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AI is changing entry-level work, but the evidence does not show that entry-level jobs are universally disappearing. Some employers report stopping certain junior hires, while other surveyed employers expect AI to increase entry-level hiring. Employment data and job-posting analyses point to pressure in some settings and rising expectations in others. For job seekers, the practical shift is that employers may expect new hires to use AI while also showing judgment, communication, and the ability to check their work.
Is AI increasing or decreasing entry-level hiring?
There is no single answer across employers or industries. Surveyed employers report different plans, and plans are not the same thing as completed hires. Employment data can show what happened in a defined group of places and workers, but it does not necessarily identify AI as the sole cause.
| Evidence | What it found | How to read it |
|---|---|---|
| Gartner employer survey, July 27, 2026 | 22% of surveyed CHROs said at least one business leader in their organization had stopped hiring for entry-level roles because of AI automation. | This captures reported decisions at some organizations, not the share of all employers eliminating entry-level roles. |
| Strada Education Foundation survey, May 19, 2026 | Nearly three times as many of nearly 1,500 U.S. executives and senior talent leaders expected AI use to increase rather than decrease entry-level hiring in 2026. | This is an expectation about hiring, not a count of realized hires. |
| U.S. Census Bureau Center for Economic Studies working paper, April 2026 | The paper estimated a 12% decline in early-career employment in the most AI-exposed quintile of industry-state cells over the ten quarters after ChatGPT’s introduction. | The paper reports that hiring largely recovered by early 2025 against a smaller employment base and discusses possible confounders; the estimate does not establish AI as the only cause. |
| NACE employer evidence, 2026 | More than one-third of entry-level jobs were reported to require AI skills; 28% of employers said they seek early-career talent able to use AI at work. AI skills appeared in 16.5% of job descriptions in the spring survey, compared with 10.5% in the fall survey. | The Spring Update survey ran February 12–March 17, 2026, and had 185 total respondents. These are employer survey and job-description findings, not a universal requirement for every entry-level vacancy. |
The numbers describe different outcomes: hiring decisions, expectations, employment levels, and job requirements. They also cover different populations and use different definitions of AI exposure. Taken together, they suggest uneven change—not a reliable prediction that AI will either erase or create entry-level work everywhere.
Are entry-level jobs starting to disappear?
Some routine tasks that once gave new employees a way into an occupation may be automated or reassigned. But that is not the same as all junior positions disappearing. Employers may reduce hiring for particular roles, keep hiring but change the work, or ask new hires to take on tasks that previously came later in a career.
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The Census Bureau Center for Economic Studies paper examined early-career workers ages 22–24 using matched employer-employee administrative data. Its reported decline applies to the most AI-exposed quintile of industry-state cells, not to every young worker or every industry. The authors also discuss other possible explanations, including remote work and increased educational attainment. The finding is important evidence of pressure in a defined setting, but it should not be read as a clean, nationwide count of jobs lost solely to AI.
What is changing about the work and the requirements?
Employers are asking for AI use alongside human judgment
NACE’s 2026 findings indicate that employers are increasingly looking for early-career candidates who can use AI. The share of job descriptions mentioning AI skills rose between its fall and spring surveys. At the same time, the Spring Update included 185 respondents, so its findings describe participating employers rather than every employer or every occupation.
NACE also found a gap between some employers’ expectations and graduating seniors’ views: 31% of surveyed graduating seniors considered AI skills of little or no importance to their future career, and 50.5% said they were not currently building AI skills for the future. NACE president and CEO Shawn VanDerziel described the divide: “Employers are increasingly asking graduates to be ready for AI, while a significant portion of students are asking whether AI deserves a place in their work at all.” Those responses are views and reported preparation, not a measure of students’ actual ability to use AI.
Some junior roles are taking on more senior-level expectations
PwC’s 2026 Global AI Jobs Barometer analyzed global job postings and found that the most AI-exposed junior roles were seven times more likely than the least exposed junior roles to call for traditionally senior skills such as leadership. PwC also reported that “seniorised” entry-level roles grew 35% since 2019. These figures reflect PwC’s job-posting definitions and comparisons; they describe changing advertised requirements, not a direct count of workers promoted, hired, or displaced.
This shift can make the entry point less straightforward: routine starter tasks may shrink while expectations for communication, problem-solving, or ownership rise. A job posting can signal what an employer wants, but it cannot show whether the employer provides the training, supervision, and authority needed to meet those expectations.
What should early-career job seekers do?
AI experience is a sensible part of preparation where it is relevant to the work, but no particular skill guarantees a job. Focus on being able to show how you use tools responsibly and verify the result, rather than simply listing a tool name.
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- Learn tools relevant to your target work. Practice using AI for appropriate tasks, such as organizing information or drafting, while respecting confidentiality and employer policies.
- Check and explain your work. Be ready to describe how you verified facts, corrected errors, and decided what should not be automated.
- Show the skills behind the output. Use examples that demonstrate communication, judgment, collaboration, and follow-through, especially where job descriptions ask for responsibilities that sound advanced for a new hire.
- Ask about the role’s support. In interviews, ask what onboarding looks like, who reviews early work, and how new employees learn tasks that require judgment.
For personal finances, the evidence supports preparation, not panic: hiring conditions vary, and the available figures do not provide a dependable forecast for an individual’s job prospects or income. Avoid treating a broad AI prediction—or a single employer’s hiring decision—as a reason to assume your career path is closed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should employers change when AI removes starter tasks?
Entry-level work has often served two purposes: producing useful work and giving new employees a safe way to learn. If automation removes routine tasks, employers need to deliberately replace the learning opportunities those tasks provided. Gartner recommends redesigning early-career roles and building support structures as routine development opportunities diminish. PwC likewise calls for rethinking mentoring and training.
- Map which tasks AI can handle and which still require a person to decide, review, or take responsibility.
- Give junior employees structured practice on more complex work, with clear review and escalation paths.
- Provide regular feedback and mentoring instead of assuming that employees will learn informally from tasks that have disappeared.
- Assess candidates on the ability to use AI appropriately and evaluate its output, not just on whether they have encountered a particular tool.
Gartner Director Analyst Kaelyn Lowmaster warned that “Organizations that respond by cutting their early career talent pipelines altogether risk creating significant workforce challenges down the road.” That is a warning about the consequences of removing development pathways, not a guarantee that any single organization will face a particular outcome.
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