AI Is Changing Entry-Level Jobs. Here's What to Do If You're Just Starting Out

Let's start with the number that's been making rounds in career forums and LinkedIn posts: entry-level hiring at the 15 biggest tech companies fell 25% from 2023 to 2024, according to a SignalFire report . That's not a blip. That's a structural shift.

And if you're fresh out of school, or switching careers, or trying to land your first real job in 2026, you've probably felt it. More applications going nowhere. Roles that used to hire juniors now asking for two to three years of experience. Internship programs quietly shrinking. The job market for new graduates is at its most pessimistic since 2020, according to the National Association of Colleges and Employers.

So what's actually happening? And more importantly, what do you do about it?

I'm not going to tell you everything is fine. It's not, for specific roles. But I'm also not going to tell you the story is simple, because it isn't. The honest picture is more nuanced and more navigable than the headlines suggest.

What AI Is Actually Replacing (And What It Isn't)

Here's the thing most articles get wrong. They talk about AI replacing "jobs" when what's actually happening is more precise than that. AI is replacing tasks. And the tasks it's best at replacing are the ones that make up the bulk of entry-level work.

Think about what a first-year analyst does. They gather data, format reports, write first drafts, do research, transcribe meeting notes, handle customer inquiries from a script, process documents. These are all codifiable tasks. Tasks where the knowledge required is the kind you learn from textbooks, training manuals, or onboarding decks.

Research from the Dallas Federal Reserve puts it clearly: AI can replicate codified knowledge but struggles with tacit knowledge, the understanding that comes from lived experience and judgment built over years. The implication is uncomfortable but important. AI substitutes for entry-level workers, who have book learning but little experience, while it complements experienced workers, who have something AI can't easily learn from a PDF.

This is why the employment data breaks down the way it does. Workers aged 22 to 25 in AI-exposed roles have seen employment fall 6 to 20%. Workers aged 30 and above in the same roles have seen employment grow 6 to 13%. The technology isn't hitting everyone equally. It's hitting people at the start of their careers hardest.

The roles with the most pressure right now: data entry clerks (95% automation risk), customer service representatives (projected 5% employment decline through 2033), bank tellers (15% projected decline), paralegals (80% risk by 2026), medical transcriptionists (4.7% projected decline), and junior content writers in marketing (job postings down 50% projected by 2030).

The roles that are holding or growing: software development (17.9% projected growth through 2033), skilled trades like electrical and plumbing work (40% of young graduates are now choosing these paths specifically because they can't be automated), healthcare hands-on roles, teaching, and anything requiring sustained human judgment in unpredictable environments.

The Experience Trap

Here's the part nobody talks about enough. AI isn't just replacing entry-level work. It's creating a new kind of career ladder problem.

The traditional path was: get an entry-level job, do the foundational work, build experience, move up. The entry-level role was how you learned. It was how you converted your textbook knowledge into the tacit knowledge that made you valuable at the next level.

AI is collapsing that rung.

Companies are discovering they can use AI to handle the foundational tasks that junior roles used to do, which means they need fewer juniors. But they still need experienced people who have the judgment to direct the AI, catch its mistakes, and do the work that AI genuinely can't do. So they're hiring fewer entry-level people and expecting more from the ones they do hire.

Hugo Malan from Kelly Services called this a "tectonic shift" in how roles are defined. The expectation for new hires has changed. Where companies once expected to spend a year training someone on the basics, they now expect faster time-to-value. Industry experience and demonstrated skills are the top factors employers now look for, ahead of GPA or the name of your school.

The entry-level bar has moved up. The junior role now expects what the mid-level role used to expect.

The Skills That Actually Matter Now

So if the traditional path is harder, what's the alternative path?

The data points in a pretty consistent direction. Professionals with demonstrated AI skills command salaries up to 56% higher than peers in identical roles without them. Jobs affected by AI are seeing required skills evolve 66% faster than they were in 2024, according to PwC. The workers who are thriving are the ones who figured out how to work with AI rather than around it or instead of it.

But "learn AI" is too vague to be useful. Here's what that actually means in practice.

AI fluency is not the same as AI tool usage. Using ChatGPT to write emails is not a skill that differentiates you. Understanding how to structure prompts to get reliable outputs, knowing when AI output needs to be verified and how to verify it, using AI to accelerate research and then adding genuine analysis on top of it, building workflows that combine AI tools with human judgment: that's what employers mean when they say they want AI-fluent candidates.

If you're figuring out which high-income skills are actually worth building right now , AI fluency sits near the top of that list precisely because it compounds. It makes every other skill more productive.

Demonstrated experience beats credentials. Mike Roberts from Creating Coding Careers put it bluntly: recent graduates may not be ready to ship code on day one, but AI can. The way you beat that comparison is by having experience you can show, not just knowledge you can talk about. Portfolio projects. Freelance work. Open source contributions. Apprenticeship programs. Real things you built or improved, with outcomes you can describe.

This is why the advice to "just get certified" is less reliable than it used to be. Certifications prove you absorbed information. They don't prove you can do anything with it. If you're thinking about how certifications actually fit into career building , the lesson from that space applies broadly: credentials that come with demonstrated practical skill outperform credentials that are purely knowledge-based.

Soft skills are becoming hard differentiators. This sounds like corporate speak, but the data backs it up. AI is very good at information processing and very bad at the things that require reading a room, building trust, navigating conflict, and making judgment calls in ambiguous situations. The skills that were always considered "nice to have" are becoming the actual differentiators at the entry level.

We covered why soft skills are now more valuable than technical skills in depth, and if you're starting your career right now, that piece is worth your time.

Which Roles Are Still Worth Pursuing at Entry Level

Given all of this, here's the honest read on where entry-level opportunities still exist and where they're shrinking.

The roles that are still accessible and growing for new entrants: AI prompt engineering and evaluation, data analysis with visualization skills, cybersecurity (the talent gap here remains massive), UX research, technical writing and documentation for AI products, skilled trades, healthcare support roles, and sales.

Notice something about that list. Most of these roles share a characteristic: they involve judgment, physical presence, or human relationships in ways that make full automation impractical, expensive, or socially unacceptable. A cybersecurity analyst making real-time decisions under pressure during an active breach can't be fully automated. A UX researcher figuring out why users behave unexpectedly requires human observation skills. A skilled electrician working in a building that doesn't match the blueprints needs adaptability that no current robot can match.

The roles getting squeezed: junior content writing, data entry, basic customer service, administrative assistance, entry-level coding that involves mostly boilerplate, basic financial analysis, and report generation.

If you're already in one of the squeezed roles or training for one, this isn't a reason to panic. It's a reason to think now about where you want to be in three years and what skills bridge from where you are to where the demand is.

What to Actually Do

Here's the practical part. Not general advice. Specific things worth doing.

Build in public. The single biggest differentiator for new entrants right now is having real work people can see. GitHub repositories, a writing portfolio, a project you built and deployed, before-and-after analyses of problems you solved. Anything that shows your reasoning and output to someone who doesn't know you yet. Employers are skeptical of resumes because AI has made it easy to write impressive-sounding ones. Tangible work is harder to fake.

Get industry experience before you need it. Apprenticeship programs, internships, volunteer work at organizations in your target industry, freelance projects for small businesses, part-time work while studying. The experience premium in AI-exposed jobs has gone up, not down. The faster you can convert your book knowledge into real judgment, the better positioned you are.

Find the humans-in-the-loop. Every AI-automated workflow still needs someone who understands what the AI is doing and can catch when it's wrong. Quality control, AI output review, prompt refinement, workflow design: these are real jobs that are growing precisely because AI is being deployed at scale. They're not always listed under obvious job titles, but they exist in almost every industry where AI has been adopted.

Don't sleep on trades. The data here is striking. 40% of young university graduates in 2025 chose careers in plumbing, electrical, construction, and similar trades, with researchers explicitly citing automation resistance as a major factor. Average wages for electricians in the US are $61,000 with strong upward potential, and journeyman electricians in high-cost-of-living areas can earn over $100,000. These roles require physical presence, real-time problem solving, and human judgment. They're not going anywhere.

Think about where AI needs a human partner. The most durable career positioning in 2026 isn't to be AI-resistant. It's to be AI-complementary. Find the places where AI output is useful but needs human direction, judgment, or oversight, and build your skills there. That's where the experience premium is going up, not down.

The Bigger Picture

By 2030, the World Economic Forum estimates 170 million new jobs will be created while 92 million are displaced, for a net gain of 78 million jobs. That's the optimistic aggregate number. The honest caveat is that those 78 million new jobs aren't going to the same people who lost the 92 million displaced ones, at least not automatically. The workers trapped between displacement and reskilling are the real challenge.

If you're starting your career right now, you're entering at an unusual moment. The old path is harder. But the new path is actually clearer than it's ever been for anyone paying attention: build demonstrable skills, get real experience faster than traditional education timelines allow, develop the judgment and human capabilities that AI genuinely can't replicate, and position yourself as someone who makes AI more useful rather than someone who competes with it directly.

If you're trying to make a full switch into a new field and figure out what that transition actually costs and takes, we covered the realistic picture of getting into tech from a non-tech background which walks through timelines, real costs, and the paths that actually work.

The entry-level market is harder. It's not impossible. And the people who understand what's changing and adapt to it early are going to be in much better shape than the people who are waiting for it to go back to how it was.

It won't go back. But that's not the same as it being hopeless.