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economist J. Bradford DeLong ’82, Ph.D. ’87, looking back on the twentieth century two decades after its end, comes to a similar conclusion but on different grounds.

DeLong, professor of economics at Berkeley, looks to matters of “contingency” and “choice”: at key junctures the economy suffered “bad luck,” and the actions taken by the responsible policymakers were “incompetent.”

these were “the most consequential years of all humanity’s centuries.” The changes they saw, while in the first instance economic, also “shaped and transformed nearly everything sociological, political, and cultural.”

DeLong’s look back over the twentieth century energetically encompasses political and social trends as well; nor is his scope limited to the United States. The result is a work of strikingly expansive breadth and scope

labeling the book an economic history fails to convey its sweeping frame.

The century that is DeLong’s focus is what he calls the “long twentieth century,” running from just after the Civil War to the end of the 2000s when a series of events, including the biggest financial crisis since the 1930s followed by likewise the most severe business downturn, finally rendered the advanced Western economies “unable to resume economic growth at anything near the average pace that had been the rule since 1870.

d behind those missteps in policy stood not just failures of economic thinking but a voting public that reacted perversely, even if understandably, to the frustrations poor economic outcomes had brought them.

Within this 140-year span, DeLong identifies two eras of “El Dorado” economic growth, each facilitated by expanding globalization, and each driven by rapid advances in technology and changes in business organization for applying technology to economic ends

from 1870 to World War I, and again from World War II to 197

fellow economist Robert J. Gordon ’62, who in his monumental treatise on The Rise and Fall of American Economic Growth (reviewed in “How America Grew,” May-June 2016, page 68) hailed 1870-1970 as a “special century” in this regard (interrupted midway by the disaster of the 1930s).

Gordon highlighted the role of a cluster of once-for-all-time technological advances—the steam engine, railroads, electrification, the internal combustion engine, radio and television, powered flight

Pessimistic that future technological advances (most obviously, the computer and electronics revolutions) will generate productivity gains to match those of the special century, Gordon therefore saw little prospect of a return to the rapid growth of those halcyon days.

DeLong instead points to a series of noneconomic (and non-technological) events that slowed growth, followed by a perverse turn in economic policy triggered in part by public frustration: In 1973 the OPEC cartel tripled the price of oil, and then quadrupled it yet again six years later.

For all too many Americans (and citizens of other countries too), the combination of high inflation and sluggish growth meant that “social democracy was no longer delivering the rapid progress toward utopia that it had delivered in the first post-World War II generation.”

Frustration over these and other ills in turn spawned what DeLong calls the “neoliberal turn” in public attitudes and economic policy. The new economic policies introduced under this rubric “did not end the slowdown in productivity growth but reinforced it.

the tax and regulatory changes enacted in this new climate channeled most of what economic gains there were to people already at the top of the income scale

Meanwhile, progressive “inclusion” of women and African Americans in the economy (and in American society more broadly) meant that middle- and lower-income white men saw even smaller gains—and, perversely, reacted by providing still greater support for policies like tax cuts for those with far higher incomes than their own.

Daniel Bell’s argument in his 1976 classic The Cultural Contradictions of Capitalism. Bell famously suggested that the very success of a capitalist economy would eventually undermine a society’s commitment to the values and institutions that made capitalism possible in the first plac

In DeLong’s view, the “greatest cause” of the neoliberal turn was “the extraordinary pace of rising prosperity during the Thirty Glorious Years, which raised the bar that a political-economic order had to surpass in order to generate broad acceptance.” At the same time, “the fading memory of the Great Depression led to the fading of the belief, or rather recognition, by the middle class that they, as well as the working class, needed social insurance.”

what the economy delivered to “hard-working white men” no longer matched what they saw as their just deserts: in their eyes, “the rich got richer, the unworthy and minority poor got handouts.”

As Bell would have put it, the politics of entitlement, bred by years of economic success that so many people had come to take for granted, squeezed out the politics of opportunity and ambition, giving rise to the politics of resentment.

The new era therefore became “a time to question the bourgeois virtues of hard, regular work and thrift in pursuit of material abundance.”

DeLong’s unspoken agenda would surely include rolling back many of the changes made in the U.S. tax code over the past half-century, as well as reinvigorating antitrust policy to blunt the dominance, and therefore outsize profits, of the mega-firms that now tower over key sectors of the economy

He would also surely reverse the recent trend moving away from free trade. Central bankers should certainly behave like Paul Volcker (appointed by President Carter), whose decisive action finally broke the 1970s inflation even at considerable economic cost

Not only Galbraith’s main themes but many of his more specific observations as well seem as pertinent, and important, today as they did then.

What will future readers of Slouching Towards Utopia conclude?

If anything, DeLong’s narratives will become more valuable as those events fade into the past. Alas, his description of fascism as having at its center “a contempt for limits, especially those implied by reason-based arguments; a belief that reality could be altered by the will; and an exaltation of the violent assertion of that will as the ultimate argument” will likely strike a nerve with many Americans not just today but in years to come.

what about DeLong’s core explanation of what went wrong in the latter third of his, and our, “long century”? I predict that it too will still look right, and important.

, we know from the science of linguistics and the philosophy of knowledge that they differ profoundly from how humans reason and use language. These differences place significant limitations on what these programs can do, encoding them with ineradicable defects.

It is at once comic and tragic, as Borges might have noted, that so much money and attention should be concentrated on so little a thing — something so trivial when contrasted with the human mind, which by dint of language, in the words of Wilhelm von Humboldt, can make “infinite use of finite means,” creating ideas and theories with universal reach.

The human mind is not, like ChatGPT and its ilk, a lumbering statistical engine for pattern matching, gorging on hundreds of terabytes of data and extrapolating the most likely conversational response or most probable answer to a scientific question

the human mind is a surprisingly efficient and even elegant system that operates with small amounts of information; it seeks not to infer brute correlations among data points but to create explanations

such programs are stuck in a prehuman or nonhuman phase of cognitive evolution. Their deepest flaw is the absence of the most critical capacity of any intelligence: to say not only what is the case, what was the case and what will be the case — that’s description and prediction — but also what is not the case and what could and could not be the case

Those are the ingredients of explanation, the mark of true intelligence.

Here’s an example. Suppose you are holding an apple in your hand. Now you let the apple go. You observe the result and say, “The apple falls.” That is a description. A prediction might have been the statement “The apple will fall if I open my hand.”

an explanation is something more: It includes not only descriptions and predictions but also counterfactual conjectures like “Any such object would fall,” plus the additional clause “because of the force of gravity” or “because of the curvature of space-time” or whatever. That is a causal explanation: “The apple would not have fallen but for the force of gravity.” That is thinking.

The crux of machine learning is description and prediction; it does not posit any causal mechanisms or physical laws

any human-style explanation is not necessarily correct; we are fallible. But this is part of what it means to think: To be right, it must be possible to be wrong. Intelligence consists not only of creative conjectures but also of creative criticism. Human-style thought is based on possible explanations and error correction, a process that gradually limits what possibilities can be rationally considered.

ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible.

Whereas humans are limited in the kinds of explanations we can rationally conjecture, machine learning systems can learn both that the earth is flat and that the earth is round. They trade merely in probabilities that change over time.

For this reason, the predictions of machine learning systems will always be superficial and dubious.

some machine learning enthusiasts seem to be proud that their creations can generate correct “scientific” predictions (say, about the motion of physical bodies) without making use of explanations (involving, say, Newton’s laws of motion and universal gravitation). But this kind of prediction, even when successful, is pseudoscienc

While scientists certainly seek theories that have a high degree of empirical corroboration, as the philosopher Karl Popper noted, “we do not seek highly probable theories but explanations; that is to say, powerful and highly improbable theories.”

The theory that apples fall to earth because mass bends space-time (Einstein’s view) is highly improbable, but it actually tells you why they fall. True intelligence is demonstrated in the ability to think and express improbable but insightful things.

This means constraining the otherwise limitless creativity of our minds with a set of ethical principles that determines what ought and ought not to be (and of course subjecting those principles themselves to creative criticism)

True intelligence is also capable of moral thinking

To be useful, ChatGPT must be empowered to generate novel-looking output; to be acceptable to most of its users, it must steer clear of morally objectionable content

In 2016, for example, Microsoft’s Tay chatbot (a precursor to ChatGPT) flooded the internet with misogynistic and racist content, having been polluted by online trolls who filled it with offensive training data. How to solve the problem in the future? In the absence of a capacity to reason from moral principles, ChatGPT was crudely restricted by its programmers from contributing anything novel to controversial — that is, important — discussions. It sacrificed creativity for a kind of amorality.

Here, ChatGPT exhibits something like the banality of evil: plagiarism and apathy and obviation. It summarizes the standard arguments in the literature by a kind of super-autocomplete, refuses to take a stand on anything, pleads not merely ignorance but lack of intelligence and ultimately offers a “just following orders” defense, shifting responsibility to its creators.

In short, ChatGPT and its brethren are constitutionally unable to balance creativity with constraint. They either overgenerate (producing both truths and falsehoods, endorsing ethical and unethical decisions alike) or undergenerate (exhibiting noncommitment to any decisions and indifference to consequences). Given the amorality, faux science and linguistic incompetence of these systems, we can only laugh or cry at their popularity.