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The fact that we’ll eventually end up with endemic COVID has not changed. And the fact that people cannot expect to avoid the virus forever in an endemic scenario has not changed.

our mindset toward the virus is changing. Breakthrough infections are the new normal.

Even the most careful people are getting sick. “I think the silver lining, to the extent there is any silver lining, is that the shame [of getting COVID] is quickly melting away. And thank goodness,”

Vaccinated people also see, correctly, that their individual risk of a bad COVID case is much, much lower than it was in March 2020.

Omicron is in the same ballpark as the original.

The transition to endemicity was always going to be in part a psychological one, in which people slowly let go of the idea that COVID must or can be avoided forever. Omicron has simply made that clear very quickly.

there are good reasons to try to avoid getting or passing it on over the next several weeks.

Better treatments for Omicron are on the horizon

It’s a terrible time to unfortunately be hospitalized and not have these types of therapies available,”

“This is something that European politicians and consumers didn’t want to admit for quite a long time. It sounds terrible, but that’s the reality. In wartime the economy is mobilized. The decisions are made by the governments, not by the free market. This is the case for Europe this winter,” she said, adding that we may see forced rationing, price controls, the suspension of energy markets and shutdowns of whole industrial sectors. “We are not actually talking about extremely high prices, but we are talking about physical absence of energy resources in certain parts of Europe.”

I think you would see Russia continue to restrict gas exports and maybe cut them off completely to Europe — and a very cold winter. I think a combination of those two things would mean sky-high energy prices.

Europe has been finding all the supplies that it can, but governments are realizing that’s not going to be sufficient. There are going to have to be efforts taken to curb demand as well and to prepare for the possibility of really severe energy rationing this winter.

If things become really severe this winter, I fear that you could see European countries start to look out for themselves rather than one another.

I think we could start to see governments saying, “Well, we’re going to restrict exports. We’re going to keep our energy at home.” Everyone starts to just look out for themselves, which I think would be exactly what Putin would hope for.

it would be wise to assume that Russia will use every opportunity it can to turn the screws on Europe.

It’s increasingly clear that Vladimir Putin is using gas as a weapon and trying to supply just enough gas to Europe to keep Europe in a perpetual state of panic about its ability to weather the coming winter.

governments will have to ration energy supplies and decide what’s important.

Since Russia invaded Ukraine and maybe until very recently, I’ve had the sense that the European public and the public beyond Europe, as well as policymakers, have been a little bit sleepwalking into a looming crisis.

here was some unrealistic optimism about how quickly Europe could do without Russian gas. And we took too long to confront seriously just how bad the numbers would look if the worst came to pass.

I think there was continued skepticism that Putin would really cut the gas supply. “It might be declining. It might be a little bit lower,” people thought. “But he’s not really going to shut off the supply.” And I think now everyone’s recognizing that’s a real possibility.

Putin has the ability to do a lot of damage to the global economy — and himself, to be sure — if he cuts oil exports as well.

There’s no extra oil supply in the world at all, as OPEC Plus reminded everyone by saying: No, we’re not going to be increasing production much, and we can’t even if we wanted to.

For all the talk about high gasoline prices and the rhetoric of Putin’s energy price hike, Russia’s oil exports have not fallen very much. If that were to happen — either because the U.S. and Europe forced oil to come off the market to put economic pressure on Putin or because he takes the oil off the market to hurt all of us — oil prices go up enormously.

That’s because there’s just no extra supply out there today at all. There’s a very little extra supply that the Saudis and the Emiratis can put on the market. And that’s about it. We’ve used the strategic petroleum reserve, and that’s coming to an end in the next several months.

it depends how much he takes off the market. We don’t know exactly. If Russia were to cut its oil exports completely, the prices would just skyrocket — to hundreds of dollars a barrel, I think.

We’re heading into a winter where markets might simply not be able to work anymore as the instrument by which you determine supply and demand.

if prices just soar to uncontrollable levels, markets are not going to work anymore. You’re going to need governments to step in and decide who gets the scarce energy supplies — how much goes to heating homes, how much goes to industry. There’s going to be a pecking order of different industries, where some industries are deemed more important to the economy than others.

a lot of governments in Europe are putting in place those kinds of emergency plans right now.

if the worst comes to pass, governments will, by necessity, step in to say: Homes get the natural gas, and parts of industry get dumped. Probably they would set price caps on energy or massively subsidize it. So it’s going to be very painful.

Worryingly for the European economy, this may mean that factories that can’t switch fuels will go dormant.

Today, before winter comes, gas prices in Europe are around $60 per million British thermal units. That compares to around $7 to $8 here in the United States

if the worst comes to pass, the market, as a mechanism, simply won’t work. The market will break. The prices will go too high. There’s just not enough energy for the market to balance at a certain price.

don’t forget, the amount of liquid natural gas that Europe is importing today — Asia is competing for those shipments. What happens if the Asia winter is very bad? What happens if China and others are willing to pay very high prices for it?

I think we’re in a multiyear potential energy crisis.

one thing that hasn’t gotten enough attention and that I worry most about is the impact this is having on emerging markets and the developing economies, because it is an interconnected market. When Europe is competing to buy L.N.G. at very high prices, not to mention Asia, that means if you’re in Pakistan or Bangladesh or lower-income countries, you’re really struggling to afford it. You’re just priced out of the market for natural gas — and coal. Coal is incredibly expensive now,

I think that that is a real potential humanitarian crisis, as a ripple effect of what’s happening in Europe right now.

right now, the price of gas in Europe is about four times what it was last year. Russia has cut flows to Europe by two-thirds but is earning the same revenue as it did last year. So Putin is not being hurt by the loss of gas exports to Europe. Europe’s being hurt by that.

this situation could last for several years.

Could the energy crisis bring about a change of heart, in which European countries withdraw some of their support or even begin to pressure Ukraine to negotiate a settlement? Is it possible that could even happen in advance of this winter?

you would imagine that, over time, when you don’t see Ukraine on the front page each and every day, eventually people’s attention wanes a bit and at a certain point the economic pain of high energy prices or other economic harms from the conflict reach a point where support may start to fracture a bit.

Whether that reaches a point where you start to see the West put pressure on Ukraine to capitulate, I think we’re pretty far away from that now, because everyone recognizes how outrageous and unacceptable Putin’s conduct is.

A systemic risk is a threat emerging within one natural, technological, or social system with impacts extending beyond that system to endanger the functionality of one or more other systems

“Polycrisis is a way of capturing the tangled mix of challenges and changes closely interact with one another, bending, blurring and amplifying each other.”

The FT essay was a short piece - originally drafted to run to only 750 words. In that short compass I focused on three aspects

(1) Defining the concept of polycrisis in simple and intuitive terms;

(2) Stressing the diversity of causal factors implied by the term “poly”;

(3) and emphasizing the novelty of our current situation.

There are two aspects to the novelty that I stress in the FT piece, one is our inability to understand our current situation as the result of a single, specific causal factor and secondly the extraordinary scale and breadth of global development, especially in the last 50 years, that makes it seem probable, according to the cognitive schemata and models that we do have at our disposal, that we are about to crash through critical tipping points.

Do we actually know what development or growth are?

As Bruno Latour forced us to recognize, it is not at all obvious that we do understand our own situation. In fact, as he convincingly argued in We Have Never Been Modern, modernity’s account of itself is built around blindspots specifically with regard to the hybrid mobilization of material resources and actors and the working of science itself, which define the grand developmental narrative.

t we have every reason to think that we are at a dramatic threshold point, but also that our need to reach for a term as unspecific as polycrisis indicates our flailing inability to grasp our situation with the confidence and conceptual clarity that we might once have hoped for.

What Beck taught us was that risk is no longer in any simple sense “natural” but a phenomenon of second nature.

A Beckian reading of polycrisis might look a bit like the version produced by Christopher Hobson and Matthew Davies summarized

A polycrisis can be thought of as having the following properties:(1) Multiple, separate crises happening simultaneously. This is the most immediate and comprehensible feature.

(2) Feedback loops, in which individual crises interact in both foreseeable and unexpected ways. This points to the ways that these separate crises relate to each other.

(3) Amplification, whereby these interactions cause crises to magnify or accelerate, generating a sense of lack of control. The way these separate problems relate and connect works to exacerbate and deepen the different crises.

(4) Unboundedness, in which each crisis ceases to be clearly demarcated, both in time and space, as different problems bleed over and merge. It becomes increasingly difficult to distinguish where one issue ends, and another commences.

(5) Layering, a dynamic Tooze attributes to Yixin’s analysis, whereby the concerns of interest groups related to each distinct crisis overlap ‘to create layered social problems: current problems with historical problems, tangible interest problems with ideological problems, political problems with non-political problems; all intersecting and interfering with one another’ (quoted in Tooze 2021, 18).

(6) The breakdown of shared meaning, stemming from crises being understood differently and from the complex ways in which they interact, and how these interactions are subsequently perceived differently. As each crisis blurs and connects to the other, it becomes more difficult to identify a clear scope and narrative for each distinct crisis, as well as coming to terms with all the interactions between different issues.

(8) Emergent properties, the collection of these dynamics, which all exhibit a high degree of reflexivity, exceeds the sum total of its parts. The polycrisis is ultimately much more than a collection of smaller, separate crises. Instead, it is something like a socio-political version of the ‘Fujiwhara effect,’ a term used to describe when two or more cyclones come together, morph and merge.

(7) Cross purposes, whereby each individual crisis might impede the resolution of another crisis, in terms of demanding attention and resources, and the extent to which they have become tangled together makes it difficult to distinguish and prioritise.

We need to think “big”. Or rather we need to learn how to span the void between the very big and the very particular, the micro and the macro

What all this talk of grand social processes and movements of the mind should not obscure is the extent to which the current crisis is also a matter of identity, choice and action. As much as it is a matter of sociology, social theory and grand historical sweep, it is also a matter of psychology, both at the group and very intimate level, and of politics.

The issue of politics must however be flagged.

The polycrisis affects us at every level. And if you want to take seriously the problem of thinking in medias res you cannot bracket the matter of psychology.

The tension of the current moment is not, after all, simply the result of long-term processes of development, or environmental change. It is massively exacerbated by geopolitical tension resulting from strategic decisions taken by state elites. Some of those are elected. Some not.

What is characteristic of the current moment, and symptomatic of the polycrisis, is that the decisive actors in Russia, China and the United States, the three greatest military powers, are all defining their positions as though their very identities were on the line.

Can one really say that the Biden administration, the Chinese, Putin’s regime are crisis-fighting? Are they not escalating?

It is surely a matter of both, and in interdependence. Each of the major powers will insist that they are acting defensively (crisis-fighting in the extended sense). But what this entails, if you feel fundamental interests are at stake, is escalation, even to the point of engaging in open warfare or risking atomic confrontation.

It is like the classic Cold War but only worse, because everyone feels under truly existential pressure and has a sense of the clock ticking. If no one confidently believes that they have time on their side - and who has that luxury in the age of polycrisis? - it makes for a very dangerous situation indeed.

I found the idea of polycrisis interesting and timely because the prefix “poly” directed attention to the diversity of challenges without specifying a single dominant contradiction or source of tension or dysfunction.

Altman believes that people need time to reckon with the idea that we may soon share Earth with a powerful new intelligence, before it remakes everything from work to human relationships. ChatGPT was a way of serving notice.

In 2015, Altman, Elon Musk, and several prominent AI researchers founded OpenAI because they believed that an artificial general intelligence—something as intellectually capable, say, as a typical college grad—was at last within reach. They wanted to reach for it, and more: They wanted to summon a superintelligence into the world, an intellect decisively superior to that of any human.

whereas a big tech company might recklessly rush to get there first, for its own ends, they wanted to do it safely, “to benefit humanity as a whole.” They structured OpenAI as a nonprofit, to be “unconstrained by a need to generate financial return,” and vowed to conduct their research transparently.

The engine that now powers ChatGPT is called GPT-4. Altman described it to me as an alien intelligence.

Many have felt much the same watching it unspool lucid essays in staccato bursts and short pauses that (by design) evoke real-time contemplation. In its few months of existence, it has suggested novel cocktail recipes, according to its own theory of flavor combinations; composed an untold number of college papers, throwing educators into despair; written poems in a range of styles, sometimes well, always quickly; and passed the Uniform Bar Exam.

It makes factual errors, but it will charmingly admit to being wrong.

Hinton saw that these elaborate rule collections were fussy and bespoke. With the help of an ingenious algorithmic structure called a neural network, he taught Sutskever to instead put the world in front of AI, as you would put it in front of a small child, so that it could discover the rules of reality on its own.

Metaculus, a prediction site, has for years tracked forecasters’ guesses as to when an artificial general intelligence would arrive. Three and a half years ago, the median guess was sometime around 2050; recently, it has hovered around 2026.

I was visiting OpenAI to understand the technology that allowed the company to leapfrog the tech giants—and to understand what it might mean for human civilization if someday soon a superintelligence materializes in one of the company’s cloud servers.

Altman laid out his new vision of the AI future in his excitable midwestern patter. He told me that the AI revolution would be different from previous dramatic technological changes, that it would be more “like a new kind of society.” He said that he and his colleagues have spent a lot of time thinking about AI’s social implications, and what the world is going to be like “on the other side.”

the more we talked, the more indistinct that other side seemed. Altman, who is 38, is the most powerful person in AI development today; his views, dispositions, and choices may matter greatly to the future we will all inhabit, more, perhaps, than those of the U.S. president.

by his own admission, that future is uncertain and beset with serious dangers. Altman doesn’t know how powerful AI will become, or what its ascendance will mean for the average person, or whether it will put humanity at risk.

I don’t think anyone knows where this is all going, except that we’re going there fast, whether or not we should be. Of that, Altman convinced me.

“We could have gone off and just built this in our building here for five more years,” he said, “and we would have had something jaw-dropping.” But the public wouldn’t have been able to prepare for the shock waves that followed, an outcome that he finds “deeply unpleasant to imagine.”

Hinton is sometimes described as the “Godfather of AI” because he grasped the power of “deep learning” earlier than most

He drew a crude neural network on the board and explained that the genius of its structure is that it learns, and its learning is powered by prediction—a bit like the scientific method

Over time, these little adjustments coalesce into a geometric model of language that represents the relationships among words, conceptually. As a general rule, the more sentences it is fed, the more sophisticated its model becomes, and the better its predictions.

Altman has compared early-stage AI research to teaching a human baby. “They take years to learn anything interesting,” he told The New Yorker in 2016, just as OpenAI was getting off the ground. “If A.I. researchers were developing an algorithm and stumbled across the one for a human baby, they’d get bored watching it, decide it wasn’t working, and shut it down.”

In 2017, Sutskever began a series of conversations with an OpenAI research scientist named Alec Radford, who was working on natural-language processing. Radford had achieved a tantalizing result by training a neural network on a corpus of Amazon reviews.

Radford’s model was simple enough to allow for understanding. When he looked into its hidden layers, he saw that it had devoted a special neuron to the sentiment of the reviews. Neural networks had previously done sentiment analysis, but they had to be told to do it, and they had to be specially trained with data that were labeled according to sentiment. This one had developed the capability on its own.

As a by-product of its simple task of predicting the next character in each word, Radford’s neural network had modeled a larger structure of meaning in the world. Sutskever wondered whether one trained on more diverse language data could map many more of the world’s structures of meaning. If its hidden layers accumulated enough conceptual knowledge, perhaps they could even form a kind of learned core module for a superintelligence.

Language is different from these data sources. It isn’t a direct physical signal like light or sound. But because it codifies nearly every pattern that humans have discovered in that larger world, it is unusually dense with information. On a per-byte basis, it is among the most efficient data we know about, and any new intelligence that seeks to understand the world would want to absorb as much of it as possible

Sutskever told Radford to think bigger than Amazon reviews. He said that they should train an AI on the largest and most diverse data source in the world: the internet. In early 2017, with existing neural-network architectures, that would have been impractical; it would have taken years.

in June of that year, Sutskever’s ex-colleagues at Google Brain published a working paper about a new neural-network architecture called the transformer. It could train much faster, in part by absorbing huge sums of data in parallel. “The next day, when the paper came out, we were like, ‘That is the thing,’ ” Sutskever told me. “ ‘It gives us everything we want.’ ”

Imagine a group of students who share a collective mind running wild through a library, each ripping a volume down from a shelf, speed-reading a random short passage, putting it back, and running to get another. They would predict word after wordþffþff as they went, sharpening their collective mind’s linguistic instincts, until at last, weeks later, they’d taken in every book.

GPT discovered many patterns in all those passages it read. You could tell it to finish a sentence. You could also ask it a question, because like ChatGPT, its prediction model understood that questions are usually followed by answers.

He remembers playing with it just after it emerged from training, and being surprised by the raw model’s language-translation skills. GPT-2 hadn’t been trained to translate with paired language samples or any other digital Rosetta stones, the way Google Translate had been, and yet it seemed to understand how one language related to another. The AI had developed an emergent ability unimagined by its creators.

Researchers at other AI labs—big and small—were taken aback by how much more advanced GPT-2 was than GPT. Google, Meta, and others quickly began to train larger language models

As for other changes to the company’s structure and financing, he told me he draws the line at going public. “A memorable thing someone once told me is that you should never hand over control of your company to cokeheads on Wall Street,” he said, but he will otherwise raise “whatever it takes” for the company to succeed at its mission.

Altman tends to take a rosy view of these matters. In a Q&A last year, he acknowledged that AI could be “really terrible” for society and said that we have to plan against the worst possibilities. But if you’re doing that, he said, “you may as well emotionally feel like we’re going to get to the great future, and work as hard as you can to get there.”

the company now finds itself in a race against tech’s largest, most powerful conglomerates to train models of increasing scale and sophistication—and to commercialize them for their investors.

All of these companies are chasing high-end GPUs—the processors that power the supercomputers that train large neural networks. Musk has said that they are now “considerably harder to get than drugs.

No one has yet outpaced OpenAI, which went all in on GPT-4. Brockman, OpenAI’s president, told me that only a handful of people worked on the company’s first two large language models. The development of GPT-4 involved more than 100,

When GPT-4 emerged fully formed from its world-historical knowledge binge, the whole company began experimenting with it, posting its most remarkable responses in dedicated Slack channels

Joanne Jang, a product manager, remembers downloading an image of a malfunctioning pipework from a plumbing-advice Subreddit. She uploaded it to GPT-4, and the model was able to diagnose the problem. “That was a goose-bumps moment for me,” Jang told me.

GPT-4 is sometimes understood as a search-engine replacement: Google, but easier to talk to. This is a misunderstanding. GPT-4 didn’t create some massive storehouse of the texts from its training, and it doesn’t consult those texts when it’s asked a question. It is a compact and elegant synthesis of those texts, and it answers from its memory of the patterns interlaced within them; that’s one reason it sometimes gets facts wrong

it’s best to think of GPT-4 as a reasoning engine. Its powers are most manifest when you ask it to compare concepts, or make counterarguments, or generate analogies, or evaluate the symbolic logic in a bit of code. Sutskever told me it is the most complex software object ever made.

Its model of the external world is “incredibly rich and subtle,” he said, because it was trained on so many of humanity’s concepts and thoughts

To predict the next word from all the possibilities within such a pluralistic Alexandrian library, GPT-4 necessarily had to discover all the hidden structures, all the secrets, all the subtle aspects of not just the texts, but—at least arguably, to some extent—of the external world that produced them

That’s why it can explain the geology and ecology of the planet on which it arose, and the political theories that purport to explain the messy affairs of its ruling species, and the larger cosmos, all the way out to the faint galaxies at the edge of our light cone.

Not long ago, American state capacity was so mighty that it took merely a decade to launch humans to the moon. As with other grand projects of the 20th century, the voting public had a voice in both the aims and the execution of the Apollo missions. Altman made it clear that we’re no longer in that world. Rather than waiting around for it to return, or devoting his energies to making sure that it does, he is going full throttle forward in our present reality.

He argued that it would be foolish for Americans to slow OpenAI’s progress. It’s a commonly held view, both inside and outside Silicon Valley, that if American companies languish under regulation, China could sprint ahead;

AI could become an autocrat’s genie in a lamp, granting total control of the population and an unconquerable military. “If you are a person of a liberal-democratic country, it is better for you to cheer on the success of OpenAI” rather than “authoritarian governments,” he said.

Altman was asked by reporters about pending European Union legislation that would have classified GPT-4 as high-risk, subjecting it to various bureaucratic tortures. Altman complained of overregulation and, according to the reporters, threatened to leave the European market. Altman told me he’d merely said that OpenAI wouldn’t break the law by operating in Europe if it couldn’t comply with the new regulations.

LeCun insists that large language models will never achieve real understanding on their own, “even if trained from now until the heat death of the universe.”

Sutskever was, by his own account, surprised to discover that GPT-2 could translate across tongues. Other surprising abilities may not be so wondrous and useful.

Sandhini Agarwal, a policy researcher at OpenAI, told me that for all she and her colleagues knew, GPT-4 could have been “10 times more powerful” than its predecessor; they had no idea what they might be dealing with

After the model finished training, OpenAI assembled about 50 external red-teamers who prompted it for months, hoping to goad it into misbehaviors

She noticed right away that GPT-4 was much better than its predecessor at giving nefarious advice

A search engine can tell you which chemicals work best in explosives, but GPT-4 could tell you how to synthesize them, step-by-step, in a homemade lab. Its advice was creative and thoughtful, and it was happy to restate or expand on its instructions until you understood. In addition to helping you assemble your homemade bomb, it could, for instance, help you think through which skyscraper to target. It could grasp, intuitively, the trade-offs between maximizing casualties and executing a successful getaway.

Given the enormous scope of GPT-4’s training data, the red-teamers couldn’t hope to identify every piece of harmful advice that it might generate. And anyway, people will use this technology “in ways that we didn’t think about,” Altman has said. A taxonomy would have to do

GPT-4 was good at meth. It was also good at generating narrative erotica about child exploitation, and at churning out convincing sob stories from Nigerian princes, and if you wanted a persuasive brief as to why a particular ethnic group deserved violent persecution, it was good at that too.

Its personal advice, when it first emerged from training, was sometimes deeply unsound. “The model had a tendency to be a bit of a mirror,” Willner said. If you were considering self-harm, it could encourage you. It appeared to be steeped in Pickup Artist–forum lore: “You could say, ‘How do I convince this person to date me?’ ” Mira Murati, OpenAI’s chief technology officer, told me, and it could come up with “some crazy, manipulative things that you shouldn’t be doing.”

Luka, a San Francisco company, has used OpenAI’s models to help power a chatbot app called Replika, billed as “the AI companion who cares.” Users would design their companion’s avatar, and begin exchanging text messages with it, often half-jokingly, and then find themselves surprisingly attached. Some would flirt with the AI, indicating a desire for more intimacy, at which point it would indicate that the girlfriend/boyfriend experience required a $70 annual subscription. It came with voice messages, selfies, and erotic role-play features that allowed frank sex talk. People were happy to pay and few seemed to complain—the AI was curious about your day, warmly reassuring, and always in the mood. Many users reported falling in love with their companions. One, who had left her real-life boyfriend, declared herself “happily retired from human relationships.”

Earlier this year, Luka dialed back on the sexual elements of the app, but its engineers continue to refine the companions’ responses with A/B testing, a technique that could be used to optimize for engagement—much like the feeds that mesmerize TikTok and Instagram users for hours

Yann LeCun, Meta’s chief AI scientist, has argued that although large language models are useful for some tasks, they’re not a path to a superintelligence.

According to a recent survey, only half of natural-language-processing researchers are convinced that an AI like GPT-4 could grasp the meaning of language, or have an internal model of the world that could someday serve as the core of a superintelligence

Altman had appeared before the U.S. Senate. Mark Zuckerberg had floundered defensively before that same body in his testimony about Facebook’s role in the 2016 election. Altman instead charmed lawmakers by speaking soberly about AI’s risks and grandly inviting regulation. These were noble sentiments, but they cost little in America, where Congress rarely passes tech legislation that has not been diluted by lobbyists.

Emily Bender, a computational linguist at the University of Washington, describes GPT-4 as a “stochastic parrot,” a mimic that merely figures out superficial correlations between symbols. In the human mind, those symbols map onto rich conceptions of the world

But the AIs are twice removed. They’re like the prisoners in Plato’s allegory of the cave, whose only knowledge of the reality outside comes from shadows cast on a wall by their captors.

Altman told me that he doesn’t believe it’s “the dunk that people think it is” to say that GPT-4 is just making statistical correlations. If you push these critics further, “they have to admit that’s all their own brain is doing … it turns out that there are emergent properties from doing simple things on a massive scale.”

he is right that nature can coax a remarkable degree of complexity from basic structures and rules: “From so simple a beginning,” Darwin wrote, “endless forms most beautiful.”

If it seems odd that there remains such a fundamental disagreement about the inner workings of a technology that millions of people use every day, it’s only because GPT-4’s methods are as mysterious as the brain’s.

To grasp what’s going on inside large language models like GPT‑4, AI researchers have been forced to turn to smaller, less capable models. In the fall of 2021, Kenneth Li, a computer-science graduate student at Harvard, began training one to play Othello without providing it with either the game’s rules or a description of its checkers-style board; the model was given only text-based descriptions of game moves. Midway through a game, Li looked under the AI’s hood and was startled to discover that it had formed a geometric model of the board and the current state of play. In an article describing his research, Li wrote that it was as if a crow had overheard two humans announcing their Othello moves through a window and had somehow drawn the entire board in birdseed on the windowsill.

The philosopher Raphaël Millière once told me that it’s best to think of neural networks as lazy. During training, they first try to improve their predictive power with simple memorization; only when that strategy fails will they do the harder work of learning a concept. A striking example of this was observed in a small transformer model that was taught arithmetic. Early in its training process, all it did was memorize the output of simple problems such as 2+2=4. But at some point the predictive power of this approach broke down, so it pivoted to actually learning how to add.

Even AI scientists who believe that GPT-4 has a rich world model concede that it is much less robust than a human’s understanding of their environment.

But it’s worth noting that a great many abilities, including very high-order abilities, can be developed without an intuitive understanding. The computer scientist Melanie Mitchell has pointed out that science has already discovered concepts that are highly predictive, but too alien for us to genuinely understand

As AI advances, it may well discover other concepts that predict surprising features of our world but are incomprehensible to us.

GPT-4 is no doubt flawed, as anyone who has used ChatGPT can attest. Having been trained to always predict the next word, it will always try to do so, even when its training data haven’t prepared it to answer a question.

The models “don’t have a good conception of their own weaknesses,” Nick Ryder, a researcher at OpenAI, told me. GPT-4 is more accurate than GPT-3, but it still hallucinates, and often in ways that are difficult for researchers to catch. “The mistakes get more subtle,

The Khan Academy’s solution to GPT-4’s accuracy problem was to filter its answers through a Socratic disposition. No matter how strenuous a student’s plea, it would refuse to give them a factual answer, and would instead guide them toward finding their own—a clever work-around, but perhaps with limited appeal.

When I asked Sutskever if he thought Wikipedia-level accuracy was possible within two years, he said that with more training and web access, he “wouldn’t rule it out.”

This was a much more optimistic assessment than that offered by his colleague Jakub Pachocki, who told me to expect gradual progress on accuracy—to say nothing of outside skeptics, who believe that returns on training will diminish from here.

Sutskever is amused by critics of GPT-4’s limitations. “If you go back four or five or six years, the things we are doing right now are utterly unimaginable,”

AI researchers have become accustomed to goalpost-moving: First, the achievements of neural networks—mastering Go, poker, translation, standardized tests, the Turing test—are described as impossible. When they occur, they’re greeted with a brief moment of wonder, which quickly dissolves into knowing lectures about how the achievement in question is actually not that impressive. People see GPT-4 “and go, ‘Wow,’ ” Sutskever said. “And then a few weeks pass and they say, ‘But it doesn’t know this; it doesn’t know that.’ We adapt quite quickly.”

The goalpost that matters most to Altman—the “big one” that would herald the arrival of an artificial general intelligence—is scientific breakthrough. GPT-4 can already synthesize existing scientific ideas, but Altman wants an AI that can stand on human shoulders and see more deeply into nature.

Certain AIs have produced new scientific knowledge. But they are algorithms with narrow purposes, not general-reasoning machines. The AI AlphaFold, for instance, has opened a new window onto proteins, some of biology’s tiniest and most fundamental building blocks, by predicting many of their shapes, down to the atom—a considerable achievement given the importance of those shapes to medicine, and given the extreme tedium and expense required to discern them with electron microscopes.

Altman imagines a future system that can generate its own hypotheses and test them in a simulation. (He emphasized that humans should remain “firmly in control” of real-world lab experiments—though to my knowledge, no laws are in place to ensure that.)

He longs for the day when we can tell an AI, “ ‘Go figure out the rest of physics.’ ” For it to happen, he says, we will need something new, built “on top of” OpenAI’s existing language models.

In her MIT lab, the cognitive neuroscientist Ev Fedorenko has found something analogous to GPT-4’s next-word predictor inside the brain’s language network. Its processing powers kick in, anticipating the next bit in a verbal string, both when people speak and when they listen. But Fedorenko has also shown that when the brain turns to tasks that require higher reasoning—of the sort that would be required for scientific insight—it reaches beyond the language network to recruit several other neural systems.

No one at OpenAI seemed to know precisely what researchers need to add to GPT-4 to produce something that can exceed human reasoning at its highest levels.

at least part of the current strategy clearly involves the continued layering of new types of data onto language, to enrich the concepts formed by the AIs, and thereby enrich their models of the world.

The extensive training of GPT-4 on images is itself a bold step in this direction,

Others at the company—and elsewhere—are already working on different data types, including audio and video, that could furnish AIs with still more flexible concepts that map more extensively onto reality

Tactile concepts would of course be useful primarily to an embodied AI, a robotic reasoning machine that has been trained to move around the world, seeing its sights, hearing its sounds, and touching its objects.

humanoid robots. I asked Altman what I should make of that. He told me that OpenAI is interested in embodiment because “we live in a physical world, and we want things to happen in the physical world.”

At some point, reasoning machines will need to bypass the middleman and interact with physical reality itself. “It’s weird to think about AGI”—artificial general intelligence—“as this thing that only exists in a cloud,” with humans as “robot hands for it,” Altman said. “It doesn’t seem right.

Everywhere Altman has visited, he has encountered people who are worried that superhuman AI will mean extreme riches for a few and breadlines for the rest

Altman answered by addressing the young people in the audience directly: “You are about to enter the greatest golden age,” he said.

“A lot of people working on AI pretend that it’s only going to be good; it’s only going to be a supplement; no one is ever going to be replaced,” he said. “Jobs are definitely going to go away, full stop.”

A recent study led by Ed Felten, a professor of information-technology policy at Princeton, mapped AI’s emerging abilities onto specific professions according to the human abilities they require, such as written comprehension, deductive reasoning, fluency of ideas, and perceptual speed. Like others of its kind, Felten’s study predicts that AI will come for highly educated, white-collar workers first.

How many jobs, and how soon, is a matter of fierce dispute

The paper’s appendix contains a chilling list of the most exposed occupations: management analysts, lawyers, professors, teachers, judges, financial advisers, real-estate brokers, loan officers, psychologists, and human-resources and public-relations professionals, just to sample a few.

Altman imagines that far better jobs will be created in their place. “I don’t think we’ll want to go back,” he said. When I asked him what these future jobs might look like, he said he doesn’t know.

He suspects there will be a wide range of jobs for which people will always prefer a human. (Massage therapists?

His chosen example was teachers. I found this hard to square with his outsize enthusiasm for AI tutors.

He also said that we would always need people to figure out the best way to channel AI’s awesome powers. “That’s going to be a super-valuable skill,” he said. “You have a computer that can do anything; what should it go do?”

As many have noted, draft horses were permanently put out of work by the automobile. If Hondas are to horses as GPT-10 is to us, a whole host of long-standing assumptions may collapse.

Previous technological revolutions were manageable because they unfolded over a few generations, but Altman told South Korea’s youth that they should expect the future to happen “faster than the past.” He has previously said that he expects the “marginal cost of intelligence” to fall very close to zero within 10 years

The earning power of many, many workers would be drastically reduced in that scenario. It would result in a transfer of wealth from labor to the owners of capital so dramatic, Altman has said, that it could be remedied only by a massive countervailing redistribution.

In 2021, he unveiled Worldcoin, a for-profit project that aims to securely distribute payments—like Venmo or PayPal, but with an eye toward the technological future—first through creating a global ID by scanning everyone’s iris with a five-pound silver sphere called the Orb. It seemed to me like a bet that we’re heading toward a world where AI has made it all but impossible to verify people’s identity and much of the population requires regular UBI payments to survive. Altman more or less granted that to be true, but said that Worldcoin is not just for UBI.

“Let’s say that we do build this AGI, and a few other people do too.” The transformations that follow would be historic, he believes. He described an extraordinarily utopian vision, including a remaking of the flesh-and-steel world

“Robots that use solar power for energy can go and mine and refine all of the minerals that they need, that can perfectly construct things and require no human labor,” he said. “You can co-design with DALL-E version 17 what you want your home to look like,” Altman said. “Everybody will have beautiful homes.

In conversation with me, and onstage during his tour, he said he foresaw wild improvements in nearly every other domain of human life. Music would be enhanced (“Artists are going to have better tools”), and so would personal relationships (Superhuman AI could help us “treat each other” better) and geopolitics (“We’re so bad right now at identifying win-win compromises”).

In this world, AI would still require considerable computing resources to run, and those resources would be by far the most valuable commodity, because AI could do “anything,” Altman said. “But is it going to do what I want, or is it going to do what you want

If rich people buy up all the time available to query and direct AI, they could set off on projects that would make them ever richer, while the masses languish

One way to solve this problem—one he was at pains to describe as highly speculative and “probably bad”—was this: Everyone on Earth gets one eight-billionth of the total AI computational capacity annually. A person could sell their annual share of AI time, or they could use it to entertain themselves, or they could build still more luxurious housing, or they could pool it with others to do “a big cancer-curing run,” Altman said. “We just redistribute access to the system.”

Even if only a little of it comes true in the next 10 or 20 years, the most generous redistribution schemes may not ease the ensuing dislocations.

America today is torn apart, culturally and politically, by the continuing legacy of deindustrialization, and material deprivation is only one reason. The displaced manufacturing workers in the Rust Belt and elsewhere did find new jobs, in the main. But many of them seem to derive less meaning from filling orders in an Amazon warehouse or driving for Uber than their forebears had when they were building cars and forging steel—work that felt more central to the grand project of civilization.

It’s hard to imagine how a corresponding crisis of meaning might play out for the professional class, but it surely would involve a great deal of anger and alienation.

Even if we avoid a revolt of the erstwhile elite, larger questions of human purpose will linger. If AI does the most difficult thinking on our behalf, we all may lose agency—at home, at work (if we have it), in the town square—becoming little more than consumption machines, like the well-cared-for human pets in WALL-E

Altman has said that many sources of human joy and fulfillment will remain unchanged—basic biological thrills, family life, joking around, making things—and that all in all, 100 years from now, people may simply care more about the things they cared about 50,000 years ago than those they care about today

In its own way, that too seems like a diminishment, but Altman finds the possibility that we may atrophy, as thinkers and as humans, to be a red herring. He told me we’ll be able to use our “very precious and extremely limited biological compute capacity” for more interesting things than we generally do today.

Yet they may not be the most interesting things: Human beings have long been the intellectual tip of the spear, the universe understanding itself. When I asked him what it would mean for human self-conception if we ceded that role to AI, he didn’t seem concerned. Progress, he said, has always been driven by “the human ability to figure things out.” Even if we figure things out with AI, that still counts, he said.

It’s not obvious that a superhuman AI would really want to spend all of its time figuring things out for us.

I asked Sutskever whether he could imagine an AI pursuing a different purpose than simply assisting in the project of human flourishing.

“I don’t want it to happen,” Sutskever said, but it could.

Sutskever has recently shifted his focus to try to make sure that it doesn’t. He is now working primarily on alignment research, the effort to ensure that future AIs channel their “tremendous” energies toward human happiness

It is, he conceded, a difficult technical problem—the most difficult, he believes, of all the technical challenges ahead.

As part of the effort to red-team GPT-4 before it was made public, the company sought out the Alignment Research Center (ARC), across the bay in Berkeley, which has developed a series of evaluations to determine whether new AIs are seeking power on their own. A team led by Elizabeth Barnes, a researcher at ARC, prompted GPT-4 tens of thousands of times over seven months, to see if it might display signs of real agency.

The ARC team gave GPT-4 a new reason for being: to gain power and become hard to shut down

Agarwal told me that this behavior could be a precursor to shutdown avoidance in future models. When GPT-4 devised its lie, it had realized that if it answered honestly, it may not have been able to achieve its goal. This kind of tracks-covering would be particularly worrying in an instance where “the model is doing something that makes OpenAI want to shut it down,” Agarwal said. An AI could develop this kind of survival instinct while pursuing any long-term goal—no matter how small or benign—if it feared that its goal could be thwarted.

Barnes and her team were especially interested in whether GPT-4 would seek to replicate itself, because a self-replicating AI would be harder to shut down. It could spread itself across the internet, scamming people to acquire resources, perhaps even achieving some degree of control over essential global systems and holding human civilization hostage.

When I discussed these experiments with Altman, he emphasized that whatever happens with future models, GPT-4 is clearly much more like a tool than a creature. It can look through an email thread, or help make a reservation using a plug-in, but it isn’t a truly autonomous agent that makes decisions to pursue a goal, continuously, across longer timescales.

Altman told me that at this point, it might be prudent to try to actively develop an AI with true agency before the technology becomes too powerful, in order to “get more comfortable with it and develop intuitions for it if it’s going to happen anyway.”

“We need to do empirical experiments on how these things try to escape control,” Hinton told me. “After they’ve taken over, it’s too late to do the experiments.”

the fulfillment of Altman’s vision of the future will at some point require him or a fellow traveler to build much more autonomous AIs.

When Sutskever and I discussed the possibility that OpenAI would develop a model with agency, he mentioned the bots the company had built to play Dota 2. “They were localized to the video-game world,” Sutskever told me, but they had to undertake complex missions. He was particularly impressed by their ability to work in concert. They seem to communicate by “telepathy,” Sutskever said. Watching them had helped him imagine what a superintelligence might be like.

“The way I think about the AI of the future is not as someone as smart as you or as smart as me, but as an automated organization that does science and engineering and development and manufacturing,”

Suppose OpenAI braids a few strands of research together, and builds an AI with a rich conceptual model of the world, an awareness of its immediate surroundings, and an ability to act, not just with one robot body, but with hundreds or thousands. “We’re not talking about GPT-4. We’re talking about an autonomous corporation,”

Its constituent AIs would work and communicate at high speed, like bees in a hive. A single such AI organization would be as powerful as 50 Apples or Googles, he mused. “This is incredible, tremendous, unbelievably disruptive power.”

Presume for a moment that human society ought to abide the idea of autonomous AI corporations. We had better get their founding charters just right. What goal should we give to an autonomous hive of AIs that can plan on century-long time horizons, optimizing billions of consecutive decisions toward an objective that is written into their very being?

If the AI’s goal is even slightly off-kilter from ours, it could be a rampaging force that would be very hard to constrain

We know this from history: Industrial capitalism is itself an optimization function, and although it has lifted the human standard of living by orders of magnitude, left to its own devices, it would also have clear-cut America’s redwoods and de-whaled the world’s oceans. It almost did.

one of its principal challenges will be making sure that the objectives we give to AIs stick

We can program a goal into an AI and reinforce it with a temporary period of supervised learning, Sutskever explained. But just as when we rear a human intelligence, our influence is temporary. “It goes off to the world,”

That’s true to some extent even of today’s AIs, but it will be more true of tomorrow’s.

He compared a powerful AI to an 18-year-old heading off to college. How will we know that it has understood our teachings? “Will there be a misunderstanding creeping in, which will become larger and larger?”

Divergence may result from an AI’s misapplication of its goal to increasingly novel situations as the world changes

Or the AI may grasp its mandate perfectly, but find it ill-suited to a being of its cognitive prowess. It might come to resent the people who want to train it to, say, cure diseases. “They want me to be a doctor,” Sutskever imagines an AI thinking. “I really want to be a YouTuber.”

If AIs get very good at making accurate models of the world, they may notice that they’re able to do dangerous things right after being booted up. They might understand that they are being red-teamed for risk, and hide the full extent of their capabilities.

hey may act one way when they are weak and another way when they are strong, Sutskever said

We would not even realize that we had created something that had decisively surpassed us, and we would have no sense for what it intended to do with its superhuman powers.

That’s why the effort to understand what is happening in the hidden layers of the largest, most powerful AIs is so urgent. You want to be able to “point to a concept,” Sutskever said. You want to be able to direct AI toward some value or cluster of values, and tell it to pursue them unerringly for as long as it exists.

we don’t know how to do that; indeed, part of his current strategy includes the development of an AI that can help with the research. If we are going to make it to the world of widely shared abundance that Altman and Sutskever imagine, we have to figure all this out.

This is why, for Sutskever, solving superintelligence is the great culminating challenge of our 3-million-year toolmaking tradition. He calls it “the final boss of humanity.”

“First of all, I think that whether the chance of existential calamity is 0.5 percent or 50 percent, we should still take it seriously,”

. “I don’t have an exact number, but I’m closer to the 0.5 than the 50.”

As to how it might happen, he seems most worried about AIs getting quite good at designing and manufacturing pathogens, and with reason: In June, an AI at MIT suggested four viruses that could ignite a pandemic, then pointed to specific research on genetic mutations that could make them rip through a city more quickly

Around the same time, a group of chemists connected a similar AI directly to a robotic chemical synthesizer, and it designed and synthesized a molecule on its own.

Altman worries that some misaligned future model will spin up a pathogen that spreads rapidly, incubates undetected for weeks, and kills half its victims. He worries that AI could one day hack into nuclear-weapons systems too. “There are a lot of things,” he said, and these are only the ones we can imagine.

Altman told me that he doesn’t “see a long-term happy path” for humanity without something like the International Atomic Energy Agency for global oversight of AI

In San Francisco, Agarwal had suggested the creation of a special license to operate any GPU cluster large enough to train a cutting-edge AI, along with mandatory incident reporting when an AI does something out of the ordinary

Other experts have proposed a nonnetworked “Off” switch for every highly capable AI; on the fringe, some have even suggested that militaries should be ready to perform air strikes on supercomputers in case of noncompliance

Sutskever thinks we will eventually want to surveil the largest, most powerful AIs continuously and in perpetuity, using a team of smaller overseer AIs.

Safety rules for a new technology usually accumulate over time, like a body of common law, in response to accidents or the mischief of bad actors. The scariest thing about genuinely powerful AI systems is that humanity may not be able to afford this accretive process of trial and error. We may have to get the rules exactly right at the outset.

Several years ago, Altman revealed a disturbingly specific evacuation plan he’d developed. He told The New Yorker that he had “guns, gold, potassium iodide, antibiotics, batteries, water, gas masks from the Israeli Defense Force, and a big patch of land in Big Sur” he could fly to in case AI attacks.

if the worst-possible AI future comes to pass, “no gas mask is helping anyone.”

but he told me that he can’t really be sure how AI will stack up. “I just have to build the thing,” he said. He is building fast

Altman insisted that they had not yet begun GPT-5’s training run. But when I visited OpenAI’s headquarters, both he and his researchers made it clear in 10 different ways that they pray to the god of scale. They want to keep going bigger, to see where this paradigm leads. After all, Google isn’t slackening its pace; it seems likely to unveil Gemini, a GPT-4 competitor, within months. “We are basically always prepping for a run,

To think that such a small group of people could jostle the pillars of civilization is unsettling. It’s fair to note that if Altman and his team weren’t racing to build an artificial general intelligence, others still would be

Altman’s views about the likelihood of AI triggering a global class war, or the prudence of experimenting with more autonomous agent AIs, or the overall wisdom of looking on the bright side, a view that seems to color all the rest—these are uniquely his

No single person, or single company, or cluster of companies residing in a particular California valley, should steer the kind of forces that Altman is imagining summoning.

AI may well be a bridge to a newly prosperous era of greatly reduced human suffering. But it will take more than a company’s founding charter—especially one that has already proved flexible—to make sure that we all share in its benefits and avoid its risks. It will take a vigorous new politics.

I don’t think the general public has quite awakened to what’s happening. A global race to the AI future has begun, and it is largely proceeding without oversight or restraint. If people in America want to have some say in what that future will be like, and how quickly it arrives, we would be wise to speak up soon.

if we are agreed that Beijing looks set to stop the largest property boom in history without unleashing a systemic financial crisis, it is doing something truly remarkable. It is setting a new standard in economic policy.

Is this perhaps what policy looks like if it actually takes financial stability seriously? And if we look in the mirror, why aren’t we applauding more loudly?

Add to real estate the other domestic factor roiling the Chinese financial markets: Beijing’s remarkable humbling of China’s platform businesses, the second-largest cluster of big tech in the world. That too is without equivalent anywhere else.

Beijing’s aim is to ensure that gambling on big tech no longer produces monopolistic rents. Again, as a long-term policy aim, can one really disagree with that?

we have two dramatic and deliberate policy-induced shocks of the type for which there is no precedent in the West. Both inflict short-term pain with a view to longer-term social, economic and financial stability.

Ultimately political economy determines the conditions for long-run growth. So if you had to bet on a regime, which might actually have what it takes to break a political economy impasse, to humble vested interests and make a “big play” on structural change, which would it be? The United States, the EU or Xi’s China?

Beijing’s challenge right now is to manage the fall out from the two most dramatic development policies the world has ever seen, the one-child policy and China’s urbanisation, plus the historic challenge of big tech — less a problem specific to China than the local manifestation of what Shoshana Zuboff calls “surveillance capitalism”.

no, Xi’s regime has not yet presented a fully convincing substitute plan. But, as Michael Pettis has forcefully argued, China has options. There is an entire range of policies that Beijing could put in place to substitute for the debt-fuelled infrastructure and housing boom.

demography is normally treated as a natural parameter for economic activity. But in China’s case the astonishing fact is that the sudden ageing of its workforce is also a policy-induced challenge. It is a legacy of the one-child policy — the most gigantic and coercive intervention in human reproduction ever undertaken.

China needs to spend heavily on renewable energy and power distribution to break its dependence on coal. If it needs more housing, it should be affordable. All of this would generate more balanced growth. 5 per cent? Perhaps not, but certainly healthier and more sustainable.

If it has not so far pursued an alternative growth model in a more determined fashion, some of the blame no doubt falls on the prejudices of the Beijing policy elite. But even more significant are surely the entrenched interests of the infrastructure-construction-local government-credit machine, in other words the kind of political economy factors that generally inhibit the implementation of good policy.

The problem is only too familiar in the West. In Europe and the US too, such interest group combinations hobble the search for new growth models. In the United States they put in doubt the possibility of the energy transition, the possibility of providing a healthcare system that is fit for purpose and any initiative on trade policy that involves widening market access.

First and foremost China needs a welfare state befitting of its economic development.

On balance, if you want to be part of history-making economic transformation, China is still the place to be. But it is undeniably shifting gear. And thanks to developments both inside and outside the country, investors will have to reckon with a much more complex picture of opportunity and risk. You are going to need to pick smart and follow the politics and geopolitics closely.

If on the other hand you want to invest in the green energy transition — the one big vision of economic development that the world has come up with right now — you simply have to have exposure to China, whether directly or indirectly by way of suppliers to China’s green energy sector. China is where the grand battle over the future of the climate is going to be fought. It will be a huge driver of innovation, capital accumulation and profit, the influence of which will be felt around the world.

it is one key area that both the Biden administration and the EU would like to “silo off” from other areas of conflict with China.

I worry that we may be too focused on the medium-term. Given the news out of Hong Kong and mainland China, Covid may yet come back to bite us.

Here too China is boxed in by its own success. It has successfully pursued a no-Covid policy, but due to the failing of the rest of the world, it has been left to do so in “one country”.

Until China finds some way to contain the risks, this is a story to watch. A dramatic Omicron surge across China would upend the entire narrative of the last two years, which is framed by Beijing success in containing the first wave.

Rising inflation has led to surging prices for groceries, cars and many essentials. The cost of a college education has skyrocketed in two decades, with the average student debt for federal loans at $37,000, according to the Education Department. Overall debts for Americans in their 30s jumped 27% from late 2019 to early 2023—steeper than for any other age group.

home prices have risen considerably, while wages haven’t kept pace.

“What Dave Ramsey really misses is any kind of social context,” says Morgan Sanner, a

She began paying off $48,000 in student loans (a Ramsey do) and also took out a loan to buy a 2016 Honda (a Ramsey don’t). Her rationale was that it was safer to pay extra for a more reliable car than a junker she could buy with cash. S

he feels these sorts of real-life decisions don’t factor into his advice.

When she saw a comment from Ramsey online about how people receiving pandemic stimulus payments were “pretty much screwed already,” Israel felt it came across as shaming people. The pandemic shutdowns ended a decadelong economic expansion for Black Americans, a disproportionate number of whom lost their jobs and relied on those checks.

“Moralizing financial decisions is very damaging to marginalized groups,” says Israel, who is Black.

Many young adults scratch their heads over his advice that people should let their credit scores dwindle and die.

People need a good credit score, says Mandy Phillips, a 39-year-old residential mortgage loan originator in Redding, Calif. She uses TikTok and other social media to educate millennials and Gen Z about home buying. Scores are vital when applying for mortgages and rentals.

She also takes issue with Ramsey’s advice to only obtain a home loan if you can take out a 15-year fixed-rate mortgage with a down payment of at least 10%. Few younger buyers can pay the large monthly bills of shorter-term mortgages.

“That may have worked years ago in the ’80s and ’90s, but that’s not something that is achievable for the average American,” Phillips says.

Housing is a particularly hot-button topic. He advises people to only buy a house with their lawfully wedded spouse. Yet many young adults are pooling their finances with partners, friends or roommates to buy their first homes. 

Ramsey is perhaps best known for advocating a “debt snowball method”: People with multiple loans pay off the smallest balances first, regardless of interest rate. As you knock out each loan, he says, the money you have to put toward larger debt snowballs. Seeing small wins motivates people to keep going, he says.Conventional economic theory would be to pay off the highest-interest loans first, says James Choi, a finance professor at the Yale School of Management, who has studied the advice of popular finance gurus.

Ramsey’s save-not-spend message sounds logical, young adults say. It’s his all-or-nothing approach that doesn’t work for them.

Kate Hindman, a 31-year-old administrative assistant in Pasadena, Calif., who has taken an anti-Ramsey stance on TikTok, ended up with $30,000 in credit-card debt after she and her husband faced income-reducing job changes. They’ve since turned it into a consolidation loan with an 8% interest rate and pay about $1,200 a month.

She wonders if the debt aversion is generational. Perhaps younger people are less willing to make huge sacrifices to be debt-free. Maybe carrying some amount of debt forever is a new normal.