This week I audited every AI workflow I run - cost vs value, no mercy. Here's what I found: > 70% of my workflows are net negative. Most AI setups I've built burn more time and money than they save. > The 30% that work? Easily 10x more efficient than doing it manually. > 65% of the winning workflows are ones I already understood end-to-end - engineering, UI/UX, long-form content, product management, research, mechanism simulations. I'd done them myself dozens of times before automating. > The other 35% worked because there was rich existing context and clear use-cases available. I didn't always know what the output would look like, but I could learn through the process. > Going deep in one domain >>> going wide. Trying to automate a broad range of workflows at once was the biggest cost sink - in both time and money. > Most futile workflow: AI work planners. Overwhelm me more than they help. Tools I've found genuinely powerful but haven't scratched the surface of: Hermes, OpenClaw, Autoresearch, Paperclip. Honest admission: I'm still early in this journey. X makes it feel like everyone has it figured out.