AI Speeds Up Coding, But Speed Isn't Judgment
There's no disputing the productivity gains from AI coding tools. GitHub Copilot has been shown to help developers code up to 55% faster, and MIT research suggests junior developers may see a 27% to 39% increase in output when using AI assistance—an even bigger boost than their more experienced peers. The numbers are impressive.
But those numbers measure output, not understanding. The critical question isn't how fast you can produce code; it's whether you know why that code works, how it fits into the larger system, and what happens when it fails. That kind of expertise—the ability to think critically about new problems and take a systems view—is what keeps software resilient, scalable, and secure. AI accelerates how quickly we ship code; human judgment determines whether that code is worth shipping at all. Without that judgment, the risk isn't building slower, it's building faster and breaking more.
As GitHub CEO Thomas Dohmke put it bluntly at VivaTech: "Startups can launch with AI‑generated code, but they can't scale without experienced developers." Developer expertise is the multiplier on AI, not the bottleneck.
This doesn't mean rejecting AI to be a great developer. The role of the developer in the age of AI is to be the human-in-the-loop: the person who knows why code works, why it sometimes doesn't, what the key requirements are in a given environment, and how to debug and guide AI tools beyond simple vibe coding. AI can help you write code faster; only developer expertise turns that speed into resilient, scalable, and secure software.
Without that judgment, speed can lead to security vulnerabilities that static analysis can't spot on its own, architecture choices that don't scale beyond the demo, and documentation drift that leaves both humans and models guessing. The remedy is doubling down on the fundamentals that AI still can't master.
1. Push for Excellence in the Pull Request
Pull requests are about more than merging code—they're about clearly communicating intent, explaining reasoning, and making it easier for others, both human and AI, to engage with your work. A well‑scoped PR communicates why a change exists, not just what changed. That context feeds both human reviewers and AI tools like Copilot.
GitHub developer advocate Kedasha Kerr advises keeping pull requests small and focused. A tight, purposeful PR is easier to review, less likely to introduce bugs, and faster to merge. It also gives reviewers and AI tools a clean scope to work with.
The PR description is where clarity counts most. Explain why the change was made, include links to related issues or tracking tickets, and if changes span multiple files, suggest where to start reviewing. Be explicit about what kind of feedback you're looking for—a quick sanity check or a deep dive.
Before requesting a review, review your own work. Run tests, preview changes, and catch anything unclear or unpolished. This respects reviewers' time and deepens your understanding of the code. A thoughtful pull request is a signal of craftsmanship—it builds trust, strengthens communication skills, and gives Copilot better context to support future work.
Here's a quick checklist to reference:
- Scope ≤ 300 lines (or break it up).
- Title = verb + object (e.g., Refactor auth middleware to async).
- Description answers "why now?" and links to the issue.
- Highlight breaking changes with ⚠️ BREAKING in bold.
- Request specific feedback (e.g., Concurrency strategy OK?).
Consider dropping this snippet into .github/pull_request_template.md.
2. Rev Up Your Code Reviews
AI can generate code in seconds, but knowing how to review that code is where real expertise develops. Every pull request is a conversation: "I believe this improves the codebase, do you agree?" As GitHub staff engineer Sarah Vessels explains, good code reviews don't just catch bugs—they teach, transfer knowledge, and help teams move faster with fewer costly mistakes.
Developers often read and review far more code than they write. This is true whether the code comes from a colleague or an AI tool—which makes a strong code review practice critical as the volume of generated code increases.
Start by reviewing your own PRs before assigning them. Leave comments where you'd have questions as a reviewer to provide context for your teammates. Keep pull requests small; the smaller the diff, the easier it is to review, debug, and roll back if something breaks. In large DevOps organizations, small, frequent commits also reduce merge conflicts and keep deployment pipelines flowing.
As a reviewer, focus on clarity. Ask questions, challenge assumptions, and check how code handles edge cases or unexpected data. Offer specific examples when suggesting a better solution rather than just saying "this could be better." Affirm good choices too—calling out strong design decisions reinforces shared standards.
Code reviews provide daily reps for building technical judgment and earning trust with your team. In an AI‑powered world, they're a key way to slow down, ask the right questions, and spot patterns AI might miss. Keep these heuristics in mind:
- Read the tests first—they encode intent.
- Trace data flow from user input to DB writes to external calls.
- Look for hidden state in globals, singletons, and caches.
- Ask "What happens under load?" even if performance isn't in scope.
- Celebrate good patterns to reinforce team standards.
3. Invest in Documentation
Strong PRs and code reviews help your team build better software today. Documentation makes it easier to build better software tomorrow. In an AI era where code can be generated in seconds, clear documentation remains one of the most valuable—and overlooked—skills a developer can master.
Good docs keep teams, new contributors, and stakeholders aligned. They also make great context for AI models: the clearer your docs, the more effective tools like Copilot can be when generating code, tests, or summaries that depend on understanding your project's structure. GitHub software engineer Brittany Ellich and technical writer Sam Browning note that well-structured docs accelerate onboarding, increase adoption, and reduce back‑and‑forth in collaboration.
Keep documentation clear, concise, and structured. Use plain language, focus on what people actually need, and avoid overwhelming readers with unnecessary edge cases. The Diátaxis framework is a useful way to organize docs into four categories:
- Tutorials for hands-on learning with step-by-step guides
- How-to guides for task-oriented steps with bulleted or numbered lists
- Explanations for deeper understanding
- Reference for technical specs such as API specs
A clear doc structure means contributors know exactly where to find what they need and where to add new information as the project evolves. Writing great documentation also forces you to sharpen your own understanding of the system—the kind of clarity that compounds over time and strengthens your critical thinking.
The Bottom Line
AI is changing how we write code, but curiosity, judgment, and critical thinking are needed more than ever. The best developers don't just accept what AI suggests—they ask why. They provide context. They understand the fundamentals, think in systems, write with intention, and build with care.
That's what sets great developers apart. It's also how you'll not just survive but thrive in an AI-powered future: by keeping the human judgment in the loop.
