Uber disclosed that artificial intelligence adoption has reached nearly complete penetration across its engineering team of 4,500–5,000 people, resulting in each engineer roughly doubling their coding output.
The company is using these productivity gains to slow hiring in engineering rather than expand headcount proportionally, supporting a broader cost-efficiency drive.
While Uber frames the effort as still in early stages, CEO Dara Khosrowshahi indicated the company has been deploying AI tools for years and expects agents to account for roughly 10% of committed code.
What happened
Uber reported during an earnings call that AI adoption has reached nearly 100% among its 4,500–5,000 engineers, with each engineer doubling their coding output. The company is using this efficiency gain to moderate (slow) the pace of new engineering hires.
Why it matters
The doubling in output per engineer allows Uber to achieve more software development without proportional headcount growth, directly supporting the company's cost-efficiency program. CFO Balaji Krishnamurthy emphasized the company is measuring results carefully and not "getting ahead of our skis," suggesting Uber sees AI-driven productivity as a near-term lever for controlling labor costs.
What to watch
CEO Dara Khosrowshahi noted in May that Uber had been using AI tools "for years and years" and predicted 10% of its code was "committed" by agents. The company describes itself as still "early" in its AI rollout, signaling that further gains in engineering productivity may be possible.
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Uber's disclosure reveals a mature, company-wide embrace of AI-assisted coding in one of the world's largest engineering organizations. The nearly universal adoption across 4,500–5,000 engineers and the doubling of output per person mark a significant shift in how the company develops software. Rather than treating AI as an experimental pilot, Uber has rolled it out broadly and is now reaping measurable productivity gains—gains substantial enough to allow the company to moderate hiring rather than scale headcount with business growth.
The CFO's careful framing—that the company is measuring impact thoughtfully and "not getting ahead of our skis"—suggests Uber is aware of earlier corporate claims about AI productivity that later proved overstated. At the same time, the CEO's comment that the company has been using AI tools "for years and years" and expects agents to write roughly 10% of code indicates this is not a sudden overnight shift but the culmination of an incremental, multi-year effort to integrate AI into the development workflow. The company's characterization of the rollout as still "early" implies that the productivity gains may deepen further as tooling and engineering practices mature.
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