OpenAI is moving beyond general-purpose chatbots and into specialized business applications while cutting prices on lower-cost models, as the ChatGPT maker seeks to accelerate enterprise adoption and defend its position against Anthropic, Chinese AI developers and open-weight competitors.
Chief Financial Officer Sarah Friar said Monday that OpenAI is developing applications for industries including chip design, life sciences and financial services. Speaking at Goldman Sachs’ Communacopia + Technology Conference in San Francisco, she said businesses are increasingly seeking AI systems built around specific workflows, data sets and performance requirements rather than one model intended to serve every use case.
The shift reflects a maturing AI market. Early competition centered largely on model capability and benchmark performance. Corporate buyers are now placing greater emphasis on cost, reliability, security, integration and measurable business results. That is pushing AI companies to compete not only as model providers, but also as suppliers of specialized software and infrastructure.
OpenAI is testing pricing models tied to business outcomes rather than usage. Under a traditional model, customers pay according to the number of tokens processed or the amount of computing consumed. Outcome-based pricing could instead link fees to results such as faster software development, higher research productivity, or increased revenue.
Such a model could give OpenAI access to a larger share of the value created by its systems. It could also make AI easier for companies to budget if they are paying for a defined business result rather than unpredictable usage. However, the move is expected to introduce new risks for OpenAI, including disputes over how outcomes are measured and how much of an improvement can be attributed to the AI system.
The move into specialized applications also gives OpenAI a way to defend its margins as model prices fall. General-purpose models are becoming increasingly interchangeable for some tasks, especially as open-weight systems improve and can be customized or deployed through cloud providers. Industry-specific products, by contrast, can be differentiated through proprietary data, workflow integration, compliance features, and domain expertise.
OpenAI faces pressure from both established rivals and lower-cost alternatives. Anthropic has expanded its enterprise presence, particularly in coding and business applications, while Chinese developers are offering open-weight models that companies can run and modify with greater control over deployment. Those systems can reduce dependence on a single AI provider and may offer lower costs for organizations with the technical capacity to operate them.
Friar said OpenAI is responding with aggressive pricing on its own lower-cost models. The company recently cut the price of its Luna model by 80%, contributing to an approximately 10-fold increase in usage, she said.
The price reduction illustrates the trade-off facing AI companies. Lower prices can stimulate demand and help models become embedded in customer workflows, but they can also intensify pressure on revenue per query and raise questions about whether usage growth will translate into profitable growth. OpenAI is therefore likely seeking to use cheaper models as an entry point while steering customers toward higher-value products and specialized applications.
Friar also cited strong demand for Codex, OpenAI’s coding tool, which has reached 25 million users. Coding is among the most commercially important AI applications because productivity gains can be measured more directly than in many consumer use cases. It is also a highly competitive market, with products from Anthropic, Microsoft, Google and a growing number of specialized developers.
OpenAI has used its own systems internally as evidence that specialized AI can produce tangible engineering benefits. Friar said the company used its models in developing its Jalapeno chip, which was “taped out” within nine months. Tape-out is the stage at which a semiconductor design is finalized and submitted for manufacturing.
The example is strategically important because chip design is a complex, high-value workflow where even modest improvements in speed can have significant financial consequences. It also supports OpenAI’s argument that its models can be embedded in technical processes rather than used only for drafting text or answering questions. However, industry analysts note that the broader commercial significance will depend on whether similar gains can be reproduced across customers and measured against the cost of deploying the systems.
OpenAI’s enterprise business is growing faster than its overall business. Friar said enterprise revenue increased 32% from June to July, compared with 20% growth in overall annualized revenue during the same period.
By the middle of the year, enterprise and consumer businesses had reached roughly an even split, ahead of OpenAI’s previous target of achieving that balance by year-end. The change suggests that OpenAI is becoming less dependent on consumer subscriptions and more focused on large organizations with recurring contracts and broader deployment opportunities.
Enterprise customers could provide a more durable revenue base, but they also impose higher demands. Companies typically require data protections, administrative controls, auditability, service guarantees, and integration with existing software. Winning those contracts can take longer and require more support than selling subscriptions to individual users.
The enterprise push may also help OpenAI offset the high cost of developing and operating frontier models. Large corporate deployments can generate substantial revenue, but they require significant computing capacity. The company must balance the need to make models affordable enough to encourage widespread use with the need to preserve sufficient margins to fund research, infrastructure and future model development.
Friar said OpenAI’s lower-cost models can compete with Chinese open-weight alternatives once cloud deployment costs are included.
“If you’re deploying Luna and compare that to (Z.ai’s) GLM 5.3, for example, on a cloud layer, we are cheaper,” Friar said.
The comparison underscores a growing distinction in AI economics. The headline price of a model does not necessarily reflect the total cost of ownership. Buyers must also consider cloud infrastructure, engineering staff, model maintenance, security, latency, customization, and the cost of switching providers. A proprietary model with a higher listed price may still be cheaper overall if it requires less operational support or delivers better results with fewer queries.
At the same time, open-weight models remain a strategic threat because they give customers more control. Companies can host them on their own infrastructure, fine-tune them for specific tasks, and reduce exposure to changes in a vendor’s pricing or product strategy. That flexibility could be attractive in regulated industries such as financial services and life sciences.
OpenAI’s response is to compete on several fronts at once: frontier model performance, lower-cost inference, specialized applications, enterprise distribution and measurable business outcomes. The approach is expected to strengthen its position if the company can turn its technical lead into products that are deeply embedded in customer operations.
The risk is that the market may commoditize faster than OpenAI can build defensible applications. If customers view models as interchangeable, price cuts could become necessary simply to maintain market share. If specialized products require extensive customization, OpenAI may face higher sales and implementation costs, limiting the benefits of scale.
The company’s expansion into chip design, life sciences, financial services, and coding is seen as an indication that its next phase will be defined less by the number of people using ChatGPT and more by how deeply AI is integrated into professional workflows.
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