Uber tells Axios it stabilized AI spending after earlier cost burn, as usage rose

Uber has moved to rein in the cost of running artificial intelligence after what it described as an initial burn of its information-technology budget, even as overall usage of AI continued to climb, according to a report by Axios.

The company’s comments, shared with Axios in connection with its August updates, suggest Uber shifted from a “build and spend” phase early in the year to a more controlled posture once more of its AI-related work moved into steady operation. Uber did not publicly provide new, specific dollar figures or unit economics for AI in the account described by Axios.

Axios reported that Uber’s AI spending has been “stabilized” after the first quarter, framing the change as a response to earlier expense levels rather than a decision to reduce AI deployment. In other words, the company’s approach appears to be about managing cost growth while continuing to use AI more widely.

The report also says Uber is seeing AI usage increase. That matters because AI systems typically become more expensive as demand rises, due to costs such as data processing, model inference, and supporting infrastructure. Uber’s claim of stabilized spending alongside higher usage implies it has found efficiency gains, shifted workloads, or adjusted pricing and scaling practices, though the details were not laid out in the Axios account.

What Uber did not disclose in the Axios report is as important as what it did. The company did not specify which AI categories were driving the early-year costs, whether the cost stabilization came from model optimization, changes in vendor arrangements, internal tooling, or a reduction in non-essential experiments.

Uber’s public footprint for AI is broad. Like many large consumer platforms, it can apply AI to tasks that include routing and dispatch, fraud detection, customer support automation, and personalization. When usage expands across these areas, costs often rise, but companies typically aim to offset that through better engineering efficiency and more targeted deployment.

In the Autos and Transport sector, competition for customer experience and operational reliability has increasingly pushed companies toward automation and AI-driven decision-making. However, margins are sensitive to technology spend, especially during periods when companies are also funding growth, reliability improvements, and infrastructure upgrades.

Analysts and investors are likely to watch whether Uber’s “stabilized” AI costs persist over subsequent quarters, and whether higher AI usage continues without visible margin pressure. Another key question is whether Uber’s cost control will show up in broader operating cost trends, and whether the company will eventually provide more granular disclosures about AI spending in financial reporting.