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What skills matter when AI can write code?
Think less about a fixed list of tasks AI will never perform and more about the abilities that let you guide, evaluate, and take responsibility for software. A useful map spans four areas: using AI, core software engineering, adjacent engineering, and adjacent non-engineering skills. A May 2025 preprint developed this framework from interviews with 21 developers, mapping 12 work goals to 75 tasks across a six-step workflow. It is exploratory qualitative research, not a representative survey or a definitive forecast of what AI can do. Read the paper.
- Problem framing: Clarify what a user needs, what constraints matter, and what a successful outcome looks like before generating code.
- Software foundations: Follow control flow and data, understand interfaces and system behavior, and reason about design choices—not just syntax.
- Reading and debugging: Inspect unfamiliar code, reproduce failures, trace causes, and decide whether a proposed fix actually addresses them.
- Testing and review: Check expected behavior and edge cases, and assess reliability, security, and maintainability before integration.
- Communication and context: Explain trade-offs, understand how software fits deployment and users’ workflows, and work through decisions with teammates.
These capabilities help you make AI-assisted work useful and safe; the available evidence does not establish that any of them are impossible to automate.
How should you build those skills?
1. Learn foundations through working examples
Choose a small, real feature and trace how input moves through it: what calls what, where state changes, which interfaces are involved, and what assumptions the design makes. Then change one behavior and predict the result before running the code. This connects fundamentals to system behavior rather than treating memorized syntax as mastery.
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2. Diagnose a failure before requesting a fix
- Reproduce the failure and record the inputs and conditions that trigger it.
- Inspect the relevant state and trace the execution path around the failure.
- Write down a plausible cause and the smallest test that could distinguish it from alternatives.
- If you ask an AI assistant for help, ask it to explain its suspected cause and propose a test—not only to return a patch.
- Run the test, inspect the change, and confirm that the fix addresses the original behavior without creating a new failure.
Anthropic’s 2025 learning experiment found its largest score gap between AI-assisted and hand-coding groups on debugging questions. The researchers suggested that resolving errors independently may have helped participants learn; that is a hypothesis, not a proven explanation.
3. Use AI as a tutor, then verify what you learn
Ask an assistant to explain an unfamiliar API, compare two approaches, name assumptions, or quiz you on code it generated. Try to answer before reading its explanation, then check the answer against documentation and tests. In Anthropic’s small study, participants who asked follow-up or conceptual questions tended to show stronger mastery than those who delegated code generation or debugging. That pattern is suggestive, not a guaranteed learning method.
4. Practice tests and code review
For each change, identify the expected behavior and at least one meaningful edge case. Run checks that cover both, then read the diff as if someone else had written it: look for unintended behavior, unsafe assumptions, hard-to-maintain choices, and whether it meets the actual need. Microsoft Research’s October 2025 study found developers often sought AI help for coding and testing, while systems-facing work raised reliability and security concerns.
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5. Learn the engineering around the code
Understand how a feature is deployed, monitored, secured, and used in the surrounding workflow. Practice explaining trade-offs to teammates: what a change improves, what it risks, and what evidence would show it works. The 2025 skills framework includes adjacent engineering and non-engineering knowledge, though its abstract does not prescribe a specific competency list.
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Practice eliciting needs, mentoring, negotiating trade-offs, and communicating impacts with attention to context and fairness. Microsoft Research found limits around relationship-centric work such as mentoring and emphasized contextual fairness and inclusiveness for human-facing tasks. That does not mean AI has no assisting role; it means the quality of the interaction still matters.
How much does AI assistance help—and what does that mean for learning?
Productivity and learning are different outcomes, so evidence about one does not settle the other.
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| Study | What it measured | Result and context |
|---|---|---|
| Anthropic, 2025 | Immediate quiz performance after learning Python’s Trio library | Among 52 participants, mostly junior software engineers who used Python weekly for more than a year and did not know Trio, the AI-assisted group scored 17% lower than the hand-coding group; group means were 50% and 67%. Participants worked on two features and then took an immediate quiz. This is not evidence of long-term skill loss. AI-assisted participants finished about two minutes faster on average, but the difference was not statistically significant. Study details. |
| Microsoft Research, June 2025 | Completed tasks in three workplace field experiments | A pooled estimate across 4,867 developers at Microsoft, Accenture, and an anonymous Fortune 100 company found a 26.08% increase in completed tasks, with a standard error of 10.3%, for developers given AI coding assistance. This is an aggregate estimate from those experiments, not a guarantee for other tasks or workplaces, and it does not measure learning. Study details. |
| Microsoft Research, October 2025 | Reported AI use and desired support | The mixed-methods study included 860 developers. It found strong use and demand for improvements in coding and testing, interest in reducing documentation and operations toil, and limits around mentoring and other relationship-centric work. These are reported perceptions and usage patterns, not a measured boundary on AI capability. Study details. |
The Anthropic experiment examined immediate learning on a narrow task with a new Python library; it cannot establish what happens to experienced developers over time or across all AI coding workflows. The field experiments measured task completion in different workplaces. Their results are not contradictory: more completed tasks in a setting does not imply stronger learning in a separate experiment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you decide when to use AI?
Match the way you use an assistant to the task, the cost of an error, and whether doing the work yourself would help you learn.
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- Routine, low-consequence work: Delegate a draft or repetitive step when you can quickly review the result and verify it.
- New concepts or unfamiliar code: Ask for an explanation, alternatives, or a quiz; stay involved enough to understand the answer.
- Systems-facing changes: Give extra weight to testing and review when reliability or security matters. Do not treat generated code as validated code.
- Human-facing decisions: Use AI, if helpful, to prepare or organize information, but keep attention on users, teammates, context, and trade-offs.
- Learning practice: Before delegating a task you are trying to learn, attempt the diagnosis or design yourself; use assistance to expose gaps rather than conceal them.
Who is responsible for AI-generated code?
The engineer integrating a change must judge whether it is correct and appropriate to ship. In an Associated Press interview published September 29, 2025, Anthropic Claude Code project manager Cat Wu said, “We definitely want to make it very clear that the responsibility, at the end of the day, is in the hands of the engineers.” That is a product leader’s statement, not an experimental finding, but it captures the practical obligation: understand and verify the work you approve.
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