New York's New Law to Track AI Job Losses

Why Counting AI-Related Job Losses Is Harder Than It Looks

Artificial intelligence is taking people’s jobs. At least, that is what a growing number of layoff announcements seem to suggest. U.S. employers cited AI in connection with 10,970 announced job cuts in July, according to Challenger, Gray & Christmas. AI was the leading reason for announced cuts for the fifth consecutive month. Through June, employers had attributed more than 100,000 planned job cuts to AI in 2026.

Those numbers appear to tell a clear story about AI’s impact on employment. Look more closely, however, and a harder question emerges: How do we actually know when AI caused someone to lose a job?

New York is trying to find out.

Earlier this year, New York lawmakers passed legislation that would require many businesses to report annually on how AI affected their workforces. Assembly Bill A9581B passed both chambers in June and, as of this writing, has not yet been delivered to Gov. Kathy Hochul.

If signed, the legislation could create one of the more ambitious state efforts to measure what AI is actually doing to jobs. It could also reveal just how difficult that is to quantify.

New York Has Already Started Counting

The effort did not begin with A9581B. In her 2025 State of the State agenda, Hochul directed the New York Department of Labor to require businesses submitting Worker Adjustment and Retraining Notification, or WARN, notices to disclose whether a layoff was related to the employer’s use of AI. The administration described the initiative as a way to understand the effects of new technology through “real data.”

New York’s WARN Act generally requires covered businesses to provide 90 days’ notice before certain plant closings, mass layoffs, relocations and reductions in work hours. Adding AI to the state’s WARN reporting system created a relatively straightforward experiment: When employers conduct covered layoffs, ask whether AI played a role.

The early results are revealing. New York’s 2026 WARN data currently include one employment action that expressly identifies artificial intelligence as a reason. Nespresso filed a WARN notice concerning an action affecting 46 workers at its New York City location. The Department of Labor lists the reasons as “Relocation of Business, Artificial Intelligence.”

The entry raises as many questions as it answers. The public record does not indicate how much of the employment action resulted from relocation and how much resulted from AI. Nor does it explain whether AI directly replaced particular employees, enabled a broader restructuring or contributed in some other way.

Workforce decisions rarely have a single cause, which makes counting AI-related job losses complicated.

A Much Bigger Experiment

While WARN notices capture only the most disruptive workforce reductions, A9581B aims to track a far broader range of AI-related employment changes across New York. The proposed legislation would apply to any business operating in the state with more than 50 employees, as well as publicly traded companies. Notably, an earlier draft of the bill set the threshold at more than 100 employees, but lawmakers expanded its coverage before passage.

Under the bill’s framework, covered businesses would be required to file an annual report with the Department of Labor by March 1, detailing AI’s impact on their workforce during the preceding calendar year. The employment data submitted would need to include:

• Estimates of employees displaced or whose hours were reduced due to AI
• Employees hired or whose hours increased because of AI
• Previously filled positions the business decided not to fill due to AI

A critical phrase appears repeatedly throughout the bill: “due in full or in part” to the use of artificial intelligence. This language is designed to capture situations where AI is one contributing factor among several, not necessarily the sole cause of a workforce change.

Beyond raw employment numbers, employers would also need to disclose how they use AI, including its objectives, the extent of human oversight, frequency and duration of use, any application involving sensitive personal data, and measures in place for oversight and risk reduction. The Department of Labor would then aggregate all submissions and publish an annual report analyzing AI’s employment effects by sector, geography, and business size. Businesses that fail to submit the required reports could face civil penalties of up to $500 per day.

AI Can Change a Workforce Without a Layoff

WARN notices capture only one way technology can affect employment, and A9581B appears designed to address the many subtler shifts that never trigger a mass layoff notice.

Consider a common scenario: an employee leaves a company voluntarily, and AI allows the remaining team to absorb that person’s work. The company decides not to replace the departing employee. No one was laid off, and the departure itself would not ordinarily trigger WARN. Yet a position that once existed has disappeared. Did AI eliminate a job?

Similar effects could appear in other forms. An employer might reduce hiring, reallocate work among existing employees, or create entirely new positions to implement and oversee AI systems. Employees’ duties and work hours may also shift in ways that are difficult to quantify. None of those developments necessarily produces a mass layoff, and none would be visible in traditional WARN data.

A9581B appears specifically designed to capture these less visible workforce changes. The bill’s sponsor memorandum acknowledges uncertainty about whether AI-related displacement will occur through direct layoffs or through reduced hiring as workers leave their jobs. This distinction matters because the two scenarios produce very different visible outcomes, even when the net effect on employment is similar.

Much of the policy focus on workplace AI has centered on how employers use algorithmic tools to make decisions about applicants and employees. A9581B takes a different approach by asking employers to quantify how AI affected workforce size and composition after deployment. The challenge, however, is determining when and to what extent those changes occurred because of AI, particularly in workplaces where multiple factors influence hiring and retention decisions simultaneously.

When Does AI “Cause” a Job to Disappear?

Determining the precise role artificial intelligence plays in workforce reductions is rarely straightforward. Consider a hypothetical company that deploys generative AI tools across its operations. Productivity rises, but simultaneously, the broader economy softens and leadership initiates a cost-cutting program. Over the following year, 100 employees depart. The company backfills only 70 of those roles because managers determine that the remaining staff, augmented by AI, can absorb the extra workload.

So, how many of those 30 unfilled positions were eliminated because of AI? The honest answer is that it depends on how you look at it. Some of those roles might have vanished anyway due to the company’s financial struggles. In other cases, AI might have been just one factor in a wider restructuring decision, rather than the sole cause.

Assembly Bill A9581B attempts to address this complexity by asking employers for their own estimates and specifically capturing effects caused “in full or in part” by AI. While this approach acknowledges the messy reality of corporate decision-making, it also introduces a significant degree of judgment into the data collection process.

The result is that two companies experiencing nearly identical workforce changes could file vastly different reports. One might classify an unfilled position as AI-related because automation allowed other teams to absorb the duties. Another might label the same decision as routine attrition, a simple restructuring, or a cost-reduction measure. This means that how businesses characterize their decisions could become an influential variable in the final statistics.

Consequently, the methodology developed by the Department of Labor will likely be just as important as the reporting requirement itself. The bill directs the department to create standardized forms and processes, and it permits the agency to build out additional reporting rules. Clear definitions and a consistent methodology will be essential if the statewide data is to offer a meaningful, reliable picture of AI’s true impact on the labor market.

What Exactly Are We Counting?

The challenge of measuring AI’s impact is already evident in national data. For instance, Challenger, a firm that tracks job-cut announcements, reported that employers cited AI in connection with 54,836 announced cuts in 2025. By the end of June 2026, that figure had already climbed to 101,743 for the year. In July alone, employers attributed an additional 10,970 announced cuts to AI.

However, even Challenger has cautioned against interpreting these numbers as a precise measurement of cause and effect. Earlier this year, the firm highlighted the inherent difficulty of isolating AI’s specific influence on layoffs, especially as companies frequently discuss AI implementation and markets react to those announcements.

There is a fundamental difference between two questions: counting the number of job cuts that companies associate with AI versus determining how many jobs would have existed had AI not been introduced. The former is a matter of corporate attribution; the latter is a far more complex counterfactual analysis.

New York’s proposal seeks to get closer to that second, more difficult question by collecting detailed data on layoffs, hiring, hours, and vacancies directly from employers. Yet, even with this granular approach, the responsibility for making the underlying causal judgment still falls on the businesses themselves. The state’s final statistics will therefore be a reflection of thousands of individual decisions about what constitutes an AI-related workforce change, making the accuracy of the entire dataset contingent on the consistency of those judgments.

The Next Phase Of AI Employment Regulation

New York’s legislative approach signals a broader shift in how policymakers are grappling with the impact of artificial intelligence on the workforce. The proposed bill represents an attempt to move beyond theoretical debates and establish a concrete mechanism for tracking real-world changes in employment.

Context: A State Focused on AI’s Workforce Impact

Governor Hochul’s administration has pursued a dual strategy toward artificial intelligence, actively encouraging investment and adoption while simultaneously acknowledging the technology’s disruptive potential for workers. This balancing act has been a recurring theme in state policy. In April, the Governor characterized AI as a significant transformation in the labor market, announcing a state initiative dedicated to studying and addressing its effects on workers. That effort has already led to the expansion of AI training programs to more than 100,000 state employees, reflecting a commitment to preparing the public sector for the changes ahead.

The A9581B bill fits squarely within this broader initiative. Its core purpose is to fill a void that currently exists in the policymaking toolkit: the absence of a recurring, reliable dataset that captures how businesses themselves report AI’s influence on their employment decisions.

What the Data Could Reveal

If the bill is enacted and the reporting system functions as designed, New York could eventually produce a far more granular picture of AI’s workforce effects than traditional layoff statistics alone can provide. The data collected through this mechanism could help policymakers identify:

• Whether AI-related job displacement is concentrated in specific industries or geographic regions.
• Whether companies are creating new roles to complement the positions they eliminate.
• Whether the primary employment impact manifests as formal layoffs or as positions that are quietly left unfilled when workers depart.

This level of detail could meaningfully inform future policy debates across several domains, including worker retraining programs, education and curriculum development, regional economic development strategies, and the broader regulatory framework for AI itself.

The Central Challenge: Defining Causation

The practical value of this entire exercise will ultimately hinge on a single, deceptively simple question: can employers consistently and accurately determine when AI was the actual cause of a job’s creation, elimination, or transformation?

While the concept of measuring AI’s employment impact may sound straightforward in principle, the reality is considerably more complex. Employers must navigate the difficult task of attributing causation in an environment where multiple factors—market conditions, technological change, operational restructuring, and shifting consumer demand—often overlap. Deciding exactly what qualifies as an AI-related job change requires clear definitions and consistent judgment, and that challenge sits at the heart of whether this reporting system can deliver on its promise.

The success of New York’s approach, therefore, depends not just on legislative passage, but on the ability to create a workable framework for answering these difficult questions.