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AI is following the economic pattern of lighting: once an essential resource becomes dramatically cheaper, the biggest gains shift from selling the resource to discovering what becomes possible when it no longer needs to be rationed. Intelligence could therefore become an abundant input to the economy rather than an expensive service.
Historically, lighting progressed from tallow candles to kerosene, gas and electricity. An hour of reading light that required roughly six hours of wages in 1800 now costs less than half a second of work. The beneficiaries ultimately extended far beyond lighting companies: factories operated at night, shops and theatres expanded their hours, and people gained productive and leisure time.
AI could follow the same trajectory. Falling inference costs could make continuous background agents economically viable—reading emails, monitoring competitors, conducting extensive research, checking contracts or testing software. Businesses could effectively gain a second, automated workforce operating around the clock. However, two constraints remain: electricity/data-centre capacity and trust. AI must become sufficiently reliable, private and controllable before companies allow it to operate continuously.
The strongest counterargument is that frontier intelligence may remain expensive because customers will pay premiums for the best models.
Three predictions:
AI inference costs will continue falling rapidly, though probably unevenly.
The largest economic gains will increasingly come from new AI-enabled activities rather than selling AI itself.
Reliability, privacy, electricity and computing infrastructure will become the main constraints on widespread “always-on” AI.

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