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Uber Lays Off 10% of Customer Service Staff as AI Takes Over More Support Tasks

Uber Lays Off 10% of Customer Service Staff as AI Takes Over More Support Tasks.

Uber has laid off about 10% of its customer service workforce as part of a broader restructuring aimed at expanding the use of artificial intelligence across its support operations.

The company confirmed the job cuts on Thursday, joining a growing list of technology firms replacing routine customer support work with AI-powered tools.

Although Uber did not disclose the exact number of employees affected, the reduction is expected to impact a significant number of workers, given the company’s operations in more than 70 countries.

The ride-hailing giant has increasingly relied on AI to manage common customer requests, including trip disputes, refund claims, lost items, and account-related issues. These repetitive tasks can be handled more quickly and at a lower cost by AI systems, allowing the company to reduce operating expenses.

Uber’s decision reflects a wider trend across the technology industry.

Earlier this year, Klarna said its AI assistant was performing work previously handled by about 700 customer service agents, while Duolingo reduced contractor roles as it expanded its use of AI. Salesforce also announced it would slow software engineering recruitment because AI was taking over parts of the workload.

The layoffs have sparked mixed reactions online, with many users questioning whether AI can effectively resolve complex customer complaints.

While AI performs well with straightforward requests, critics argue that more complicated issues—such as disputed charges, lost valuables, account suspensions, or complaints requiring human judgment—still need real customer support representatives.

Many also expressed concern about the growing impact of AI on employment, as more companies automate jobs traditionally performed by human workers.

For Uber, the challenge will be balancing cost savings with customer satisfaction. While automation can improve efficiency, customers facing complicated or unusual issues may still expect access to human support when AI cannot provide an adequate solution.

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