Introduction
Recently, I asked a developer on our team a seemingly simple question:
“If you’re using GitHub Copilot, why does it still take time to push commits?”
You’d think with AI writing code, delivery would happen at lightning speed, right?
That’s the assumption we’re all tempted to make when new tech promises a boost in productivity. But the answer I got – from one of our engineers, Yuri – was more thoughtful than I expected.
And it made me reflect on what “velocity” really means when you care about software quality.
What is Github Copilot?
GitHub Copilot is an AI-powered tool that acts like an enhanced autocomplete, offering full code snippets based on what you type.
Key Points:
- Saves time for experienced developers: Great for automating repetitive tasks, like writing boilerplate code.
- Helps when learning new languages: Can speed up your process when switching between programming languages or frameworks.
- Not ideal for beginners: Relying on Copilot can prevent you from learning core coding concepts.
- Can lead to misunderstanding code: Copilot-generated code might be hard to understand or debug if you don’t know how it works.
- Use sparingly as a beginner: It’s better to focus on learning the basics before using tools like Copilot.
“I’m still using Copilot. But I take time to understand the implementation first.”
That’s what Yuri said.
He wasn’t using Copilot as a shortcut. He was using it as a precision tool: only after taking time to understand the bug, the current implementation, and how the fix might ripple through the codebase.
That’s not slow. That’s responsible engineering.
He also mentioned something else that stuck with me:
“It’s not just about asking Copilot to generate code. I’m writing prompts based on the exact change I want. Prompt engineering.”
AI won’t replace thinking; it actually demands more of it
Copilot can write the code.
What it can’t do is understand the why behind your system:
- Why this module was structured this way
- Why this edge case matters in production
- Why a quick fix might break authentication logic two levels down
So when a developer takes a bit longer, even with AI in the loop, it might not be inefficiency.
It might be intentional caution – the kind that avoids regressions and protects your product.
We confuse velocity with value all the time
It’s easy to assume speed = performance.
But in testing, we know that what you don’t break matters more than how fast you commit.
That’s where QA, architecture awareness, and a healthy Dev+QA handshake come in.
At BetterQA, we’ve seen this pattern before:
- Developers move faster with Copilot, yes.
But bugs can slip in faster too – especially if there’s no test coverage, no second set of eyes, and no structured validation process.
Which is why we still stand by this:
🚫 Copilot ≠ Autopilot.
✅ QA is still your safety net.
Final thought: maybe slow is the new smart
The next time someone on your team “takes longer than expected” even with Copilot, ask why – not why so slow, but what are they protecting?
Because maybe, just maybe…
Slowness is just quality moving carefully.
Like this thought? Let’s connect
We’re exploring how AI and QA intersect – not as hype, but as real practice.
Ping me at calendly.com/betterqa or check out BetterQA – or feel free to reuse this reflection in your own team retros. The conversation is just getting started.
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