Sam Altman Explores New Economics for AI-Driven Scientific Discovery

Sam Altman used his appearance at Cisco’s AI Summit on Tuesday (Feb. 3) to sketch a future in which OpenAI’s role extends beyond selling access to models and into selectively backing the outcomes those models help produce.

Speaking with Cisco President and Chief Product Officer Jeetu Patel, Altman cast this idea in cautious language, noting that what he described is not something OpenAI is doing today but a possibility on the horizon.

“This is not something we’re doing now,” Altman said, “but I think the frontier of scientific discovery with AI will require so much capital that maybe we think of ourselves as an investor in some of those cases.” He added that this would apply to work so resource-intensive that customers alone could not shoulder it.

Altman’s Summit remarks sit at the intersection of two emerging trends. First is the evolution of artificial intelligence from reactive tools to persistent collaborators embedded across workflows, systems that observe, act and manage context over time.

In domains like drug discovery and scientific research, that transition exposes gaps not just in capability but in economics: the compute, infrastructure and expertise required to convert hypotheses into tangible breakthroughs can be enormous.

That insight drove Altman’s framing that OpenAI might consider sharing risk, not just selling compute. Unlike traditional software pricing based on subscriptions or usage, this approach imagines the company participating financially in the long-term value its systems help unlock.

Second, there is emerging evidence that OpenAI’s leadership is publicly contemplating new revenue models, a trend signaled by comments beyond the Cisco stage.

According to The Information, OpenAI’s CFO has suggested the company could take a cut of value created when customers’ AI-aided discoveries become commercially valuable, for example, by securing a license to a drug discovered with the help of OpenAI’s technology or taking a share of profits from a successful outcome.

That idea of “value sharing” represents a shift from the traditional model of charging for access and usage toward outcome-based pricing, where the provider’s financial returns are tied to the success customers achieve using its tools.

It raises questions about how AI platforms position themselves relative to customer value creation and whether intelligence providers should share in the upside when their systems materially contribute to profitable discoveries.

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