OpenAI’s CFO, Sarah Friar, highlighted the company’s growing enterprise focus during a speech at the Goldman Sachs Communacopia + Technology Conference on September 8, revealing strategic moves into specialized AI applications and pricing models. The announcement underscores OpenAI’s push to differentiate itself in a competitive market where open-source alternatives like Z.ai’s GLM 5.3 are gaining traction.
Enterprise Growth and Pricing Strategy
Friar emphasized OpenAI’s accelerating enterprise revenue growth, noting a 32% increase in corporate client earnings from June to July. This followed a broader 20% annualized revenue rise during the same period, with enterprise and consumer segments nearing parity by midyear. The CFO attributed this momentum to tailored AI solutions for industries like chip design, life sciences, and finance, alongside innovative pricing strategies.
The company slashed the price of its Luna model by 80%, leading to a tenfold surge in usage.
Friar also noted that OpenAI had a positive experience of its own, using its own models in developing its Jalapeno chip,
which reached the tape-out
stage—finalizing a design for manufacturing—in nine months. This internal application aligns with OpenAI’s broader focus on industry-specific AI, as enterprises increasingly seek systems adapted to specific tasks,
according to Friar.
Chip Design Breakthrough
OpenAI revealed its internal use of AI in developing the Jalapeno chip, which reached the tape-out
stage—finalizing a design for manufacturing—in nine months. Friar called this a positive experience,
showcasing the company’s ability to leverage its own models for hardware innovation.
This internal application aligns with OpenAI’s broader focus on industry-specific AI. The CFO noted that enterprises increasingly seek systems adapted to specific tasks,
a trend the company is capitalizing on through vertical-specific tools and partnerships. The Jalapeno project also highlights OpenAI’s dual role as both an AI developer and a user of its own technology.
Market Competition and Cost Comparisons
Friar directly challenged open-source competitors, stating that deploying OpenAI’s Luna model could be cheaper than using Z.ai’s GLM 5.3 on cloud platforms. This positioning reflects OpenAI’s efforts to counter the cost advantages of open-weight models, which are often perceived as more accessible for businesses.
The statement also underscores the evolving dynamics between proprietary and open-source AI. While open-source models like GLM 5.3 offer flexibility, OpenAI’s pricing strategy and enterprise support aim to attract clients prioritizing reliability and measurable ROI. Friar’s remarks suggest a broader industry shift toward evaluating AI not just by technical performance, but by total cost of ownership and business outcomes.
OpenAI’s Codex programming tool, with 25 million users, further illustrates its enterprise appeal. The company’s ability to balance consumer and corporate growth—now nearly even—positions it as a key player in the AI ecosystem, where specialization and cost efficiency are becoming critical differentiators.
OpenAI’s enterprise revenue increased 32% from June to July, compared with 20% growth in overall annualized revenue during the period. Enterprise and consumer businesses had reached roughly an even split by the middle of the year, ahead of OpenAI’s target of reaching that balance by year-end, she said. Deploying OpenAI’s lower-cost model could be cheaper than running Chinese open-source alternatives through cloud providers, she added.
