Whose Judgement? Artificial Intelligence, Anthropology and the New Scarcity

The implications of generative artificial intelligence have rapidly become one of the defining conversations of our time.

Yet the conversation looks different depending on where you’re standing. A software engineer, a university lecturer and a clinician may have access to the same tools, and still inhabit very different symbolic worlds — in one setting, using AI signals innovation and technical competence; in another, it still invites questions about authenticity, professional judgement or legitimacy. The technology is shared. Its meaning is not.

This matters, because artificial intelligence is not entering a cultural vacuum. It is being interpreted, adopted, resisted and reshaped within communities that already possess their own histories, identities, values and moral expectations.

Much of the current conversation — whether optimistic or critical — remains focused on AI itself: what it can do, what it cannot, how quickly it is improving, and what risks or opportunities it presents.

Those are important questions. But history suggests that transformative technologies rarely reshape societies simply because of what they are capable of. Their deeper significance lies in how they alter the conditions under which human societies create value.

When Scarcity Changes, Societies Change

The printing press did more than widen the distribution of ideas. It made literacy itself — with all the exclusive power it once bestowed on its bearers — less scarce. The Industrial Revolution did not eliminate human labour, but it changed the relationship between human effort and productive capacity, and entire professions evolved in response.

From its dawn in the middle of the last century, the Information Age, made information itself abundant; the challenge stopped being how to obtain it, and became how to evaluate, interpret and apply it. Education systems built around knowledge retention are still struggling to adapt to that one.

Each of these shifts precipitated a redistribution of power. Artificial intelligence may be initiating another.

Not because machines have become “intelligent” in the way humans are (an idea most would reasonably contest) but because forms of cognitive or analytical labour that were previously scarce can now be harnessed, augmented or distributed at remarkable speed and scale.

Which means the most urgent question might not be what AI will become capable of. It’s what becomes civilisation’s limiting resource once intelligence no longer is.

Because whenever one form of scarcity diminishes, another inevitably emerges. The question is not whether scarcity disappears. It’s where it migrates to.

A fairly consistent answer is already forming across boardrooms, conference stages and business commentary: judgement, trust, discernment, understanding, even wisdom. Once intelligence is cheap, these become the differentiators — the new scarcities.

It’s a reasonable answer, and I’d broadly agree with it.

But stated on its own, it’s incomplete — and I think it’s worth pushing one level further.

Whose Judgement?

Treating “judgement” or “wisdom” as the new scarce resource makes a quiet assumption that deserves scrutiny.

Leadership literature increasingly speaks about judgement as though it were a universal competency: something leaders accumulate through experience and then carry with them from one context to another.

Anthropology invites a different possibility.

What counts as sound judgement, legitimate authority or trustworthy conduct is not fixed. It is constituted differently within different communities, shaped by their own histories, values and moral expectations. A decision read as decisive and wise in one institutional culture can be read as reckless or presumptuous in another.

A leader who has developed astute, nuanced judgement in one professional world can walk into another and discover that same judgement isn’t automatically recognised — not because it has weakened, but because the system that gives judgement its meaning has changed.

So the sharper question isn’t simply who has good judgement. It’s whose judgement — legitimated by which values, and according to whose history.

That reframing is important, because much of the current commentary treats “judgement” or “wisdom” exactly the way earlier commentary treated “intelligence”: as a single, universal currency, equally valid wherever it’s spent.

This piece opened by noting that the same AI tools carry different meanings in different professional worlds — that the technology is shared, but its meaning is not. Leadership commentary risks making the identical assumption about judgement: treating it as one thing, measured the same way, everywhere it appears.

Context Gives Judgement Its Meaning

For more than a century, anthropologists have studied exactly this problem: how human beings construct meaning, establish legitimacy and negotiate difference within particular communities, rather than according to some universal standard of “good” behaviour imported from outside. The discipline starts from a simple premise: to understand a human system, you must first understand the world as it makes sense to the people within it.

That is precisely the capability leadership now needs, and precisely the one most AI conversation skips past.

Because although a shortage of intelligence may be becoming less likely, at least for some, a different risk is emerging in its place: an excess of certainty. When answers become easier to generate, it becomes easier to mistake fluency for understanding.

But complex human systems rarely yield to rapid explanation. The most serious leadership failures are rarely failures of intelligence. They happen when leaders confidently apply solutions without understanding the context — the specific community, its history, its own definitions of legitimacy — into which those solutions are introduced.

Elsewhere I have called this context blindness: the tendency to misread the human landscapes within which decisions are made and lived.

AI can amplify that tendency as easily as it can reduce it. It can analyse information, recognise patterns and generate sophisticated output at extraordinary speed. It cannot, by itself, tell you what a particular community values, why one symbol carries legitimacy while another provokes resistance, whose perspectives have been overlooked, or how people themselves understand the change they’re living through.

Those remain fundamentally human questions — and answering them well, for a specific community rather than in the abstract, is a discipline in its own right.

Leadership Beyond Intelligence

If intelligence is becoming abundant, the task facing leaders isn’t simply to become wiser — it’s to become fluent readers of the particular human systems they’re responsible for, and honest about the fact that wisdom itself doesn’t look identical from one system to the next.

That shifts attention away from optimisation and towards judgement, away from generating answers and towards asking better questions, away from individual expertise and towards collective sensemaking — and, beyond that, towards a genuine curiosity about why a decision that lands well in one part of an organisation can land badly in another.

Leadership has always meant making decisions under uncertainty. What’s changing is the nature of that uncertainty. When AI can generate the analysis, the recommendation and the plan in seconds, the responsibility for deciding what ought to be done — and for whom, and according to whose sense of what’s right — doesn’t disappear. It becomes more consequential.

That responsibility was never technological. It belongs to us.

In an Age of Abundant Cognition, the defining question of leadership won’t be how to produce more intelligence. It will be whether leaders can learn to recognise that wisdom, trust and legitimacy are not singular things to be optimised for, but plural ones to be understood — differently, and deliberately, in every human system they touch.

Artificial intelligence may reshape what organisations can do. Anthropology helps us understand what they should become — and for whom.