Getting good at AI takes time and practice

The post argues that becoming skilled with AI requires durable, long-term abilities rather than disposable prompt tricks, including supplying the right context, learning what models can and can't do, recognizing characteristic AI mistakes when reviewing code, and quickly skimming verbose output. It contends that new models improve mostly on tasks older models already handled well, so these skills remain useful, and that software's human-machine "centaur age" may last decades. The author concludes that deliberately avoiding AI likely leaves people behind, while going fully into AI is also a mistake since LLMs reward expertise best gained by hand.