How many jobs is your state losing to AI? This dashboard tracks the cuts – and the cost

An AI-attributed job cut is a layoff that a company publicly connects to artificial intelligence. The label does not prove that AI directly replaced every affected worker. Companies may provide limited explanations, and there is no single definition of an AI-related cut. That makes the category useful but imperfect.

CAIS relies on announced layoffs and applies the national share of cuts citing AI to state totals. Its methodology does not require an executive to say, “we replaced a team with AI.” Instead, it captures the broader pattern of workforce reductions justified by AI while excluding cuts that are clearly unrelated.

This means the dashboard measures reported attribution, not confirmed technological obsolescence. The figures can show how companies are describing workforce changes, but they cannot establish every layoff’s exact cause. CAIS also warns that the dashboard excludes jobs created by AI, productivity gains, and re-employment.

The national dashboard reports just over 117,000 job cuts attributed to AI in 2026. It estimates that these cuts represent $8.5 billion in annual wage losses. These figures translate a headline about layoffs into a measure of income potentially removed from households and local economies.

The calculation is straightforward. CAIS multiplies the number of AI-attributed job cuts by the average annual salary. It uses layoff announcements compiled by Challenger, Gray, and Christmas, along with annual mean wage data from the Bureau of Labor Statistics. Population estimates come from the US Census Bureau.

The total is an estimate, not a direct payroll audit. The dashboard does not include jobs created by AI, productivity gains, re-employment, or secondary economic effects. It also depends on companies’ own statements about whether AI caused particular cuts. The figure therefore measures reported wage exposure, not the economy’s final outcome.

States with large technology and media sectors are experiencing greater estimated impact because AI-attributed cuts are not spread evenly. California illustrates this concentration, while Alaska shows a much smaller estimated effect. The dashboard’s state comparisons reveal how national layoffs can create very different local exposure.

California has just over 27,000 layoffs attributed to AI and an estimated $56 annual wage loss per person. Alaska has 138 estimated job losses and $14 per person. These are not exact counts of people actually laid off for AI in each state. CAIS applies each month’s national share of AI-citing cuts to each state’s announced job cuts.

The estimates therefore reflect both local layoffs and population size. California’s larger technology and media presence helps explain its higher exposure, while Alaska’s smaller reported number produces a lower estimate. The dashboard highlights geographic concentration, but it cannot prove every state-level cut was directly caused by AI.

The Wage Loss Dashboard converts announced layoffs into an estimated average loss per person. First, it estimates AI-attributed cuts in each state. Then it converts those cuts into annual wage loss using average wages. Finally, it divides the state total by the state population. The same process produces a national average.

For example, the dashboard estimates $8.5 billion in annual wage losses from just over 117,000 national AI-attributed cuts. Divided across the country, that equals $25 per person per year. California’s estimate is $56 per person, while Alaska’s is $14. The figures are presented alongside everyday expenses, such as a tank of gas.

This method describes a population-wide average, not an individual severance payment or verified household loss. State estimates are not observed AI layoffs. They use each month’s national share of cuts citing AI and apply it to that state’s announced job cuts, so the results remain estimates.

A layoff removes income from a household, but the effects can extend beyond that family. Workers with less money may reduce spending at grocery stores, small businesses, and other local services. Rent and other household costs still matter, while businesses can lose revenue from customers who have less income.

CAIS describes this as a ripple effect. One person’s layoff means less income for every business that person supports. Multiplied across thousands of workers and several industries, those individual reductions can create a larger economic weight. The dashboard expresses that potential effect in annual wage dollars and per-person averages.

The full outcome is uncertain. CAIS does not include secondary economic effects in its calculations, even though it discusses these local ripples. It also excludes re-employment, jobs created by AI, and productivity gains. The dashboard therefore shows a measure of possible wage pressure, not a complete forecast of community conditions.

Several forces could reduce the net impact shown by the dashboard. AI may create new jobs, raise productivity, or help companies expand. Workers who lose jobs may also find new employment. These outcomes could replace some lost wages or generate new income, although the dashboard does not measure them.

CAIS deliberately focuses on announced AI-attributed cuts and their estimated annual wages. It excludes jobs created by AI, productivity gains, re-employment, and secondary economic effects. Its national figure therefore captures one side of the transition: reported wage exposure associated with layoffs.

The future balance remains uncertain. The article notes that experts do not agree on whether, or how much, AI will stimulate the economy or outpace job displacement. Companies’ explanations of AI-related cuts are also imperfect. The dashboard can illuminate current reported costs, but it cannot determine whether broader gains will eventually outweigh them.

Automation changes the link between technology and work by allowing the same output with fewer worker hours, or more output with the same workforce. When productivity rises, companies may gain efficiency and potentially support higher pay or expansion. But if automated systems replace tasks, employers may need fewer workers in affected roles. The result depends on how gains and losses are distributed.

The article’s dashboard captures the displacement side. It counts job cuts companies attribute to AI and converts them into estimated lost wages. It does not measure higher productivity, new jobs, or re-employment. That leaves the relationship between improved output and worker income outside the calculation.

The article does not settle whether AI will raise wages, reduce employment, or do both across different occupations. It notes that experts lack consensus about whether AI will stimulate the economy or outpace job displacement. The dashboard therefore documents reported cuts rather than a final productivity verdict.