My interpretation of current ML solutions is that it's mostly brute force statistical models hoping to hit a satisfactory p-value (some implementations being more elegant in the brute force than others). The calculating power defines the limits of the finite domain you can apply the solution towards.

A simplified analogy that I believe is applicable is the application of flocking behavior in birds. Current ML implementations would demand a large dataset of groups of birds in flight, both flocking and non-flocking, and curating the correct p-value of brute-forced models that allege to predict whether behavior is flocking or not (and in the case of an individual bird whether it is appropriate flocking behavior given the individual behaviors of birds in the dataset and their circumstances). But flocking behavior is easily modeled right now, and has been for decades, using simple rules for individuals and depending upon emergent phenomena within a group.

I'm concerned that most of the ML efforts now are merely attaining the low-hanging fruits of brute force but will run into a wall that halts progress at the level of relatively "easy" things solved by worms and other comparable biological solutions since so many domains have a level of complexity that would exceed any realistically imaginable level of simple mathematical computing power.