AI Workforce PREPARE Act Mandates Layoff Transparency and Boosts Job Training Data

The AI Workforce PREPARE Act is essentially the government hiring a full-time, highly paid crystal ball gazer to figure out what Artificial Intelligence is going to do to our jobs over the next 10 to 20 years. This bill directs the Department of Labor (DOL) to stop guessing and start producing hard data on which jobs are at risk, which new skills are needed, and how existing federal job training programs measure up. The goal is simple: give workers, students, and educators the kind of detailed, forward-looking information they need to make smart career choices in an AI-driven economy.

The AI Forecaster Gets Hired

This legislation is all about getting the right data and the right people to analyze it. Title I gives the DOL the authority to quickly hire up to 20 highly qualified AI and data science experts—bypassing some of the usual hiring bureaucracy—to staff an AI Workforce Research Hub. Think of this as bringing in a SWAT team of tech-savvy analysts whose only job is to track the labor market impact of AI. They’ll be producing recurring analyses and running pilot projects to track detailed data on how workers move between jobs, especially in occupations most affected by automation. For a software developer or a factory worker wondering if their skills will be obsolete in five years, this is the data that could guide their next certification or career pivot.

New Rules for Layoffs: The AI Disclosure Clause

Title II introduces a major transparency change by amending the Worker Adjustment and Retraining Notification (WARN) Act. Currently, if your employer lays off a large group of people, they have to give notice. Under this bill, if AI is a substantial factor in a mass layoff, the employer must disclose it in the notice. That means they have to specify the type of AI used and estimate the percentage of job losses linked to it. This is a big deal for workers; it moves the conversation from vague 'restructuring' to a clear statement about automation. However, this is also where things get a little squishy. Defining 'substantial factor' and accurately quantifying the 'percentage of job losses' due to AI will be a massive headache for employers and could lead to legal challenges or inconsistent reporting. Still, the intent is clear: workers deserve to know if a robot took their job.

Better Training, Better Forecasts

Titles III and IV focus on making sure job training programs actually prepare people for the future, not the past. The DOL must identify at least 15 AI-sensitive occupations and publish detailed employment forecasts for them every year, looking out 2, 4, and 8 years. To keep these predictions sharp, the bill establishes a public prize competition run by the National Science Foundation, offering cash prizes for the most accurate short-term labor market forecasts. This crowdsourcing of intelligence aims to tap into the best minds outside of government. Furthermore, states and local workforce boards will be required to use these new AI-driven forecasts when deciding which industries and occupations they deem 'in-demand' for their job training programs. This should mean that federal training dollars are spent teaching skills that are actually needed in the future, helping both the newly laid-off and those just entering the workforce.