Since March 2025, New York State has required companies filing mass layoff notices to answer one question: did “technological innovation or automation” drive this workforce reduction? One year in, 162 companies covering 28,300 affected workers have all answered no. Not one checked the box.
That’s not a coincidence. It’s exactly what you’d expect from a voluntary mechanism with no enforcement teeth.
This article breaks down why the current system is built to produce this result, names the specific legislative fixes on the table, and spells out what meaningful enforcement would actually require. If your company has 100 or more employees federally — or 50 or more in New York State — this regulatory trajectory is heading your way.
For broader context on AI-washing and why layoff disclosure fails, see the pillar page in this cluster.
What does the WARN Act currently require companies to disclose about AI-driven layoffs?
The federal WARN Act requires companies with 100 or more employees to give 60 days’ advance notice before a mass layoff. No reason required. No AI-specific provisions.
New York’s mini-WARN law sets a higher bar. Ninety days’ notice, a 50-employee threshold, and a broader definition of covered layoffs. In January 2025, Governor Kathy Hochul directed the NY Department of Labour to add an AI disclosure checkbox to WARN forms. The checkbox asks whether “technological innovation or automation” contributed — and if yes, employers need to specify whether that means AI, robotics, or software modernisation.
There are three structural problems with this approach. First, it’s voluntary self-reporting with no audit. Second, the $500-per-day maximum civil penalty applies to notice failure, not to AI checkbox omission. Third, the attribution language is vague enough that non-disclosure is easy to rationalise. As NY DOL Commissioner Roberta Reardon acknowledged: “defining an AI-related layoff is challenging.”
What did zero out of 162 disclosures in New York actually reveal?
Between March 2025 and early 2026, 162 companies filed WARN notices with the NY DOL. Zero checked the AI checkbox.
Amazon filed 660 New York WARN notices citing economic conditions — while CEO Andy Jassy had publicly linked AI benefits to future job cuts. Goldman Sachs and Morgan Stanley both attributed cuts to economic reasons in their filings while investor communications referenced AI productivity gains. Nationally, Challenger, Gray and Christmas found AI or automation drove over 48,400 job cuts in 2025 — the second-most cited reason for layoffs.
Zero AI attribution across 162 New York filers is a structural outcome. The evidence this disclosure regime was designed to surface cannot be surfaced when companies have every incentive to stay quiet and no reason to speak up.
Why does a $500 daily fine fail to change corporate disclosure behaviour?
Do the maths. Maximum annual WARN exposure is $182,500. Goldman Sachs reported approximately $53 billion in net revenue in 2024. A full-year WARN violation represents 0.000345% of that. It doesn’t register in any legal risk budget.
More importantly: the $500/day applies to notice failure, not AI checkbox omission. There is currently zero specific penalty for omitting AI as a layoff cause. Checking the box creates a documented admission that employment lawyers can anchor discrimination claims against. Voluntary self-reporting in a domain with asymmetric incentives will always produce biased data. Every time.
What do Goldman Sachs and Morgan Stanley’s WARN filings tell us about the gap?
Goldman Sachs and Morgan Stanley are among the most sophisticated compliance organisations in the United States. Neither checked the AI box — not because they missed the requirement, but because they rationally weighed the options.
Goldman’s “OneGS 3.0” linked workforce decisions to AI productivity in investor communications. The WARN filing cited economic conditions and annual talent review. Morgan Stanley’s 260 New York cuts were attributed to automation by an unnamed Bloomberg source; the WARN filing made no mention of it. Wharton management professor Peter Cappelli puts it plainly: “The headline is, ‘It’s because of AI,’ but if you read what they actually say, they say, ‘We expect that AI will cover this work.’ Hadn’t done it.”
That earnings call / WARN filing gap is the AI-washing mechanism in its most documented form.
For detailed analysis of the companies at the centre of the disclosure gap, see the case study coverage in this cluster.
What would the Harry Bronson bills change and why does tying enforcement to grant eligibility matter?
Assembly Labour Committee chair Harry Bronson introduced two bills in January 2026 to address the enforcement gap. They go after the problem from different angles.
Bill A9581 — annual AI impact disclosure — would require businesses with more than 100 employees to file annual estimates on unfilled roles attributable to AI and how many employees’ hours changed due to automation. A prospective disclosure model that builds a record over time.
Bill A9533 — WARN Act expansion — would require companies with at least 50 employees to give 90-day written notice before implementing AI causing significant workforce cuts. Violators face $10,000 fines and lose access to New York State grants, loans, and tax incentives for five years.
The key innovation in A9533 is the type of consequence, not the fine size. Losing state grant and tax incentive eligibility for five years targets financial benefits companies actively budget for — orders of magnitude more impactful than any fine. You can’t calibrate a fine to the size of Goldman Sachs. But you can take away grant and tax incentive access, and that hits differently.
For the broader accountability context, see the series overview on AI-washing and why layoff disclosure fails.
What would actually create honest AI layoff disclosure?
Three changes would meaningfully shift the disclosure calculation.
Enforcement with real financial consequences. The A9533 benefit-loss model is the right direction. Fine-based penalties can’t be set high enough for large companies without proportionality challenges; benefit-loss doesn’t need to be calibrated to company size.
Mandatory independent verification. The OSHA analogy is instructive here: workplace safety disclosure changed because companies couldn’t control the audit record. Applying this to AI disclosure would require the NY DOL to develop audit capacity — board minutes, deployment timelines, headcount changes. Neither A9533 nor A9581 includes this yet.
Proactive disclosure triggers. A9581’s annual reporting model is a step in this direction. The EU AI Act classifies AI in employment contexts as “high risk,” requiring pre-deployment documentation by August 2026 — directionally closer to A9581 than the current WARN checkbox.
If you’re deploying AI in ways that could reduce headcount — customer support automation, task redistribution, role elimination — start building a deployment impact record now. What was deployed, when, against which roles. That documentation will be defensible under any compliance framework that follows.
For what this means for WARN Act compliance obligations and practical workforce planning, see the compliance guidance in this cluster. For the full picture of AI-washing and why layoff disclosure fails, including the broader evidence base this regulation was designed to surface, see the series overview.
Frequently Asked Questions
What is the NY WARN Act AI disclosure checkbox?
Added to New York WARN forms in March 2025 under Governor Hochul’s direction. It asks whether “technological innovation or automation” contributed to the workforce reduction. Voluntary self-reporting, no independent audit. Zero of 162 companies who filed WARN notices in the following year checked the box — confirmed by the NY DOL as of end of January 2026.
Why have companies not checked the AI box on NY WARN filings?
No penalty for non-disclosure — only the underlying WARN notice failure carries a $500/day fine. Checking the box creates a documented admission usable in discrimination claims. No legal downside to omission, significant legal downside to disclosure. That’s a pretty easy calculation.
What are Assembly Bills A9533 and A9581?
Both introduced by Assemblyman Harry Bronson in January 2026. A9533 requires 90-day notice before implementing AI causing significant workforce cuts; violators lose access to New York State grants and tax incentives for five years. A9581 requires annual estimates of roles unfilled and hours changed due to AI — a prospective longitudinal record.
Why is the $500 daily WARN fine not enough to change disclosure behaviour?
Maximum annual exposure is $182,500 — 0.000345% of Goldman Sachs’s 2024 net revenue. Zero specific penalty exists for omitting AI as a cause. The litigation exposure from checking the box exceeds the penalty from not checking it.
Does the WARN Act apply to my company if I deploy AI tools that reduce headcount?
Federal WARN applies to 100 or more employees; New York WARN to 50 or more. Obligations attach to mass layoff events. Under A9533/A9581’s trajectory, obligations would extend upstream to AI deployment decisions before layoff events. Start maintaining deployment-impact records now.
What is the difference between genuine AI-driven layoffs and AI-washing?
Genuine AI displacement involves AI systems demonstrably replacing task categories. AI-washing involves attributing layoffs to AI in investor communications while filing WARN notices citing economic reasons — the Goldman Sachs and Morgan Stanley pattern.
Is the zero-disclosure finding specific to New York?
New York is the only US state with an operational AI layoff disclosure checkbox. Challenger, Gray and Christmas found AI drove over 48,400 job cuts nationally in 2025, yet zero New York WARN filings reflect it. Zero disclosures doesn’t mean zero AI-driven layoffs — it means zero companies had a reason to disclose.
What does the EU AI Act require regarding AI and workforce disclosure?
The EU AI Act classifies AI in employment as “high risk,” requiring pre-deployment documentation by August 2026 — pre-deployment regulatory compliance rather than post-event disclosure. Directionally closer to A9581’s annual reporting model than the current WARN checkbox.
What can AI displacement policy learn from Trade Adjustment Assistance?
Trade Adjustment Assistance (TAA), established in 1962, struggled with proving “caused by trade” attribution — the same causal attribution problem now plaguing AI disclosure. Congress let TAA expire in 2022 without resolving it. Bronson’s A9581 annual reporting approach could build the workforce-impact record that any future AI Adjustment Assistance programme would need.
