https://en.wikipedia.org/wiki/Kolmogorov_complexity

In algorithmic information theory (a subfield of computer science and mathematics), the Kolmogorov complexity of an object, such as a piece of text, is the length of a shortest computer program (in a predetermined programming language) that produces the object as output. It is a measure of the computational resources needed to specify the object, and is also known as algorithmic complexity, Solomonoff–Kolmogorov–Chaitin complexity, program-size complexity, descriptive complexity, or algorithmic entropy. It is named after Andrey Kolmogorov, who first published on the subject in 1963 and is a generalization of classical information theory.

Put another way, it's a measure of how compressible information is. If you have a file that's 1 GB, but it's all 0's, you can compress that down to 4 bytes by simply storing the number of 0's. On the flip side, random data can't really be meaningfully compressed.

Another example from the link:

This image illustrates part of the Mandelbrot set fractal. Simply storing the 24-bit color of each pixel in this image would require 23 million bytes, but a small computer program can reproduce these 23 MB using the definition of the Mandelbrot set, the corner coordinates of the image and the parameters of the color mapping. Thus, the Kolmogorov complexity of this image is much less than 23 MB in any pragmatic model of computation. PNG's general-purpose image compression only reduces it to 1.6 MB, smaller than the raw data but much larger than the Kolmogorov complexity.