Recent college graduates are unique—and not in a good way.
Unlike preceding generations, graduates aged 22-27 have a higher unemployment rate than the total working-age population. The prevailing hypothesis, of course, blames the rise of generative AI.
The problem with that alarmist narrative is that data doesn’t back it up. AI isn’t stealing new grads’ jobs—yet. That should be a relief, because America’s unemployment system isn’t ready for the disruptions that would come if it did.
Recent graduates’ unemployment rates have been drifting in the wrong direction since the 2010s, long before generative AI models hit the market. And many occupations with moderate to high exposure to AI disruptions are actually faring better over the past few years.
According to recent data for young workers, there has been employment growth in roles typically filled by those with college degrees related to computer systems, accounting and auditing, and market research. AI-intensive sectors like finance and insurance have also seen rising employment of new graduates in recent years.
Since ChatGPT’s release, sectors in which more than 10% of firms report using AI and sectors in which fewer than 10% reporting using AI are hiring relatively the same number of recent grads. It is true that employment for new graduates has fallen in sectors that use AI hyper-intensively, such as management, scientific, and technical consulting services. But plenty of low AI-use sectors have experienced similar hiring declines. So the data is mixed—and there is no clear link yet between higher AI use and worse outcomes for young workers.
A more pressing question is what will happen if and when that link does materialize. Extreme estimates project unemployment would rise to 20%. More modest (and realistic) projections say millions of American workers could be displaced over the next decade.
I don’t believe we are ready for either situation—or any major employment shock for that matter.
The U.S. unemployment system is riddled with systematic issues. During the Covid-19 pandemic, fraudsters siphoned off an estimated $135 billion from state and temporary federal unemployment programs. Years later, the average state still misses federal benchmarks for unemployment benefit payment timeliness and accuracy. Workers are forced to wait too many weeks before getting cash benefits and re-employment services. Meanwhile, 16% of benefit payments contained errors in 2024. These should be flashing warning signs for the next economic upheaval—whether it be driven by AI, a geopolitical shock, or any other unpredictable economic event.
Two fixes stand out.
First, we must correct how unemployment administration is financed. Since the early 2000s, the federal dollars that state unemployment agencies rely on to run their operations have shrunk by about one-third when adjusted for inflation. And the specific amounts provided to states change unpredictably year to year. This seesaw in funding makes it nearly impossible to plan hiring or invest in better fraud detection tools. Both the Department of Labor and Government Accountability Office know this. They have identified declining funding and “funding uncertainties” as a “historical issue” plaguing state agencies.
In fact, hundreds of millions of dollars earmarked for the administration of benefits tend to be repurposed each year—not because states don’t need the money—but as a result of a strict federal cap on the size of the federal administrative fund where the program resources are kept. Once the cap is hit, excess cash is shifted into a separate account for emergency benefits, leaving states without resources intended for program management.
This has real consequences. In the four years before the onset of the pandemic, nearly $5 billion that could have upgraded systems, hired field experts, and improved fraud controls was swept away. As a result, states entered the Covid-19 crisis with one hand tied behind their backs. The same dynamic could play out again.
We must also adjust how unemployment eligibility is verified. Before issuing benefits, state unemployment agencies need to know why a claimant left their job. But agencies generally question claimants’ employers after they file a claim—opening the door to long delays and mistaken payments. Response deadlines vary by state, and employers often answer late or not at all.
It would be a mistake to solely blame employers for these errors. The Department of Labor estimates that three-quarters of overpayments can’t be caught by agency procedures. They have found, however, that changing how employer information is gathered would reduce errors. A simple change—collecting separation information at the moment employees leave—could reduce payment mistakes by about 20% and speed up approvals. Congress should work to make that the standard.
If and when AI displaces workers, recent graduates and many others will feel pain. They should be able to trust that their transition between jobs will be smooth. That isn’t a reliable bet right now.
Without reforms to the unemployment insurance system, workers will be stuck between a rock and a hard place.
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