The equality machine The right sort of AI can level the playing field

In our meritocratic age, intelligence remains the ultimate golden ticket. As the political scientist Charles Murray has been telling us for decades, the correlation between cognitive ability and lifetime earnings is substantial. While we may prefer to attribute inequality to schooling,  discrimination, or access to power, the uncomfortable reality is that differences in human cognitive capacity continue to drive large differences in economic outcomes. Of course, intelligence operates within social and institutional contexts that shape how it is developed and rewarded. Still, the link between cognitive performance and economic reward remains a central feature of modern market societies.

Now, along comes artificial intelligence.

A major question — and source of anxiety — provoked by AI concerns its effects on economic outcomes: who will thrive, and who will be displaced? Will firms require far less human labor to produce greater output, consigning vast swaths  of workers to the margins? Or will entirely new forms of work emerge, sustaining broad-based prosperity?

These are important questions. But they aren’t the most fundamental ones. The deeper issue is how artificial intelligence will interact with natural human intelligence — and thus how it will reshape the relationship between cognitive ability and economic reward.

In our meritocratic age, intelligence remains the ultimate golden ticket. As the political scientist Charles Murray has been telling us for decades, the correlation between cognitive ability and lifetime earnings is substantial. While we may prefer to attribute inequality to schooling, discrimination, or access to power, the uncomfortable reality is that differences in human cognitive capacity continue to drive large differences in economic outcomes. Of course, intelligence operates within social and institutional contexts that shape how it is developed and rewarded. Still, the link between cognitive performance and economic reward remains a central feature of modern market societies.

Now, along comes artificial intelligence.

A major question — and source of anxiety — provoked by AI concerns its effects on economic outcomes: who will thrive, and who will be displaced? Will firms require far less human labor to produce greater output, consigning vast swaths of workers to the margins? Or will entirely new forms of work emerge, sustaining broad-based prosperity?

These are important questions. But they aren’t the most fundamental ones. The deeper issue is how artificial intelligence will interact with natural human intelligence — and thus how it will reshape the relationship between cognitive ability and economic reward.

Crucially, this isn’t simply a question about what AI is, as a technology. It is a question about how it is used, and by whom. Whether AI widens or narrows inequality will depend not only upon its technical capabilities, but on the social and developmental conditions that determine who is able to use it effectively.

Economists capture this distinction by asking whether two inputs are complements or substitutes. If artificial intelligence is a complement to human intelligence, it makes capable people more productive. Those who are already skilled can use AI to extend their reach, scale their output, and outperform others by an even greater margin. If, by contrast, AI is a substitute, it performs tasks that previously required human intelligence, thereby reducing the advantage of being especially smart. Work that once demanded elite cognitive ability becomes accessible to a much broader range of people.

The difference matters enormously. In one case, inequality widens; in the other, it may narrow.

Much of the anxiety surrounding AI assumes the first scenario. People fear the emergence of a permanent over-class of AI-enhanced cognitive elites, with the majority relegated to precarious and lower-value work. That outcome is certainly possible. But it isn’t inevitable.

Even those closest to the technology appear uncertain. Some suggest that AI may erode the value of highly specialized technical skills while elevating more general capacities — judgment, communication, adaptability. Others anticipate the opposite: that those best able to harness these tools will pull further ahead. These conflicting predictions aren’t especially informative in themselves. But they do highlight a central point: how we implement the AI revolution will determine whether machines will primarily amplify human intelligence or replace it.

To see what is at stake, consider how technology has historically amplified talent. As the economist Sherwin Rosen demonstrated, prior to the advent of music streaming services, a slightly better singer could reach millions more people than her nearest competition. A top singer would have access to resources, such as record deals, radio airplay, television appearances, and prime concert venues, that the second-best singer might not, translating a marginal edge into a massive payday. AI might operate on the same principle — as a “superstar” enabler. 

A top-tier lawyer, equipped with AI tools, can survey vast bodies of case law, identify relevant precedents, and draft arguments more quickly, while still relying on unique human judgment to determine what matters. A skilled programmer can generate, test, and refine complex systems at unprecedented speed. In such cases, the technology doesn’t replace human intelligence; it amplifies it. Those who are already highly capable benefit the most.

In this world, where AI and human intelligence are complements, exceptional minds multiply their output, while routine cognitive tasks — data entry, basic analysis, even portions of coding — are increasingly automated. The result is a steeper earnings curve, with modest differences in cognitive ability yielding large differences in income. We’ve seen glimpses of this already. Tech giants like Google and OpenAI are hoovering up talent, paying premiums for the brightest, while entry- and mid-level jobs in fields like journalism or graphic design face AI encroachment. At the same time, the economic rents generated by frontier AI systems may accrue disproportionately to a small number of firms and their owners, further concentrating rewards.

But there is another possibility. Artificial intelligence might instead function as a substitute for certain forms of human cognition. In that case, it would reduce the premium placed on raw intellectual ability by making high-level performance accessible to a broader population.

An analogy may help. Eyeglasses once revolutionized life for the visually impaired. They didn’t turn everyone into hawks with perfect vision, but they prevented poor eyesight from being a lifelong handicap. Bad eyes no longer meant exclusion from reading, driving, or fine craftsmanship. Now, with a few exceptions, poor eyesight is irrelevant to one’s employment prospects. AI could do something similar for intelligence, acting as a corrective lens for the mind. It might not make us all geniuses, but it could mitigate the penalties of average cognition, allowing more people to compete on equal terms. A nurse practitioner using diagnostic tools might approximate aspects of specialist judgment. A small-business owner could perform functions that previously required multiple professionals.

In such a world, what matters less is the speed or depth of one’s unaided reasoning, and more the ability to deploy these new tools effectively — to exercise judgment, to work reliably, and to integrate machine outputs into meaningful action. Traits like discipline, judgment, and social intelligence could become relatively more valuable.

It is tempting to think that whether AI complements or substitutes for human intelligence is a fixed property of the technology itself. But this is misleading. The same tools can function differently depending on the context in which they are used. Where individuals are well-trained and capable of directing these systems, AI will tend to amplify their abilities. Where such capacities are lacking, it may instead replace them.

My own expectation is that, at least in the near to medium term, AI will function more as a complement than a substitute for high-level human cognition, thereby intensifying inequality before any equalizing diffusion takes hold.

Even so, markets don’t stand still. People and institutions adapt. As the value of certain skills rises, individuals respond by acquiring them, and organizations adjust how work is structured accordingly. Over time, these processes may soften the initial effects of technological change. This raises another issue — one that is emphatically social, not technological.

Adaptation is sure to be uneven. Families with resources — stable homes, access to quality education, and strong social networks — are better positioned to respond. They are more likely to cultivate the habits and dispositions that make effective use of these new technologies possible. Others may lag behind not because they inherently lack relevant abilities, but because they lack access to environments that support their development.

A key constraint, then, isn’t simply access to AI tools. It is access to the conditions under which people learn to use those tools well. The relevant “skill” here is judgment — knowing when to rely on the machine, when to question it, and how to integrate its outputs into meaningful action. Consider a junior analyst using an AI system to generate a financial report. The machine can produce a polished document in seconds — complete with charts, projections, and plausible-sounding explanations. But the real task is not producing the report; it is knowing whether the underlying assumptions make sense, whether the data are reliable, and whether the conclusions follow. A less experienced worker may accept the output at face value. A more seasoned one will probe it, adjust it, and in some cases reject it entirely. The difference is not access to the tool, but the judgment required to use it well.

The question, therefore, is not simply what AI will do to us, but what we will do with it. If it becomes a tool that extends the reach of those already advantaged, inequality will rise. If it becomes a widely usable aid to competence, it may narrow the gap.

Either way, the decisive factor will not be the machine itself, but the social arrangements through which human capacities are formed and deployed. For despite AI’s power, it cannot determine how we use our new and improved vision. But it will force us to reckon with the world we’re building. The degree to which AI polarizes or equalizes us will depend upon our values and our foresight. Major technological disruptions inevitably reveal our flaws — in social organization, in the ways we apportion rewards, in our determination of virtuous living — but technology alone does not determine our fate. That’s the difference between us and our eyeglasses.