I keep thinking about one line from a recent paper: the early-career hiring landscape has shifted dramatically in the last year. When I read those findings, it hit me personally — friends who graduated with computer science degrees are suddenly competing in a different market. The phrase AI entry-level jobs feels less like a topic and more like a life change for people starting their careers.
What the data shows
A new paper from three Stanford economists examined anonymized data on millions of employees across tens of thousands of firms, and the results are hard to ignore. They looked closely at age cohorts and specific occupations, and the clearest signal was that younger workers — especially those in roles where generative AI can automate routine tasks — have experienced a sharp divergence in employment trends since late 2022.
“There’s a clear, evident change when you specifically look at young workers who are highly exposed to AI,” said Erik Brynjolfsson, one of the paper’s authors.
To put it bluntly: among software developers aged 22 to 25, headcount was nearly 20% lower this July versus its late 2022 peak. That’s a big swing for one of the most popular majors and career tracks of the last decade. When the entry-level pipeline constricts like this, it ripples through student decisions, internship markets, and even what professors emphasize in classrooms.
Why young developers are most affected
There are a few straightforward reasons why early-career workers feel the impact more acutely than seasoned hires:
- Nature of tasks: Entry-level roles often include repetitive coding, debugging, documentation, or writing test cases — precisely the kinds of tasks that generative-AI tools can accelerate or automate.
- Cost and risk: Employers trying to optimize budgets may prefer fewer junior hires if a combination of experienced staff plus tools yields similar short-term output.
- On-the-job learning: Junior roles traditionally serve as training grounds. If firms cut those roles, they reduce the number of places newer workers can gain real-world experience.
All of this is amplified in fields like software development, where tooling can replace parts of a person’s output without a dramatic reorganization. That doesn’t mean the job goes away entirely — it often changes. But for someone trying to break into the field, the opportunity to learn by doing has diminished in many places.
Navigating AI entry-level jobs as a grad
If you’re staring at graduation and wondering how to make yourself marketable, the situation feels daunting but not hopeless. Here are practical, human-centered steps that can help you stand out beyond what an AI tool can produce.
- Show your decision-making: Tools can generate code or answers, but they can’t explain why you chose one architecture over another or how you weighed trade-offs under constraints. Build a portfolio that highlights that judgment.
- Prioritize projects with messy context: Real products live in messy ecosystems — legacy systems, unclear requirements, or ambiguous user needs. Demonstrating success in those areas signals adaptability.
- Invest in communication skills: Early-career roles that require cross-team collaboration, stakeholder management, or clear product thinking are harder to automate.
- Seek internships and apprenticeships: Smaller programs and bootcamps that emphasize mentorship can substitute for lost entry-level headcount at larger firms.
- Learn to use the tools: Paradoxically, being proficient with AI-assisted coding, prompt engineering, or model evaluation can be a differentiator — it shows you can partner with the tools.
If you’re chasing AI entry-level jobs, consider emphasizing internships and contract work that let you demonstrate impact quickly. Employers often hire contractors or short-term contributors and then convert them to full-time when budgets permit.
Where to find AI entry-level jobs
Knowing where to look makes a huge difference. Here are some smart places to target:
- Startups and small companies: They still need scrappy developers who can wear many hats, and they often value demonstrated output over pedigree.
- Industry-specific roles: Healthcare, education, and manufacturing are using AI but often need domain knowledge combined with technical skills.
- Remote contract platforms: Short gigs let you build a track record and often lead to repeat clients or referrals.
- Research labs and academic groups: If you like deep learning or model evaluation, contributing to papers or open-source projects raises your profile.
What employers could do differently
From the hiring side, there are creative ways to preserve pathways into the workforce even as tools automate tasks:
- Apprenticeship programs: Structured, paid apprenticeships can provide on-the-job training while giving employers confidence in talent pipelines.
- Rotations and mentoring: Rotational programs broaden experience and help new hires learn non-automatable aspects of the business.
- Rethinking measurement: Evaluate new hires on learning velocity, collaboration, and product outcomes — not just lines of code.
Balancing realism and optimism
It’s tempting to sound alarmist: AI is changing the job market, and early-career workers are unlucky casualties. But there’s another side to the story. New technologies have historically removed some tasks while creating others, often in adjacent or unforeseen areas. The difference this time is the speed and breadth of change.
What feels like an existential shock today may become an opportunity for creative restructuring of early-career training. Universities, bootcamps, and employers can collaborate to create new types of internships, projects, and credentials that signal on-the-job readiness in an AI-enhanced workplace.
Wrapping up
If you’re an early-career worker or a student, acknowledge the reality: the hiring landscape has shifted, and the path most people expected may be narrower. But you can adapt. Focus on the skills that augment judgment and collaboration, build demonstrable work, and look for roles where human context matters.
For employers and educators, there’s a responsibility to keep openings and pathways for learners. Apprenticeships, mentorship, and project-based evaluation can ensure the next generation gains experience rather than being sidelined by automation.
Q&A
Q: Are entry-level tech jobs disappearing because of AI?
A: Not entirely disappearing, but many routine tasks within entry-level roles are being automated. That reduces some traditional openings, so early-career workers need to show strengths that AI tools can’t easily replicate, like product judgment and cross-team communication.
Q: What should students studying computer science do now?
A: Focus on building a portfolio that demonstrates impact in messy, real-world settings; seek mentorship and internships; learn to collaborate with AI tools; and cultivate communication and problem-solving skills that go beyond code generation.