The core idea: AI is crossing from novelty to infrastructure, and the economics shift from 'should we adopt?' to 'how efficiently do we consume it?' The framing is sound — it echoes the electrification lag economists like Paul David documented, where real productivity gains arrived decades after the technology itself. But the weak spot is measurement: electricity had clean units like kWh, while AI output quality is heterogeneous and enterprise ROI accounting is still immature, so 'efficiency' risks staying a buzzword before it becomes a metric. There's also a countervailing force the analogy misses — inference prices are collapsing fast, which can hide bad unit economics for years and delay the reckoning. And frankly, most companies are still stuck in pilot purgatory, so for them this is a preview of a problem they haven't yet earned. Worth reading as a signal of where the enterprise AI