Not really. Your task might be to compress random weather data or historical stock info or historical lottery results.

In those datasets you could improve compression by adding apriori knowledge about their context in the real world - if the compression program knows about historical S&P500 prices, it will be pretty good at compressing all historical stock info. Kolmogorov complexity is indistinguishable from AI

They are not random (e.g. weather data should have a daily or seasonal pattern). And the fact that there does exist a random-looking data doesn't refute the original claim because AI can conclude that the data has a close-to-maximal entropy (i.e. "incompressible") instead.