Microsoft’s AI sidekick is writing more code than ever, but the devs maintaining it are quietly losing their sanity. Here’s why it’s not just a GitHub problem it’s an industry symptom.
Every dev’s had that moment when Copilot confidently suggests a chunk of code that looks perfect until it absolutely detonates your build. You sit there, blinking at your screen, wondering how you just got gaslit by an autocomplete.
It’s the same feeling you get when a senior dev reviews your PR and says, “Looks fine,” but you know it’s not fine. That’s Copilot energy.
The wild part? Microsoft engineers the people building Copilot are now living in that chaos too. Inside Redmond and across GitHub repos, AI-generated pull requests are quietly piling up. Some devs call it “helpful.” Others call it “Stockholm Syndrome as a Service.”
Meanwhile, dev forums, Reddit, and Hacker News have turned into Copilot group therapy. You’ll find everything from “It finished my CRUD in seconds” to “It hallucinated an entire API, and I merged it anyway.”
TL;DR: This isn’t a Copilot hate post. It’s a reality check. We’re watching a cultural shift happen in real time where AI tools are outpacing our ability to understand what they create. Microsoft’s internal engineers are the canary in the coal mine. Their struggle with Copilot isn’t just about buggy code it’s about how engineering itself is evolving (and breaking).
The honeymoon phase when Copilot felt magical
The first time you try GitHub Copilot, it feels like discovering cheat codes for coding. You write half a function name, and boom it finishes your thought like a telepathic junior dev who actually knows regex. For a few glorious weeks, you’re convinced you’ll never touch Stack Overflow again.
I remember that phase vividly. You start using Copilot to crank out boilerplate, generate mock data, even write tests. It nails 80% of what you need. You start flexing in stand-ups “yeah, I finished the API early.” You conveniently skip the part where Copilot imported the wrong version of Axios and wrote a promise chain straight out of 2015.
Microsoft leaned into that magic too. Their engineers showcased how Copilot “cut code review time” and “increased dev velocity.” It’s the kind of language that makes product managers salivate. The productivity graphs went up, the dopamine followed, and soon everyone was bragging about how Copilot “understood their style.”
Reddit and Hacker News threads mirrored the hype. Developers called it “the future of software.” Others joked it was “like hiring an intern who doesn’t sleep.” It really did feel like AI was finally the teammate we always wanted fast, confident, and endlessly available.
But here’s the catch: speed creates trust. And trust, in engineering, is dangerous when it’s blind. We started hitting Tab like it was a muscle reflex. Copilot didn’t just autocomplete our code it started autocompleting our thinking.
And once you stop thinking, you stop noticing. That’s where the chaos quietly began.
When autocomplete becomes autopilot
Here’s the thing Copilot was never supposed to replace us. It was supposed to assist us. But somewhere between “autocomplete” and “autopilot,” we all got too comfortable letting it drive.
You’ve seen it: that split second when you’re mid-function, Copilot whispers a 10-line solution that looks right. You nod, hit Tab, and move on. Then you merge, deploy, and six hours later, QA finds out Copilot’s “perfect” code was just confidently wrong.
Now scale that up to enterprise. Inside Microsoft, teams reportedly found themselves reviewing PRs where 60–70% of the code came from Copilot suggestions. And sure, it works until someone asks, “Wait, who actually wrote this?” Cue the silence.
Developers start trusting the pattern, not the logic. It’s overconfidence bias in code form. When AI gets it right enough times, you stop questioning it. You skip the test. You assume it’s fine. You assume it’s you that’s overthinking things. That’s the gaslight.
It’s not just anecdotal either. GitHub’s own blog posts mention “increased velocity” and “decreased mental load.” Translation: we’re coding faster, thinking less, and debugging more. There’s a reason Reddit threads titled “Copilot made me lazy” hit the front page.
It’s like flying on autopilot with a system that doesn’t understand turbulence.
AI doesn’t “know” context it predicts it. And those predictions look stunningly accurate until they crash into edge cases.
Competitors like Cursor and Codeium are learning from this adding self-review prompts, code diff explainers, and citation systems. But the culture is already shifting. The more Copilot handles, the less devs feel responsible for the code they ship.
And that’s how we end up debugging strangers’ logic written by an AI, approved by a human who didn’t read it.
Debugging AI code you didn’t write
If you’ve ever spent a night debugging a bug that “wasn’t yours,” congratulations you’ve already experienced the future. Except now, that mystery code isn’t from a sleep-deprived teammate… it’s from your friendly neighborhood Copilot.
Here’s how it usually happens: you ship something fast, it works in dev, everyone claps in Slack. Then, three sprints later, an issue surfaces a subtle data mismatch, a cache that never invalidates, or a function that returns undefined in the weirdest corner case. You trace it back, open the file, and realize: you didn’t write this. You just… pressed Tab.
It’s a surreal feeling debugging AI-generated logic that technically came from your account. You find elegant function names and clever abstractions, but the moment you step through the code, it’s like reading an alien language that just happens to be syntactically correct.
Copilot writes like a senior dev structured, confident, maybe even poetic. But it fails like a junior who didn’t test a thing.
Researchers at Microsoft published a paper last year The False Sense of Security in AI Pair Programming showing that developers reviewing AI-generated code missed 40% more bugs than those reviewing human-written code. Why? Because AI code “looks clean.” And clean code is seductive.
There’s also what I call responsibility diffusion:
- Devs assume Copilot’s suggestions are correct.
- Copilot assumes devs will verify them
- Nobody actually does.
One Hacker News comment summed it up perfectly:
“Copilot saves me 30 minutes writing code and costs me 2 hours debugging it.”
That’s the new equilibrium productivity on paper, chaos in production.
AI didn’t eliminate grunt work; it just moved it downstream.
So when something breaks, you’re not debugging your logic anymore you’re debugging a model’s assumptions about your logic.
Dev culture vs AI-driven deadlines
The real Copilot crisis isn’t code quality it’s culture. Somewhere along the line, management saw “AI productivity” dashboards and decided engineers could magically double their velocity. Spoiler: we didn’t. We just doubled our review backlog.
You can see the ripple effects across teams. PMs love AI-generated commits they look impressive on graphs. More commits, more progress, right? Except half of those “lines of code” are later rolled back because no one actually understood them.
It’s like watching someone floor a Tesla on autopilot and brag about how fast they’re going ignoring the fact they’re headed straight into a wall of merge conflicts.
Developers feel it most in reviews. PRs that used to be 200 lines of clean logic are now 700 lines of Copilot fan fiction. And when you try to leave a comment like, “Why does this exist?”, your teammate shrugs: “It’s what Copilot suggested.”
The result? Code review fatigue.
Everything looks syntactically perfect but semantically cursed. Reviewing AI code feels like grading an essay that ChatGPT wrote about you technically correct, emotionally hollow.
This shift changes team dynamics, too. Junior devs learn less because they’re following AI patterns instead of understanding fundamentals. Senior devs spend more time cleaning up invisible messes. And managers think it’s all progress because the metrics look shiny.
Microsoft’s internal dev chats (the ones that occasionally leak onto Reddit) show a weird mix of awe and exhaustion. Some engineers swear Copilot cuts meetings and boilerplate; others say it erodes craftsmanship. Both are right.
Because here’s the uncomfortable truth: AI didn’t change what we build it changed how we think about building.
And when velocity becomes vanity, quality quietly dies.
What’s next coexistence or collapse?
By 2025, every IDE ships with “AI mode.” What started as an optional plugin is now baked into the dev stack like linting or Git blame. You open VS Code, and it’s already whispering, “Want me to finish that function for you?” It’s not the future anymore. It’s the default.
But here’s the paradox: we’re writing more code than ever, and understanding less of it. The more Copilot evolves, the less connected we feel to the logic beneath our own commits. You can almost sense it engineers aren’t shipping software anymore, they’re shipping suggestions.
So what’s the path forward? It’s not ditching AI; that ship’s sailed. It’s coexistence with discipline.
- Write code reviews like they matter again.
- Keep a “code journal” to remind yourself why decisions were made.
- Use AI as a sparring partner, not a crutch.
- Slow down once in a while and actually read the diff before merging.
And maybe, just maybe, stop bragging about 10x productivity if half of it goes to debugging Copilot’s “creative liberties.”
There’s an old-school mindset worth reviving here craftsmanship. The quiet pride of code you fully understand. The joy of a function that passes tests not because AI said so, but because you made it elegant.
Mildly controversial take: the best AI engineer isn’t the one with the most automation it’s the one who still writes boring manual tests.
Soon, Copilot will review Copilot. Multi-agent IDEs are already experimenting with self-verification loops. But if we’re not careful, we’ll end up in a recursive blame game where the machines argue over which AI caused the bug.
AI won’t replace engineers but it might replace craftsmanship if we let it.
Helpful resources
If you want to dive deeper or fact-check some of the receipts behind this piece, here’s a short list worth bookmarking:
- GitHub Copilot official documentation setup, configs, and team usage data.
- Reddit: r/programming “Copilot horror stories” real dev chaos, daily.
- Hacker News: “AI code review burnout” threads sharp takes from senior engineers.
- Devlink Studio: AI workflow hygiene guide how to use AI tools without losing your sanity
Top comments (5)
Problem is not with AI. Problem is with how we're writing software in general. AI just made flaws so evident that we can't turn a blind eye on them anymore. Software development deserves to be an engineering, not art. As soon as we start writing code using mechanical rules instead of "conceptual" art shenanigans of personal tastes and preferences, issue disappear. There barely will be any difference between code written by AI or human.
P.S. Here is my take on it.
First, Microsoft has been gaslighting and handcuffing software developers for decades; that'll never change.
Second, I've been trying to warn about this for the past three years. We're auto-shipping software that's heavier, more brittle, and way more bug-ridden than ever before, and with an order-of-magnitude drop-off in comprehension with regard to being able to reason about those bugs and breaks, much less jump in with any capability to actually find and fix them as efficiently as yester-decade's quality-minded engineers were capable of.
Finally, to those of you that have been around for two or more decades: I see you. Solidarity.
Having coded for 35 years I've seen it all. The first 17 years there was simpler more reliable code options. COBOL, SQL/DB2, JCL, CICS, a simple editor, line commands and library tools to migrate code. I could write the code half asleep. We were responsible for several business departments. If there was a problem with an accounting system you walked over to Accounting, started with a Manager and worked your way down to the end user. Went over the issue, reproduced the problem and report to your IT Manager the problem and what you planned to look at to debug. Found the problem and told the manager your plan to fix and permission to go ahead. The code patterns were simple enough to follow and it did not take long to isolate problem blocks. Placed your hand under your chin, rubbed a bit and ran through possible bad outcomes. Then true experimentation by trying the top 3 solutions in your head. By that time you see the linear trace and the point in which things turned. Trace back up for a decision outcome and discover the undhanled/mishandled piece of the process and adjust it. Most of the issues were due to missed or lack of communication from upstream processes or poor code choices like hard coding values before we learned we should never do that.
I've been through the code generators, canned software, reporting tools, screen scrapers and the worst were the code generators that came from design tools and didn't allow a line of code to be changed without regenerating. The months wasted learning a bloated tool to be able to fix a block of code. Now I was fine with build my web UI model's GET/SET functions for data exchange. The point here is that some tools arrive that help and are unobtrusive and others can cause complete chaos and add techinical debt.
I use DeepSeek to run an idea or raw high level plan and get some feedback on whether this is currently industry standard conceptually sound. Some back and forth on options and try to get to the most optimal combinations of options but with the understanding that even the best of plans can change as we progress in building solutions.
There are many cases in which the AI suggests code and the code is bad. That is to say that it will not build or the combination violates package dependencies or is not capable of reach a finite solution because it lacks capabilities in a given scenario which on the surface is difficult to predict.
Copilot is there now when I code C#.NET applications. I seldom use it. The way I code is to build from templates or reuse code I trust and runs cleanly. I have used Copilot in MS Word to built an outline for a project document that I do. I'm still writing task documents because I like to keep up my writing skills and having a reference if nothing else for myself to use as notes and steps to follow when I move on/off code languages between C#.NET, Java, Python, Angular and React.
I have made a living form Microsoft but I have also known they are not my friend and could care less about my well being or purpose on the planet. I will code in spite of them is my philosophy. I did drop ChatGPT because of OpenAI and its recent push to monetize AI models. In learning LLM-NLP with RAG it felt wrong to have to setup an account on a website that looked a lot like Azure or other Cloud companies who push tier structures and have no care for open source or academic community. I just could not trust OpenAI and Microsoft in the backround anymore and when I asked ChatGPT about cancelling it told me that I would lose my ChatGPT access as well. The decision was easy.
Why is there so much punctuation missing from this article?
Food for thought! Pause, reflect, correct course ...