March 9, 2026. I sit in my office staring at my screen, surrounded by AI copilots—glimmering windows that offer suggestions as to how I can make the mundane tasks smoother or more efficient. Kubernetes is running boringly well, eBPF scripts are quietly monitoring our services, and Wasm containers are everywhere, like tiny computational islands in a sea of microservices.

Today, I spent some time working on a project involving a new AI-native tool that promised to simplify my life by automatically managing the complexity of deploying models at scale. The tool was supposed to handle everything from infrastructure setup to version control, but it felt like every time I made a change, the copilot would suggest something slightly different or even outright conflicting.

I found myself in an endless loop of tweaking configurations and trying to understand why my carefully crafted changes were being overruled by the AI. It was like having a co-pilot who didn’t quite know the rules of the road yet. The tool claimed it could handle everything, but I kept running into situations where I needed context that only humans could provide.

For example, I had to debug an issue with a model not loading properly on production servers. The AI suggested adding more logging or adjusting the GPU configuration, both of which seemed sensible on their surface. But after a few iterations, it became clear that the root cause was a subtle networking glitch in our multi-cloud setup. The AI’s suggestions were just a veneer over the underlying issue.

I’m reminded of Tony Hoare’s passing earlier this month. His work on null references was both a blessing and a curse; it made programming easier in some ways, but also introduced new challenges that we’re still grappling with today. Similarly, these AI tools are making our lives easier, but they’re introducing their own set of challenges.

I spent part of the day arguing with an AI copilot about whether or not to implement a feature using eBPF instead of a traditional container approach. The copilot insisted on the latter, citing stability and ease of debugging as key reasons. But I felt that for this specific use case, eBPF would offer better performance and lower latency. In the end, we compromised by implementing it in both ways to see which one performed better.

At the same time, there’s a growing sense of unease among platform engineers about how much AI is really helping versus how much it’s complicating things. The “Copilot edited an ad into my PR” incident from Hacker News earlier this week was just another reminder that these tools are still learning and can make mistakes.

The MacBook Neo announcement has sparked a lot of discussion around hardware and the role of AI in embedded systems. While I’m excited about the potential for AI to enhance device functionality, I can’t shake the feeling that we might be losing sight of what makes good engineering—principles like simplicity, robustness, and maintainability.

In my personal project today, I needed to run an AI model locally to debug some issues in a production environment. The idea that local development could involve running complex models was both thrilling and daunting. It’s one thing to have an AI tool manage the infrastructure for me, but quite another to trust it with the critical task of local development where context is king.

As I wrap up my day, I’m left thinking about the balance between convenience and control. These tools are powerful, no doubt, but they come at a cost—namely, that we need to spend time understanding their limitations and making sure they’re serving us rather than the other way around.

In the end, as much as AI can help, it’s still up to us to maintain that crucial context. Whether I’m tweaking configurations or arguing with an AI copilot about the best approach, I’ll keep striving for a balance where these tools enhance my work without taking away from what makes engineering so rewarding: solving problems and building something that works.

---

[End of Blog Post]