ICI Founding Humanity Partner Quoted in Bloomberg Law Article Re AI-Related Layoffs

Image from Bloomberg Law captioned "state efforts to track AI's role in layoffs is proving difficult." Photographer: Joan Cros/NurPhoto via Getty Images

ICI Founding Humanity Partner Andi Mazingo was recently quoted in Bloomberg Law’s Daily Labor Report in an article examining the growing difficulty regulators face in tracking AI-related layoffs.

The piece, “AI-Related Layoffs Test New York’s Ability to Track Job Losses,” explores New York’s first-in-the-nation attempt to measure the workforce impact of artificial intelligence. Under a recent update to the state’s Worker Adjustment and Retraining Notification (WARN) reporting system, companies conducting mass layoffs must disclose whether “technological innovation or automation” played a role in the job cuts.

The early data reveals a striking paradox.

More than 160 companies filed mass layoff notices with New York’s Department of Labor after the rule was introduced—including major firms integrating AI into their operations—yet none attributed the layoffs to automation or AI.

The result highlights a structural challenge for policymakers: when understanding technological change relies on institutional self-reporting, the true drivers of workforce transformation can remain difficult to observe.

As the article notes, companies often cite broader factors such as restructuring, economic conditions, or operational shifts rather than explicitly attributing job cuts to automation.

Mazingo’s comments in the piece reflect a broader concern that the problem is not simply technological change itself, but how institutions document and explain the decisions surrounding that change.

“Companies are using narratives about AI to frame layoffs in ways that are most likely to be well received by shareholders and potential shareholders. . . .”

The Bloomberg Law article ultimately raises a question that sits at the center of ICI’s work: if AI adoption is reshaping organizations gradually—through productivity shifts, internal restructuring, and changing expectations—how can existing regulatory frameworks capture that transformation?

Mazingo’s closing observation in the article captures the challenge succinctly:

“I don’t think litigation and existing legal structures are the solution. . . . It needs collaboration with the corporations themselves” to understand the nuances.

At ICI, we view this issue as part of a larger governance challenge.

Artificial intelligence is not just introducing new technologies into workplaces—it is exposing the limits of institutional systems designed for earlier industrial eras. Laws built to track factory closures and sudden plant shutdowns are now being asked to explain gradual, distributed changes in organizational decision-making.

As AI continues to reshape how work is organized, the ability to trace the reasoning behind institutional decisions may become as important as measuring the outcomes themselves.