What Keynes imagined was a society advanced enough to solve what he called the “economic problem” of basic material provision. If technology kept improving, and societies kept growing richer, then fewer hours of human labor would be needed to produce the necessities and comforts of life.
And then, others make the opposite claim: that AI’s efficiency will create more work, not less. Drawing on the Jevons paradox—the idea that when a technology becomes more efficient and cheaper to use, total demand for what it enables can rise rather than fall—this view posits that the routinization of cognitive work will expand the need for “relational work,” and the amount of human communication and coordination organizations want to do.
What Keynes underestimated, and what many of today’s AI evangelists tend not to confront, is that progress unfolds inside social systems structured by power. In today’s capitalist system, machines do not simply lighten labor; they are deployed according to the interests of firms, investors, and managers. The important question we face now is how the transformation from AI will be organized, and crucially, whether it will widen human freedom or tighten control, concentrate wealth, and extract still more from labor.
Early machines, for instance, could spin, weave, stamp, and cut far faster than individual artisans. Yet these technologies did not lead to long-term elevated unemployment, nor lighten the burdens put on workers. Factory production moved work out of skilled, relatively autonomous craft systems and into centralized regimes of supervision, discipline, and timing. Workers did not just do less of the old labor; they entered a world of clock-based work, managerial oversight, and repetitive tasks. The machine changed who controlled labor and the terms on which it was done.
Who reaps the benefits of increased productivity?
The computer revolution followed this pattern, too. When I started work in the 1990s, many people talked about the paperless office and a dramatic reduction in routine work. Some clerical tasks certainly disappeared or changed. Yet office work expanded, as recent invocations of the Jevons paradox rightly note. But the lesson is not simply that cheaper tools create more and different types of demand and with that new occupations. It is that, inside firms, efficiency gains were rarely passed on to workers as free time. Instead, they were typically used to help businesses become more profitable.
This leads us to recent discussions of the Jevons paradox, which can obscure as much as it reveals. True, in Jevons’ original example, Watt’s steam engine made coal more productive, and Britain consumed more coal, not less. A more recent technological example people will cite to support this line of thinking is the LED lightbulb. As these energy-saving bulbs became cheaper and more efficient, societies found more occasions for lighting rather than simply consuming less electricity.
Early use of algorithmically controlled work shows similar patterns. Gig platforms were sold as a new “free agent” economy, promising autonomy, flexibility, and freedom from the traditional boss. In practice, many gig platforms have become systems of control without the protections of employment: software sets prices, routes workers, and disciplines behavior through ratings, penalties, and opaque incentives. All the while, many of the companies have shed their need to provide benefits and other human resources, and their investors have become rich.
The politics that Keynes missed
But capitalism has rarely treated “enough” as a stopping point. Each productivity gain becomes a fresh opportunity for business owners to maximize their own gains while using the same technology to further control their employees. Indeed, one could argue that the legal structures of modern capitalism requires leaders to maximize gains to boost shareholder value.
This is where authority matters. Sadly, too many of the people building and promoting AI portray their private-sector ambitions to dominate markets, build government-subsidized infrastructure, capture consumer data, and weaken labor power as historical inevitability, a supposed attempt to normalize disruption as a natural force instead of a political and institutional choice. This is why recent appeals to Jevons paradox, and how the need for human interaction may mean less job loss than prior predictions by AI leaders such as OpenAI’s Sam Altman are so revealing. By centering the debate on whether AI will create or destroy jobs, these perspectives obscure the deeper question at the heart of the issue: whether AI’s productivity gains will expand workers’ freedom or deepen firms’ power over them.
Recent episodes of students openly booing AI-themed graduation speeches shows the unease today’s youth have about whether an economy organized around machine intelligence will still protect human creativity and judgment. Pope Leo XIV has also raised a similar warning, framing AI as a moral and political test of whether technology will serve humans or concentrate power in ways that diminish our dignity and flourishing.
Without taking these kinds of actions, warnings about job loss will remain in vain and incomplete. AI may eliminate some jobs, but it is also likely to deepen a quieter transformation: the movement of judgment, discretion, and power out of workers’ hands.
Keynes’s lost 15-hour workweek was not simply a failed forecast about productivity. It was a question about whether societies would use technological abundance to enlarge freedom or to deepen accumulation.
We invented the technology.
The issue is that we still have not invented the necessary politics.
Hence then, the article about replace or reshape how ai could change the way we work was published today ( ) and is available on Time ( Middle East ) The editorial team at PressBee has edited and verified it, and it may have been modified, fully republished, or quoted. You can read and follow the updates of this news or article from its original source.
Read More Details
Finally We wish PressBee provided you with enough information of ( Replace or Reshape: How AI Could Change the Way We Work )
Also on site :