AI Fatigue? Yea I Know. Here's How I Set Boundaries That Actually Hold

I hit a wall around month three of "AI for everything." Every tool promised 10x. Every morning brought three new products to evaluate. I was spending more time managing AI tools than doing the work they were supposed to accelerate. Sound familiar?

AI fatigue is real, and it's not a motivation problem. It's a signal that your tooling has outpaced your workflow. Here's what actually helped.

Stop evaluating, start finishing

I set a rule: no new AI tool enters my rotation unless it replaces something I already use. Not "complements" — replaces. This killed 90% of my tool churn overnight. The bar isn't "is this good?" It's "does this make my current setup obsolete?"

Use AI at the edges, not the core

The tools that stuck all share one trait: they handle the tedious edges of my work, not the core. Autocomplete for boilerplate. Summarizing long threads. First-draft outlines I'll rewrite anyway. The moment AI touches the part of the work where I actually think — architecture, trade-offs, the judgment calls — it slows me down because I'm reviewing instead of building.

Batch the "check what's new"

I used to read AI news daily. Now I do one weekly pass. Nothing important breaks in 24 hours. The tools that matter are still there in a week, and they're better documented by then.

Keep a "good enough" default

My default stack is boring and it works: one model for drafting, one for code, the editor's built-in completion. I only pull out something special when a task genuinely warrants it. Most tasks don't.

The uncomfortable truth

A lot of AI fatigue comes from tools that are fun to try and painful to depend on. The best test isn't the demo — it's whether you'd notice if it disappeared tomorrow. If the answer is no, it's not part of your workflow, it's a subscription.

What's your boundary? I'm curious what rules other people have found that hold up past the honeymoon phase.