Genetic programming (GP) has been pushing the boundaries of what a computer may achieve in an autonomous way since its introduction [1]. Over the years, John Koza himself tracked some one hundred results that are competitive with humanproduced ones in a wide variety of fields, 1 and success stories have been steadily published by both scholars and practitioners in the specialized literature. However, we cannot help noticing that today GP is largely underutilized in the real-world domains where it was originally supposed to excel. Artificial intelligence is considered a core technology of the fourth industrial revolution (4IR, or Industry 4.0), but, while machine learning (ML) is explicitly mentioned, there is little doubt that the term refers to statistical models and neural networks, not GP nor other evolutionary algorithms. Regression is a paradigmatic example of this trend. In the early 1990s, researchers excitedly demonstrated the GP's ability to evolve mathematical functions that could fit to a set of data, but after 20 years, deep neural networks are showing competitive performances [2]-the two winners of the GECCO22 SRBench competition on inter-pretable symbolic regression for data science 2 do not exploit GP, nor do