Uber executive questions AI spend as budget exhausted in four months
The ride-hailing giant has spent its annual artificial intelligence budget early in 2026, prompting internal scrutiny over the trade-off between technology investment and workforce reductions.
Uber president Andrew Macdonald has raised concerns regarding the company’s artificial intelligence expenditure, noting a significant disconnect between rising token consumption and tangible productivity gains or consumer features. Speaking in an interview with Rapid Response, Macdonald stated that the company is finding it increasingly difficult to justify its investment in AI technologies, particularly as the firm reportedly exhausted its annual AI budget within the first four months of 2026.
Macdonald highlighted that while underlying usage metrics for tools such as Claude Code are trending upwards, a direct correlation to shipping useful features remains unclear. He noted that it is currently difficult to draw a line between rising usage statistics and the delivery of specific improvements, such as a 25 per cent increase in useful consumer features. He suggested that the link may become clearer over the coming quarters and years, but emphasized that the current data does not yet support the expenditure.
The financial scale of these investments is substantial. Uber spent $3.4 billion on research and development efforts in 2025, representing a 9 per cent increase from the previous year. This surge in spending coincides with a strategic shift in workforce planning. Earlier this month, Uber chief executive Dara Khosrowshahi indicated that the company was offsetting increasing AI investments by hiring fewer human employees, a move that Macdonald now describes as harder to defend without clear evidence of productivity returns.
Macdonald argued that the company must begin to explicitly weigh token consumption and associated costs against headcount reductions. He stated that if the firm cannot draw a direct line between AI usage and the volume of useful functionality shipped to users, the trade-off between technology costs and reduced human headcount becomes increasingly difficult to justify to stakeholders.
The comments from Uber’s leadership underscore a broader industry challenge as technology firms grapple with the return on investment for large language models and automated development tools. While the trajectory of AI adoption remains steep, the immediate translation of these capabilities into measurable operational efficiency or consumer value continues to be a point of internal debate at major tech companies.
