Part of it is that there's very casual, conversational usages of Big O vs. much more formal usages when trying to actually quantify performance. Conversational

Part of it is that there's very casual, conversational usages of Big O vs. much more formal usages when trying to actually quantify performance. Conversational Big O tends to include a lot of spherical cows (https://en.wikipedia.org/wiki/Spherical_cow https://en.wikipedia.org/wiki/Spherical_cow), because when Big O comes up in conversation, people are more likely to be talking in generalities about high level designs. More detail could be counterproductive in that context. When someone is actually trying to quantify and predict performance of an algorithm, then the other extreme becomes desirable- the more detailed and specific the function, the better.