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AI is putting pressure on some entry-level hiring and routine junior tasks, but current evidence does not show that it has broadly eliminated entry-level jobs. The concern for new graduates is that the tasks employers automate can also be how beginners gain practice, feedback, and a path to more complex work. That risk is real, but uneven—and it depends on whether employers replace the learning as they redesign the work.
What the evidence says about entry-level hiring
Different studies measure different things: what employers say they are doing, what happened to employment and earnings, and how exposed particular jobs are to AI-related change. Those measures point to pressure in some settings, not a single economy-wide count of jobs lost to AI.
Employers report fewer junior tasks and some hiring changes
| Source and scope | Reported finding | How to read it |
|---|---|---|
| Gartner, survey of 110 heads of HR in the fourth quarter of 2025; findings published July 27, 2026 | 22% said at least one business leader in their organization had stopped hiring for entry-level roles because of AI automation. | This is a share of surveyed HR respondents reporting a decision by at least one leader in their organization—not a finding that 22% of organizations, jobs, or entry-level positions disappeared. |
| D2L/Morning Consult, U.S. survey of 546 HR leaders fielded in January 2026 | 30% said their talent-acquisition strategy was shifting toward fewer entry-level workers and more mid-level talent using AI for the same tasks. 56% reported fewer basic tasks delegated to early-career staff, and 48% said AI raised productivity expectations for entry-level jobs. | These are respondents’ reports about organizational strategies, task assignments, and expectations—not observed national job-loss rates. |
The two surveys suggest that some employers are changing how they staff and assign work. They do not establish how many jobs have vanished, whether plans were implemented, or whether AI alone caused the changes.
Administrative data show weaker outcomes in some highly exposed groups
Two 2026 working papers from the U.S. Census Bureau’s Center for Economic Studies use regression-adjusted comparisons. They identify groups with higher exposure to AI, rather than counting positions that employers confirmed were eliminated:
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| Group studied | Estimate | Important qualification |
|---|---|---|
| Early-career workers aged 22–24 in the most AI-exposed quintile of industry-state cells | Employment declined 12% over the ten quarters after ChatGPT’s introduction. | The paper says hiring rates had largely recovered by early 2025, but against a smaller employment base. It also discusses earlier trend shifts and possible explanations, including remote work and rising educational attainment in exposed occupations. The estimate does not prove AI alone caused the decline. |
| Graduates in the most AI-exposed decile of college majors | The estimated likelihood of initial employment fell by 5 percentage points, and full-quarter initial earnings fell 13%. | These are regression-adjusted estimates from the September 2026 working paper. The effects attenuate as graduates move further from labor-market entry; exposure and timing do not establish that AI caused every difference. |
The findings matter to graduates because initial employment and earnings can affect a household’s ability to cover expenses, build savings, and manage debt. But they should not be read as a forecast for every new graduate: the estimates are concentrated in the most exposed groups, and the papers describe associations with AI exposure, not a proven single cause.
Why automating junior work can also remove a training route
Entry-level work is more than a first rung on a pay scale. It can give a new employee repeated practice, feedback on mistakes, familiarity with how an organization makes decisions, and gradual responsibility for harder assignments. Basic research, drafting, checking, data preparation, and routine customer or administrative work may be candidates for automation; those assignments can also be where beginners learn how to do the job.
Jobs and Skills Australia describes entry-level roles as pathways where graduates and other new entrants develop capabilities, including roles with significant on-the-job training. Its 2025 analysis says employers it consulted reported shifts toward experienced workers and changes in junior recruitment and work. Some described junior staff checking AI output. The organization also notes that official labor collections often do not separately monitor entry-level roles, which makes broad comparisons difficult.
In its January 2026 U.S. survey, D2L/Morning Consult found that 58% of surveyed HR leaders worried that reducing entry-level roles because of AI could contribute to a shortage of qualified senior leaders within five years. The same survey found that 74% said their organization did not yet have active upskilling or development programs to replace on-the-job learning lost to automation. These figures describe respondents’ concerns and reported program status; they do not prove a future leadership shortage.
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Which entry-level work is most exposed?
Exposure is about how AI may change tasks, not a verdict that a job or occupation will disappear. Risk is higher when a role consists largely of routine tasks that AI can perform with limited human judgment. Work that requires context, accountability, communication, or judgment may instead be reshaped around using and checking AI.
- More exposed: roles where a large share of beginner assignments involve repeatable, clearly specified tasks that can be automated.
- More complementary: roles where AI can help with parts of the work but people still need to interpret results, verify accuracy, understand context, communicate with others, or take responsibility for decisions.
- More uncertain: roles where employers are still testing tools or where the work combines automatable tasks with substantial human interaction and judgment.
The International Monetary Fund’s 2026 discussion note distinguishes between high AI exposure with low complementarity to human work and high exposure with high complementarity. That distinction matters: high exposure alone does not mean a job is likely to be lost. The World Economic Forum reported in June 2026 that more than one in three young workers globally are employed in occupations with medium to high exposure to AI-driven task change. This is a measure of potential task change, not a claim that those workers will be displaced.
The International Labour Organization’s June 2026 review provides a counterweight to the most alarming predictions: it found large-scale job displacement remained limited in the evidence it reviewed, while identifying risks to younger workers’ employment opportunities and to job quality and work organization. Taken together, these sources support concern about uneven changes to work and early-career pathways—not a claim that all graduates or all entry-level roles face the same outcome.
How employers can preserve learning while adopting AI
Automating a task need not mean removing the opportunity to learn. Employers can redesign early-career roles so that efficiency gains do not leave new hires without practice, guidance, or a route to more complex work.
Map tasks before cutting roles
Separate the tasks AI can perform from the parts that require human judgment, context, communication, or accountability. Then decide which safe, meaningful assignments early-career staff can own, including reviewing AI output where that develops sound judgment rather than merely shifting error-checking onto juniors.
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Make development part of the job design
Replace routine assignments that no longer provide practice with structured learning: progressively harder work, clear feedback, mentoring, team support, internal apprenticeships, or rotations. Gartner recommends analyzing how AI changes tasks, moving appropriate work into early-career roles, and providing development support such as tools, guidance, and peer connections.
Teach people to work with and evaluate AI
AI literacy should include knowing when a tool is useful, how to check its output, and when to seek human review. D2L’s recommendations also include structured learning, internal apprenticeships and rotations, AI-enabled training simulations, and skills-based hiring that values critical thinking and communication alongside AI literacy.
These approaches have trade-offs: a firm may gain short-term efficiency by assigning fewer basic tasks, but it still needs a way for employees to build the capabilities required for more senior work. The key test is whether a redesigned role includes deliberate practice, feedback, and increasing responsibility—not simply a higher output expectation.
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Graduates cannot control employer hiring plans or the pace of automation, and the evidence does not identify one universally safe major or occupation. They can, however, look for roles where learning is explicit and build skills that help them contribute when AI handles part of a task.
- Ask prospective employers how new hires are trained, who reviews their work, and how responsibilities grow over the first year.
- Look for concrete signs of development—mentoring, rotations, supervised projects, or a clear progression from basic to more complex assignments—rather than relying on a job title alone.
- Build AI literacy alongside communication, critical thinking, and domain knowledge; being able to verify an AI-generated result is more useful when you understand the subject it concerns.
- When evaluating an opportunity, distinguish a role that uses AI to extend what a junior employee can learn from one that expects AI to replace both the tasks and the training.
For personal finances, the practical implication is uncertainty rather than a uniform prediction of lower pay: the Census estimates concern the most exposed early-career groups, and the graduate study finds effects that lessen as workers move further from entering the labor market.
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