Giant robot flicking tiny man. Ai technologies and unemployment problem concept.

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Is AI the death knell for entry-level jobs, or is it a convenient scapegoat? Youth unemployment has surged to a four-year high, leading many to assume ChatGPT is replacing college graduates. However, a closer look at the timeline reveals a fatal flaw in that theory, suggesting AI can be merely an alibi.

One thing is clear: U.S. unemployment among youth (ages 16-24) is the highest it’s been in four years. Youth unemployment stood at 10.9% in February 2021, compared with 10.5% in August 2925, according to the Federal Reserve Bank of St. Louis. In comparison, the overall U.S. unemployment rate was 4.3% in August 2025.

In March 2023, Goldman Sachs predicted that AI would destroy or degrade 300 million full-time jobs. This came on the heels of OpenAI’s launch of ChatGPT and DALL-E, which they touted at the time as capable of “generating content that is indistinguishable from human-created output and to break communication barriers between humans and machines.” Goldman Sachs’ research, leveraging data on job tasks in the U.S. and Europe, estimated that two-thirds of existing jobs would be exposed to AI automation, for which generative AI could conceivably substitute up to one-fourth of them.

Recently, several studies have been released. One paper from two Harvard economics Ph.D. candidates, “Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data,” concluded, “Following adoption, junior employment declines sharply in adopting firms relative to non-adopters, while senior employment remains largely unchanged. The junior decline is concentrated in occupations most exposed to GenAI and is driven by slower hiring.”

A study from Stanford, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence,” came to a similar conclusion. It sought to determine the effects of labor markets exposed to gen AI and concluded: given the “widespread adoption of generative AI, early-career workers (ages 22-25) in the most AI-exposed occupations have experienced a 13% relative decline in employment.” Similar conclusions from the World Economic Forum and MIT have been published in their versions of the AI-killed-junior-jobs report.

Jing Hu is a seasoned researcher and the author of 2nd Order Thinkers. Her recent analysis, “Are Junior-Level Jobs Really Killed by AI?” weighs the Harvard students’ results against broader economic data. She questioned, “Can a 62-million data study make a wrong and dangerous assumption?”

According to Hu, the study reveals that the “junior hiring crash started in 2022, before many CEOs knew what prompt engineering was. In March 2022, an inflation surge, driven by pandemic supply and demand imbalances, marked the start of the Federal Reserve’s rate hikes, raising rates by 25 basis points from near zero to 0.25-0.5%. But that was just the beginning.” She adds, “Over the next 16 months, the Fed executed 11 consecutive rate hikes, driving rates to 5.5% by July 2023.”

She describes this cumulative spike as the “most aggressive tightening campaign in decades,” effectively ending the longest period of cheap money in modern history.

The timeline for AI as a causal instrument is misleading, as she notes, “The junior hiring collapse started in Q1 2023, when the rate shock began its stride, months before most companies had even figured out what to do with generative AI and LLMs. AI is likely a convenient and more strategic sounding excuse, adding marginal pressure only from late 2024 onward, long after the real damage was done.”

Between 2022 and 2025, Hu reports that tech job posts “fell 36% below pre-pandemic levels as of mid-2025.” However, postings for roles with less than one year of experience experienced a larger 50% decline between 2019 and 2024.

I reached out to Harvard students Guy Lichtinger and Seyed Mahdi Hosseini Maasoum, the authors who cited the decline in junior roles concentrated in roles exposed to gen AI. They emphasize that their “identifying strategy was to compare adopters and non-adopters” by looking at LinkedIn job postings of gen AI integrator roles.” They add, “Those adopting firms tended to be much bigger and less affected by monetary policy compared to small firms… and therefore unlikely the big adopting firms were more responsive to interest rate hikes.” They tested this concern by extending their analysis back to 2015. They conclude: “The Federal Reserve implemented a similar rate hike that year and we find no differential effect in our data.”

Below is a chart of the Federal Reserve interest rate hikes between 2015-2025. Hu points out that between 2015-2018, the rate hikes were gradual (0.25% increments over four years). There was no significant economic event that distinguished this period. In comparison, between 2022 and 2023, the vertical slope showed a steeper rate hike (0%-5%) over the year.

She explains that in 2015, companies were able to adjust their balance sheets; however, in 2022, “the cost of capital exploded overnight. CFOs reacted to the shock differently. The brutal 2022 hike from near-zero levels since the pandemic forced immediate, drastic cost-cutting measures (firing juniors). This did not happen in 2015.”

Fortune’s recent article, “Top economists and Jerome Powell agree that Gen Z’s hiring nightmare is real—and it’s not about AI eating entry-level jobs,” cited a slowing economy and hiring restraint as the main drivers of junior job declines. UBS chief economist Paul Donovan points to varying global employment levels among young workers: “UK unemployment falling steadily, while participation by young Japanese workers is near all-time highs. It seems highly implausible that AI uniquely hurts the employment prospects of young US workers.”

He asserts that it is tempting to “blame technology for the plight of the Gen Z would-be entry-level worker,” calling it an “ever-popular dystopian scenario.” He argues that the more plausible explanation is that “the U.S. pattern more convincingly fits a broader hiring freeze narrative, affecting new entrants to the workforce.”

First to Go, Last to Return: The Cycle of Entry-Level Collapse

Hu is adamant, “Every economic shock, every time… the same pattern. Junior hiring craters. It stays depressed. And when it comes back, if it comes back, it never returns to the old baseline.”

She alludes to the economic shocks of 1990-91 and the 2001 dot-com bubble. In both cases, high unemployment resulted and persisted while the economy recovered. After the dot-bomb of 2001, Hu adds, “what followed was a prolonged and severe contraction in employment. Entry-level positions disappeared as companies retained senior staff, eliminating junior roles.”

According to the Economic Policy Institute, this 2001 bubble burst was marred by a slower recovery, leading to a “tougher economy for highly educated workers and resulting in large declines in employment opportunities for new graduates.” In the downturns between 1979 and 2002, the report concluded that entry-level jobs had been disproportionately hurt by the weak labor market.”

The Fortune article also recognized that recent graduates are “empirically hit the hardest” during “no fire, no hire” periods and The Great Recession between 2007 and 2011, “when entire industries froze hiring,” college graduates felt the effects more deeply.

A recent Economist podcast noted that post-pandemic hiring was significantly above pre-pandemic levels. Tech hiring in 2021 and 2022 was twice the hiring levels. A similar trend followed white-collar jobs in professional and information services, for which college grads are typically hired — the very professions that have experienced more stagnant growth today. Indeed, the job market platform noted varied contributing elements, “that the earlier hiring boom, broader economic conditions and interest in AI could explain this year’s crash in demand for tech workers.”

Hu mentions that since 1980, every major economic shock has resulted in “1) disproportionate cuts to entry-level hiring, 2) permanent downward shifts in youth labor force participation, 3) long-term wage scarring for affected cohorts and 4) failure to recover to pre-shock hiring levels even during expansions.”

State of AI in 2025: AI Adoption is Poor

The impact of a new technology is not felt immediately. Transformation typically takes years. For gen AI to have an indelible impact on the job market, let alone be a disruptor on junior employment, organizations need to firmly adopt it. The recent McKinsey Report on the State of AI in 2025 claims that the technology is still very much in the early stages of adoption and “Nearly two-thirds of respondents say their organizations have not yet begun scaling AI across the enterprise.” In addition, the curiosity with agents remains just that, as 62% indicate they are still experimenting with the technology.

The report indicates that most organizations are still at the stage of value creation, with only 39% reporting EBIT (earnings before interest and taxes) impact: “most have not yet embedded them deeply enough into their workflows and processes to realize material enterprise-level benefits.” In the coming year, the outlook for AI varies: 32% expect decreases, while 13% expect increases in AI use. The remainder sees no change.

MIT’s similar report on the State of AI in 2025 references the “Gen AI Divide,” highlighting the “billions of dollars invested in enterprise gen AI pilots” that have failed to yield value.

Klaas Ardinois is a seasoned CTO and tech advisor who has witnessed the emergence of technology through the rise of mobile and tablets, blockchain, Web3 and LLMs. Ardinois says that hiring as a direct sign of adoption is nebulous. Adoption levels vary and if a company has enabled Copilot for a few employees, they can also claim they have adopted AI. He explains, “In my experience, the companies that are seriously looking at this follow a well-trodden path of finding prototype opportunities. Organizations, where teams are free to experiment, may prove out some value. And if those show results and are successful, they shift to more live implementations and much larger rollouts.”

He suggests that adoption among larger firms is much more gradual but argues, “in reality, those prototype and R&D activities are not where juniors are being hired, but rather, are where the most experienced and skilled people are.”

Ardinois added that two firms that were more vocal and jumped in with both feet in AI — Shopify, whose CEO committed a full integration of AI into its culture, and Klarna, which went all in on AI, fired over 700 staff only to rehire them back — did not necessarily stop hiring juniors when they announced their AI ambitions, “To make the decision that AI is delivering enough value to no longer hire juniors, in particular, would require substantial evidence that work is getting done that juniors would otherwise do,” Ardinois said. “There is rarely evidence of this after one quarter after the release of a tool that isn’t even embedded in the organization.”

There have been more ebbs in the journey of LLMs than significant milestones and with the billions in circular deals that hyperscalers, cloud providers and AI chip providers spend on each other, the illusion of massive revenue is enough to overvalue each entity. Pundits believe this AI bubble is going to burst.

In the meantime, speculation of a recession looms. The Trump tariffs have set in motion a domino effect that has increased operational costs, led to sudden shifts in consumer spending and compounded the unemployment drama. Data from the October Jobs Report from Challenger, Gray and Christmas shows U.S. employers announced 153,074 job cuts, up 183% from September. The report attributes this surge to “softening consumer spending,” corrections after the pandemic hiring boom and “rising costs.” As it concludes, this is a period of “belt-tightening” and “hiring freezes” in response to economic conditions, not because of AI’s influence.

Time will tell whether AI will shatter the future of work and wreak havoc on a global economy highly dependent on labor. That inevitability will be a future state. In the meantime, breathe a sigh of relief that, at least for now, AI is not eating entry-level jobs.