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 →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.
Is AI Smarter Than We Think, or Just Luckier?
When AI suddenly solves a complex physics problem, is it reasoning or pattern matching? The grokking phenomenon shows the answer is stranger than either: models that have memorized their training data sometimes develop genuine generalization long after they appear to have stopped learning, and the conditions under which this happens are not yet well-characterized.
Building AI Learning Curves: A Vibe Coding Journey
How an interactive AI learning curves visualization was built in under two hours using Claude Code, and what the heterogeneous post-ChatGPT acceleration across domains reveals about how to measure AI effectiveness.
Deere's Physical-World Data Loop
John Deere's See & Spray puts computer vision on the sprayer boom at the bottleneck of row-crop agriculture: herbicide cost per acre. Every pass produces labeled image-and-outcome data only Deere observes.