Mile-hi · Field Notes on AI Effectiveness
From efficiency
to effectiveness.
What if, instead of measuring how fast or cheap AI runs, we measured how much it helps people, teams, and organizations evolve? Perhaps effectiveness — not efficiency — is the lens we've been missing.
The Framework
Read the thesis →Sixteen cells. Four tiers of effectiveness × four technical lenses. Each cell holds essays in its quadrant.
The Series
All essays →Each series is a book — multi-part essays designed to be read together. Pull one off the shelf.
Latest Thinking
View all →AI Built Layer 1. The Seam Still Needed a Human Adversary.
AI collapsed the cost of building the pieces of a data pipeline that ingested nearly half a billion rows. It did not collapse the cost of integrating them honestly. The data-loss bugs lived in the seam between the pieces — where no review of any single piece could see them.
When You're More Correct Than the Source of Truth
We set out to reproduce the numbers a reference dataset published, and found the reference was wrong. The hard part of the Information layer is rarely access. It is the judgment to know when you are more correct than the thing you are validating against — and a gate that can tell a fix from a regression.
Two Thousand Pages, One Moving Median
The day you turn raw data into a sentence a human can read, you sign up to keep that sentence true as the data keeps moving. Most data-driven writing is trusted because nobody re-checks it — not because it is still correct.
Building AI That Learns From Its Mistakes
The fix for brevity bias and context collapse is not a bigger context window. It is smarter context. The evolving playbook approach turns each AI interaction into institutional learning through three components: context templates, error patterns, and feedback loops.