ZDNET’s key takeaways
- AI-related job losses impact local community economics.
- The CAIS tracker puts these losses in dollars and cents.
- Understanding how AI will change the economy still involves guesswork.
Fears of how AI will impact the economy and job availability are running rampant after a few years of steady layoffs at major companies. Much of that fear can be attributed to labs and CEOs juicing their own relevance, but some job cuts have been explicitly tied to AI’s rapid acceleration.
The Center for AI Safety (CAIS) maintains a tracker that keeps tabs on how these changes are impacting family wages.
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The Wage Loss Dashboard includes a map of where layoffs have occurred since January 2026 (it was just updated this month to include September data) and a drop-down that shows data for the entire country or an individual state. As of now, the dashboard finds just over 117,000 job cuts attributed to AI nationally this year — $8.5 billion in estimated annual wage losses.
The Wage Loss Dashboard multiplies the number of AI-attributed job cuts by the average annual salary to estimate annual wage loss. According to the dashboard, wage estimates “begin to show what those cuts can mean for households and local economies.”
The dashboard also displays the estimated average wage loss per person based on that layoff data, presented relative to everyday expenses, like a tank of gas. For the country as a whole, it’s $25 per person per year. Not much, but the numbers shift by state: In California, for example, it’s $56. That’s thanks to a much higher concentration of job cuts (just over 27,000 layoffs attributed to AI). Alaska, on the other hand, claims only 138 job losses ($14 per person).
Those aren’t the exact numbers of people laid off for AI in California and Alaska this year, however. As CAIS explains, “The dashboard estimates AI-attributed cuts for each state, converts them to estimated annual wage loss, then divides by state population to estimate the average loss per person. State estimates are not observed AI layoffs. We apply each month’s national share of cuts citing AI to that state’s announced job cuts.”
From layoffs to dollars and cents
The dashboard uses a few sources of data: monthly reporting from Challenger, Gray, and Christmas, which compiles layoff announcements from across the US by state as well as national job cuts credited to AI, and data from the Bureau of Labor Statistics on every state’s annual mean wage. CAIS also refers to population estimates from the US Census Bureau.
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In 2025, the Challenger, Gray, and Christmas reports identified over 54,000 job cuts that companies publicly linked to AI. That translates to one in 20 layoffs from last year. But tracking job losses alone is a more abstract metric than what those layoffs mean in monetary terms to the people experiencing them.
“There has been significant news coverage of job loss from AI, but it can be hard to quantify how these cuts affect the average person’s finances,” CAIS wrote of the project. “This dashboard aims to help illuminate the monetary costs of AI on American families and communities.”
As CAIS points out, the impact of layoffs shows up in ripples across grocery stores, small businesses, rent, and other local costs.
“One person’s layoff means less income for every business they support. Multiply that effect across thousands of workers in multiple industries, and you start to see the real economic weight of AI displacement,” the researchers behind the project wrote. “In addition, AI-attributed job cuts aren’t spread evenly. States with large tech and media sectors are experiencing greater impact.”
A few caveats: CAIS noted that this dashboard doesn’t include “jobs created by AI, productivity gains, re-employment, or secondary economic effects.” That’s partly because experts don’t have a consensus on whether or how much AI will stimulate the economy or outpace job displacement. AI-attributed layoffs are themselves murky: The data here are job cuts companies themselves said were tied to AI. Without a single definition for what that means or complete transparency from companies, it’s not always 100% provable that a layoff was the direct result of obsolescence or replacement spurred by AI.
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“We don’t require an executive to say ‘we replaced a team with AI.’ This is a moderate standard, deliberately: it captures the broad pattern of AI-justified workforce reductions while excluding clearly unrelated cuts,” the Alliance for Secure AI wrote of its methodology.
Other trackers like Jobloss.ai, which is run by the Alliance for Secure AI, also catalog job loss data by company, date, and how explicitly companies cited AI around those layoffs. Because of how amorphous these data can be to track, Jobloss.ai only logs layoffs that meet certain criteria: at least 100 jobs in one fell swoop, accompanied by some mention of AI (or proxy terms, like “automation” and “productivity”).
The dashboard aims to make the data we do have more accessible — not to shame any particular industry or company for its AI adoption choices.
“AI is a technology being adopted by companies for many reasons,” CAIS wrote. “Our goal is to make the costs of that adoption visible, so workers, communities, and policymakers can respond with real information instead of guesses.”
Automating remote labor
The Wage Loss Dashboard also includes a section on how well AI is completing remote freelance tasks. Using data from the Remote Labor Index (RLI) benchmark CAIS co-developed, which tests agents on real-world freelance tasks like web development and video production, CAIS shows how quickly agents are evolving.
Also: The top AI fear for 6,000 tech pros isn’t losing their jobs
“When RLI launched, the best AI agent completed just 2.5% of projects at professional quality. Eight months later, that number has risen to 15.8%,” researchers wrote. As of now, AI can fully automate almost 16% of remote tasks, a percentage that’s doubling every 3.7 months. Claude Fable 5 comes in on top, followed by OpenAI’s GPT-5.5.
But that doesn’t mean AI is gobbling up those projects from human freelancers. RLI measures what AI models are capable of doing, not what they’re actively taking on. That said, CAIS maintains alarm about what it sees as a trend: the convergence of AI-attributed job cuts with increasingly work-capable agents.
While legislation can’t explicitly prevent companies from opting for AI systems over hiring humans in certain cases, recent laws in a few states, including the tech-sector-heavy state of California, aim to make sure firing and other disciplinary decisions aren’t completely automated by AI systems.
