
OpenAI's latest data reveals a significant and widening gap between enterprises that have adopted AI aggressively and those moving more cautiously.
The top 10% of AI users consumed 8.3 times as many output tokens per user as the median company in June, a dramatic jump from a 2.6× gap in January.
This acceleration is being driven partly by a shift from conversational AI tools to agentic platforms designed for autonomous work, suggesting that leading adopters are moving beyond asking AI questions to deploying it for independent task execution.
What happened
OpenAI data shows the disparity between heavy and moderate AI users among its business customers has grown sharply. Firms in the top 10% of AI usage consumed 8.3 times as many output tokens per user compared with companies in the middle of the pack in June—up from a 2.6× gap in January.
Why it matters
The widening gap signals that enterprises making aggressive AI commitments are pulling ahead of slower adopters in their ability to automate and scale operations. This split is likely to deepen competitive advantages for early movers, while laggards risk falling further behind in operational efficiency.
What to watch
OpenAI's customers are shifting usage toward Codex, the company's agentic coding platform, rather than the traditional ChatGPT interface—a sign that businesses are moving from AI for answering questions to AI for autonomous task execution.
OpenAI's latest internal data provides a clear picture of diverging adoption patterns among its business customers. The gap between the top 10% of AI users and the median company has expanded dramatically in a six-month window. In January, firms in the top decile utilized 2.6 times the median token consumption per user. By June, that ratio had ballooned to 8.3×, indicating a sharp acceleration in the pace at which leading adopters are outpacing the rest of the enterprise market. The data was shared first with Semafor and reflects consumption across OpenAI's entire business customer base. The shift in which tools companies are using offers insight into what this acceleration means in practice. Collectively, OpenAI's business customers are now consuming more tokens on Codex, the company's agentic coding platform, than on the traditional ChatGPT interface. This pivot is significant because it signals a move away from interactive, human-directed AI queries toward AI systems designed to operate more autonomously. According to Ronnie Chatterji, OpenAI's chief economist, businesses are embracing AI for "work that's more doing rather than asking." In other words, rather than using AI to answer questions or draft content on demand, enterprises are building AI into their operations to execute tasks independently and continuously. This distinction helps explain both the widening gap and its rapid acceleration: early adopters are not just using AI more; they are embedding it deeper into their production systems, where it generates far more tokens through continuous autonomous operation than traditional question-and-answer interfaces ever could.
The widening gap between enterprise AI adopters reflects a fundamental shift in how companies are deploying artificial intelligence. In January, the ratio between top-tier and median users was already 2.6×, indicating early stratification. By June, that gap had more than tripled to 8.3×, suggesting that businesses with aggressive AI strategies are not merely adopting the technology faster but accelerating their token consumption at an exponential rate compared to the rest of the market. This trend aligns with OpenAI chief economist Ronnie Chatterji's observation that companies are moving toward AI for "work that's more doing rather than asking"—a shift evidenced by increased usage of Codex, the company's agentic coding platform. Where ChatGPT represents conversational, question-and-answer interaction, Codex enables autonomous task execution, indicating that leading adopters are moving beyond exploratory AI use toward embedding AI directly into business workflows for continuous, independent operation.
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