AI-assisted code creates knowledge gaps developers can't recover from

The Herrengt scenario isn't primarily about bad code quality; it's about the disappearance of the mental models developers build by doing hard work themselves. When an LLM handles the reasoning loop, the human never forms the internal map that makes future debugging possible, and that loss is invisible until something breaks badly.

This sits in a different register from the infrastructure bets we've been tracking. The IBM $240M Nvidia cluster deal (covered August 12) reflects enterprise confidence that AI workloads will keep scaling, but Herrengt's critique asks what happens to the human side of that equation as reliance deepens. Those two stories aren't in direct tension, but together they sketch a gap: capital is flowing toward more AI capacity while the organizational consequences of that capacity remain largely unexamined. The concern here belongs to a conversation about developer tooling and team structure, not compute markets.

Watch whether engineering teams at companies that have publicly committed to heavy LLM-assisted workflows (Fable is one named example) begin reporting measurable increases in onboarding time or incident resolution time over the next 12 to 18 months. That kind of operational data would move this from plausible theory to documented pattern.