What assistants are good at
- Generating boilerplate, such as forms, API clients and configuration
- Explaining unfamiliar code and libraries
- Writing unit tests and suggesting edge cases
- Translating code between languages or frameworks
- Drafting documentation and commit messages
These tasks are time-consuming but low in judgement, which makes them a natural fit.
Where developers stay essential
Architecture, data models, security decisions, performance trade-offs and understanding what the business really needs are still human work. An assistant can propose a solution; an experienced developer decides whether it is the right one.
Risks to manage
- Plausible but wrong code: it compiles, looks right and fails in edge cases
- Security gaps: missing input validation, unsafe queries or outdated patterns
- Licence and dependency issues: suggested packages must be checked like any other
- Skill erosion: junior developers still need to understand what they ship
Habits that work
- Review AI-written code as carefully as code from a new teammate.
- Keep automated tests and code review in place for everything.
- Ask the assistant to explain its code; if the explanation is weak, so is the code.
- Use it to explore options quickly, then decide deliberately.
The bottom line
AI assistants change how fast code is written, not who is responsible for it. Teams that combine them with strong review and testing ship faster without lowering quality.