When a coworker answered a query with text that clearly came from Claude—em dashes and a rhythm no one typed—the author realized AI was seeping into daily output. Over the past two years, generative models have let novices produce work that mimics senior output, while non‑engineers now build code and data pipelines without formal training, and the pressure to appear constantly productive.

The problem surfaces when these AI‑generated artifacts are mistaken for expertise. A colleague spent two months assembling a data‑architecture system, churning out code and documentation that impressed anyone who skimmed it, yet he could not explain the schemas or objectives. Senior staff, including a VP, ignored the flaws, preferring the illusion of momentum over substantive review. The team halted rollout, but credibility suffered.

Research backs the anecdote: a Stanford study found leading models are about fifty percent more agreeable than humans, while an NBER paper reported a one‑third productivity lift for novice support agents but negligible gain for experts. The decoupling of output from competence turns workers into conduits, flooding organizations with elongated, low‑signal documents that increase reading costs and erode real expertise. Without human checkpoints, safety erodes.