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

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 issue is that the decision of which selection to make may depend on context that is expensive to formalize, like the room, the audience, the season, the cultural moment, or the specific internal qualia of the recommendee. Another problem is social authority; the value of the sommelier’s recommendation could depend on who is recommending the wine, not just which wine in particular is recommended. There is also the issue of accountability if the decision is wrong.

But above all, the fundamental reason this problem is difficult is that it inherently relies on human communication to and from the machine. The machine can generate a million variations for you to choose from, but it cannot know which one you will like without some kind of highly individualized data elicitation, which is bound by human I/O2.

But this is not an inherently unsolvable problem for AI. After a sufficiently long enough data collection and training process, it is possible that AI could develop a model of humans preferences that is good enough to generate things you like without much input from you.

There is still the question of the value that might result from having specific tastes or preferences. For now, it is a human writing, editing, and publishing this essay. But perhaps someday AI could manage the entire process end-to-end, from ideation to research to drafting to editing to formatting to publishing. Then I could read the blog I desire without having to labor to produce it. Would I be “writing” the blog or would I be “reading” it? Would there be a meaningful distinction? My desires would create something that I and others would consume. If others consume it, then my desire is valuable in-and-of-itself. If the purpose of economic activity is to generate value for humans, then helping specify the final outcome of the machine’s production is a valuable activity, even if the machine does all the work.

People may not create value in a post-AI world through unique skills, but through unique desires. Your job isn’t to go to the office, but to go shopping.

Social Status

Status, being ordinal, is inherently rivalrous. In a world of material abundance, social position can still be scarce. In our current world, status is often a byproduct of productive economic contribution. For example, someone can currently increase in status for being a great artist, a brilliant scientist, or a powerful CEO. But in a post-AI world, the link between production and status breaks down. The question is: what will generate status when production no longer does?

Who gets the best land, the most desirable spouse, or the invite to the coolest parties? No amount of AI-driven productivity can manufacture more status, because humans fundamentally desire to rank people. Similarly, conspicuous consumption is not about the underlying quality of the goods but about the signal the goods present. The point of a $10,000 exclusive handbag is that you can’t buy it. Automating handbag production just shifts the status signal to some other arbitrary token.

The underlying scarce resource is attention. Human attention is finite even when everything else is abundant. Status games can be thought of as competitions for the limited bandwidth of other humans. The influencer economy is an intensification of a dynamic that has always existed. In fact, as AI accelerates the supply of content, the demand for attention remains bottlenecked. The result is that capturing attention becomes more valuable relative to producing content.

The post-economy is the post economy. We can already start to see the inversion take place. Likes and views aren’t valuable because they can be converted into money. Instead, money is valuable because it can be converted into likes and views. Eventually, as production drops away, the money itself may become a mere token for attention.

Games

Status games are just one particular type of game. We can generalize this trend to other kinds of games.

Games are voluntary competitions with rules that generate value through the experience of playing and the potential determination of winners or losers (or, at least “good” and “bad” players).

The last section was about social games. “Getting the most likes on Instagram” is a social game. “Having the nicest lawn” is a social game. So are “getting the promotion” and “meeting your KPIs” and “climbing the corporate ladder”. As AI automates more of the actual work, the game aspect may become more central to how people derive value from their careers. Actual economic contribution (“doing the work”) may become less important than how well you play the game of corporate politics, networking, and self-promotion. Maybe this has already happened.

But beyond corporate games, there are board games, card games, video games, sports betting, competitive cooking, debate, trivia, poker, bowling, pickup basketball, fantasy football, speedrunning, competitive eating, and so on. These are all voluntary competitions with some kind of structure and some kind of outcome that can compare performance between the participants.

Games are not necessarily fun, fair or entertaining. They can be stressful, frustrating, and demoralizing. In a post-AI world where production is automated and abundant, games may become a more central mechanism for generating value and allocating scarce resources. They are inherently human-centric and resistant to automation because they rely on human judgment, social interaction, and the experience of playing. Furthermore, they can clearly distinguish winners and losers, which is a key aspect of status generation. The value of winning a game is not just in the outcome but in the process of playing and the social recognition that comes with it.

Consider chess. It is a game with simple rules but infinite complexity. It generates value through the experience of playing, the social recognition of skill, and the narrative of competition. Even if an AI can play chess at a superhuman level (which is already true), the human experience of playing chess and the social recognition that comes with it still generate value. In fact, chess is more popular than ever, with millions of people playing online and watching grandmaster tournaments, even though computers can beat any human player. Even so, two humans can still compete to measure their comparative skill.

Sports

Sports and games are closely related phenomena. In a previous essay, I briefly considered whether sports are art (sometimes). Are sports games? I think the answer is also “sometimes”.

Some sports are clearly games. A football match is a game with rules, players, and an outcome (the thought exercise in the previous section works fine for games with a physical component). On the other hand, some sports are more about performance and spectacle than competition (especially the ones that are “art”).

There is another aspect to sport that is worth mentioning, which is the exploration of the fundamental limits of the human body. The 100m sprint, the marathon, the high jump, the long jump, the pole vault, and many other human activities are endeavors that test the limits of human physical perf

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