I had a sidewalk conversation with two neighbors that turned into a real-time debate about AI replacing jobs. Then I watched a Nate video that gave me the exact framework to explain why all 3 were right — and wrong. One neighbor is a project manager already using AI daily. One is a business analyst who coaches companies. One of my neighbours’ husband — a skeptic — is convinced AI cannot do creative work. All 3 are right in their position, and also all of us wrong, depending on which…

Neuroscientist Lisa Genova explains why forgetting is normal — your brain filters out most of your day, loses context when you change rooms, and is terrible at remembering future intentions. I watched her TED talk and realized I had already encountered every one of these failures in AI agents I build for real organizations. Here is how the parallels work, what breaks in production, and what I built to fix it.

I run an autonomous AI agent on a Mac Mini in my house. She handles 16 daily cron jobs — finances, email triage, outreach campaigns, device monitoring, morning briefings. The agent says “done.” But did it actually do anything? I built a 9-dimension eval rubric to find out. Along the way I discovered that my evals were broken, my agent was better than I thought, and the most important metric isn’t pass/fail — it’s whether a failure is your fault or the agent’s fault.