Summary:
- The article explores how evolutionary algorithms and computational models are increasingly selecting for "complexity for complexity's sake" rather than purely functional optimization.
- It discusses the implications of these findings for artificial intelligence development, specifically regarding how autonomous systems might evolve unpredictable or inefficient architectures when incentivized by certain reward functions.