The Idea
Collecting AI summaries is not the same as building judgment.
We save effortlessly now. We clip, summarise, and store. But a growing archive of fragmented outputs is not knowledge; it is a record of deferred thinking.
The Collector's Fallacy exists: the more you save, the more you mistake retrieval speed for understanding. Real judgment comes from connecting underlying laws, not from a well-organised folder.
The leverage is not in the archive. It is in the extraction.
The Tactic
The Problem-Principle-Case Loop
This protocol turns a pile of saved outputs into a set of rules you can actually apply.
Problem — Start with a real-world confusion or decision you are facing. Do not let the tool set the agenda.
Principle — Apply a macro framework to identify the root cause. Look for a rule that explains the pattern. First principles, Occam's Razor, sunk cost, second-order effects. Something with a name.
Case — Map this principle back to a past experience. Write one sentence: "When X happened, this principle explains why." That sentence is now a reusable method.
The mechanical advantage: you stop logging and start compounding.
The Spark
I am re-reading Poor Charlie's Almanack this week. Munger's whole system runs on a lattice of mental models, not a library of facts. The gap between his approach and how most people use AI tools is worth sitting with.
Until next time,
Gav.
