Scientific models tend to express a common computational relationship. That's because we like to quantify things in scientific models, and perhaps unsurprisingly, we have a fairly standard paradigm for quantitative analysis in our mathematical algebraic, geometric and topological models.
The physicists here are discussing a feature of using information theory to generalize how certain fixed parameters can take values at different scales while still preserving most of their predictive structure. That's all.
Science journalists need to stop sensationalizing mathematically interesting results. This is a neat account of scale and pattern matching in applied mathematics, but it's not a "unified theory of all scientific theorising" any more than, say, Bayesian Inference is.