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The Pareto principle

Also called the 80/20 rule · Economics & productivity

The Pareto principle — the 80/20 rule — observes that for many outcomes, roughly 80% of effects come from 20% of causes. A small share of inputs (customers, effort, code, sources) produces most of the results.

How it works

Effects are rarely distributed evenly; they concentrate. Identifying the vital few inputs that drive most of the output lets you focus effort where it compounds and stop spreading it thinly across the trivial many.

How to use it


  • Find the ~20% of tasks, customers, or products driving ~80% of results — and double down there.
  • Cut or de-prioritise the low-yield majority.
  • When overwhelmed, ask: which few inputs actually move the outcome?

Worked example

A business often finds ~80% of revenue comes from ~20% of clients; a codebase has ~80% of crashes from ~20% of bugs. Fixing the vital few beats spreading effort evenly across everything.

Where it fails

The ratio is a rough observation, not a law — it may be 90/10 or 70/30. And the “unimportant” 80% isn’t always disposable (the long tail can matter). Use it to prioritise, not to justify neglect.

The deeper point

The 80/20 rule eats itself: apply it to the vital 20%, and inside that there’s another 20% doing most of the work. The real move isn’t finding the 20% once — it’s applying the lens recursively until the leverage is obvious.

Frequently asked


What is the 80/20 rule?
The Pareto principle: roughly 80% of results come from 20% of causes — a small share of inputs drives most outcomes, so focus on the vital few.
Is the Pareto principle always exactly 80/20?
No — it’s a rough pattern, not a fixed law. The real split might be 90/10 or 70/30. What matters is that effects concentrate, not the exact ratio.
How do you apply the 80/20 rule?
Identify the ~20% of inputs driving ~80% of results and concentrate effort there, while cutting or minimising the low-yield majority.

Related


Editorial synthesis © ReadGlobe 2026, drawing on the mental-models tradition (Charlie Munger, Farnam Street) and the primary sources for each model. · Last reviewed 2026-05-29.