r.Appiah2025

Title: "The Age of De-Skilling"
Year: 2025
Authors: Kwame Anthony Appiah
Status: read
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Type: literature-note
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the anxiety has shifted, too, from apocalypse to atrophy. Page 1

The term for it is unlovely but not inapt: de-skilling. Page 1

Use it or lose it was the basic takeaway Page 1

But the real puzzle isn’t whether de-skilling exists—it plainly does—but rather what kind of thing it is. Page 1

To grasp what’s at stake, we have to look closely at the ways that skill frays, fades, or mutates when new technologies arrive Page 1

Socrates Page 2

Writing, he warns, will do the opposite: It will breed forgetfulness, letting people trade the labor of recollection for marks on papyrus, mistaking the appearance of understanding for the thing itself Page 2

Written words, he complains, never answer your particular questions; reply to everyone the same way, sage and fool alike; and are helpless when they’re misunderstood Page 2

Of course, the reason we know all this—the reason the episode keeps turning up in Whiggish histories of technology—is that Plato wrote it down Page 2

You could now take in ideas without wrestling with them Page 2

Reading, by contrast, lets you bask in another’s brilliance, nodding along without ever testing yourself against it. Page 2

What looks like a loss from one angle, though, can look like a gain from another Page 2

“Writing is a technology that restructures thought.” Page 2

Later, satellite navigation brought an end to sextant skills. Page 2

Slide rules yielded to calculators, calculators to computers. Each time, individual virtuosity waned, but overall performance advanced. Page 2

their old skills unexercised and unvalued Page 3

The new system was faster, cleaner, safer; it also drained the work of its meaning. Page 3

One told Sennett, half-joking, “Baking, shoemaking, printing—you name it, I’ve got the skills.” She meant that she didn’t really need any. Page 3

To hear the music you wanted, you had to practice. Page 3

The modern music lover may have been less of a performer but, in a sense, more of a listener. Page 3

Still, breadth came at the expense of depth. Practicing a piece left you with an intimate feel for its seams and contours. Did your kid with the shiny Victrola get that? Page 3

That sense of estrangement—of being a step removed from the real thing—shows up whenever a powerful new tool arrives. Page 4

Pressing “Cos” on a keypad got you a number, but the meaning behind it could slip away. Page 4

“The computer understands the answer,” he told them when they handed him their printouts, “but I don’t think you understand the answer.” Page 4

A found that, in certain circumstances, people would remember where a fact could be found rather than the fact itself. Page 4

This compounding of insight—externalized, preserved, shared—is what sets Homo sapiens apart. Bonobos live in the ecological present. We live in history. Page 4

Over time, the division of labor became, inevitably, a division of cognitive labor. Page 5

Human capability resides not solely in individuals but in the networks they form, each of us depending on others to fill in what we can’t supply ourselves. Scale turned social exchange into systemic interdependence Page 5

The result is a world in which, in a classic , nobody knows how to make a pencil. Page 5

Scientific mastery now means knowing more and more about less and less. Page 5

When Andrew Wiles proved Fermat’s Last Theorem, he didn’t re-derive every lemma himself; he assembled results that he trusted but didn’t personally reproduce, building a structure he could see whole even if he hadn’t cut each beam. Page 5

Knowledge, once imagined as a possession, has become a relation—a matter of how well we can locate, interpret, and synthesize what others know. Page 6

We live inside a web of distributed intelligence, dependent on specialists, databases, and instruments to extend our reach. Page 6

It was only a matter of time before the network acquired a new participant—one that could not just store information but imitate understanding itself. Page 6

The old distinction between information and skill, between “knowing that” and “knowing how,” has grown blurry in the era of large language models Page 6

But most modern work is collaborative, and the arrival of AI hasn’t changed that. Page 6

The issue isn’t how humans compare to bots but how humans who use bots compare to those who don’t. Page 6

It’s an echo of the old debate over “risk compensation”: Add seatbelts or antilock brakes, some social scientists argued a few decades ago, and people will simply drive more recklessly, their tech-boosted confidence leading them to spend the safety margin. Research eventually showed a more encouraging result: People do adjust, but only partially, so that substantial benefits remain. Page 7

(The AI here was an expert system—a narrow, reliable form of machine learning, not the generative kind that powers chatbots.) Page 7

In other domains, the more skillful the person, the more skillful the collaboration—or so some recent studies suggest. Page 7

The outcome depended on the task. Where human intuition was weak, as with the hotel reviews, people second-guessed the bot too much and dragged the results down. Page 7

Once a machine enters the workflow, mastery may shift from production to appraisal Page 8

The skill migrated from composition to supervision. Page 8

Expertise shifts from producing the first draft to editing it, from speed to judgment. Page 8

When the stakes are real, skilled human agents have to remain accountable for the call—noticing when the model has drifted from reality, and treating its output as a hypothesis to test, not an answer to obey Page 8

The future of expertise will depend not just on how good our tools are but on how well we think alongside them. Page 8

But collaboration presupposes competence Page 8

. You can’t become de-skilled if you were never skilled in the first place. Page 8

Our old standbys need a rebuild; in the past couple of years, too many college kids have, in an unsettling phrase, ended up “majoring in ChatGPT.” Page 8

In both rounds, the AI-tutored students came out ahead—by a lot. They didn’t just learn more. They worked faster, too, and reported feeling more motivated and engaged. The system had been designed to behave like a good coach: showing you how to break big problems into smaller ones, offering hints instead of blurting out answers, titrating feedback and adjusting to each student’s pace Page 8

If custom-fitted in the right way, large language models promise to mass-produce that kind of attention—not the cardigan, not the burnished briar, not the pensive moue, but the steady, responsive pressure that turns confusion into competence. Page 9

And we should be mindful that those physics students put their tutor bot to good use because they had in-class exams to face—a proctor, a stopwatch, a grader’s cold eye. Page 9

We should also be mindful that what works for STEM courses may not work for the humanities. Page 9

In a curious cultural rewind, orality may have to carry more of the load. Will Socrates, dialogue’s great defender, have the last word after all? Page 9

Erosive de-skilling remains a prospect that can’t be wished away: the steady atrophy of basic cognitive or perceptual capacities through overreliance on tools, with no compensating gain. Page 10

a system’s reservesabilities you seldom need but must have when things go wrong. Page 10

Some automation theorists distinguish between “humans in the loop,” who stay actively engaged, and “humans on the loop,” who merely sign off after a machine has done the work. Page 10

The remedy probably lies in institutional design. For example, a workplace could stage regular drills—akin to a pilot’s recurrent flight-simulator training—in which people must challenge the machine and ensure that their capacities for genuine judgment haven’t decayed in the long stretches of smooth flight. Page 10

That’s why the Naval Academy, alarmed by the prospect of GPS jamming, brought back basic celestialnavigation training after years of neglect. Most sailors will never touch a sextant on the high seas, but if a few of them acquire proficiency, they may be enough to steady a fleet if the satellites go dark. The goal is to ensure that at least some embodied competence survives, so that when a system stumbles, the human can still stand—or at least stay afloat. Page 10

The most troubling prospect of all is what might be called constitutive de-skilling: the erosion of the capacities that make us human in the first place. Judgment, imagination, empathy, the feel for meaning and proportion—these aren’t backups; they’re daily practices. Page 11

What might vanish is the tacit, embodied knowledge that underwrites our everyday discernment. Page 11

shallower conversation, a reduced appetite for ambiguity, a drift toward automatic phrasing where once we would have searched for the right word, the quiet substitution of fluency for understanding. Page 11

Most forms of de-skilling, if you take the long view, are benign. Page 11

Another kind of de-skilling represents the elimination of drudgery. Page 11

He’s still responsible for the content, but if his grant-writing chops decline, he’s unbothered. That’s not science, in his view; it’s a performance demanded by the research economy. Offloading some of it gives him back time for discovery. Page 11

Occupational de-skilling can, in fact, be democratizing, widening the circle of who gets to do a job. Page 12

. The craft had shrunk; the eligible workforce had grown. (And yes, their labor had grown cheaper: a wider gate, a lower wage.) Page 12

Zuboff called this reskilling: action skills giving way to abstraction and procedural reasoning, or what she termed “intellective skills.” Page 12

Something similar happened with accountants after the arrival of spreadsheet programs such as VisiCalc; no longer tasked with totting up columns of numbers, they could spend more time on tax strategy and risk analysis. Page 12

Each leap enlarged the field of the possible Page 12

Working with large language models, my younger colleagues insist, is already teaching a new kind of craftsmanship—prompting, probing, catching bias and hallucination, and, yes, learning to think in tandem with the machine. These are emergent skills, born of entanglement with a digital architecture that isn’t going anywhere. Important technologies, by their nature, will usher forth crafts and callings we don’t yet have names for. Page 12

The hard part is deciding, without nostalgia and inertia, which skills are keepers and which are castoffs. Page 13

Throughout human history, our capabilities have never stayed put. Know-how has always flowed outward—from hand to tool to system Page 13

Generative AI—a statistical condensation of human knowledge—is simply the latest chapter in our long apprenticeship to our own inventions. Page 13