Today I ran into a textbook example of a problem that AI just couldn’t solve. I ended up spending almost an entire day digging through the database and eventually found out that the issue wasn’t just related to some unique key conflicts—it was also due to the database field types being insufficient to store the data, which meant there were no logs recorded.
In other words, when more than one variable is involved, it becomes very difficult for AI to pinpoint the issue. I even asked it to suggest "possible causes", but it still couldn’t help—it didn’t mention either of the problems I found.
Of course, when you're dealing with issues caused by large volumes of data, directly connecting AI to the database over SCP is far too risky—especially since this particular problem only occurred in the production environment, not in staging or dev. So clearly, that’s not the right approach. Hopefully, no one ever thinks it’s a good idea to hook AI up to a nuclear weapon system...
Another challenge is when there are multiple variables at play, and the project you’ve inherited already has tons of issues—ones that just weren’t caught during client acceptance testing. You only realize them when you dig into the code. So when you try to fix one bug but end up uncovering two or three more, AI also struggles to help in that situation.
At the end of the day, no matter how far AI advances, we need to build a solid foundation ourselves if we want to maintain our edge.
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