I've watched this pattern play out in systems administration over two decades. The pitch is always the same: higher abstractions will democratise specialist work. SREs are "fundamentally different" from sysadmins, Kubernetes "abstracts away complexity."

In practice, I see expensive reinvention. Developers debug database corruption after pod restarts without understanding filesystem semantics. They recreate monitoring strategies and networking patterns on top of CNI because they never learned the fundamentals these abstractions are built on. They're not learning faster: they're relearning the same operational lessons at orders of magnitude higher cost, now mediated through layers of YAML.

Each wave of "democratisation" doesn't eliminate specialists. It creates new specialists who must learn both the abstraction and what it's abstracting. We've made expertise more expensive to acquire, not unnecessary.

Excel proves the rule. It's objectively terrible: 30% of genomics papers contain gene name errors from autocorrect, JP Morgan lost $6bn from formula errors, Public Health England lost 16,000 COVID cases hitting row limits. Yet it succeeded at democratisation by accepting catastrophic failures no proper system would tolerate.

The pattern repeats because we want Excel's accessibility with engineering reliability. You can't have both. Either accept disasters for democratisation, or accept that expertise remains required.