Even if AI cannot do your job, it can change what someone will pay you to do it. Most conversations about artificial intelligence focus on replacement: Will the machine take my job? How long before it can do what I do? These questions treat AI as a tool or an automation technology, heir to the power loom, the typewriter, and the computer. This is a useful but incomplete analogy. Artificial intelligence is also a medium, heir to the phonograph, the radio, and the camera.
That second lineage illuminates what the first cannot.
A tool extends a person’s ability. A medium scales a person’s reach: it distills their performance, separates it from them, and distributes it widely. Media rarely wipe out whole professions. They do something subtler and more destabilizing: they leave the occupation standing while upending its income distribution, job stability, and paths to success. AI does not have to replace you to reprice you.
Consider the music industry. Successive waves of recording and distribution technology did not make musicians disappear. Since 1970, their share of the American labor force has remained roughly stable.
Yet the profession changed beyond recognition. Technology allowed musicians to reach far beyond local audiences. At the same time, it also exposed them to competition from the best performers everywhere. Today, the top stars are billionaires while most musicians earn less than the median national salary.
Meanwhile, technology democratized music production and distribution, empowering millions of people to upload their performances to platforms like YouTube, TikTok, and Spotify. Most of these individuals are not counted in formal labor data, but their content competes for attention and revenue with traditional musicians. This competition is destabilizing, as it makes even top performers depend on platform algorithms and unpredictable shifts in attention rather than traditional measures of quality.
Similar dynamics have been visible across creative work long before AI, as new communications and transportation technology enabled individuals to scale their reach and lowered barriers to entry. Economists have been studying the “superstar” economics of markets like these for at least 45 years.
Even physical occupations — from fitness instructors to adult performers — have developed markets with a handful of superstars and a vast field of competitors. Some would argue that a Peloton instructor or OnlyFans performer is not a perfect substitute for the offline experience. But that’s exactly the point: Their existence reshapes the economics of the whole profession even without fully replicating the original in-person version.
AI will intensify these dynamics and spread them into many more industries. It enables top professionals to amplify their impact. At the same time, it distills some of their capabilities and makes them accessible to anyone. The result is a market with fewer, larger winners, a longer tail of semi-professionals, and a hollowing middle. Expanding access to expertise does not necessarily expand the share of people who can earn a dependable living from it.
Silicon Valley offers a preview of this polarization: Last year, companies like Meta, Google, and Microsoft courted elite AI researchers with pay packages reportedly running into the hundreds of millions of dollars. At the same time, they fired thousands of experienced employees and scaled their businesses without the hiring that such growth once required.
By taking on any task that could be routinized or measured, AI is pushing humans to do things that are more creative, harder to quantify, and more “distinctly human,” as Richard Florida recently put it. But such jobs also tend to be inherently less stable and less equal. In addition, AI is quickly becoming very good at producing creative work and exhibiting taste. The internet is already flooded with AI-generated content that many humans seem to enjoy. Many of this year’s winners of prestigious journalism and literary prizes are using AI for research, and some prominent authors have been accused of relying on it for actual writing. AI is even solving mathematical problems, including some that humanity’s greatest minds have grappled with for generations.
Trying to race ahead of the machine toward white-collar tasks that seem “distinctly human” is a losing strategy. AI is catching up faster than we can run. In the end, the only thing distinctly human is being an actual person. People will pay a premium for human attention, opinion, and creation — not because it is better, but because it is human.
Even before AI, many of us were paid for things that software and robots could already do: to attend meetings that could have been emails, to ask questions that could have been a form, and to give one another attention and personal service. AI will free us (or force us, depending on your perspective) to do more of this kind of work. But demand for human attention is not a guarantee of a middle-class income.
We cannot stop AI any more than the musicians of the 1920s stopped the gramophone. But we can still learn from their experience. In 1948, they negotiated a royalty agreement that ensured a piece of record label sales would go into a trust fund that has financed live performances by real musicians across America ever since. That is the task now, at the scale of the economy rather than the orchestra pit: to build institutions that turn greater productive capacity into broadly shared prosperity, and allow people to build dependable lives even as the market value of their efforts keeps changing.