The AI fight brewing inside The New York Times

How newsrooms should use AI — or if they should at all — has been a recurrent debate within the media industry over the last several years. Increasingly, these rules are being hammered out at the bargaining table between unions and publishers. Right now, employees at *The New York Times *are gearing up for a fight.

The AI fight brewing inside The New York Times

Unionized tech employees at the *Times *say the company is violating their contract by using AI tools to monitor employee performance.

Unionized tech employees at the *Times *say the company is violating their contract by using AI tools to monitor employee performance.

Unionized staff with the Tech Guild say Times management has refused to provide the union with information related to how the company has used AI, its plans for AI use in the future, and how it will affect employees’ jobs and workflow. (The union filed an unfair labor practice charge earlier this month.) The Tech Guild, a NewsGuild of New York unit of around 700 software engineers, designers, product and project managers, and data analysts, also filed grievances saying *Times *management violated their collective bargaining agreement when it started using two internal AI tools that track and evaluate employee performance and activity.

One of the AI tools, called DX, advertises itself as an engineering productivity tool that lets companies track employees’ output, generative AI use, and efficiency, among other metrics. DX was originally announced internally as a way to improve the developer experience, says Ben Harnett, a software engineer at the *Times *and chair of the unit’s generative AI committee. The goal, at least according to *Times *management, was to measure the company as a whole. Over the last few months, though, the DX data has become more personalized, with benchmarks being applied to individuals, Harnett says.

“Now people in disciplinary situations are suddenly having read back to them, ‘You only did one [pull request] per week, per whatever, and that’s 25 percent below industry standard,’” Harnett says. He is concerned that the blanket metrics flatten all work the unit members do and erase the nuance of engineering into an opaque set of metrics that can be used against staff in disciplinary or performance review settings. The metrics don’t correlate to quality of work or the actual number of features an employee delivers, Harnett says.

“All this [data] reasonably could be expected to … help us understand how