Why developer expertise matters more than ever in the age of AI

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AI tools seem to be everywhere. With the tap of a key, they provide ready answers to queries, autocomplete faster than our brains can, and even suggest entire blocks of code. Research has shown that GitHub Copilot enables developers to code up to 55% faster. Junior developers, specifically, may see a 27% to 39% increase in output with AI assistance according to MIT, showing even greater productivity gains from their adoption of AI than more experienced developers. 

But here’s the question: you may be coding faster with AI, but when was the last time you asked yourself why before adopting a suggestion from an AI coding assistant? 

Being a developer is not just about producing code. It’s about understanding why the code works, how it fits into the bigger picture, and what happens when things break down. The best developers know how to think critically about new problems and take a systems view of solving them. That kind of expertise is what keeps software resilient, scalable, and secure, especially as AI accelerates how quickly we ship. Without it, we risk building faster but breaking more.

Our 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.

We’re not saying you have to reject AI to be a great developer. At GitHub, we believe AI is a superpower, one that helps you move faster and build better when used thoughtfully. Your role as a 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 in your environment are, and how to debug, guide AI tools, and go beyond vibe coding. 

After all, AI can help you write code a lot faster, but only developer expertise turns that speed into resilient, scalable, and secure software.

TL;DR: AI pair‑programming makes you faster, but it can’t replace the judgment that keeps software safe and maintainable. This article offers three concrete ways to level‑up your expertises.

AI’s productivity dividend + developer experience = greater impact

Benefit	How human judgment multiplies the value
⏱️ Faster commits (up to 55 % quicker task completion)	Devs run thoughtful code reviews, write tests, and surface edge cases so speed never comes at the cost of quality.
🧠 Lower cognitive load	Freed-up mental bandwidth lets developers design better architectures, mentor teammates, and solve higher-order problems.
🌱 Easier onboarding for juniors	Senior engineers provide context, establish standards, and turn AI suggestions into teachable moments building long-term expertise.
🤖 Automated boilerplate	Devs tailor scaffolding to real project needs, question assumptions, and refactor early to keep tech-debt in check and systems secure.

Speed without judgment can mean:

• Security vulnerabilities that static analysis can’t spot on its own.
• Architecture choices that don’t scale beyond the demo.
• Documentation drift that leaves humans and models guessing.

The remedy? Double down on the fundamentals that AI still can’t master.

Mastering the fundamentals: 3 key parts of your workflow to focus on when using AI

As the home for all developers, we’ve seen it again and again: becoming AI-savvy starts with the old-school basics. You know, the classic tools and features you used before AI became a thing (we know, it’s hard to remember such a time!). We believe that only by mastering the fundamentals can you get the most value, at scale, out of AI developer tools like GitHub Copilot. 

A junior developer who jumps into their first AI-assisted project without having a foundational understanding of the basics (like pull requests, code reviews, and documentation) may ship fast, but without context or structure, they risk introducing bugs, missing edge cases, or confusing collaborators. That’s not an AI problem. It’s a fundamentals problem.

Let’s revisit the core skills every developer should bring to the table, AI or not. With the help of a few of our experts, we’ll show you how to level them up so you can dominate in the age of AI.

1. Push for excellence in the pull request

At the heart of developer collaboration, pull requests are about clearly communicating your intent, explaining your reasoning, and making it easier for others (humans and AI alike!) to engage with your work.

A well‑scoped PR communicates why a change exists—not just what changed. That context feeds human reviewers and Copilot alike.

As GitHub developer advocate Kedasha Kerr advises, start by keeping your pull requests small and focused. A tight, purposeful pull request is easier to review, less likely to introduce bugs, and faster to merge. It also gives your reviewers, as well as AI tools like Copilot, a clean scope to work with.

Your pull request description is where clarity counts. Don’t just list what changed—explain why it changed. Include links to related issues, conversations, or tracking tickets to give your teammates the full picture. If your changes span multiple files, suggest where to start reviewing. And be explicit about what kind of feedback you’re looking for: a quick sanity check? A deep dive? Let your reviewers know.

Before you ask for a review, review it yourself. Kedasha recommends running your tests, previewing your changes, and catching anything unclear or unpolished. This not only respects your reviewers’ time, it improves the quality of your code and deepens your understanding of the work.

A thoughtful pull request is a signal of craftsmanship. It builds trust with your team, strengthens your communication skills, and gives Copilot better context to support you going forward. That’s a win for you, your team, and your future self.

Here’s a quick 5‑item PR checklist to reference as you work: 

1. Scope ≤ 300 lines (or break it up).
2. Title = verb + object (e.g., Refactor auth middleware to async).
3. Description answers “why now?” and links to the issue.
4. Highlight breaking changes with ⚠️ BREAKING in bold.
5. Request specific feedback (e.g., Concurrency strategy OK?).

Drop this snippet into .github/pull_request_template.md and merge.

Learn more about creating a great pull request > 

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.

And let’s be honest: as developers, we often read and review far more code than we actually write (and that’s ok!). No matter if code comes from a colleague or an AI tool, code reviews are a fundamental part of being a developer—and building a strong code review practice is critical, especially as the volume of code increases. 

You should start by reviewing your own pull requests before assigning them to others. Leave comments where you’d have questions as a reviewer. This not only helps you spot problems early, but also provides helpful context for your teammates. Keep pull requests small and focused. The smaller the diff, the easier it is to review, debug, and even roll back if something breaks in production. In DevOps organizations, especially large ones, small, frequent commits also help reduce merge conflicts and keep deployment pipelines flowing smoothly. 

As a reviewer, focus on clarity. Ask questions, challenge assumptions, and check how code handles edge cases or unexpected data. If you see a better solution, offer a specific example rather than just saying “this could be better.” Affirm good choices too: calling out strong design decisions helps reinforce shared standards and makes the review process less draining for authors.

Code reviews give you daily reps to build technical judgement, deepen your understanding of the codebase, and earn trust with your team. In an AI-powered world, they’re also a key way to level up by helping you slow down, ask the right questions, and spot patterns AI might miss.

Here are some heuristics to keep in mind when reviewing code:

• Read the tests first. They encode intent.
• Trace data flow for 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.

Learn more about how to review code effectively >

3. Invest in documentation 

Strong pull requests and code reviews help your team build better software today. But documentation makes it easier to build better software tomorrow. In the AI era, where code can be generated in seconds, clear, thorough documentation remains one of the most valuable—and overlooked—skills a developer can master.

Good documentation helps everyone stay aligned: your team, new contributors, stakeholders, and yes, even AI coding agents (docs make great context for any AI model, after all). The clearer your docs, the more effective AI tools like Copilot can be when generating code, tests, or summaries that rely on understanding your project’s structure. As GitHub’s software engineer Brittany Ellich and technical writer Sam Browning explain, well-structured docs accelerate onboarding, increase adoption, and make collaboration smoother by reducing back and forth.

The key is to keep your documentation clear, concise, and structured. Use plain language, focus on the information people actually need, and avoid overwhelming readers with too many edge cases or unnecessary details. Organize your docs with the Diátaxis framework, which breaks documentation into four categories:

• Tutorials for hands-on learning with step-by-step guides
• How-to guides for task-oriented steps with bulleted or numbered list
• Explanations for deeper understanding
• Reference for technical specs such as API specs

When your docs follow a clear structure, contributors know exactly where to find what they need and where to add new information as your project evolves.

In short: great documentation forces you to sharpen your own understanding of the system you’re building. That kind of clarity compounds over time and is exactly the kind of critical thinking that makes you a stronger developer.

Learn more about how to document your project effectively >

A level‑up dev toolkit

To make things simple, here’s a skills progression matrix to keep in mind no matter what level you’re at. 

Skill	Junior	Mid‑level	Senior
Pull requests	Describes what changed	Explains why and links issues	Anticipates perf/security impact & suggests review focus
Code reviews	Leaves 👍/👎	Gives actionable comments	Mentors, models architecture trade‑offs
Documentation	Updates README	Writes task‑oriented guides	Curates docs as a product with metrics

And here are some quick‑wins you can copy today:

• .github/CODEOWNERS to auto‑route reviews
• PR and issue templates for consistent context
• GitHub Skills course: Communicating with Markdown

The bottom line

In the end, AI is changing how we write code, and 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. They think in systems, write with intention, and build with care. 

So keep asking why. Stay curious. Continue learning. That’s what sets great developers apart—and it’s how you’ll survive and thrive in an AI-powered future.

The post Why developer expertise matters more than ever in the age of AI appeared first on The GitHub Blog.

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