Why Tech Gets Cheap but People Don’t

Productivity surges in one sector (like AI) trigger Jevons-style demand explosions and cheaper outputs there, while pushing up wages and prices in less-productive, human-intensive services via the Baumol effect. This can even occur within a single job as AI automates most tasks, leaving a scarce ‘human-in-the-loop’ bottleneck that earns a premium. The result is a wealthy but odd-looking economy where goods get cheap, services get costly, and the right strategy is to keep driving productivity.

Key Points

• Jevons paradox: large productivity gains lower costs and unlock vast new demand, expanding jobs and uses (e.g., computing, AI tokens).
• Baumol effect: sectors with limited productivity growth still see rising wages and prices because they compete in the same labor market as booming sectors.
• AI will amplify both forces: some services will get radically cheaper and more abundant, while many human-intensive services will get pricier yet remain in demand.
• Within single jobs, the human ‘last 1%’ (often mandated by regulation) can become a high-wage bottleneck until fully automated.
• Prosperity looks weird: goods get cheap while human services get costly, but overall wealth increases make those costs bearable.

Sentiment

The community is predominantly skeptical. While commenters acknowledge the Baumol effect and Jevons paradox as legitimate economic phenomena, they broadly agree the article misapplies and oversimplifies these concepts. The childcare thread shows genuine intellectual engagement with the ideas, but the overall tone is corrective — commenters feel the author got the economics partly wrong and wrote from too privileged a vantage point for the examples to land. The criticism is substantive rather than hostile, more professorial red-ink than dismissive.

In Agreement

• Childcare is a perfect Baumol example where labor intensity, regulation, and opportunity costs combine to make it expensive for parents while caregivers still earn very little
• The human-in-the-loop bottleneck is a real and unresolved problem — maintaining attention when agreeing with AI assessments most of the time is genuinely difficult
• Regulations that govern inputs rather than outputs structurally prevent productivity improvements in sectors like childcare, even when technology could theoretically help
• Productivity growth in some sectors does pull up wages elsewhere through opportunity cost, validating the core Baumol mechanism even if the article describes it imprecisely

Opposed

• The article mischaracterizes Jevons paradox (consuming more is not spending more) and wrongly claims we'll consume more of everything — entire industries like horses simply disappeared
• The drywall-vs-TV comparison is rhetorical trickery from an extremely privileged perspective that reflects billionaire contractor bids, not typical American labor costs
• Productivity and wages are already disconnected in practice, which limits the real-world impact of the Baumol effect the article depends on
• Diseconomies of scale and Dutch disease may be better frameworks than Jevons/Baumol for explaining why certain services get more expensive
• The article concludes with admitted speculation about radiologist workflows, undermining its credibility on the very point it tries to make most dramatically
• Accountability cannot be delegated to AI — someone must be personally liable when things go wrong, which is a more fundamental reason for human involvement than regulatory protectionism