Mistakes and Anti-Patterns
I’ve made most of these mistakes. I’ve built tools that nobody used. I’ve chased releases and spent weeks catching up on things that ultimately didn’t change how I worked. I’ve seen people over-automate themselves into irrelevance and I’ve watched smart engineers build technically impressive things that solved problems nobody had. These are not hypothetical cautionary tales. They’re patterns I’ve watched repeat across a lot of people trying to navigate this moment.
The reason this chapter exists is that the AI era has a particular flavor of distraction that’s hard to recognize because it feels productive. You’re building things. You’re learning things. You’re staying current. But underneath all that activity, the question of whether any of it is actually serving real people with real problems often goes unasked. That question is the antidote to almost every mistake in this chapter.
The through-line here is staying grounded in reality. Real businesses. Real customers. Real friction. Real accountability. The more time you spend in the abstract world of models and benchmarks and demos, the more drift you build up between your work and the thing it’s supposed to serve. This chapter is about recognizing that drift early and correcting it before it costs you a year.