Blog-4

AI, or 'Artificial Disruption'? Wolf and Krugman in Dialogue

By any honest measure, the word “revolution” has been lavished on more than its share of arrivals—be they technological wonders or philosophical fads. Yet only rarely does a technology command, in equal measure, the attention of Nobel laureates, Financial Times commentators, yogis, and warehouse managers. Artificial intelligence, with its stochastic parroting and ticklish ambitions, presently occupies that rare intersection. On a Friday in June, Martin Wolf, chief economics commentator at the Financial Times, and Paul Krugman, Nobel laureate and sometime aperitif enthusiast, met to examine what lies beneath the surface drama of AI’s ascent—and what it means for the generations tasked with managing its consequences.

One quickly learns, in the presence of Krugman, that the idea of “artificial intelligence” is itself something of a conjurer’s trick. “What we’re calling artificial intelligence really isn’t, at this point, intelligence,” he muses, sounding less like an emissary of cold equations and more like one of those fabled American skeptics who came in with the telegraph and never left. He describes machines built not to think, but to slosh immense data sets through clever, often unintelligible algorithms—algorithms that answer, more or less, in the manner of an educated but distractible dinner guest.

Wolf, who takes his role as discussant seriously but not somberly, reminds us of Alan Turing, that patron saint of computation and ambiguity. How strange, he notes, that the Turing Test—once the grand hurdle—has been so easily cleared. The machines converse; the machines fool. Yet, as Wolf observes, “we don’t think that they’re people yet.” The bar, as so often in life, has proven portable.

If the machines have advanced, our anxieties have kept pace. Krugman, whose tenure spans booms, busts, and the brief reign of “learn to code,” finds in history a series of rehearsals for our present confusion. The Luddites feared the power loom, farmers surrendered to the tractor, and forty years passed between the invention of electricity and its quiet remaking of the American factory. Now, AI’s impact is visible, if not yet truly measurable. “We find other stuff to do,” he says, and history, his perennial companion, seldom contradicts.

Wolf, ever lucid, notes the peculiar geography of risk. “The safest job in the world is probably gardener,” he quips. The greatest danger, he suggests, lurks not in the coal mines or cotton fields but in the arid middle spaces of white-collar work—the places where analysis eclipses judgment and where, increasingly, software finds purchase. There is an irony here: the university boom, the proliferation of “middle-grade analysts,” both now threatened by their own intellectual offspring.

Krugman wonders aloud whether AI might, perversely, return dignity to manual skills long derided in the managerial imagination. “Maybe we go back to that. Maybe we go to a situation where people who can actually deal with the material world become appropriately valued again... computers can also push symbols around.” In the long view, the gardener may yet inherit the earth.

Yet, as Krugman observes, a peculiar haste distinguishes our current AI moment from previous upheavals. “What we’re seeing is a rush to implement AI before it’s been proved that it’s useful.” Search engines, once lithe, now stagger under the weight of generative protocol. There is a suspicion, in both men’s voices, that AI—like blockchain, or perhaps Aperol Spritz—may be another fashion disguised as destiny.

Of course, Wolf is not naïve to the implications for power. AI’s infrastructure, Krugman notes, is oddly corporeal: “giant server farms, huge amounts of power consumption... people with lots of money to invest in largely physical capital.” It is not so much ingenious code as invested capital that determines who inherits this new earth—a familiar melody, rearranged for fewer instruments.

The conversation circles, as good conversations do, toward education and the university. Wolf invokes the pedagogical conundrum: is it wise to overhaul curricula in the face of uncertainty, or to trust, as previous generations did, to the slow grinding virtues of analytical training? Krugman, with the lively skepticism of a man who’s heard too many slogans, mourns the obsolete “learn to code”—now that AI does just that.

And yet, neither succumbs to despair. Wolf imagines, with a touch of optimism, that AI’s advance may be less concentrated—less the China shock and more the cotton gin—allowing societies to absorb change without disaster. Krugman, ever empirical, suggests that any prediction may be upended by the next white paper, or by the unpredictable logic of the market.

In the end, what remains is the cheerful uncertainty of two men who, having seen enough technological revolutions to distrust easy omens, offer no prophecy but plenty of nuance. To the management and data science students preparing to navigate this era, they offer only this: be alert to both fashion and substance, and remember that the future rarely unfolds as swiftly as its loudest prophets insist.

To believe anything about AI, Krugman reflects, is to find an expert to confirm it. The wise, he implies, proceed with humility—and a gardener’s patience