Unemployment rates among recent graduates could climb above 30 per cent because so many early career routine tasks will be performed by AI agents, ServiceNow CEO Bill McDermott has said. Speaking to CNBC, McDermott emphasised the severity of the challenge, pointing to a stark gap between current conditions and what lies ahead.
The Federal Reserve Bank of New York put the unemployment rate for recent college graduates at the end of 2025 at about 5.7 per cent. Yet the underemployment rate of 42.5 per cent was the highest level since 2020. McDermott's projection represents a tripling of joblessness rates over just two years. According to McDermott, "for the non-differentiating roles, so much of the work is going to be done by agents. It's going to be challenging for young people to differentiate themselves in the corporate environment."
The warning arrives as companies move quickly to automate routine white-collar work. McDermott also claimed that ServiceNow's AI platform could handle 90 per cent of the tasks that humans provide in customer service. This is not merely hypothetical; across industries, businesses are slashing costs and cutting jobs with the help of new AI tools. Last month, Block announced plans to cut nearly half its workforce as AI automates more work. Meanwhile, software firm Atlassian, which has seen its stock dive 54% this year on AI disruption fears, said this week it would lay off about 10% of its workforce to support AI investments.
Yet McDermott's assertion sits amid broader expert warnings about the structural damage such hiring cuts could inflict. Tech analyst group Gartner previously warned that disproportionate cuts to graduate jobs could have a long-term impact on workforce structure. Highlighting the danger of "pipeline choke," Gartner pointed out that mundane bits of work on which junior staff once cut their teeth would no longer be available in the same numbers, reducing the supply of more experienced staff in years to come. "When a senior staff delegates to AI some of the work that juniors used to do... that approach captures value, but it can stall your growth, so pair it with a robust talent development strategy, or risk choking your future pipeline," said Gabriela Vogel, Gartner VP analyst.
Research from Stanford University suggests the trend is already underway. The analysis revealed a 13 per cent relative decline in employment for early-career workers in the most AI-exposed jobs since the widespread adoption of generative-AI tools, even after controlling for firm-level shocks. A Harvard University study tracking 62 million workers across 285,000 US firms found junior positions "shrinking at companies integrating AI" since 2023. The researchers warn that AI is "eroding the 'bottom rungs' of career ladders."
The economic logic driving these decisions is straightforward. If AI can replicate codified knowledge but not tacit knowledge, AI will automate jobs requiring codifiable (textbook) knowledge but complement jobs demanding experiential tacit knowledge. The distinction between codifiable and tacit knowledge further suggests that AI may substitute for entry-level workers but augment the efforts of experienced workers. This creates an immediate incentive for firms to cut junior roles while retaining senior staff.
However, the long-term consequences warrant caution. The current model of white-collar career progression involves taking an entry level job right out of school and doing codifiable tasks while slowly learning the tacit knowledge to become an experienced worker. Firms are going to find that AI is making this method of employee development cost-ineffective, at least in the short run. Of course, leaving new employees off the job ladder is not sustainable in the long run. In the long run, AI adoption will require rethinking how entry-level employees gain experience on the job.
Compared to previous technological revolutions, experts say AI is chipping away at many white-collar jobs, including coding and marketing roles, and allowing companies to reduce hiring and improve productivity with fewer workers. Yet unlike past automation waves, the speed of AI adoption is compressing timelines and leaving little room for workforce adjustment. The question for policymakers and business leaders is whether intentional investment in alternative training pathways can emerge before McDermott's forecast materialises.