March 23, 2026. I woke up to yet another news item about AI co-pilots doing my work for me. Yet again, another blog post by some self-styled guru touting their latest shiny toy. But today, it’s different. Today, I’m writing this from the vantage point of someone who’s been living with copilot for months, and the reality isn’t as glamorous as those marketing pitches would have you believe.

Last year, my team adopted an AI co-pilot to help us manage our Kubernetes clusters more efficiently. We thought it would be a game-changer, but let me tell you—it’s like having a teenager helping you with your taxes. The initial excitement waned quickly as we encountered more issues than I care to admit.

One day, while I was reviewing logs for a cluster that had been acting up, the co-pilot decided to “help” by suggesting an update to our eBPF program. Now, I’ve worked with eBPF enough to know it’s not something you just bang out in a hurry. This particular script was complex and required a lot of care to get right.

The co-pilot suggested adding a line that would essentially log every function call, which sounded like a good idea at first glance. But after running the code, we noticed a massive spike in logging activity that was causing performance issues. The script had added a call to log::info for almost every single function invocation. My face turned red as I realized the co-pilot had not properly understood the context of our application.

It’s moments like these that make me appreciate the old saying, “Better to do it yourself than ask for help.” Sure, having an AI co-pilot can save you time on repetitive tasks, but when the stakes are high and the consequences of a mistake can be severe (like a production outage), I prefer my own judgment.

But let’s not throw the baby out with the bathwater. The copilot also has its merits. For instance, it helped us quickly prototype a solution to an edge case that we hadn’t anticipated in our service mesh configuration. It suggested some improvements that I would never have thought of on my own. And while it was making some mistakes, those were usually minor and easily corrected.

The real question is how do you make AI co-pilot tools useful without them becoming a liability? The key lies in integrating them with robust review processes and human oversight. We’ve established a workflow where the copilot provides suggestions that we then review line by line. If it suggests something outlandish, we can easily revert or modify the suggestion.

Another challenge is keeping up with the constant updates to these AI tools. They learn quickly, but so do the environments they operate in. I find myself spending more time debugging issues caused by recent copilot changes than I did on actual ops work. It’s like having a pet that demands attention 24/7—it can be exhausting.

This brings me back to last month’s HN post about not posting AI-generated comments. While the tech industry is awash with buzzwords and hype, it’s important to remember that real work still needs real humans. Even with copilot’s help, there are things that I wouldn’t trust it to handle, like debugging production issues or making critical architectural decisions.

As I type this, a notification pops up from my co-pilot suggesting another improvement for our monitoring setup. It’s tempting to just click “apply” and see what happens. But experience has taught me that the best way forward is to think through every suggestion carefully and ensure it aligns with our goals.

So here’s my advice: embrace AI copilots but don’t let them replace your judgment or oversight. They’re tools, not saviors. And as always, remember to keep your guard up against those pesky ad edits sneaking into PRs!

Stay humble and stay vigilant. The future of ops might be AI-native, but the work will still require human touch.

Until next time, folks—keep coding!