Cognitive science faces a radical challenge in explaining the richness of human learning and cognitive development. My work proposes that developmental theories can address the challenge by adopting perspectives from computer science. Many of our best models treat learning as analogous to computer programming because symbolic programs provide the most compelling account of sophisticated mental representations. I specifically propose that learning from childhood onward is analogous to a style of programming called hacking—making code better along many dimensions through an open-ended and internally-motivated set of diverse values and activities. I also present a first attempt to formalize and assess the child as hacker view through an in-depth empirical study of human and machine concept learning. We find that while a concept’s syntactic complexity (i.e. its description length) predicts learning, predictions are much better when accounting for various semantic features. We further show that a computational model with a hacker-like design uses these semantic features to better predict human performance than several alternative models of learning. These results lay groundwork for a new generation of computational models and demonstrate how the child as hacker hypothesis can productively contribute to our understanding of learning.