Big O notation describes how the work done by an algorithm grows as the input grows. It does not try to predict the exact number of milliseconds on your laptop. Instead, it gives you a stable way to compare strategies as the problem gets larger.
Why Engineers Use Big O
Two pieces of code can solve the same task but scale very differently. A loop that runs once over an array behaves very differently from a loop inside another loop. Big O gives you the language to explain that difference before the system is under load.
Common Complexity Classes
Constant time stays flat. Logarithmic time grows slowly. Linear time grows in proportion to the input. Quadratic time appears when one pass is nested inside another. Exponential growth is usually a sign that brute force or repeated recursion needs to be replaced.
How To Read It In Real Code
Start by counting the loops, recursion depth, and any extra data structures. Then ask how those parts depend on input size. If you repeatedly halve the search space, you are likely looking at logarithmic growth. If every element compares with every other element, the runtime is probably quadratic.
TimeComplexityAI is useful here because it translates the code into a readable explanation instead of making you infer every cost manually.