GitHub outlines three skills for developers to stand out in the AI era

AI is reshaping the developer career ladder by shifting the value of execution from writing code to directing AI, evaluating output, and making technical decisions. While coding remains essential, developers must now master skills that complement AI capabilities to advance their careers.

Directing AI Agents

Great execution increasingly means defining problems clearly and providing context rather than implementing every piece manually. Developers should learn to coordinate multiple AI agents, where the human role shifts to defining work, reviewing outputs, and integrating components. For example, instead of a linear workflow of creating branches and writing code, a developer might oversee agents handling authentication, documentation, and tests simultaneously.

Critically Evaluating Output

AI's first answer is not always the best, making human judgment and code review skills more critical than ever. Developers should use a second AI model to critique the first model's work, a technique mirrored by GitHub Copilot's built-in Rubber Duck agent. This approach helps catch issues like missing index recommendations or poor performance on large tables that a single model might overlook.

Solving Bigger Problems

AI frees up time for developers to focus on higher-level tasks such as understanding customer needs, evaluating architectural tradeoffs, and defining success metrics. As AI handles implementation details like building features and generating tests, the distinguishing skills for engineers become exercising sound judgment and solving the right problems. Developers who strengthen these areas while effectively leveraging AI will be best positioned to thrive.