How Will Humans Generate Value In a Post-AI Society? – demonstrandom■

How Will Humans Generate Value In a Post-AI Society?

Technology and Society

Essays

Speculation

Published

February 22, 2026

Introduction

The current dominant AI narrative asserts that “white-collar jobs are next”. This includes lawyers, software engineers, radiologists, writers, mathematicians, artists, and ultimately any job that can be done with a computer.

Suppose this is true. Furthermore, suppose that robotics will eventually usher in a world of true abundance, where the production of goods and services is essentially free. In such a world, how do humans generate value? What do we do that is worth doing? What do we do that machines cannot do? What will we do that machines will not do?

Income is merely a proxy for value. Money and the capitalist system are abstractions that emerged to coordinate human economic activity and expand the frontier of possible “real” outcomes. If, in a world of abundance, AI handles economic production, income as we currently understand it may become obsolete. The question is not “what jobs will be left” but “what mechanisms will generate value for humans when economic production is no longer a meaningful source of value?” Furthermore, even in a world of abundance, there will still be scarcity of some goods that are, to whatever degree, inherently finite and rivalrous, such as attention, status, meaning, and position. How will humans allocate these scarce resources if the usual channels of value generation and resource allocation are automated away?

Mechanisms of Value Generation

I’ll propose and explore various mechanisms in this section, roughly but uncertainly sequenced by predicted order of obsolescence.

Physical Work

Even if we fully believe that all desktop work will be automated, it will take some time before the human hand and body are replaced in meatspace. Care work, construction, plumbing, cooking, surgery, massage, sex work, eldercare, childcare, and many other occupations require direct interaction with reality.

Despite some inertia in the current state of affairs, it is expected that human dominance in physical work will merely be a temporary state of affairs. As robotics improves, the set of tasks requiring human bodies shrinks, and will ultimately reduce to a small subset of things that are either too complex, too delicate, or too expensive to automate, and then vanish entirely. In the limit, we’d expect physical work to be fully replaced.

Taste Work

If AI can produce anything, the bottleneck shifts from execution to specification. Can you determine what you want, and if you can, how do you specify it to the machine? This is the taste problem, and it is harder than it seems at first glance, even for a perfect model.

Taste work can be taxonomized into three different operations. The first is creation, which is making a new thing that some group or individual desires (this could be a a director making a blockbuster movie for a huge audience, a musician composing for their specific muse, or a blogger writing for a future version of himself). The next operation is curation, which is putting together lists that adhere to a certain aesthetic or a given quality level. This is done by museum curators when they choose what paintings to hang, by bookstore owners when they choose how to stock their shelves, or by film institutes when they select the quality films. Finally, the last operation is selection, which is choosing one thing from a set of options to apply attention to. This may actually be a long chain of decisions (a “demand chain”). For example, a restaurant might choose which wines to stock, a sommelier may recommend a shortlist, and the restaurant patron ultimately orders a single wine.

AI already provides value in these domains. For example, Spotify playlists, search ranking, and recommendation engines are all AI-driven tools for curation. Generative models can, to some extent, produce novel images, music, and text on demand. Personalized advertising can suade your tastes, partially dictating your personal preferences.

There’s a further distinction worth making: taste-for-others versus taste-for-self. Taste-for-others is about predicting what someone else will like. This is fundamentally a prediction problem, and AI can produce for the masses with enough data.

Taste-for-self is slightly different. You might walk into a restaurant not knowing what you want, read the menu, and then decide on an option (or even order “off-menu”). You might not have been able to communicate what you wanted before you saw the menu. The preference didn’t exist until the moment of contact with the options. Similarly, desires can be very, very particular. There is still more value to be generated by human taste work in the selection of things for ourselves. And the specification cost doesn’t vanish just because generation becomes free1.

What makes taste work resistant to automation? One...