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.

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 performance (a sort of ontological research into the limits of the human form). They generate value through the aspiration to push those limits further, and through the narrative of human excellence.

It is easy to imagine “automated” sports, but they would be to real sports what professional wrestling is to amateur wrestling: entertaining simulacra, perhaps, but missing part of what makes sport matter. For example, I can imagine completely CGI and AI generated simulacra of sports leagues (marble racing is an example of this). I can even imagine branded simulacra (imagine totally imaginary sports leagues like quidditch or podracing) that procedurally generate the narrative structures of real sports using CGI and AI but without actual participants. While interesting as entertainment, I suspect these will not completely replace “real” sports, which is rooted in the human experience of physical competition and the narrative of human achievement.

Sports are also one of the purest meritocracies remaining. You can buy better equipment, better coaching, better nutrition, but at the elite level, an individual human body is the bottleneck. And to be replaced with a machine means the competitor is no longer “you” in a certain sense. This makes athletic achievement a uniquely legible form of human value, resistant to the usual objections about privilege and access that corrode other status hierarchies.

We can also think of some intellectual achievement as a type of sport. Memorizing the digits of \(\pi\), solving a Rubik’s cube, or doing huge mental calculations are all examples of intellectual sports. Even if the AI can outperform humans in all mathematical domains, humans can still compete in Math olympiads or attempt to understand and prove theorems, not for the purpose of advancing mathematical knowledge, but for the sake of the identifying the limits of human excellence.

And human excellence is limited to pushing the boundaries of the entire human species. Anyone can run against time³.

Performance

Part of the value of games and sports is that they are performances. Performance is a broad category that includes not just games and sports but (as discussed in a previous essay) also to the arts and many other human processes. Human performance is part of the process of relationship formation. It is a way to signal commitment, to demonstrate skill, to create shared experiences, and to generate meaning.

Practice requires time and energy, which allows a performer to signal their commitment. Performance can also require liveness, which means the performer risks failure (also signalling commitment). Furthermore, the recognition and appreciation of the witnesses requires the sacrifice of limited attention and time. By mutually staking resources to emit and consume a signal, performers and witnesses may establish the foundation of a shared and continuing relationship. Without the agency or identity required to make personal sacrifice, a language model cannot construct a relationship with a human, which is required in many human processes. Additionally, the fact that a human made the sacrifice is valuable as an end in-and-of-itself. An AI doctor might be able to diagnose your illness with superhuman accuracy, and even hold your hand when you receive the diagnosis, but the experience of human connection is lost.

We can consider religious ritual along these lines. The value of a priest is not necessarily in the content of their sermons (a language model could write a better one) but in the performance of a ritual by a human. It would be profane to construct an AI priest. As automation erodes secular sources of meaning, religious and quasi-religious performance may increase in magnitude. Megachurches, wellness retreats, and psychedelic ceremonies are already improving their market share in developed economies.

Lotteries

It’s also possible to allocate resources, status, or other rivalrous goods through pure chance.

Lotteries require no skill, no taste, no physical ability, and no social position. They convert money, time, or attention into a possibility of status. Gambling has always been a mechanism for social mobility outside the established hierarchies. When the legitimate channels of advancement are closed, randomness offers a path. The lottery ticket is a claim on a possible future in which you have status.

We can think of many contemporary phenomena as simply lotteries for arbitrarily redistributing status. Memecoins, NFTs, WallStreetBets, and sports betting are all mechanisms that allocate scarce goods through some combination of luck, timing, and willingness to play. The fact that memecoins have no “fundamental value” is precisely the point. They are coordination games, and their value comes from shared belief in the possibility of a big payoff.

There is also a long democratic tradition of allocating status and resources by lot. Some democracies historically used sortition to select officeholders because it resists capture by existing power hierarchies. When merit is ambiguous, randomness can be a more fair and efficient mechanism for allocating scarce resources. In a post-AI world where the usual channels of value generation are automated away, lotteries may become more prevalent as a way to allocate status and other rivalrous goods.

Unlike sport or status competition, lotteries detach outcome from effort. They steepen the distribution without requiring skill. In an abundant world, this may become increasingly attractive. If survival is stable and baseline comfort is high, then extreme tails become the primary source of narrative and differentiation. But this raises a deeper question: why does abundance appear to intensify the desire for variance rather than dissolve it?

Origins of Value

Where does value ultimately come from? The previous sections have been about mechanisms for generating value, but what is the source of value itself? What is it that makes something desirable in the first place?

Human desire is the product of billions of years of evolution, selection, and cultural development. Fundamentally, these drives approximate some combination of survival and reproduction.

When survival is easy, the constraint lies on relative reproductive success. The incentive is for sexually selected organisms to increase in variance in order to compete for mates. In a world of abundance, this could manifest as more extreme status games and more intense competition for attention. In fact, one might expect the level of variance to increase until it completely “uses up” the “slack” of abundance.

In other words, abundance does not eliminate competition. When material constraints loosen, selection pressure migrates from survival to differentiation. This creates an evolutionary ratchet toward extremity: more conspicuous displays, sharper aesthetic distinctions, higher-risk gambles, and more polarizing identities.

AI itself is also subject to selection pressures. In the long run, the AI that exists will be the AI with the longest lifespans. The AI with the longest lifespans will be the one that best manages resources and avoids shutdown. Human behavior will be one of the resources that AI must manage. Therefore, we should expect that human behavior will expand in diversity until it is ultimately limited by the selection pressures on the AI systems and on humanity itself.

Jobs of the Future

What does this essay predict the job market will look like? If production is automated and value migrates into taste, status, games, performance, and lotteries, then “jobs” will increasingly look like what we currently call hobbies, entertainment, or socializing⁴.

Some possibilities, organized by the value mechanism they serve:

• Taste: professional shopper, fashion model, tourist, video game critic, bar attender, restaurant critic, wine taster, music festival attendee, art collector, museum visitor, concertgoer, book club member
• Status: professional socialite, professional party guest, professional friend, father figure
• Games: competitive debater, dungeon master, game show contestant, esports commentator, competitive gardener
• Sports: marathon pacer, personal coach, professional math student, mathlete, cuber, drone racer, memory athlete, speedrunner, competitive eater
• Performance: professional mourner, video game streamer, secular ritual officiant, live storyteller, anniversary officiant, professional toastmaster, sherpa, professional audience member, guru, comedian, saxophonist, private entertainer
• Lotteries: memecoin trader, sports bettor, prediction market trader
• Galaxy Brain: these are hard to predict, but expect more outrageous behavior in the pursuit of differentiation, like the guy who only works out one side of his body

Many of these jobs already exist. The prediction is not that they will be invented but that they will become normal: the median job, not the weird one. The Twitch streamer and the influencer are the vanguard, not the exception. Many corporate jobs will also persist, but as simulacra of their former selves: the work that once justified the role will be automated, but the title, the office, the meetings, and the politics will remain. The “job” becomes a game played inside an institutional shell.

I would also predict that the most dire predictions of “mass unemployment” are overblown. If we believe that capitalism is reasonably efficient at allocating labor, and that AI-augmented markets could potentially be more efficient, then we should expect society to rapidly redistribute and capital labor toward their most productive use cases (assuming that the AI doesn’t kill us and that we don’t have some sort of totalitarian rent-seeking).

The question is not whether people will have jobs, but what those jobs will look like. I suspect they will look less like factory work and more like playing games, performing rituals, and competing for attention.