GitHub Copilot launched its technical preview in June 2021. Six months in, the developer community has produced a range of reactions from enthusiastic adoption to serious concerns about code quality, copyright, and the future of software development as a career.
What the enthusiasts say
Developers who use Copilot for routine coding tasks report genuine productivity gains. Boilerplate generation, test scaffolding, documentation comments, and repetitive patterns across large codebases are the strongest use cases. The completion is contextually aware of the surrounding code and can generate working implementations of standard patterns on the first try. The experienced developer using Copilot as an advanced autocomplete tool gets real value.
The code quality concerns
Copilot generates code that compiles but may have subtle bugs, security vulnerabilities, or licensing issues. Researchers found that Copilot generates insecure code in a significant fraction of cases when prompted with security-sensitive contexts. The code looks correct to a junior developer but would be caught by a thorough code review. The concern is that developers with less experience to evaluate Copilot's suggestions are more likely to accept problematic code.
The copyright question
Copilot was trained on public GitHub code, which includes code under GPL, MIT, Apache, and other open source licences. Copilot sometimes reproduces verbatim code from training data. The legal question of whether this constitutes copyright infringement has not been resolved in 2021. The Software Freedom Conservancy and individual developers raised concerns about the licence implications. The litigation that would eventually test these questions was filed in November 2022.
The career implications
The developer community discussion about whether AI coding tools threaten developer jobs is loud in 2021 and unresolved. The empirical evidence in 2021 is that Copilot assists experienced developers rather than replacing them. The code generation quality degrades significantly outside well-understood patterns. The more plausible near-term effect is that the ratio of senior to junior developers that a team needs may shift as junior-level boilerplate work is automated.