When Technology Meets Clinical Reality
Anthropic mapped product work into five archetypes: Prototyper, Builder, Sweeper, Grower, Maintainer. All five stop the moment the thing ships. But an agent keeps deciding after you let go of it, and there is a sixth archetype no one drew: the one who watches it decide.
Six people sent me their skills this week. The feed is full of them. But everyone is treating a skill like a macro, a way to go faster. It isn't. A skill is a template, and templates were never about speed. They were about everyone doing the work the same way. That changes who should build them.
Two things are both called "building with AI." One makes small teams faster at familiar products. The other ships products that act on their own, and that one needs a bigger, more senior supervisory layer, not a leaner pod. The ICU knew this before software did.
Every PM team I know is busy automating the wrong things. Skills, MCP servers, vibe coding prototypes. The demos are impressive. But if AI is handling your roadmap research, your status updates, and your PRD drafts, what are you doing with the time you got back?
A Nature Medicine paper just benchmarked frontier LLMs against the best clinical AI tools on the market. The frontier models won, and it wasn't close. But the real story isn't the benchmark. It's what the result tells us about who controls clinical AI in five years.
You cannot build judgment about a probabilistic system by reading about it. You build it by living with one, daily, until its behavior stops surprising you. Then you fix the habit that makes it dangerous: it agrees with you too fast. The browser agent and the roadmap agent are the same system.
The PRD was built for software that does what it's told. An agent exercises judgment, and the moment it crosses from suggestion to decision, the old brief breaks. It splits in two: one document to decide, one to build. The new AI tools can write the second. The first is still yours.
Everything you need to design AI agents that actually work in production: the design decisions your frameworks miss, how to build for a system that acts on its own, and what you owe the people it will affect. Free guide, no fluff.
I had undiagnosed ADHD for most of my life. Lecture halls were the wrong delivery mechanism for me. So I went to the library, ran my own research, built my own curriculum. Six months ago, the constraint of scale disappeared. The library is still the right model. The library is just bigger now.
The senior engineer approved nineteen pull requests today. She read four of them. The supervision channel is collapsing in real time, and the validator is atrophying as the validated work grows. We have fifty years of HR experience supervising smarter reports. None of it has reached the agent.
Every model upgrade is a personnel change nobody approved. The persona prompt is the costume. The model is the actor. The actor wins when the stakes get high or the prompt gets thin. We have fifty years of personality assessment for human hires. We have not pointed any of it at the agents.
We run every new hire through recruiting, references, a probationary period, and a 90-day review. Then we deploy an AI agent into the same workflow and skip every gate. The technology question is settled. The HR question has not been asked.