Is GitHub Copilot Actually Going to Change How We Write Code?

I’ve had GitHub Copilot running in VS Code for a few weeks now, ever since it landed in technical preview back on June 29, and I still catch myself doing a double take when it finishes a function correctly from nothing but a comment. Type // parse a CSV row into a dict and it just… does it. Sometimes it’s wrong. Often it’s not.

Copilot is built on Codex, an OpenAI model trained on billions of lines of public code, and the pitch is right there in the name: an AI pair programmer. It doesn’t just autocomplete a variable name or close a bracket the way a normal IDE does. It reads the surrounding context — your comments, your function signatures, the style of the file you’re in — and proposes entire blocks of logic. Loops, error handling, regex you’d normally go copy off Stack Overflow. For boilerplate-heavy work it feels like magic.

But the honeymoon phase comes with an asterisk, and it’s a big one: licensing. Early testers have already caught Copilot reproducing snippets of code verbatim from public repositories, including chunks that carry copyright notices or specific license terms. If a tool trained on “all of GitHub” occasionally spits back someone else’s GPL-licensed function without attribution, what happens when that ends up in a commercial codebase? Nobody has a clean answer yet, and I don’t think a clean answer exists until either GitHub changes how the tool works or courts weigh in.

The junior developer question

The other conversation happening in every Slack and Twitter thread I’m in right now is what this does to how people learn to code. There’s a real argument that autocompleting whole functions robs newer developers of the struggle that actually teaches you something — you don’t learn how a hash map collision works if the tool just writes the collision-resistant version for you. There’s an equally real counterargument that Copilot is just a faster Stack Overflow, and nobody complained when IDEs started autocompleting method names.

My honest take, a few weeks in: it’s genuinely great at the boring 70% of programming — glue code, test scaffolding, parsing, the stuff you’ve written a hundred times and don’t want to write again — and mediocre at the interesting 30%, where actual design decisions live. It suggests plausible-looking code with unnerving confidence even when it’s subtly wrong, which is its own kind of danger for someone who doesn’t yet have the experience to spot the bug.

Whether this becomes a normal part of every developer’s toolkit or gets bogged down in licensing disputes and quality concerns is genuinely an open question right now. Given how fast GitHub and OpenAI are iterating, I’d bet we get more clarity — one way or the other — well before the preview period ends.