Group items matching

in title, tags, annotations or url

Mr. Gensler’s central claim is simple: For all their novel attributes, most cryptocurrencies are securities, like stocks or other investment products. That means the developers who issue cryptocurrencies must register with the U.S. government and disclose information about their plans.

Even before FTX’s collapse, the debate was reaching an inflection point: A federal judge is expected to rule in the coming months in a lawsuit brought by the S.E.C. that charges the cryptocurrency issuer Ripple with offering unregistered securities. A victory for the government would strengthen Mr. Gensler’s hand, establishing a precedent that could pave the way for more lawsuits against crypto companies.

A former Goldman Sachs partner, Mr. Gensler became one of the most aggressive financial regulators in Washington after the 2008 recession. As chairman of the Commodity Futures Trading Commission, an agency that regulates the financial markets, he helped carry out the 2010 Dodd-Frank Act, which aimed to protect consumers and rein in Wall Street.

when Mr. Gensler took over the S.E.C., the crypto industry hailed him as an enthusiast who understood the technology’s potential. Bitcoin “is in good hands,” one venture investor tweeted.

But it soon became clear that Mr. Gensler would take a hard-line approach. In July 2021, he met with a group of industry representatives, including the leader of the Blockchain Association, a prominent crypto trade group. He bluntly informed her that most of the organization’s members were probably violating federal rules, two people familiar with the meeting said.

Rather than devise new rules for crypto, Mr. Gensler has focused on enforcing the current ones as broadly as possible.

A few days later, Mr. Gensler called crypto “the Wild West” while speaking at a national security conference in Washington.

Behind closed doors, Mr. Gensler has been equally aggressive. “I’ve heard about other groups going in and getting in arguments,” said Perianne Boring, the founder of the Chamber of Digital Commerce, a crypto advocacy group. “You want to have a fight, you can have one.”

In crypto circles, mentioning Mr. Gensler’s name elicits quivers of fury. A Twitter account for the crypto company LBRY once called him “a demon wearing human flesh.”

The basis for Mr. Gensler’s claim that cryptocurrencies are securities is a legal analysis known as the Howey Test, which the Supreme Court outlined in 1946. Under the framework, a financial product is deemed a security when it offers the chance to invest in a “common enterprise” with the expectation of profiting from the efforts of others.

FTX’s collapse has unleashed a new level of scrutiny. Screenshots of Mr. Gensler’s public meeting schedule, which show multiple sessions with Mr. Bankman-Fried, have circulated on Twitter, where crypto fans who once said Mr. Gensler was overly aggressive have now accused him of cozying up to a criminal.

“If you don’t like him, you don’t like the current S.E.C., then of course you’re just going to blame him, regardless of the facts,” Mr. Reiners said. “If Sam Bankman-Fried tried to get a meeting with the S.E.C., and Gary Gensler said absolutely not, I’ll never talk to you, the Republicans would’ve gone ballistic prior to the collapse.”

“Why we often separate these things out is so that the public is better protected about the inherent conflicts,” he said. “It’s really important to make sure that this field comes in, gets registered, gets regulated.”

In public remarks shortly after FTX imploded, Mr. Gensler argued that too many crypto companies performed multiple financial roles at the same time — like running an exchange and making trades, an apparent reference to the close relationship between FTX and Alameda.

The outcome will also draw attention in Congress, where a slate of crypto-related bills was introduced this year. When Mr. Gensler testified in front of the Senate Banking Committee in September, he was grilled by Republican senators, who said the S.E.C. was offering insufficient legal guidance to crypto companies that wanted to comply with federal law.“Not liking the answer from the S.E.C.,” he shot back, “doesn’t mean there isn’t guidance.”

the broader economic and cultural landscape is so different now than it was just a few years ago that public investments of even this somewhat smaller scale appear poised to make an enormous difference.

That’s because those public investments are being made not against dirty-energy headwinds but with the support of much broader tailwinds

Thanks to technological change and the plunging cost of renewables, a growing political and cultural focus on decarbonization and increasing awareness of the public health costs of pollution and market trends for things like electric vehicles and heat pumps, it’s genuinely a whole new world out there. Not that long ago, the upfront cost of a green transition looked almost incalculably large. Today it seems plausible that quite dramatic emissions gains can be achieved for just, say, $369 billion

For 90 percent of the world, clean energy is now cheaper than dirty alternatives, and while countries like Spain are boasting about more than tripling solar power capacity by 2030, in Texas, solar output has grown 39-fold in just six years. Globally, renewable output has grown fourfold in the past decade

Ten years ago, when the United States endeavored to tackle the problem of climate change, it tried to do so largely by punishing the cost of dirty energy with a cap-and-trade system. This time, it’s giving a kick-start, or a boost of momentum, to an already ongoing green transition.

this strategic choice of carrots rather than sticks has received some deserved praise: It’s better and more popular to subsidize cheap, clean energy than it is to make the bad stuff more expensive

A “fair share” analysis suggests the United States — today the world’s second largest emitter, and historically the largest by far — should be moving faster than any nation in the world.

The models may ultimately prove optimistic, given the complications of infrastructure build-out

it is fair to wonder about the uncertain economics of some of the bill’s technological bets, like carbon capture and storage, which could allow emissions from industry and power generation to be trapped and sequestered, and which some climate activists and environmental justice advocates distrust

Jesse Jenkins, who leads the REPEAT Project, says he believes that the tech problems of C.C.S. have been solved and that, with tax credits, the bill will address its cost problem, leading to a dramatic scale-up in use. Julio Friedmann, a former Obama-era Energy Department official turned carbon removal advocate, says that a rapid scale-up of C.C.S. would be, while miraculous, also plausible.

the fact that this much climate progress appears even remotely possible for less than the annualized budget of the State Department, as Ben Dreyfuss recently put it, is a remarkable reflection of the state of green energy today, even without the new law. When it comes to emissions, we are no longer fighting an uphill battle, at least in the United States and many other countries like it. We are deciding how quickly to race downhill.

at the risk of playing Pollyanna, I think it is also possible to see the size of the bill — its relative smallness — as at least a mark of good news

The headline projection of the I.R.A. impact appears, if inadequate by the standards of the Paris agreement, nevertheless impressive: a 40 percent reduction in just eight years

already today the United States has reduced emissions 20 percent from 2005 levels, and was projected to reduce them further even without the benefit of the I.R.A. As recently as a few weeks ago, before the bill was revived, it might have felt like the United States was permanently stalled on climate action, but in fact the country was already moving to decarbonize, if not fast enough.

peed really matters; as the writer and activist Bill McKibben put it, when it comes to warming, “winning slowly is the same as losing.” Simply moving in the right direction isn’t enough, and too much time has been squandered — within the United States and globally — to avoid what was once described as a catastrophic climate future.

If the United States achieves that 40 percent reduction, that’s still well short of the country’s target of a 50-52 percent reduction by 2030. The gap may seem relatively small, but it represents more than half a billion tons of carbon each year. That’s a lot.

the I.R.A. is a compromise, obviously and outwardly, tying new leases for wind power development to new ones for oil and gas, only moderately reducing the country’s demand for oil and gas over the next decade and investing less in environmental justice measures than Biden himself promised not too long ago

But its basic bet — that many of these markets and technologies are close enough to tipping points that relatively small public support can get them racing toward inevitability — also means the ultimate impacts could be larger and far-reaching.

The effects on prices and markets could make state and local action cheaper and easier, and even federal regulation more palatable

the bill includes some unheralded provisions to help retire coal power more quickly, as Keane Bhatt, the policy director for the Progressive Caucus, has pointed out, as well as an under-discussed “stick” in the form of a fee for methane

The impact of its “green bank” and Energy Department loans could be quite large — some estimates have suggested they could run into the hundreds of billions, and the $27 billion handed to the Green Bank could catalyze ten times as much private capital

because much of the I.R.A.’s top-line “investment” comes in the form of tax credits, its outlays — and impacts — could ultimately grow substantially if certain sectors (wind, solar and C.C.S., for instance) really do take off.

This might not ultimately be just a $369 billion package, in other words, but something quite a bit bigger. Enough to get us to 50 percent by 2030? “I think we have a pretty good chance,” Jenkins says.

it is striking that, given where we were not that long ago, such a proposition seems credible at all. Here’s hoping.

The provisions tying future auctions for wind power to leases for oil and gas development have been called “poison pills,” because they appear to lock in future emissions. But the ultimate impact is likely to be quite small. (Energy Innovation estimates at most 50 million tons of additional annual carbon emissions, compared with a billion in reductions from other measures in the bill.)

The third factor is the speed with which higher interest rates pass through to homeowners. The biggest risk is to borrowers on floating-rate mortgages

not all fixed-term loans are alike. In America the bulk of them are fixed for two or three decades. In other countries, even fixed-rate borrowers will face soaring mortgage costs soon enough

all the ingredients for a deep housing slump are in place. This time, though, it is likely to be led not by America, but by Canada, the Netherlands, Australia, New Zealand and Norway

First-time buyers and recent borrowers are especially vulnerable. Many stretched their finances to buy a home, leaving less spare cash to cover a jump in mortgage costs

First-time buyers have also had less time to accumulate equity. Oxford Economics estimates that a 15% drop in house prices in America over a year would cancel out two-thirds of the housing equity they have accumulated since the start of the pandemic

the housing squeeze will have profound consequences. “The housing cycle IS the business cycle”, wrote Edward Leamer of the University of California, Los Angeles, in a paper published in 2007

The link between the two cycles arises because housing confers “wealth effects” on owner occupiers. When house prices rise, people feel good about their financial situation, so borrow and spend more.

It noted that housing slowdowns had preceded eight of the past ten recessions in America

2019 research by the Bank of England found that a 10% increase in house prices raises consumption by 0.35–0.5%

Another important channel between the housing market and the rest of the economy is investment. Capital spending associated with housing, especially house building, can be extremely volatile—and is often the difference between a growing or shrinking economy.

Some people see an upside to a housing crash. They hope lower prices will allow young folk to buy their first houses. These hopes are almost certain to be dashed. In housing corrections, and sometimes for years after, home ownership rates tend to fall, rather than rise

Economic conditions that cause house prices to fall simultaneously imperil the chances of would-be homeowners. Unemployment rises and wages decline. If interest rates jump, people are able to borrow less and mortgage lenders tend to become more skittish about lending

The biggest effect of a housing downturn may be in politics

In countries where home ownership is seen as a rite of passage, lower prices without any increase in affordability will rub salt in already sore wounds. “Falling to what? Falling to absurdly grotesque prices instead of just unthinkable?”

For years more established homeowners took comfort in the thought that, even if real-wage growth was terrible, at least the price of their house was rising. Those days are over. Even baby-boomers, the great winners from a decade of price growth, now face the prospect of living off a smaller nest-egg in retirement, as downsizing becomes less lucrative

All this means rising interest rates will have unpredictable political repurcussions, as people who once benefited from the status quo discover what it feels like to lose out.

Do not be surprised, then, if policymakers launch enormous rescue operations

while Trump deserves credit for upsetting the apple cart in terms of conventional wisdom with regards to China relations, the Biden administration is correct to pursue those previous actions to their logical conclusion. . . .

it bans chips but it also bans equipment as well (and, given the restrictions it places on U.S.-persons, also bans the service of existing equipment).

That certainly increases the motivation for China to build alternatives, but it is tough to get strong legs when you have to first figure out how to make weights (but the weights involve the most complex tools ever invented by humans).

now the Chinese have to reinvent every wheel in the process just to get to par as it exists in the West circa 2022.

we talked about an accusation we sometimes hear:It’s nice that you right-wingers have come around since 2016, but the Republican party was always like this.

I argued that I don’t think this criticism is really right. Let’s pretend that you were a Republican in 2000 and you cared about:Robust foreign policyThe spread of democracy abroadThe rule of lawFree trade

Well, guess what: The Democratic party is now your natural home for those priorities. Sure, the Democrats also have some stuff you’re against, like political correctness and student loan forgiveness and expansion of the welfare state.

I hope you’ll watch this video clip. Because it’s not what Tuberville is saying so much as the crowd’s reaction to it. The guy is basically doing a Supreme Grand Wizard routine—all that’s missing is the n-word—and the crowd forking loves it.

at the same time, I understand—I think—what these critics mean. What they mean is:

Republican voters were always revanchists motivated not by high-minded intellectual arguments, but by simple animosities. Like racism.

And when you put it this way, I think the criticism is valid. For example:

The point is that the Republican party has changed along some very important, policy and ideological vectors. It really wasn’t always like this.

the actual Republican voters at this rally? They got crazy for it. They are into it.

Is there any way to read this except as an expression of cut-and-dried, out-and-proud, no dog whistle racism?

we can stipulate that the majority of Republican voters aren’t motivated in large part by racial animosity. I want to be as generous as possible so that Republicans reading this don’t think that they, personally, are being accused.

However small the minority of out-and-out racists in the Republican voting ranks might be, it’s much larger than people like me thought it was 20 years ago.

And any Republican/conservative who can’t come to grips with that today—who is still pretending that their coalition is motivated either by either high-minded political theory or benign tribalism—has to be trying (hard) not to see the truth.

But the federal rules do not dictate how poor a patient needs to be to qualify for free care

In exchange, the Internal Revenue Service requires Allina and thousands of other nonprofit hospital systems to benefit their local communities, including by providing free or reduced-cost care to patients with low incomes.

In 2020, thanks to its nonprofit status, Allina avoided roughly $266 million in state, local and federal taxes, according to the Lown Institute, a think tank that studies health care.

Doctors and patients described being unable to complete medical forms that children needed to enroll in day care or show proof of vaccination for school.

Allina is one of Minnesota’s largest health systems, having largely grown through acquisitions. Since 2013, its annual profits have ranged from $30 million to $380 million. Last year was the first in the past decade when it lost money, largely owing to investment losses.

The financial success has paid dividends. Allina’s president earned $3.5 million in 2021, the most recent year for which data is available. The health system recently built a $12 million conference center.

Allina sometimes plays hardball with patients. Doctors have become accustomed to seeing messages in the electronic medical record notifying them that a patient “will no longer be eligible to receive care” because of “unpaid medical balances.”

In 2020, Allina spent less than half of 1 percent of its expenses on charity care, well below the nationwide average of about 2 percent for nonprofit hospitals

Serena Gragert, who worked as a scheduler at an Allina clinic in Minneapolis until 2021, said the computer system simply wouldn’t let her book future appointments for some patients with outstanding balances.

Ms. Gragert and other Allina employees said some of the patients who were kicked out had incomes low enough to qualify for Medicaid, the federal-state insurance program for poor people. That also means those patients would be eligible for free care under Allina’s own financial assistance policy — something many patients are unaware exists when they seek treatment.

Allina says the policy applies only to debts related to care provided by its clinics, not its hospitals. But patients said in interviews that they got cut off after falling into debt for services they received at Allina’s hospitals.

Jennifer Blaido lives in Isanti, a small town outside Minneapolis, and Allina owns the only hospital there. Ms. Blaido, a mechanic, said she racked up nearly $200,000 in bills from a two-week stay at Allina’s Mercy Hospital in 2009 for complications from pneumonia, along with several visits to the emergency department for asthma flare-ups

Ms. Blaido, a mother of four, said most of the hospital stay was not covered by her health insurance and she was unable to scrounge together enough money to make a dent in the debt.

Last year, Ms. Blaido had a cancer scare and said she couldn’t get an appointment with a doctor at Mercy Hospital. She had to drive more than an hour to get examined at a health system unconnected to Allina

In court filings, the couple described how Allina canceled Ms. Anderson’s appointments and told her that she could not book new ones until she had set up three separate payment plans — one with the health system and two with its debt collectors.Even after setting up those payment plans, which totaled $580 a month, the canceled appointments were never restored. Allina allows patients to come back only after they have paid the entire debt.

When the Andersons asked in court for a copy of Allina’s policy of barring patients with unpaid bills, the hospital’s lawyers responded: “Allina does not have a written policy regarding the canceling of services or termination of scheduled and/or physician referral services or appointments for unpaid debts.”In fact, Allina’s policy, which was created in 2006, instructs employees on how to do exactly that. Among other things, it tells staff to “cancel any future appointments the patient has scheduled at any clinic.”

It does provide a few ways for patients to continue being seen despite their unpaid bills. One is by getting approved for a loan through the hospital. Another is by filing for bankruptcy.

“Ideological confrontation, geopolitical rivalry and bloc politics are not a choice for us,” Mr. Xi said in a speech at the opening of the forum at the Great Hall of the People in Beijing.

“What we stand against are unilateral sanctions, economic coercion and decoupling and supply chain disruption,” Mr. Xi said, clearly referring to efforts by the United States and its Western allies to pressure China.

The conference was virtually absent of European Union countries, largely because of the divisiveness of Russia’s war in Ukraine. Prime Minister Viktor Orban of Hungary, an authoritarian-leaning friend of Mr. Putin and Mr. Xi, was the only European Union leader to attend.

Represented instead were nearly 150 developing nations. China has disbursed close to $1 trillion through the Belt and Road initiative, largely in loans, to build power plants, seaports, and other infrastructure across Asia, Africa and Latin America, but some countries are finding their debt obligations onerous.

“The forum has clearly shown that Russia remains a massive country with massive resources, and that they are very far from isolation,” said Artem Lukin, an international relations professor at Far Eastern Federal University, in Vladivostok, Russia. “Asia, and the Global South in general, are clearly showing that the war in Ukraine is not their concern, and that they are more interested in doing business with Russia.”

On Sunday, Mr. Wang told his Saudi counterpart, Prince Faisal bin Farhan, that Israel’s retaliation in Gaza had “already gone beyond self-defense.” He also called on Israel to halt the “collective punishment of the people of Gaza.”

The pointed remarks signal a shift away from China’s stated policy of noninterference in another country’s internal affairs. China typically treads carefully when it comes to conflict in other countries, often opting for neutrality and anodyne statements about supporting peace.

They’re doing this as a way to signal to the Global South that China will support those countries in a way that they probably shouldn’t expect Western countries in general, and the U.S. in particular, to support them,” said Jonathan Fulton, a nonresident senior fellow with the Atlantic Council.

Then, in March 2022, the Fed changed course, first tightening the money supply by increasing the federal-funds rate and then introducing quantitative tightening. Between July 2022 and August 2023, the M2 supply contracted by 3.9%, the most extreme contraction since 1933.

The first factor contributing to the contraction of the money supply is the Fed’s quantitative tightening

Quantitative tightening has already produced a dramatic selloff in the bond market. But just as they did ahead of the September 2019 crunch in the repurchase-agreement market, Fed officials keep repeating their mistaken mantras that quantitative tightening can operate “in the background” and “on autopilot,” implying minimal market effect

But basic balance-sheet accounting shows that unless commercial banks are creating enough “new money” through their lending activity to offset the Fed’s balance-sheet shrinkage, quantitative tightening has a contractionary effect on the money supply.

The second factor contributing to shrinking M2 is the decreased availability of commercial bank credit—the sum of loans and bank holdings of securities. With the steep rise in rates, bank lending has slowed, and banks have been selling off securities.

This brings us to the stock-market crash of 1987. In that year the key 10-year bond yield rose steeply from January onward (from 7% in January to 10% by Black Monday in October) and the money supply slowed sharply.

In 1987 growth of M2 declined by almost half, from 9.7% year-on-year in January to 4.9% in September, while M3—no longer published by the Fed—slowed from 8.7% to 3.6% over the same period

A bond-market crunch and monetary squeeze together led to a sudden, drastic reassessment of equity-market valuations. The same could happen today, particularly since the current jump in bond yields and monetary squeeze are much more pronounced than in 1987.

So far, only the remaining excess money the Fed created between 2020 and 2021—the cumulative excess savings from the Covid handouts—has been keeping businesses hiring and consumers spending. The effects of the excess money are still giving the economy a lift, but that extra fuel is almost exhausted. When it dries up, the economy will run on fumes.

In all of this, an appreciation for time lags is critical. The Fed ignored the huge acceleration in the quantity of money and thus failed to anticipate the ensuing inflation. When inflation struck in early 2021, Fed officials tried to argue it was “transitory,” caused by supply-chain disruptions.

The Fed continues to ignore the money supply, and we now face the opposite problem. The money supply has been contracting for 18 months, and soon, after the overhanging extra money from 2020-21 has been used up, spending will plunge and inflation will fall, not simply to 2%, but below—and perhaps even into deflation in 2025.

Since Fed officials pay no attention to either monetary aggregates or their credit counterparts, they are overlooking these signals

Monetary analysis tells a very different story than the measures the Fed follows. The first effect of a monetary contraction is higher market interest rates for a brief period. Then comes an economic slump. The economy goes into recession and inflation falls. This results in a second and more permanent effect of subpar money growth, namely lower interest rates and a weaker currency.

the real problem with Mr. Andreessen’s manifesto may be not that it’s too outlandish, but that it’s too on-the-nose.

Neoreactionary thought contends that the world would operate much better in the hands of a few tech-savvy elites in a quasi-feudal system. Mr. Andreessen, through this lens, believes that advancing technology is the most virtuous thing one can do.

And the way we regard that wealth as a product of good decision-making and righteous hard work, no matter how many billions of dollars of investors’ money they may have vaporized, how many other people contributed to their success or how much government money subsidized it

In the case of ordinary individuals, however, debt is regarded as not just a financial failure but a moral one. (If you are successful and have paid your student loans off, taking them out in the first place was a good decision. If you haven’t and can’t, you were irresponsible and the government should not enable your freeloading.)

He articulates (albeit in a refrigerator magnet poetry kind of way) a strain of nihilism that has gained traction among tech elites, and reveals much of how they think about their few remaining responsibilities to society.

This strain of thinking is disdainful of democracy and opposes institutions (a free press, for example) that bolster it. It despises egalitarianism and views oppression of marginalized groups as a problem of their own making.

Who is doing the telling here, and who is being told? It’s not technology (a term so broad it encompasses almost everything) that’s reducing wages and increasing inequality — it’s the ultrawealthy, people like Mr. Andreessen.

It argues for an extreme acceleration of technological advancement regardless of consequences, in a way that makes “move fast and break things” seem modest.

Mr. Andreessen claims to be against authoritarianism, but really, it’s a matter of choosing the authoritarian — and the neoreactionary authoritarian of choice is a C.E.O. who operates as king.

it is taken seriously by people who imagine themselves potential Chief Executive Authoritarians, or at the very least proxies. This includes another Silicon Valley billionaire, Peter Thiel, who has funded some of Mr. Yarvin’s work and once wrote that he believed democracy and freedom were incompatible.

how did they sell so many other people on it? By pretending that for all their wealth and influence, they are not the real elites.

When Mr. Andreessen says “we” are being lied to, he includes himself, and when he names the liars, they’re those in “the ivory tower, the know-it-all credentialed expert worldview,” who are “disconnected from the real world, delusional, unelected, and unaccountable — playing God with everyone else’s lives, with total insulation from the consequences.”

His depiction of academics of course sounds a lot like — well, like tech overlords, who are often insulated from the real-world consequences of their inventions, including but not limited to promoting disinformation, facilitating fraud and enabling genocidal regimes.

“We are told that technology takes our jobs,” Mr. Andreessen writes, “reduces our wages, increases inequality, threatens our health, ruins the environment, degrades our society, corrupts our children, impairs our humanity, threatens our future, and is ever on the verge of ruining everything.”

You can see it in the way we valorize the C.E.O.s of “unicorn” companies who have expanded their wealth far beyond what could possibly be justified by their individual contributions

The argument for total acceleration of technological development is not about optimism, except in the sense that the Andreessens and Thiels and Musks are certain that they will succeed. It’s pessimism about democracy — and ultimately, humanity.

the billionaire classes of Silicon Valley are frustrated that they cannot just accelerate their way into the future, one in which they can become human/technological hybrids and live forever in a colony on Mars

In pursuit of this accelerated post-Singularity future, any harm they’ve done to the planet or to other people is necessary collateral damage. It’s the delusion of people who’ve been able to buy their way out of everything uncomfortable, inconvenient or painful, and don’t accept the fact that they cannot buy their way out of death.

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.

“People have faced higher prices and that is difficult, but that doesn’t explain why people have not cut back,” she said of a phenomenon known as revealed preference. “They have spent as if they see nothing but good times in front of them. So why are their actions so out of whack with their words?”

Many said their own finances were good enough — they had jobs, owned houses, made ends meet. But they felt as if they were “just getting by,” with “nothing left over.” Many felt angry and anxious over prices and the pandemic and politics.

Also, economists said, wages have increased alongside prices. Real median earnings for full-time workers are slightly higher than at the end of 2019, and for many low earners, their raises have outpaced inflation. But it’s common for people to think about prices at face value, rather than relative to their income, a habit economists call money illusion.

“The pandemic shattered a lot of illusions of control,” Professor Stevenson said. “I wonder how much that has made us more aware of all the places we don’t have control, over prices, over the housing market.”

Inflation weighed heavily on voters — nearly all of them mentioned frustration at the price of something they buy regularly.

Consumer prices were up 3.2 percent in October from the year before, a decline in the year-over-year inflation rate from more than 8 percent in mid-2022. But inflation “casts a long shadow on how people evaluate things,” said Lawrence Katz, an economist at Harvard. Some people may expect prices to return to what they were before — something that rarely happens

Those feelings may be driving attitudes about the economy, economists speculated, sounding more like their colleagues from another branch of social science, psychology.

Younger people — who were a key to President Biden’s win in 2020 but showed less support for him in the new poll — had concerns specific to their phase of life. In the poll, 93 percent of them rated the economy unfavorably, more than any other age group.

“Everyone thinks a wage increase is something they deserve, and a price increase is imposed by the economy on them,” Professor Katz said.

There’s a sense that it’s become harder to achieve the things their parents did, like buying a home. Houses are less affordable than at the height of the 2006 bubble, and less than half of Americans can afford one.

“More than likely, half my income will go toward rent,” he said. “I was really hoping on that student loan forgiveness.”

Yet overall, economists said, data shows that more people are quitting jobs to start better ones, moving to more desirable places because they can work remotely, and starting new businesses.

He said he makes almost $80,000, serving in the military and working as a DoorDash deliverer, yet feels he had more spending money a decade ago, when he was two pay grades lower.

he uncertainty Mr. Blanck and Ms. Linn share about the future ran through many voters’ stories, darkening their economic outlook.

“The degree of volatility that we’ve experienced from different events — from the pandemic, from inflation — leaves them not confident that even if objectively good things are going on, it’s going to persist,”

In response to the pandemic, the United States built an extensive welfare state, and it has since been dismantled. While wealth has increased for families across the income spectrum, data shows, and there are indications that inequality could be shrinking, the changes have been small relative to decades of growing inequality, leading to a sense for some that the system is rigged.

“When things are going well, that means rich people are getting richer and all of us are pretty much second,” said Manuel Zimberoff, 26, a manufacturing engineer in Philadelphia. “And if things are going poorly, rich people are still getting richer, and all of us are screwed.”

For roughly two decades, partisanship has increasingly been correlated with views about the economy: Research has shown that people rate the economy more poorly when their party is not in power. Nearly every Republican in the poll rated the economy unfavorably, and 59 percent of Democrats did.

He brought up U.S. funding in Ukraine and the Middle East. He wanted to know: Is that the reason our economy is “slowing down?” He wasn’t sure, but he thought it might be. He plans to vote for “the Republican, any Republican,” he said. “Democrats have disappointed me.”

It often borrows the language of social justice movements

BimboTok came about at a time when the girlboss had more or less faded as a trendy cultural type

“I don’t know what the economy is, I don’t know what supply and demand is,” Ms. Chlapecka says in one video. “All I know is that our problems would be solved if we’d just print more money.” There’s a way in which her monologues can come to feel almost incisive — why can’t we print more money? The answer is complicated, obviously, and on some level Ms. Chlapecka knows that. “I’m asking a lot of questions that, yes, I have the answer to, but are also valid,” she said. “The whole world we made is literally made up, so we can make up solutions to the issues we have.”

Bimboism is the antithesis of the mode of feminism that was dominant in the 2010s, a kind of hyperambitious you-can-have-it-all feminism that can be summed up by the label “girlboss.”

It encourages asking the dumb questions that get to the heart of things.

Amid the resurgence of leftist politics, and the disillusionment with capitalism among millennials and Gen Z, the framing of careerism and individualism as feminist rings hollow

That girlboss model was critiqued for its lack of inclusivity — its overwhelming whiteness and its focus on a level of economic success that was never attainable for a vast majority of women.

Not to mention, girlboss aesthetics are simply cringe, for a generation steeped in internet irony.

In its most interesting form, bimboism also makes a connection between the ideas of pleasure — sexual pleasure, pleasure in clothes, pleasure in simply existing as a woman in the world with a body on display — and political gains that would make it more possible, like universal health care, student loan debt cancellation and abortion rights.

It’s anticapitalist, even anti-work. (In one video, another BimboTok comedian, @brattybarbiana, dances behind text that reads, “Rule of thumb: All relationships should be 50/50 … He works for his money, I spend it!”)

Bimboism offers an opposing and, to some, refreshing premise: Value me, look at me, not because I’m smart and diligent, but for the fact that I’m not.

But to dismiss this corner of the internet as totally backward would be to miss the point.

Is it really leftist for a woman to want to just go shopping and never get a job?

“Are you a leftist who likes to have their tits out? Do you like to flick off pro-lifers?” Chrissy Chlapecka asks the camera in a 51-second TikTok captioned “BIMBOS, RISE

Since 2016, there have been two competing visions for the Democratic Party. One is the promise that began with Barack Obama of a multiracial coalition that would grow stronger as America’s demographics shifted; the other is the political revolution championed by Bernie Sanders as a way to unite nonvoters with the working class

Ms. Ocasio-Cortez bridges the gap between the two

what’s clear is that at a time when Democrats are struggling, she is quietly laying the groundwork to build a coalition broader than the one she came to power with, unafraid to take risks along the way.

After five years in Congress, she has emerged as a tested navigator of its byzantine systems, wielding her celebrity to further her political aims in a way few others have.

Three terms in, one gets the sense that we’re witnessing a skilled tactician exiting her political adolescence and coming into her own as a veteran operator out to reform America’s most dysfunctional political body.

To grasp what sets Ms. Ocasio-Cortez apart from many of her colleagues, you have to understand where she finds allies

In 2019, she and Senator Ted Cruz of Texas considered joining forces to write a bill that would bar former members of Congress from becoming lobbyists. Asked why she would consider an alliance with someone so loathed by liberals, she said, “I will swallow all of my distaste in this situation because we have found a common interest.”

It was a window into the politician she would become: pragmatic and results-driven, willing to work with people she considers her political adversaries, at least on legislation that appeals to her base

She has attributed the success of these efforts at least in part to her role as the second most powerful Democrat on the House Oversight Committee, which she said has “opened many windows” for collaboration.

while these bills may seem like small victories, they are more than that because, in a sense, she is redefining what bipartisanship looks like in Washington.

For decades, bipartisanship has meant bringing together moderates, lobbyists and establishment insiders to produce watered-down legislation unpalatable to many voters in both political partie

What Ms. Ocasio-Cortez is doing is different; she’s uniting politicians on the fringes of American politics around a broadly popular set of policies.

Americans in both parties overwhelmingly say that they don’t trust the government to do the right thing and that donors and lobbyists have too much sway over the legislative process.

more than 8 in 10 Americans believe politicians “are more focused on fighting each other than on solving problems.” One-fifth of respondents said lack of bipartisan cooperation was the biggest problem with the political system.

Ms. Ocasio-Cortez’s efforts to reach out to Republicans are offering what a sizable portion of Americans want from Congress: a return to getting things done.

The few policy matters on which progressives and conservatives align often boil down to a distrust of politicians and of big corporations, particularly technology companies and pharmaceutical giants.

Ms. Ocasio-Cortez has shrewdly made those causes her passion, building alliances with conservative colleagues interested in holding these industries accountable.

Last spring, she cosponsored a bill with, among others, Brian Fitzpatrick, a moderate Republican from Pennsylvania, and Matt Gaetz, the Florida rabble-rouser who has become one of Mr. Trump’s most steadfast allies. The legislation would bar members of Congress from trading individual stocks, a measure that as of the fall of 2022 was supported by nearly 70 percent of voters across party lines.

On Gaza, too, she has been willing to buck other members of her party to pursue an agenda that a majority of voters support. She was one of the first Democrats to call for a cease-fire; within weeks, nearly 70 percent of Americans said Israel should call one and try to negotiate with Hamas.

In March, Ms. Ocasio-Cortez was accosted by a handful of protesters who demanded that she call Israel’s war in Gaza a genocide.

Less than three weeks later, Ms. Ocasio-Cortez did accuse Israel of genocide and chastised the White House for providing military aid to the country while it blockaded Gaza. “If you want to know what an unfolding genocide looks like,” Ms. Ocasio-Cortez said in a speech on the House floor, “open your eyes. It looks like the forced famine of 1.1 million innocents. It looks like thousands of children eating grass as their bodies consume themselves, while trucks of food are slowed and halted just miles away.”

Last month, she voted against providing additional funding for Israel. Those were unpopular positions in Congress, where unconditional support for the country remains the norm, but they put her in line with a majority of Democratic voters.

These stances haven’t been enough to quell the doubts from a faction of the left that helped get her elected. Over the past few weeks, some have accused her of caving in to pressure from moderate Democrats

. Ms. Ocasio-Cortez has taken much of the heat from leftist activists who see her as a symbol of the contradictions and compromises inherent in the political system. It may not be realistic to expect absolute purity from her; she is, after all, a politician. But these critiques overlook the promise of what she’s doing behind the scenes.

Democratic pollsters and strategists are searching for ways for Mr. Biden to win back Muslims and Arab Americans in swing states such as Michigan and Georgia, recent college graduates who hoped to have their student debt forgiven, immigrant-rights activists and Latinos.

Some of the betrayal these voters feel was hardly the president’s fault; he was hampered on student loan debt by a federal judiciary stacked with judges sympathetic to conservative legal arguments, and Congress refused to pass the comprehensive immigration bill he supported in 2021, which would have provided legal status to as many as 11 million undocumented immigrants.

A more gifted orator might have been able to make the structural impediments in his way clear to voters, while also putting forth a proactive vision for dismantling the core problems baked into our politics.

In that, someone like Ms. Ocasio-Cortez, who endorsed Mr. Biden for re-election in 2023, may be able to help. She’s the Democratic Party’s most charismatic politician since Barack Obama and its most ardent populist since Bernie Sanders.

she can offer voters something more substantial than a hollow rebuke of Trumpism

Last month, when the journalist Mehdi Hasan asked her how she’d respond to “a young progressive or Arab American who says to you, ‘I just can’t vote for Biden again after what he’s enabled in Gaza,’” Ms. Ocasio-Cortez said a vote for Mr. Biden didn’t necessarily mean an endorsement of all his policies. “Even in places of stark disagreement, I would rather be organizing under the conditions of Biden as an opponent on an issue than Trump,” she said. It was a shrewd political maneuver, designed to distance herself from Democrats who support Israel unconditionally, while meeting voters — some of whom have lost family members in Gaza — where they are

There are, of course, limits to this strategy. Some on the left see Ms. Ocasio-Cortez’s endorsement of Mr. Biden as a betrayal of progressive values, particularly in the wake of the climbing death toll in Gaza.

The moderate Republicans who turned out for Mr. Biden in 2020 might shrink from a Democratic Party led by someone they consider an outspoken progressive.

But for every moderate or leftist voter lost with a strategy like Ms. Ocasio-Cortez’s, the Democratic Party may be able to win someone new — from the pool of disillusioned Americans who feel shut out of the political process.

This is May 2024: Humanity is in the early stages of a standoff with generative AI, which offers methods through which visual art, writing, music, and computer code can be created by a machine in seconds with the simplest of prompts

Most of us are still in the sizing-up phase for generative AI, staring warily at a technology that’s been hyped as world-changing and job-disrupting (even, some proponents argue, potentially civilization-ending), and been foisted on the public in a very short period of time. It’s a weird, exhausting, exciting, even tense moment. Enter: THE CRUSHER.

There is about a zero percent chance that the company did not understand the optics of releasing this ad at this moment. Apple is among the most sophisticated and moneyed corporations in all the world.

this time, it’s hard to like what the company is showing us. People are angry. One commenter on X called the ad “heartbreaking.

Although watching things explode might be fun, it’s less fun when a multitrillion-dollar tech corporation is the one destroying tools, instruments, and other objects of human expression and creativity.

Apple is a great technology company, but it is a legendary marketer. Its ads, its slickly produced keynotes, and even its retail stores succeed because they offer a vision of the company’s products as tools that give us, the consumers, power.

The third-order annoyance is in the genre. Apple has essentially aped a popular format of “crushing” videos on TikTok, wherein hydraulic presses are employed to obliterate everyday objects for the pleasure of idle scrollers.

It’s unclear whether some of the ad might have been created with CGI, but Apple could easily round up tens of thousands of dollars of expensive equipment and destroy it all on a whim. However small, the ad is a symbol of the company’s dominance.

The iPad was one of Steve Jobs’s final products, one he believed could become as popular and perhaps as transformative as cars. That vision hasn’t panned out. The iPad hasn’t killed books, televisions, or even the iPhone

The iPad is, potentially, a creative tool. It’s also an expensive luxury device whose cheaper iterations, at least, are vessels for letting your kid watch Cocomelon so they don’t melt down in public, reading self-help books on a plane, or opting for more pixels and better resolution whilst consuming content on the toilet.