Frankly, the "AI as accelerant" argument, as fomoz puts it, holds true only when you have a solid understanding of the domain. In enterprise system builds, we don't often encounter theoretical physics where errors might lead to a broken model rather than a broken system. Instead, a faked coefficient from an LLM could mean a production outage.

It's why I push for a hybrid mentor-apprentice model. We need to actively cultivate the next generation of "Schwartzes" with hands-on, critical thinking before throwing them into LLM-driven environments. The current incentive structure, as conception points out, isn't set up for this, but it's crucial if we want to avoid building on sand.