OpenAI pushes AI chip design tools, claims cost edge over open models

OpenAI is expanding into industry-specific AI applications including semiconductor design, positioning its tools as cost-competitive alternatives to open-source models despite premium pricing perceptions.

CFO Sarah Friar told attendees at Goldman Sachs' Communacopia + Technology Conference that OpenAI is concentrating on sectors such as chip design, life sciences, and financial services. The company is also testing outcome-based pricing models as enterprise customers demand clearer return on investment from AI deployments.

Chip design as proof of concept

OpenAI used its own models to develop its Jalapeno chip, reaching tape-out—the stage where a design is finalized for manufacturing—within nine months. Friar cited this internal project as evidence that the company's AI can accelerate specialized engineering workflows.

The company has also seen adoption of its Codex coding tool, which now has 25 million users, according to Friar.

Aggressive pricing to counter open alternatives

OpenAI recently slashed pricing on its lower-tier Luna model by 80 percent, driving a roughly tenfold increase in usage. Friar argued that deploying Luna through cloud infrastructure can be cheaper than running Chinese open-weight models like Z.ai's GLM 5.3 on the same platforms.

The pricing strategy addresses a persistent challenge: open-source and open-weight models are widely viewed as more economical options, particularly as competition intensifies from Chinese AI labs and rivals including Anthropic.

Enterprise momentum outpaces consumer growth

Enterprise revenue grew 32 percent from June to July, outpacing the 20 percent growth in overall annualized revenue during the same period. Enterprise and consumer businesses reached roughly equal revenue contributions by mid-year, ahead of OpenAI's year-end target for that balance.

Why it matters

OpenAI's push into vertical-specific applications and aggressive pricing signals a strategic shift as the AI market matures. Enterprise customers are moving beyond experimentation to demand measurable business outcomes, forcing frontier model providers to compete on both capability and cost. The company's claim of price competitiveness with open alternatives—long considered the budget option—suggests margin pressure across the AI stack as deployment scales.

The details were first reported by Reuters correspondent Krystal Hu from San Francisco.