Source: Nate’s Substack
Current LLMs demonstrate a sharp competency gap: they can reverse-engineer and rebuild massive codebases from scratch, yet stumble on targeted modifications to existing libraries—tasks humans consider simpler. This inverts the intuitive hierarchy of difficulty. LLMs operate well when reconstructing from general principles but break when asked to navigate specific constraints and existing architecture. The risk is not overestimated AI intelligence, but systems whose strengths are orthogonal to production software work, where understanding what *not* to break matters more than what to build from nothing.