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Junior developers can gain experience by taking on supervised work that includes more than writing code: clarifying requirements, working in an existing codebase, testing and debugging, responding to review, and learning what happens after release. AI can speed up some routine tasks, but accepting generated code is not a substitute for building judgment. Choose work you can explain, verify, and improve through feedback.
Has AI eliminated entry-level developer work?
The available evidence does not show that AI has universally eliminated entry-level software jobs. It does show that some routine programming tasks are changing, while evidence about productivity, hiring, and long-term skill development measures different things.
In three field experiments involving 4,867 developers at three companies, researchers reported a pooled 26.08% increase in completed tasks with coding-assistant access (standard error 10.3%). Less experienced developers adopted the tools more and saw greater productivity gains. The experiments measured task completion, not durable learning or entry-level hiring outcomes. Microsoft Research’s 2025 study is evidence that assistants can affect work output; it is not evidence that juniors no longer need training.
The International Labour Organization notes that AI tools can simplify some junior programming tasks. It also cites US Bureau of Labor Statistics projections of 17.9% growth in software developer employment from 2023 to 2033, compared with 4.0% for all occupations. Those are projections for US software developers as a whole, not observed growth or a forecast specifically for junior roles. The ILO’s discussion therefore supports neither a guarantee of jobs nor a claim that the junior pathway has disappeared.
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Hiring evidence points to experience as a persistent challenge. In the UK Department for Science, Innovation and Technology’s 2025 survey, 35% of organizations recruiting for AI roles said they struggled to fill them; reported barriers included candidates lacking work experience (31%) and insufficient technical skills (30%). These figures concern surveyed organizations recruiting for AI roles in the UK, not all employers or all junior software jobs. The report recommends industry-linked AI apprenticeships and internships, but that recommendation does not establish that a specific opening is available. Read the survey’s executive summary.
What counts as useful experience?
A useful experience creates a feedback loop and gives you responsibility for understanding the result, not just producing a patch. Look for work that includes several of these elements:
- Real requirements: a user, team, maintainer, or instructor can explain the need and tell you whether your solution addresses it.
- An existing codebase: you must understand conventions, dependencies, and the consequences of changing code others rely on.
- Tests and debugging: you can reproduce a problem, check behavior, investigate failures, and verify a fix.
- Review and revision: someone examines your work, explains concerns, and expects you to respond rather than merely submit it.
- Follow-through: you see the change used, maintained, or adapted after feedback.
- Explainable decisions: you can describe the trade-offs, tests, and what you learned without exposing confidential information.
These are practical criteria, not a validated scoring system. A small, carefully maintained project with real feedback can demonstrate more than a collection of tutorial clones that stop at a working demo.
How to build experience through projects
Choose a problem with a real feedback source
Pick a problem for a club, volunteer group, local organization, open-source project, or a group of users you can actually reach. Agree on what the software should do and how you will know it works. If no outside user is available, ask an instructor, mentor, or peer reviewer to challenge the requirements and inspect the implementation.
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Work through the whole change
- Write down the need. Record the user, the behavior they need, and important constraints. Turn the request into a small issue or task with a clear expected outcome.
- Inspect before changing. Read the relevant code, documentation, and existing tests. Identify what the current system does and where a change might cause side effects.
- Implement and test. Make a focused change, run the appropriate tests, and add or update tests for important behavior and edge cases. When a test fails, investigate the cause instead of changing code until the failure disappears.
- Ask for review. Share the change with a maintainer, teammate, instructor, or mentor. Explain the requirement and approach, then make considered revisions in response to feedback.
- Observe use and maintain the result. Check whether the change works for its intended users. Fix issues that emerge and note what you would do differently next time.
- Document your contribution. Keep a README, tests, issue history, and a concise account of the feedback and changes. In a portfolio or interview, distinguish your own decisions from suggestions produced by tools or collaborators.
Open-source contributions can expose you to existing code, issue discussions, and maintainer review. Start by reading a project’s contribution guidance and choosing a task that maintainers have identified as appropriate. A merged contribution is useful evidence of collaborative work, but contributions do not guarantee employment.
How to use AI without outsourcing the learning
Use an assistant to make unfamiliar work more approachable, then verify the answer yourself. A systematic review of 56 primary studies about junior developers’ use of large language models found that 83.9% of the included studies reported both positive and negative perceptions. The review identifies risks including incorrect suggestions, hallucinations, and potential data leakage. Its authors define juniors as people with five or fewer years of experience, including students, while noting that the underlying studies use inconsistent definitions. See the 2025 systematic review.
- Ask for explanation, not just a finished patch. Request a walkthrough of an unfamiliar function, an explanation of an error, or a comparison of approaches. Check the explanation against the code and documentation.
- Generate test ideas, then inspect them. Ask for edge cases or test scenarios, but make sure the tests reflect the actual requirement. AI-generated tests can be wrong or incomplete and need human review. GitHub’s survey article also describes respondents using time saved for learning, collaboration, and system design.
- Run the code and examine boundaries. Verify behavior with tests, inspect error cases and assumptions, and check dependencies or security-sensitive changes. A plausible answer is not proof that the code is correct.
- Protect code and data. Do not paste confidential source code, credentials, personal data, or other restricted material into a tool unless your organization explicitly permits it.
- Keep deliberate practice. Sometimes debug or implement a small task without autocomplete or generated solutions. Then compare your approach with other options. The aim is to build fundamentals you can use when a tool is unavailable or its suggestion is wrong.
AI use is common in some workplaces but not universal or necessarily employer-approved: a GitHub survey of 2,000 people on enterprise software teams in the US, Brazil, Germany, and India found that more than 97% had used AI coding tools at work at some point. It measured whether respondents had ever used the tools, not frequency, and employers did not all sanction their use. The survey was published in 2024 and updated in 2025.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate an internship, apprenticeship, or junior role
Do not choose solely by title. Ask questions that reveal whether the opportunity will teach you how software is built and maintained:
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- Who will review my code, and how often will I receive feedback?
- Will I work in an existing codebase with tests and maintenance responsibilities?
- How are tasks selected, and will I have contact with users or the people defining requirements?
- How does responsibility grow as I demonstrate competence?
- What are the eligibility rules, location, pay, duration, and expected hours?
- May I describe the work publicly, and what information must remain confidential?
Internships and apprenticeships can provide structured supervision, but the label alone does not guarantee mentorship or meaningful work. Check the day-to-day assignments and support before committing. The UK government’s 2025 AI labor-market survey recommends expanding AI apprenticeships with industry partners and developing internships and job opportunities; it is a policy recommendation, not a directory of current positions. The report sets out that recommendation.
What employers can do to preserve the junior pathway
Automating routine tasks does not remove the need to develop people who can understand systems, assess generated work, and take responsibility for changes. Employers can design AI-enabled work so juniors still learn the full development cycle:
- Pair newer developers with experienced colleagues and make review a teaching conversation, not only a gate.
- Assign bounded changes in real systems, including tests, debugging, maintenance, and opportunities to understand user needs.
- Increase responsibility as a developer demonstrates sound judgment, rather than equating speed or AI output with readiness.
- Maintain internship and apprenticeship routes with clear mentorship and progression.
- Set explicit rules for tool use, confidentiality, verification, and attribution.
Deloitte’s survey of 1,874 workers in the US, Canada, India, and Australia, conducted July 17–31, 2024, includes multiple industries and worker types rather than software juniors alone. It identifies learning, mentorship, and growth opportunities as ways organizations can support early-career workers. DORA’s 2025 report similarly describes AI as an “amplifier”: in its organizational context, AI magnifies strengths in high-performing organizations and dysfunctions in struggling ones. Deloitte’s findings and DORA’s report reinforce that tools alone do not create a supportive learning environment.
What to show when applying for work
Present evidence that makes your contribution and judgment visible. For one or two substantial projects, include:
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- the problem and intended user;
- your role and the parts you implemented;
- tests, debugging decisions, and important trade-offs;
- feedback you received and what you changed because of it;
- what happened after the project was used or deployed, where applicable; and
- how AI tools contributed, along with the checks you performed.
Be honest about scope and ownership. A reviewer is better able to assess a modest change you can explain than a large generated codebase whose behavior, tests, and decisions you cannot defend.
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