The Honest AI Tool Evaluation Framework Nobody Is Writing
Last October I had 14 AI tools running in parallel across three monitors. Cursor for code, Claude.ai for reasoning, Perplexity for research, Notion AI for docs, a custom GPT-4 wrapper I'd built myself, and nine others I was "evaluating." My monthly AI spend had crossed $400. My actual productive output was worse than when I had two tools. I had optimized for coverage and achieved paralysis. That embarrassing month forced me to build an actual framework for evaluating AI tools — not the listicle kind, but the kind that makes you say no to things.
The Real Cost Is Cognitive Overhead, Not the Subscription Fee
Every AI tool evaluation I've read focuses on benchmarks, pricing tiers, and feature checklists. That is the wrong unit of analysis. The correct unit is: how much mental RAM does this tool consume per hour of use?
A tool that costs $20/month but requires you to context-switch, re-explain your project, re-paste your codebase, or mentally translate its output back into your actual workflow is not a $20 tool. It is a tool that is quietly taxing every session with hidden overhead. When I audited my October stack, I found I was spending roughly 40 minutes per day on tool management — opening tabs, copying outputs between tools, re-prompting because context had been lost. That is 14 hours a month of work that produced zero output.
