A Gallup poll published this week found that young Americans are gloomier about the job market than their peers in 86 other countries. Among 141 nations surveyed, only 43% of Americans aged 15 to 34 said it was a good time to find a job locally. Among Americans aged 55 and over, that figure is 64%.

That 21-point generation gap is the widest of any country in the survey. The US sits alongside China, Hong Kong, Norway, Serbia, and the UAE as one of only six countries where young people are at least 10 points more pessimistic about finding work than older ones.

The most pessimistic are college-educated young adults who haven’t yet secured full-time employment. They are a generation that followed the rules, studied hard, earned the degree, and entered a job market that had moved on without them.

What happened to entry-level hiring

In a Yale School of Management analysis, The Real Job Destruction from AI Is Hitting Before Careers Can Start, Jeffrey Sonnenfeld and his co-authors found that unemployment among recent graduates has climbed to nearly 6%, rising twice as fast as the rest of the workforce since 2022. In an unexpected finding, computer science majors are now finding it harder to land jobs than humanities graduates, according to New York Federal Reserve research cited by the same analysis.

The explanation is structural rather than cyclical. During 2023 and 2024, many companies deployed AI tools to handle work that had previously required junior hires. The effect wasn’t mass redundancy. Senior and mid-level employees, working with AI assistance, absorbed tasks that used to belong to teams of people below them. Entry-level job postings dropped 35% from early 2023. In some tech and data roles, the fall was 67%.

Wharton economist Judd Kessler put a number to the result: entry-level unemployment peaked at 13.3% last July. He called it the worst entry-level market in 37 years.

The AI story is messier than it looks

Peter Cappelli, Professor of Management at the Wharton School and director of Wharton’s Centre for Human Resources, spent the past year examining what AI adoption has actually done inside companies. His findings push back on the more alarming projections.

“It does not cut many jobs at all,” he told Fortune in January, “although in the long-run it is improving productivity a lot.” He found that most of the difficulty with AI isn’t technological. “The key thing is just how much work is involved in doing it,” he said. “It’s hugely expensive.”

His research also documented a pattern he called AI washing. A Harris Poll from early 2025 found that 74% of CEOs globally feared losing their job within two years if they couldn’t demonstrate AI progress. In response, roughly a third admitted to performative AI adoption, initiatives with little real business value behind them. “They’re pretending so they can say they’re doing something, right?” Cappelli told Fortune. 

He added a pointed warning about where people get their information: “If you’re listening to the people who make the technology, they’re telling you what’s possible, and they’re not thinking about what is practical.”

That gap between AI’s promised capabilities and its actual performance has already started to close, in ways that matter for graduates.

Forrester Predictions 2026 found that 55% of employers regret AI-driven layoffs. Among those who cut staff, 35% rehired more than half the people they let go. One in 3 spent more on restaffing than they saved from the cuts in the first place. Companies that eliminated customer service roles, content writers, and junior software engineers because AI could theoretically do the work have discovered that theoretically and operationally are different things.

What’s actually being hired for

CNBC reported in April that entry-level job postings calling for AI skills nearly doubled year-over-year. AI-related postings now sit 134% above 2020 levels. In January 2026 alone, 275,000 US job postings required AI skills.

That gap is an opening. The Yale SOM analysis found that graduates who can work with AI tools critically, evaluating outputs, identifying limits, and making judgments the model can’t make, are finding more traction than those who can’t. The demand for that combination, technical fluency plus the judgment to use it well, is real and growing.

The evidence is clearest at the postgraduate level. GMAC’s 2025 Corporate Recruiters Survey, based on responses from 1,108 hiring managers across 46 countries, found that 90% of employers planned to hire MBA graduates, with 3 in 4 expecting to take on the same number or more than the previous year. 

For Masters in Management graduates, median pay projections for the US job market rose from $80,000 to $90,000 in a single year. MBA median starting salaries now sit at $125,000, compared with $75,000 for bachelor’s degree holders.

AI fluency ranks as the single most important future skill in GMAC’s employer survey. The demand is for fluency, not deep technical specialisation. Columbia Business School’s analysis of employer expectations found that companies want MBAs who can translate AI-generated insights into business decisions, exercise sound judgment, and communicate clearly across teams. The capacity to interrogate AI outputs, to know when to trust the model and when to push back on it, is what separates candidates in hiring.

Why MBA and MiM graduates are in demand

Columbia’s own 2025 MBA class confirmed the picture. Offers, acceptances, and pay all rose, reversing two years of declining compensation, at a moment when the bachelor’s-level market was deteriorating.

Business schools are redesigning programs around this specific skill set. IESE Business School, which rebuilt its MBA curriculum for September 2026, described the goal as developing “the capabilities required to redesign work, lead human-AI systems and make decisions under technological uncertainty, while reinforcing the formation of sound managerial judgment.” Sound managerial judgment is what AI can inform but can’t supply.

36% of current college seniors report using AI daily. 49% use it weekly. That familiarity, if it develops into genuine competence rather than passive reliance, is something older workers often don’t have. Cappelli’s research suggests that most of the challenge in making AI work inside organisations is a management problem, not a technology one. At the MBA and MiM level, employers are actively paying for graduates who understand both the tools and their limits.

For graduates crossing the stage this spring, the data is genuinely hard. Entry-level postings down, competition up, and a job market that absorbed AI faster than it created new doors. The 21-point gap in the Gallup poll reflects something real: a generation trying to start in a market that restructured before they arrived. 

That market is shifting. Entry-level AI job postings have nearly doubled in a year. MBA and MiM salaries from top-tier programmes hit a record in 2025. The pessimism is justified by what’s behind these graduates. The graduates who understand what’s being asked for next are already pulling ahead.

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