free users and creating more inventory for an advertising business.
There’s a major constraint affecting all these strategies: compute. Friar said demand for OpenAI’s services is limited less by interest than by available computing capacity, a dynamic that, in her view, makes the company’s growing menu of revenue models not just optional, but necessary.
“You can start to see how the goal in the last 12 months has been creating more and more strategic options that allow me to keep paying for the compute we need to really achieve our mission,” Friar added.
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