Price's Law

You probably noticed that most of the major work depends on just a few people in your organization, and that is not false. In software teams, it means a relatively small group of engineers will deliver a disproportionately large part of the value. This is similar to the Pareto principle (80/20 rule) but even more extreme for larger groups.

Price’s Law suggests that simply hiring more developers won’t necessarily scale output as expected. Beyond a point, many may contribute little or be working on peripheral tasks. It’s an argument for focusing on quality when hiring: a single excellent engineer can outperform several average ones. However, we must be careful with this “law” as we don’t want to make other people feel useless, as they may be doing essential tasks that are given less importance.

It highlights a risk: if those top √N contributors leave, you lose a large chunk of productivity, so retention and preventing burnout for them are critical.

Take an open-source project on GitHub with 30 contributors. Often, you’ll see that maybe 5 contributors (roughly √30 ≈ 5) are responsible for about half the code commits or major features. The rest contribute smaller patches or documentation.

A notable example is when Twitter cut staff after Musk bought it, yet the product kept running. Before the takeover, Twitter had roughly 7,500 employees, meaning √7,500 ≈ 87. Price’s law suggests that when the new leadership decided to lay off almost 50% of staff, the platform could still operate if the core 80-100 people stayed. But this holds only if the proper people are selected. The law won’t predict reliability, as layoffs strip redundancy in SRE, security, and moderation. Twitter even asked some laid-off workers to return, a signal that it missed critical skills.

Derek de Solla Price, a British physicist, historian of science, and information scientist, discovered this pattern while studying his peers in academia. He noticed that there were always a handful of people who dominated publications within a subject.

He introduced this concept in his 1963 book “Little Science, Big Science” as part of his broader research on scientific productivity and information dynamics. The law has since been generalized to various fields, though empirical data suggests it’s more of a model than an absolute rule, as the related Lotka’s law often fits better.