AI and the Devaluation of Effort (tawandamunongo.dev)

The author argues that generative AI — LLMs, copilots and autonomous agents that can spin up prose, prototypes and codebases from a few prompts — has shifted a subtle but profound value judgment in knowledge work: effort itself. Where scarcity and friction once forced prioritization and created attachment to finished work, near-costless creation now fuels “over‑creation”: dozens of half-started repos, outlines and drafts that never get finished. The piece identifies three core principles driving this — low friction leads to low attachment, abundance dulls meaning, and vanished scarcity destroys the selection pressure that made completion matter — and shows how that decoupling of input (effort) and output (value) turns creators into curators of machine output rather than authors. For the AI/ML community this isn’t just philosophical. It has practical implications for tooling, collaboration, research rigor and skill development: ease of generation can hide shallow understanding, encourage superficial experiments, and change incentive structures (more starts, fewer shipped outcomes). The remedy the author proposes is deliberate friction — intentional constraints, single‑project focus, and a discipline of finishing — to preserve the “becoming” that labour produces (debugging, failure-driven learning, ownership). In short, AI multiplies capacity but not care; the community must design workflows and norms that reintroduce meaningful limits so completion, competence and creative transformation survive automation.