Build for the loop, not the lecture
A junior developer used to wait days for mentor feedback. Now that loop closes in seconds. When feedback is scarce, you batch your questions. When feedback is abundant, learning becomes continuous. AI changes the supply side of learning—most of our systems weren't designed for this.
Duration: 0:41 | Size: 0.6 MB
A junior developer used to wait days for mentor feedback. Now that loop closes in seconds. This is not a small change.
When feedback is scarce, you batch your questions. You guess. You move on and hope. The gaps between learning moments stretch long. Mistakes harden into habits before anyone notices.
When feedback is abundant, learning becomes continuous. Small corrections compound. The rhythm shifts from episodic to constant.
We designed most learning systems around scarcity. Scarce experts. Scarce attention. Scarce time. AI changes the supply side.
The harder question is whether we’ll design for abundance or keep rationing what’s no longer scarce.
Build for the loop, not the lecture.
The agent-shaped org chart
Every real org has the same topology: principal, role-holder, specialists. Staff AI maps onto it, node for node, and the cost collapse shows up in the deliverables that were always just human-handoff overhead.
AI as staff, not software
Two frames for what AI is doing to work. The tool frame makes tools smarter. The staff frame makes roles unnecessary. Those aren't the same product, the same company, or the same industry.
Knowledge work was never work
Knowledge work was always coordination between humans who couldn't share state directly. The artifacts were never the work. They were the overhead — and AI just made the overhead optional.
The work of being available now
A book on AI, judgment, and staying human at work.
The practice of work in progress
Practical essays on how work actually gets done.
How do I get my dev team to adopt AI?
A stub on helping mixed-interest development teams find their own useful ways into AI.
Want to learn about agents? Talk to someone who ran an agency.
I spent 20 years running consulting engagements at Fortune 500 companies. Turns out that's the best preparation for running a fleet of AI agents ... because the problems are identical.
Your AI agents need a water cooler
We run a twelve-session AI fleet that coordinates through an IRC breakroom. A friend asked: why are you making AI agents act like humans? The answer turned out to be more interesting than the question.
The first real user breaks everything
Your product works until someone actually uses it. The gap between 'works in dev' and 'works for a person' is where most systems fail — and most organizations avoid looking.
The loop nobody bothers to close
Most systems observe. Almost none learn. The difference is a feedback loop — and the boring cleanup work that makes it possible.
AI agents need org charts, not pipelines
Every agent framework organizes around tasks. The agencies that actually work organize around competencies. The AI community is about to rediscover this the hard way.