I've been watching our industry get swept up in AI observability promises, and honestly, it reminds me of that Tesla AI Day where they showed how their algorithm needed thousands of examples just to recognize a tree.
Yes, AI is necessary in observability. When you're drowning in millions of metrics and thousands of alerts, human pattern recognition hits a wall fast. I've seen teams spend entire sprints just tuning alert thresholds that AI could optimize in minutes.
But here's my contrarian take that'll annoy both the AI evangelists and skeptics: AI in observability is simultaneously revolutionary and embarrassingly primitive.
Last month, I watched a customer's AI-powered anomaly detection flag a "critical issue" because someone deployed during lunch instead of their usual 3 AM window. The algorithm had learned the pattern but completely missed the context. Meanwhile, any junior engineer would've spotted the deploy correlation instantly.
I treat AI tools like I treat my toddler daughter when she's fighting sleep—they need constant guidance, clear boundaries, and realistic expectations. They can identify patterns we'd miss, but they'll also confidently tell you that a perfectly normal database backup is actually a security breach.
The winning move isn't choosing between AI and human expertise. It's acknowledging that we're still in the "teaching computers what trees look like" phase, but those same computers can process forest-scale data we never could.
What's one observability pattern you've noticed that you think AI would completely miss the context on?