 Both approaches have their value. For rigid mathematical proofs the poor machine approach is often best. For practical applications on finite data sets the rich model strategy often gets much better results, since a poor machine would have to “re-invent the wheel” every time it compresses a data set. This leads to the conclusion that Kolmogorov complexity inherently contains a theory about scientific bias and as such implies a methodology in which the class of admissible universal models should be explicitly formulated and motivated a priori. In the past decennia there have been a number of proposals to define a formal unit of measurement of the amount of structural (or model-) information in a data set.