Applying Fermi Estimation to Business and Everyday Problem Solving

In a recent discussion about Ant Group’s IPO, the author uses a Fermi‑style calculation to estimate that roughly 6,000 programmers could each earn over five million yuan, prompting readers to request a deeper explanation of the Fermi estimation method.

Executives in large tech firms often glance at a few numbers on a slide and instantly gauge operational issues, impressing audiences with rapid, seemingly magical conclusions.

Fermi estimation, popularized by Wu Jun’s column “Google Methodology,” is described as a powerful thinking technique that can make one appear exceptionally insightful and can accelerate career advancement.

The method was originally proposed by the Italian physicist Enrico Fermi, offering a systematic way to tackle problems when information and resources are limited.

Its core idea is to decompose a big problem into many smaller, mutually exclusive and collectively exhaustive (MECE) sub‑questions, allowing answers to be derived from experience and reasoning without needing expert input.

As a historic illustration, on July 16, 1945, the first atomic bomb detonated in New Mexico; 40 seconds later the shock wave reached Fermi’s base.

Fermi tore a page from his notebook, felt the shock, and instantly estimated the bomb’s energy as equivalent to about 10,000 tons of TNT.

Subsequent detailed measurements confirmed that Fermi’s quick estimate was remarkably accurate.

Fermi, a Nobel laureate and consultant to the Manhattan Project, demonstrated the profound skill behind such on‑the‑spot calculations.

One practical case examines how many piano tuners are needed in Nanjing: using assumptions about population, household size, piano ownership, and work hours, the calculation yields roughly 150,000 piano adjustments per year and about 180 professional tuners.

The example shows that by breaking the problem down and applying reasonable assumptions, one can arrive at a close‑to‑real figure.

A second case estimates daily milk‑tea sales near Shanghai’s Zhangjiang subway station: assuming 30,000 peak commuters, a 20% purchase rate, and 1.5 cups per buyer, the estimate reaches about 9,000 cups, rising to roughly 11,250 cups when including delivery orders.

These examples illustrate that the “Fermi formula” works by dissecting large questions under the MECE principle, using personal knowledge to obtain estimates that are often within an acceptable margin of error and highly useful for decision‑making.

If you found the content helpful, please like, comment, or share—it’s the greatest support for the author.