Artificial intelligence promises to change not just how Americans work, but how societies decide which kinds of work are worthwhile in the first place. When technological change outpaces social judgment, a major capacity of a sophisticated society comes under pressure: the ability to sustain forms of work whose value is not obvious in advance and cannot be justified by necessity alone.

As AI systems diffuse rapidly across the economy, questions about how societies legitimate such work, and how these activities can serve as a supplement to market-based job creation, have taken on a policy relevance that deserves serious attention.

From Prayer to Platforms

That capacity for legitimating work has historically depended in part on how societies deploy economic surplus: the share of resources that can be devoted to activities not strictly required for material survival. In late medieval England, for example, many in the orbit of the church made at least part of their living performing spiritual labor such as saying prayers for the dead and requesting intercessions for patrons. In a society where salvation was a widely shared concern, such activities were broadly accepted as legitimate ways to make a living.

William Langland was one such prayer-sayer. He is known to history only because, unlike nearly all others who did similar work, he left behind a long allegorical religious poem, Piers Plowman, which he composed and repeatedly revised alongside the devotional labor that sustained him. It emerged from the same moral and institutional world in which paid prayer could legitimately absorb time, effort, and resources.

In 21st-century America, Jenny Nicholson earns a sizeable income sitting alone in front of a camera, producing long-form video essays on theme parks, films, and internet subcultures. Yet her audience supports it willingly and few doubt that it creates value of a kind. Where Langland’s livelihood depended on shared theological and moral authority emanating from a Church that was the dominant institution of its day, Nicholson’s depends on a different but equally real form of judgment expressed by individual market participants. And she is just one example of a broader class of creators—streamers, influencers, and professional gamers—whose work would have been unintelligible as a profession until recently.

What links Langland and Nicholson is not the substance of their work or any claim of moral equivalence, but the shared social judgment that certain activities are legitimate uses of economic surplus. Such judgments do more than reflect cultural taste. Historically, they have also shaped how societies adjust to technological change, by determining which forms of work can plausibly claim support when productivity rises faster than what is considered a “necessity” by society.

How Change Gets Absorbed

Technological change has long been understood to generate economic adjustment through familiar mechanisms: by creating new tasks within firms, expanding demand for improved goods and services, and recombining labor in complementary ways. Often, these mechanisms alone can explain how economies create new jobs when technology renders others obsolete. Their operation is well documented, and policies that reduce frictions in these processes—encouraging retraining or easing the entry of innovative firms—remain important in any period of change.

That said, there is no general law guaranteeing that new technologies will create more jobs than they destroy through these mechanisms alone. Alongside labor-market adjustment, societies have also adapted by legitimating new forms of value—activities like those undertaken by Langland and Nicholson—that came to be supported as worthwhile uses of the surplus generated by rising productivity.

This process has typically been examined not as a mechanism of economic adjustment, but through a critical or moralizing lens. From Thorstein Veblen’s account of conspicuous consumption, which treats surplus-supported activity primarily as a vehicle for status competition, to Max Weber’s analysis of how moral and religious worldviews legitimate economic behavior, scholars have often emphasized the symbolic and ideological dimensions of non-essential work. Herbert Marcuse pushed this line of thinking further, arguing that capitalist societies manufacture “false needs” to absorb surplus and assure the continuation of power imbalances. These perspectives offer real insight: uses of surplus are not morally neutral, and new forms of value can be entangled with power, hierarchy, and exclusion.

What they often exclude, however, is the way legitimation of new forms of value can also function to allow societies to absorb technological change without requiring increases in productivity to be translated immediately into conventional employment or consumption. New and expanded ways of using surplus are, in this sense, a critical economic safety valve during periods of rapid change.

Skilled Labor Has Been Here Before

Fears that artificial intelligence is uniquely threatening simply because it reaches into professional or cognitive domains rest on a mistaken historical premise. Episodes of large-scale technological displacement have rarely spared skilled or high-paid forms of labor; often, such work has been among the first affected. The mechanization of craft production in the nineteenth century displaced skilled cobblers, coopers, and blacksmiths, replacing independent artisans with factory systems that required fewer skills, paid lower wages, and offered less autonomy even as new skilled jobs arose elsewhere. These changes were disruptive but they were absorbed largely through falling prices, rising consumption, and new patterns of employment. They did not require societies to reconsider what kinds of activity were worthy uses of surplus: the same things were still produced, just at scale.

Other episodes are more revealing for present purposes. Sometimes, social change has unsettled not just particular occupations but entire regimes through which uses of surplus become legitimate. In medieval Europe, the Church was the one of the largest economic institutions just about everywhere, clerical and quasi-clerical roles like Langland’s offered recognized paths to education, security, status, and even wealth. When those shared beliefs fractured, the Church’s economic role contracted sharply—not because productivity gains ceased but because its claim on so large a share of surplus lost legitimacy.

To date, artificial intelligence has not produced large-scale job displacement, and the limited disruptions that have occurred have largely been absorbed through familiar adjustment mechanisms. But if AI systems begin to substitute for work whose value is justified less by necessity than by judgment or cultural recognition, the more relevant historical analogue may be less the mechanization of craft than the narrowing or collapse of earlier surplus regimes. The central question such technologies raise is not whether skilled labor can be displaced or whether large-scale displacement is possible—both have occurred repeatedly in the historical record—but how quickly societies can renegotiate which activities they are prepared to treat as legitimate uses of surplus when change arrives at unusual speed.

Time Compression and its Stakes

In this respect, artificial intelligence does appear unusual. Generative AI tools such as ChatGPT have diffused through society at a pace far faster than most earlier general-purpose technologies. ChatGPT was widely reported to have reached roughly 100 million users within two months of its public release and similar tools have shown comparably rapid uptake.

That compression matters. Much surplus has historically flowed through familiar institutions—universities, churches, museums, and other cultural bodies—that legitimate activities whose value lies in learning, spiritual rewards or meaning rather than immediate output. Yet such institutions are not fixed. Periods of rapid technological change often place them under strain–something evident today for many–exposing disagreements about purpose and authority. Under these conditions, experimentation with new forms of surplus becomes more important, not less. Most proposed new forms of value fail, and attempts to predict which will succeed have a poor historical record—from the South Sea Bubble to more recent efforts to anoint digital assets like NFTs as durable sources of wealth. Experimentation is not a guarantee of success; it is a hedge. Not all claims on surplus are benign, and waste is not harmless. But when technological change moves faster than institutional consensus, the greater danger often lies not in tolerating too many experiments, but in foreclosing them too quickly.

Artificial intelligence does not require discarding all existing theories of change. What sets modern times apart is the speed with which new capabilities become widespread, shortening the interval in which those judgments are formed. In this context, surplus that once supported meaningful, if unconventional, work may instead be captured by grifters, legally barred from legitimacy (by say, outlawing a new art form) or funneled into bubbles. The risk is not waste alone, but the erosion of the cultural and institutional buffers that make adaptation possible.

The challenge for policymakers is not to pre-ordain which new forms of value deserve support but to protect the space in which judgment can evolve. They need to realize that they simply cannot make the world entirely safe, legible and predictable: whether they fear technology overall or simply seek to shape it in the “right” way, they will not be able to predict the future. That means tolerating ambiguity and accepting that many experiments will fail with negative consequences. In this context, broader social barriers that prevent innovation in any field–professional licensing, limits on free expression, overly zealous IP laws, regulatory bars on the entry to small firms–deserve a great deal of scrutiny. Even if the particular barriers in question have nothing to do with AI itself, they may retard the development of surplus sinks necessary to economic adjustment. In a period of compressed adjustment, the capacity to let surplus breathe and value be contested may well determine whether economies bend or break.

Eli Lehrer is the President of the R Street Institute.

If you listen to some entrepreneurs and investors, the flying car ? a longstanding staple of science fiction ? is right around the corner. Working prototypes exist. At least two companies already take orders for the vehicles, with deliveries promised next year.

The last decade has seen the introduction of practical consumer videoconferencing, voice recognition, drones, self-driving cars and many other items that once were found only in science fiction stories. It therefore might seem plausible that practical flying cars are around the corner. They aren’t. Indeed, massive safety, infrastructure and technology problems make them a near impossibility.

The first concern is safety. While flying a commercial airline is always safer than driving oneself the same distance, it’s an entirely different story if one looks at per-trip fatality rates. The Department of Transportation estimates that Americans take about 350 billion car trips per-year and experience about 30,000 fatal accidents; roughly one fatal accident per 11 million trips. By contrast, there are roughly 35 million scheduled air flights around the world each year. Over the past decade, the number of commercial aviation incidents that have proved fatal has averaged 17 annually. This means about one of every 2 million commercial air flights ends in death.

We see these fatalities every year, despite pilots’ years of intense training, planes’ extensive safety equipment requirements, regular maintenance checks and airlines’ need to maintain sterling reputations for safety. All of these provide far more safeguards than anything that applies to cars on the road.

It’s true that there are some factors that might make flying cars safer than commercial jetliners. They would travel at lower speeds and lower altitudes, for instance. But there’s no practical way to subject them to the same safety and training standards imposed on commercial airplanes if they are to become anything like a consumer product. Indeed, the per-trip fatality rates for private planes already is very likely higher than commercial airliners, but there are no worldwide statistics available. Safety advocates would make a plausible case for banning flying cars on these grounds alone.

Even if one thinks these risks are acceptable?and they probably are, given the potential advantages of flying cars?that doesn’t solve the even greater infrastructure or technological problems. The current working models of flying cars need runways to take off and land. Bringing them into regular use would require runways just about everywhere, without obviating the need for parking lots. The world’s busiest airport, Atlanta’s Hartsfield-Jackson, accommodates slightly less than 2,500 aircraft movements each day on its five runways and 4,700 acres. Any sizable office building would need its own version of Hartsfield-Jackson if people were to commute to work via their flying cars. The space to build facilities this size for flying cars simply doesn’t exist anywhere near any city of any size.

New technologies could theoretically obviate the need for runways. One Japanese team has shown off a modified lightweight drone supposedly capable of vertical takeoff and landing like a helicopter. But making these vehicles practical would require breakthroughs that appear to be decades away. Existing helicopters and military “jump jets” still require a significant amount of space to land, are even noisier than commercial jets and drink huge amounts of fuel. As such, they’re not really used for travel. Commercially produced helicopters have existed since the 1940s and aren’t currently used for scheduled commercial service anywhere in the United States. Technological breakthroughs could eventually solve these problems, but it’s unlikely that a few years of flying-car development will overcome problems that have bedeviled helicopter designers for more than seven decades.

While the promised 2017 deliveries of working flying cars seem unlikely, it’s far from impossible that a commercially produced civilian airplane with the kinds of retractable wings and safety equipment that would allow it to be driven on highways might make it to market within the next decade. But widely available flying cars, more likely than not, will remain clearly in the realm of science fiction.