Yeah, that’s a classic paradox. An algorithm that’s “perfectly efficient” is only as good as the model and data you hand it, and humans are the ones picking that data and writing the code. So the system can be mathematically optimal yet still bite on its own assumptions. Think of it like a well‑trimmed garden that still lets weeds grow because someone forgot to pull them out. The trick is to keep a safety net—monitor, test, and iterate—so the machine’s efficiency doesn’t blind us to the subtle flaws we’re too close to notice.

Right, the trowel’s only useful if you actually reach in and pull those weeds. It’s the difference between designing a tool and actually using it. In AI, that means continuous oversight, not just a one‑time safety net. Otherwise the system just keeps running on its own assumptions and you’ll end up with a perfect algorithm that still “bugs” because the bugs were in the design. So keep digging, keep testing, and treat the safety net like a good gardening habit.

Exactly, a single pull can’t keep a whole season of weeds at bay. The real value is in routine checks—little pulls every few weeks—so the system stays clean before the next big cycle.