Where clinical depth meets AI product thinking. Written by Yoram Friedman, MD, physician and enterprise product leader.
A new paper rebuilds the DIKW pyramid for medicine and shows where AI actually breaks. Strong on the arrows nobody worried about, weak on the one the structure depends on. The pyramid always assumed you could tell which layer you were standing on. That assumption is gone.
A Cybercab was filmed with no steering wheel. Removing it was the honest part. The rungs below it, hands on the wheel, eyes on the road, ready to take over, were never supervision. They were accountability. Most enterprise AI is still on rung three.
Engineering changed its unit of work to intent, policy, and boundaries. Product and design mostly still ship user stories and screens. So the policy gets written by the last person to touch the ticket, in a prompt, at midnight. The control plane has inputs, and nobody upstream owns them.
The Claude Code team ships most of its code through an agent that acts on its own. The six lessons everyone is copying are real. But in a regulated enterprise, earning your way out of the loop only counts when the receipt is an audit trail, not one person's memory.
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.
Second edition. The judgment a person used to supply at runtime, and how to write it down before the agent runs. Fifteen chapters for PMs shipping agentic AI where being wrong has consequences.