OpenAI is sharpening its enterprise strategy by concentrating on industry-specific applications and lowering costs for budget-tier models, the company’s chief financial officer said on Monday.
Speaking at the Goldman Sachs Communacopia + Technology Conference in San Francisco, the CFO said businesses are increasingly asking for AI systems tailored to particular tasks rather than broad, general-purpose models. She noted that OpenAI is exploring outcome-based pricing as an alternative to traditional usage-based fees in response to corporate demand for clearer returns on AI investments.
According to the comments, OpenAI sees a set of targeted verticals as priorities, including chip design, life sciences and financial services. The company is also adjusting pricing to remain competitive with lower-cost open-source and open-weight alternatives that many organizations evaluate when weighing AI deployments.
As part of the pricing moves, the CFO said OpenAI cut the price of its budget-tier Luna model by 80%, a reduction that the company reports has raised usage by roughly tenfold. She also highlighted adoption metrics for other products, noting that Codex, OpenAI’s coding assistant, has reached 25 million users.
On enterprise revenue, the company reported a 32% increase between June and July, a pace that outstripped the 20% growth in annualized revenue over the same interval. The CFO said enterprise and consumer revenue streams were roughly even by mid-year, putting the company ahead of the timeline it had set to reach parity between the two segments by year-end.
The CFO used OpenAI’s internal work on a custom chip as a concrete example of value derived from its models. She said the company completed the chip design and sent it for manufacturing - a phase known as tape-out - in nine months using OpenAI’s models.
Competition remains prominent in the marketplace. The CFO acknowledged rising pressure from Chinese open-weight models and competitors such as Anthropic. She argued that, on a cost basis, running Luna can be more economical than deploying some Chinese open-source models through third-party cloud layers, giving the example of GLM 5.3 as a point of comparison on cloud-based costs.
The company is also testing outcome-based pricing frameworks, moving away from pure usage metering to address corporate scrutiny around AI spending and return on investment. That shift is positioned as a response to enterprise customers who want clearer payoff profiles from their AI investments.
OpenAI’s approach combines focused industry targets, aggressive budget-tier pricing and new commercial models. The CFO’s comments framed these steps as responses to both customer demand for verticalized capability and competitive pressure from lower-cost alternatives in the market.