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Yes—but not automatically. AI may reduce some entry-level work, and it can also remove the practice through which junior engineers learn to make good decisions. Teams can respond by redesigning that work: use AI to accelerate routine coding where errors are low-risk, while giving juniors structured practice in specifying behavior, testing, reviewing generated code, and diagnosing failures. For software where mistakes could cause serious harm, preserve deliberate work without AI as well. This is a practical proposal, not a proven policy or a guarantee that employers will maintain junior hiring.
Can junior engineers survive AI?
The concern is not only whether AI can write code. It is whether organizations will still hire and train people who have not yet developed the judgment to decide what code should do, recognize when it is wrong, and recover when it fails.
There is reason to take the employment concern seriously, but the available figures do not show that AI alone caused the changes:
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- A 2025 Stanford Digital Economy Lab paper by Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen, using monthly ADP payroll records through September 2025, found a 16% relative employment decline for workers ages 22–25 in occupations classified as AI-exposed, controlling for firm-level shocks. Experienced workers remained stable in the comparison. The authors describe the result as consistent with generative AI disproportionately affecting early-career employment, while cautioning that other factors may contribute.
- In the paper’s software-developer case study, employment for workers ages 22–25 was nearly 20% below its late-2022 peak by September 2025. This occupation-specific trend is distinct from the paper’s broader relative estimate and is not proof that AI caused the decline.
The paper also finds different patterns across occupations where AI is used more to automate work and those where it is used more to augment work. Those are associations based on the paper’s exposure measures, not a prediction for every engineering role or company. Its authors note limitations in the data and classifications and say other forces may influence the results.
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So the evidence points to a real risk for early-career workers in some settings, not a settled conclusion that junior engineers are becoming unnecessary. The longer-term risk is that fewer entry-level opportunities today could mean fewer experienced engineers and architects tomorrow.
Where will the next generation of software developers come from?
Senior engineers do not appear fully formed. They gain judgment by working through requirements, making mistakes, getting feedback, and learning to tell a plausible solution from a correct one. If AI takes over routine execution, a team has to decide where that learning will happen instead.
Matt Beane, an assistant professor in UC Santa Barbara’s Technology Management Program, studied how robotic surgery affected training. When a surgeon no longer needed a trainee’s hands for parts of an operation, residents could lose opportunities to practise and receive effective instruction. Beane documented residents building capability through simulator practice and other informal routes, and argued that work and technology can be redesigned to keep apprentices involved. As he put it: “That system of learning — which we all take for granted — is under a new kind of threat from the way we’re handling new technologies. It doesn’t have to be that way.”
The lesson for software teams is not that coding practice must remain unchanged. It is that apprenticeship should be designed, not left to chance. If AI handles a first draft, juniors can still learn by defining expected behavior, writing tests that expose edge cases, reviewing the generated code, and tracing failures. But merely assigning those tasks does not guarantee learning: juniors need context, a meaningful share of the decision-making, and feedback from someone who can explain why a test or review matters.
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An accounting analogy helps clarify the shift. Automation can reduce time spent on routine calculations and increase the importance of designing checks and finding errors. Applied to software, that is a proposal for moving some junior effort from producing routine code toward verifying behavior and investigating defects—not evidence that every employer will preserve junior headcount or make the transition well.
What does the chess comparison actually show?
Chess offers a useful but limited counterexample to the idea that stronger AI must make human expertise disappear. A 2024 peer-reviewed study analyzed more than 11.6 million decisions by elite players over four decades. The authors report that human decision quality improved steadily, with no distinct periods of rapid improvement around the two AI advances they studied. In that setting, human performance improved alongside increasingly capable AI.
That result supports one narrow point: AI and growing human skill can coexist. It does not establish that chess became more popular because of chess engines, that software engineering will follow the same path, or that companies will retain junior roles for the sake of future training. Chess is a reason not to treat human skill as automatically doomed—not a labor-market forecast.
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What happens if the AI is wrong?
Start with the consequence of failure. A faulty prototype that can be rolled back is different from software whose failure could injure someone or disrupt a critical service. The greater the potential harm, the less a team should rely on AI output alone and the more deliberate practice it should preserve.
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| Software context | Useful role for AI | Junior practice to protect |
|---|---|---|
| Prototypes and lower-risk commercial software | Accelerate routine implementation so people can spend more effort evaluating the output. | Specify behavior, create tests, review generated code, and diagnose defects. |
| Mission-critical software where failure could cause harm | Assist with work, but do not let generated output replace human verification or unaided capability. | Structured debugging without AI, failure simulation, and coaching on the decisions behind a fix. |
This is a risk-based recommendation, not a validated universal standard. Managers should make the risk classification explicit and connect it to the review and practice a junior engineer receives.
Built In’s article reports a 2013 FAA warning that continuous autopilot use could degrade manual-flying skills, and says a survey found that 70% of surgical simulation centers required residents to demonstrate simulator proficiency before robotic surgery. Those details are useful illustrations of the skill-retention concern, but they are claims reported by Built In; the underlying notice and survey are not independently established here.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can teams turn AI use into training?
Make the task more than “accept or reject”
Give a junior engineer the surrounding problem: the requirement, constraints, and expected behavior. Ask them to identify assumptions in an AI-generated change, create tests that would catch plausible mistakes, and explain what evidence supports accepting the code. Understanding the client or domain still matters; speed is not a substitute for knowing what the software is supposed to do.
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When a defect appears, let juniors trace it from observed behavior to a failing test, a likely cause, and a verified fix. The objective is not to ban AI from every debugging session. It is to ensure engineers sometimes practise the reasoning themselves, especially where they may need to respond when a tool is unavailable or its answer is misleading.
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Use AI as a coach or simulator, not only a code generator
AI can pose edge cases, ask a junior to defend a design choice, or generate a failure scenario for investigation. A senior engineer still needs to judge the exercise and give feedback. The same tool that removes a trainee’s opportunity to act can, if used differently, create safe opportunities to rehearse.
Keep a deliberate-practice lane for high-consequence work
For systems with serious failure consequences, schedule debugging and review exercises in which the engineer works without AI assistance, then compare the reasoning with the tool’s output. Simulation can reveal whether someone understands the system or has only learned to prompt for a plausible answer. This should complement supervised work, not replace it.
These steps describe a way to preserve learning while using automation; they do not demonstrate that firms are already adopting it at scale.
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For junior engineers
- Ask what behavior a task is meant to produce before asking a tool to implement it.
- Practise writing tests, inspecting unfamiliar code, and explaining why a change is correct.
- Use AI explanations as hypotheses to check, not as proof that code is safe or understood.
- Seek assignments that include diagnosis and feedback, not only prompt-writing or code acceptance.
For managers and mentors
- State the consequence level of the software and what verification it requires.
- Make room for juniors to own requirements, tests, review questions, and failure analysis.
- Provide timely explanations and supervised practice instead of assuming that exposure to AI-generated code teaches itself.
- Preserve unaided exercises where independent judgment matters, and use simulations to rehearse failures safely.
Sridhar Vembu, founder of Zoho, captured the organizational tension in comments quoted by The Times of India: “AI makes senior architects more productive and reduces the need for junior engineers,” while warning that without junior engineers there may be no next generation of architects. That is a concern about how the profession renews its skills, not proof of what hiring will do. An anecdote in Built In about an intern being told not to understand client meeting transcripts captures a similar tension; it should not be generalized into advice to ignore domain knowledge.
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