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AI coding tools can help less-experienced developers, but the evidence does not show that they make every developer faster. Workplace experiments have found larger measured gains among junior developers, while a controlled lab study found that novices took longer on some difficult tasks. The practical answer is not to restrict access by seniority: give junior developers clear guidance, fundamentals training, coaching, and review expectations, then judge results by completed, correct, maintainable work—not code volume alone.
Does AI help junior developers more than senior developers?
It can, in some settings. Three randomized workplace experiments suggest that developers with less experience adopted an AI coding assistant more often and saw greater productivity gains. But the result depends on what developers were doing, how they used the tool, and what the study counted. Other evidence shows that AI can slow novices on difficult tasks.
That is why the findings should not be reduced to a rule that junior developers benefit more, or that senior developers benefit less. The studies measured different outcomes in different environments:
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|---|---|---|---|
| Microsoft Research, June 2025: three randomized field experiments at Microsoft, Accenture, and an anonymous Fortune 100 company | Completed tasks among 4,867 developers | Developers given access to an AI coding assistant completed 26.08% more tasks on average (standard error 10.3%). Less-experienced developers had higher adoption and larger measured gains. | This is a pooled estimate across three noisy experiments, not a guaranteed gain for an individual developer or team. |
| Bank for International Settlements, Working Paper 1208: a field experiment with Ant Group’s CodeFuse | Lines of code produced | The LLM group produced 55% more lines of code; roughly one third of that increase was directly attributable to AI-generated code. Statistically significant gains were primarily among junior staff. | This is a company- and tool-specific measure of code volume, not a general measure of completed or high-quality work. The paper’s publication year is not established here. |
| McKinsey & Company, 2023: controlled lab study with more than 40 developers in the United States and Asia | Time and task performance for code generation, refactoring, and documentation | Time savings varied by task complexity and experience. On some tasks, developers with less than one year of experience took 7–10% longer with AI. | A lab study of specific tasks is not a direct estimate of workplace productivity, but it shows why AI is not automatically faster for novices. |
These outcomes are not interchangeable. More lines of code do not necessarily mean more tasks completed; faster task completion does not, by itself, establish correctness, maintainability, or learning. Read each percentage as evidence about a particular population, tool, setting, and metric.
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Why can the same tool help one junior developer and slow another?
Experience affects adoption and judgment
Less-experienced developers may use an assistant more frequently and get useful help with routine or unfamiliar work. More experienced developers may use it less often, or reserve it for tasks where they can quickly assess the answer. The BIS field experiment, for example, found that less frequent engagement by senior programmers appeared to explain their smaller measured impact. That is a possible explanation in that setting, not proof that senior developers cannot benefit.
Experience also matters after the suggestion appears. A developer needs to recognize whether generated code fits the team’s architecture, handles edge cases, and meets requirements. An AI draft can reduce typing while leaving the harder work—understanding the problem and validating the change—untouched.
Task complexity changes the balance
Routine completions, documentation drafts, and some refactoring may be easier to delegate to an assistant than a change involving unfamiliar frameworks, ambiguous requirements, or complicated system behavior. In the McKinsey lab study, task complexity and developer experience affected time savings, and novices lost time on some tasks. A team should therefore be more cautious when the developer is learning both the problem domain and the underlying technology.
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Output, time, and learning answer different questions
Task counts, lines of code, elapsed time, and work allocation describe different parts of work. A rise in code volume may reflect more generated code without demonstrating better outcomes. A task completed sooner may still require substantial review. And a developer who spends more time coding may spend less time on planning or collaboration, which can matter for team learning.
An account of research on 187,000 GitHub developers published by MIT Sloan School of Management on March 10, 2026 reports increased time spent coding and less time on project-management activities after Copilot access. Less-experienced developers showed the largest increase in coding time, while peer collaboration also declined. These are findings about time allocation, not proof that the additional coding time produced better software or improved junior developers’ skills.
How should engineering teams set expectations for junior developers?
Give early-career developers access to useful tools, but make the learning and verification process explicit. The goal is to use AI to support development without outsourcing the reasoning that helps a developer build durable skills.
Teach fundamentals alongside tool use
McKinsey recommends foundational coursework for developers with less than one year of experience, including syntax, data structures, algorithms, design patterns, and debugging. Tool training should sit alongside that learning, not replace it. Developers should be able to explain the problem, understand a proposed change, and identify what still needs testing.
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Set a review standard for AI-assisted changes
Agree on what a developer must verify before requesting review. That can include whether the change meets the stated requirements, handles relevant edge cases, fits the codebase, and has appropriate tests. Reviewers should assess the code and the reasoning behind it, rather than treating AI use itself as either a defect or a guarantee of quality.
Use coaching to turn suggestions into learning
Senior developers can ask a junior colleague to explain why an AI-suggested approach works, what alternatives were considered, and how the change was validated. Shared examples of good prompts, review comments, and failure cases can make expectations visible. Coaching and code review are especially valuable when the tool proposes unfamiliar patterns or when a task is difficult to evaluate from a quick glance.
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Choose tasks with risk in mind
For routine work or first drafts, AI may be a useful starting point. For complex requirements, unfamiliar frameworks, or high-impact changes, pair the assistant’s output with closer human scrutiny and support. The point is not to ban AI from challenging work; it is to avoid mistaking a plausible answer for a validated one.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can a team tell whether AI is improving its work?
Track outcomes that correspond to the team’s actual goals. Do not use lines of code as a stand-in for productivity, and do not treat speed as the only result that matters.
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- Correctness and maintainability: Do changes pass review and testing, fit the system, and remain understandable to the next person?
- Time: Is time saved on drafting offset by time spent checking, correcting, or reworking suggestions?
- Learning: Can early-career developers explain and adapt the code, rather than only produce it?
- Collaboration: Is individual coding time increasing at the expense of discussion, mentoring, or coordination the work needs?
Interpret any result in context: tool access and adoption, task mix, developer experience, and how the team defines quality all affect what a metric can tell you. The DORA 2025 report, based on nearly 5,000 technology professionals worldwide and more than 100 hours of qualitative data, frames AI as an amplifier of existing organizational strengths and dysfunctions. That is a report conclusion about organizational conditions, not a causal estimate that AI has a particular effect on junior developers.
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What is the practical policy?
Make AI available with training, clear validation expectations, and access to senior guidance. Encourage use where it helps with drafting or routine work, while requiring more scrutiny when requirements are complex or the code is unfamiliar. Evaluate completed, correct, maintainable work and the effects on learning and collaboration—not a single productivity number.
The evidence supports differentiated coaching, not a blanket rule that junior developers should use AI more or less than senior colleagues. The right balance depends on the task, the developer’s ability to evaluate the output, and the team’s working conditions.
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