Swap the model and almost nothing changes. Swap the masking layer, the permission model, or the monitoring, and the system stops running, starts leaking, or quietly drifts wrong. The part everyone demos is the part that matters least.

Key takeaways

Model choice sits behind an abstraction in a well-built system, which means it is the cheapest part to change and the least differentiating.

The expensive 90% is plumbing: masking before any prompt, permissions that travel with the person, audit trails, kill-gated evaluation, drift detection, none of which shows up in a demo.

Research on production ML lifecycles increasingly measures workflow completion, artifact traceability and drift recovery, not just predictive accuracy, because that is where systems actually fail.

A team that can name its out-of-scope cases and its monitoring signal is pipeline-first; a team that can only name its model is not, yet.

None of this is an argument against caring about models.…