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Bonobos became famous for showing that nature didn’t always have to be red in tooth and claw. “Bonobos are an icon for peace and love, the world’s ‘hippie chimps,’” Sally Coxe, a conservationist, said in 2006.

Because bonobos live in remote, swampy rainforests, it has been much more difficult to observe them in the wild than chimpanzees. More recent research has shown that bonobos live a more aggressive life than their reputation would suggest.

In a study based on thousands of hours of observations in the wild published on Friday, for example, researchers found that male bonobos commit acts of aggression nearly three times as often as male chimpanzees do.

“There is no ‘hippie ape,’”

As our closest living relatives, bonobos and chimpanzees can offer us clues about the roots of human behavior. We and the two species share a common ancestor that lived about 7 million years ago. About 5 million years later, bonobos split off from chimpanzees.

In 2012, a trio of Harvard researchers proposed that bonobos evolved much like dogs did. Less aggressive wolves were not as likely to be killed by humans, which over time led to the emergence of dogs. In a similar fashion, the researchers argued, female bonobos preferred to mate with less aggressive males, giving birth to less aggressive offspring.

The researchers called their idea the self-domestication hypothesis. In later years, they speculated that humans may have undergone a self-domestication of their own.

Dr. Mouginot soon became perplexed, as she saw that male bonobos acted aggressively on a regular basis. Unlike male chimpanzees, who started their days in a mellow mood, the male bonobos seemed to wake up ready for a fight.

She and her colleagues trained field assistants, who made more observations throughout the pandemic. The new analysis, based on 9,300 hours of observations on 12 male bonobos and 14 male chimpanzees, found that bonobos committed aggressive acts 2.8 times as frequently as than the chimpanzees did.

Dr. Mouginot found that the frequent bonobo aggressions almost always involved a single male attacking another male. Chimpanzees, in contrast, often ganged up to attack a victim.

the study set a new standard for comparing aggression in bonobos and chimpanzees.

Dr. Mouginot speculated that male chimpanzees engage in one-on-one aggression less often because it poses bigger dangers: A victim of aggression may not want to go on a border patrol with the perpetrator, for example. Or he may bring back some of his own allies to wreak vengeance.

It may be easier for male bonobos to get away with aggression, Dr. Mouginot said, because in their female-dominated society they don’t face the risks that come with male alliances. “I think that’s why we see more aggression in bonobos — because it’s less risky to act aggressively against other males,”

the apes that carried out the most aggressive acts were also the ones who mated most often.

parts of the self-domestication hypothesis “clearly need refinement.” It may be important to consider the effect that different kinds of aggression have on a species, rather than lumping them altogether, he said.

Still, he argued that the differences between the two species remained significant. “Chimpanzees murder, and bonobos don’t,

Special effects software can give your music all sorts of subtle vibes. For $299, for instance, you can make your song sound as though it was recorded in Sound City Studios in Los Angeles, on the $20,000 vintage microphones with the $100,000 mixing console that recorded Fleetwood Mac and Nirvana. For $259 you can simulate the Beatles’ Abbey Road Studios

In the past, when you assembled all the tracks for a song, it would have to be mixed and mastered in a studio by a sound engineer adjusting the levels. Now an app will do it for you.

Now, all you need to do is get the files uploaded to streaming services. If you are signed with a label, they will do that for you, but you don’t need a record deal to do that. There are companies such as DistroKid that act as postmen: for £17.99 a year, you can ensure that the song you created in your bedroom is available on multiple streaming platforms.

Some artists thrive on social media. It suits lively, quirky performers like Ryder, and artists who want to experiment with different genres. Mxmtoon, a 23-year-old American YouTuber, singer-songwriter and ukulele player, is also a streamer on Twitch, a platform on which people watch others playing online games, has published graphic novels and made a podcast. Maia — the artist’s name — describes mxmtoon as “a multi-hyphenated project”.

More traditional musicians struggle with some aspects of tech. “We get together,” says Sital-Singh of his contemporaries in the business, “and have a little moan about how everyone’s telling us to do TikTok and we can’t bring ourselves to dance and we feel old and decrepit.”

Even digital natives struggle sometimes: “I already have nostalgia for a simpler past, even though I’m 23,” says Maia. “It’s exciting but it does put so much pressure on a normal individual to be an entrepreneur to advertise their own personal brand.”

Journalists, regrettably, no longer have the power they once did. What matters these days is social media. The A&R (artists and repertoire) people at record companies who would once have hung out in basement clubs scouting for new talent now sit in meetings examining the data on artists’ social media performance.

Now that the whole world’s music is available all over the world at the click of a play button, there’s a greater diversity among top-selling artists.

People making videos of themselves performing or dancing to the song on TikTok helped propel it to the stratosphere. It has been streamed a billion times. Sethi now plays to packed venues in America and Europe; last year, he performed at Coachella, America’s Glastonbury. “Without digital technology I would be a south Asian indy musician, working on the fringes of Bollywood,” he says from his home in New York.

For musicians, it’s more ambiguous. Because the costs of making music are lower, anybody with ambition can have a go. Many more people, as a result, are getting into the music business. According to PPL, the organisation which distributes money to music performers and rights-holders, the number of registered artists has risen from 61,310, when the industry was at its nadir, to 165,039 last year.

That makes the business fiercely competitive. As Will Page, former chief economist at Spotify and author of Tarzan Economics, points out, around 100,000 tracks are being uploaded on to Spotify every day: that’s more than were released in an entire calendar year in the 1980s

That has cemented the power of the record companies. When the digital revolution started, it was widely expected that record labels would cease to exist, and be replaced by a model in which everybody promoted and distributed their own music. That’s not what has happened: because it’s so difficult to get noticed, embryonic stars need record labels to promote them.

A label invests in the production of the music, the styling of the band, video content, interviews, touring and the crucial business of getting a song on a streaming service’s playlist that suggests the song to listeners to suit their tastes. Artists that are signed with major labels get paid more, per stream, than those that aren’t.

Three quarters of streamed tracks have one of the major record labels behind them. And even though streaming is booming, it doesn’t contribute much to the incomes of the vast majority of artists.

Most artists stay hungry. A single stream will earn a musician anywhere between 0.1p and 2.4p. Crispin Hunt reckons that on average a million streams for a song — a wild ambition for most musicians — will, if you have to pay a cut to a record company, probably make you £1,000

If you’ve then got to pay your manager 20 per cent, and divide the rest between the four band members, “it barely pays for a Sainsbury’s shop. That’s why music is dominated by middle-class people called Crispin whose parents can afford to buy them an electric guitar and a laptop.”

For Christie Gardner, half of Lilo, a two-woman band, it helps planning gigs. “You can see where your listeners are and you can tell what they’re listening to. We make decisions about shows on that basis.” But for most bands, the economics of live performance are pretty grim.

Deathcrash’s manager Joe Taylor says that the band has been offered the opportunity of a European tour supporting a much bigger act. It would be good for their career but not their bank balance. The fees per show would be €200; once the costs of a sound engineer, a tour van, a driver, fuel, hotels and a carnet for importing musical instruments into the EU had been factored in, they would lose £15,000.

music has always been an uneconomic business, which people subsidise through other activities. The fiddler in the village pub probably worked in the fields in the daytime and played for money and fun in the evenings and at the weekend, rather as Ryder ran a juice bar and sang at weddings. These days, there is also the wafer-thin chance that they might end up being one of the 1,200 artists who make more than $1 million a year on Spotify

There are some signs that in the new musical economy, the balance of power between artists and the big record companies may have tipped slightly in the artists’ favour

the share of streaming revenues going to artists increased from 19.7 per cent to 23.3 per cent between 2012 and 2021 and that going to songwriters has risen from 8 per cent in 2008 to 15 per cent in 2021. “Outcomes for consumers, artists and songwriters,” it concludes, “are getting better.”

Most of the extra revenues generated by streaming are going to the top earners. But the stars are not the only ones benefiting. Between 2017 and 2022, the number of artists earning over $1 million on Spotify more than doubled, but so did the number earning over $10,000

more than two fifths of artists who release their own music aren’t expecting to make a full-time career. They’re in it for fun, for the love of it, or to be able to show their mum that they have released a song on Spotify.

It seems likely that the more music is being created, the greater the chance that wonderful tunes are being written, but it’s not necessarily the case. The best stuff might get buried under a mound of mediocrity.

The latest study looked at data from 126,972 people over the study period. It found that in 2015, 4.2 percent of Medicaid enrollees under the age of 17 in Maryland had overlapping prescriptions of three or more different classes of psychiatric medications. That figure rose to 4.6 percent in 2020.

he trickiest question may be how to prevent abuse. AI generators have technological boundaries, but not morals, and it’s relatively easy for users to trick them into creating content that depicts violence, rape, sex with children or a celebrity — or even a crush from work who never consented to appear

In some cases, the engines themselves are trained on porn images whose subjects didn’t explicitly agree to the new use. Currently, no federal laws protect the victims of nonconsensual deepfakes.

Adult entertainment is a giant industry accounting for a substantial chunk of all internet traffic: Major porn sites get more monthly visitors and page views than Amazon, Netflix, TikTok or Zoom

The industry is a habitual early adopter of new technology, from VHS to DVD to dot com. In the mid-2000s, porn companies set up massive sites where users upload and watch free videos, and ad sales foot the bills.

At last year’s AVN conference, Steven Jones said his peers looked at him “like he was crazy” when he talked about AI opportunities: “Nobody was interested.” This year, Jones said, he’s been “the belle of the ball.”

He called up his old business partner, and the two immediately spent about $550,000 securing the web domains for porn dot ai, deepfake dot com and deepfakes dot com, Jones said. “Lightspeed” was back.

One major model, Stable Diffusion, shares its code publicly, and some technologists have figured out how to edit the code to allow for sexual images

What keeps Jones up at night is people trying to use his company’s tools to generate images of abuse, he said. The models have some technological guardrails that make it difficult for users to render children, celebrities or acts of violence. But people are constantly looking for workarounds.

So with help from an angel investor he will not name, Jones hired five employees and a handful of offshore contractors and started building an image engine trained on bundles of freely available pornographic images, as well as thousands of nude photos from Jones’s own collection

Users create what Jones calls a “dream girl,” prompting the AI with descriptions of the character’s appearance, pose and setting. The nudes don’t portray real people, he said. Rather, the goal is to re-create a fantasy from the user’s imagination.

The AI-generated images got better, their computerized sheen growing steadily less noticeable. Jones grew his user base to 500,000 people, many of whom pay to generate more images than the five per day allotted to free accounts, he said. The site’s “power users” generate AI porn for 10 hours a day, he said.

Jones described the site as an “artists’ community” where people can explore their sexualities and fantasies in a safe space. Unlike some corners of the traditional adult industry, no performers are being pressured, underpaid or placed in harm’s way

And critically, consumers don’t have to wait for their favorite OnlyFans performer to come online or trawl through Pornhub to find the content they like.

Next comes AI-generated video — “porn’s holy grail,” Jones said. Eventually, he sees the technology becoming interactive, with users giving instructions to lifelike automated “performers.” Within two years, he said, there will be “fully AI cam girls,” a reference to creators who make solo sex content.

It costs $12 per day to rent a server from Amazon Web Services, he said, and generating a single picture requires users to have access to a corresponding server. His users have so far generated more than 1.6 million images.

Copyright holders including newspapers, photographers and artists have filed a slew of lawsuits against AI companies, claiming the companies trained their models on copyrighted content. If plaintiffs win, it could cut off the free-for-all that benefits entrepreneurs such as Jones.

But Jones’s plan to create consumer-friendly AI porn engines faced significant obstacles. The companies behind major image-generation models used technical boundaries to block “not safe for work” content and, without racy images to learn from, the models weren’t good at re-creating nude bodies or scenes.

Jones said his team takes down images that other users flag as abusive. Their list of blocked prompts currently contains 1,000 terms including “high school.”

“I see certain things people type in, and I just hope to God they’re trying to test the model, like we are. I hope they don’t actually want to see the things they’re typing in.

Peter Acworth, the owner of kink dot com, is trying to teach an AI porn generator to understand even subtler concepts, such as the difference between torture and consensual sexual bondage. For decades Acworth has pushed for spaces — in the real world and online — for consenting adults to explore nonconventional sexual interests. In 2006, he bought the San Francisco Armory, a castle-like building in the city’s Mission neighborhood, and turned it into a studio where his company filmed fetish porn until shuttering in 2017.

Now, Acworth is working with engineers to train an image-generation model on pictures of BDSM, an acronym for bondage and discipline, dominance and submission, sadism and masochism.

Others alluded to a porn apocalypse, with AI wiping out existing models of adult entertainment.“Look around,” said Christian Burke, head of engineering at the adult-industry payment app Melon, gesturing at performers huddled, laughing and hugging across the show floor. “This could look entirely different in a few years.”

But the age of AI brings few guarantees for the people, largely women, who appear in porn. Many have signed broad contracts granting companies the rights to reproduce their likeness in any medium for the rest of time

Not only could performers lose income, Walters said, they could find themselves in offensive or abusive scenes they never consented to.

Lana Smalls, a 23-year-old performer whose videos have been viewed 20 million times on Pornhub, said she’s had colleagues show up to shoots with major studios only to be surprised by sweeping AI clauses in their contracts.

“This industry is too fragmented for collective bargaining,” Spiegler said. “Plus, this industry doesn’t like rules.”

At some point they will be able to, for example, suggest recipes for novel cyberattacks or biological attacks—all based on publicly available knowledge.

But as models become more sophisticated, this approach may prove insufficient. Some models are beginning to exhibit polymathic behavior: They appear to know more than just what is in their training data and can link concepts across fields, languages, and geographies.

We need to adopt new approaches to AI safety that track the complexity and innovation speed of the core models themselves.

What’s much harder to test for is what’s known as “capability overhang”—meaning not just the model’s current knowledge, but the derived knowledge it could potentially generate on its own.

Red teams have so far shown some promise in predicting models’ capabilities, but upcoming technologies could break our current approach to safety in AI. For one, “recursive self-improvement” is a feature that allows AI systems to collect data and get feedback on their own and incorporate it to update their own parameters, thus enabling the models to train themselves

This could result in, say, an AI that can build complex system applications (e.g., a simple search engine or a new game) from scratch. But, the full scope of the potential new capabilities that could be enabled by recursive self-improvement is not known.

Another example would be “multi-agent systems,” where multiple independent AI systems are able to coordinate with each other to build something new.

This so-called “combinatorial innovation,” where systems are merged to build something new, will be a threat simply because the number of combinations will quickly exceed the capacity of human oversight.

Short of pulling the plug on the computers doing this work, it will likely be very difficult to monitor such technologies once these breakthroughs occur

Current regulatory approaches are based on individual model size and training effort, and are based on passing increasingly rigorous tests, but these techniques will break down as the systems become orders of magnitude more powerful and potentially elusive

AI regulatory approaches will need to evolve to identify and govern the new emergent capabilities and the scaling of those capabilities.

But the AI Act has already fallen behind the frontier of innovation, as open-source AI models—which are largely exempt from the legislation—expand in scope and number

Europe has so far attempted the most ambitious regulatory regime with its AI Act,

both Biden’s order and Europe’s AI Act lack intrinsic mechanisms to rapidly adapt to an AI landscape that will continue to change quickly and often.

a gathering in Palo Alto organized by the Rand Corp. and the Carnegie Endowment for International Peace, where key technical leaders in AI converged on an idea: The best way to solve these problems is to create a new set of testing companies that will be incentivized to out-innovate each other—in short, a robust economy of testing

To check the most powerful AI systems, their testers will also themselves have to be powerful AI systems, precisely trained and refined to excel at the single task of identifying safety concerns and problem areas in the world’s most advanced models.

To be trustworthy and yet agile, these testing companies should be checked and certified by government regulators but developed and funded in the private market, with possible support by philanthropy organizations

The field is moving too quickly and the stakes are too high for exclusive reliance on typical government processes and timeframes.

One way this can unfold is for government regulators to require AI models exceeding a certain level of capability to be evaluated by government-certified private testing companies (from startups to university labs to nonprofit research organizations), with model builders paying for this testing and certification so as to meet safety requirements.

As AI models proliferate, growing demand for testing would create a big enough market. Testing companies could specialize in certifying submitted models across different safety regimes, such as the ability to self-proliferate, create new bio or cyber weapons, or manipulate or deceive their human creators

Much ink has been spilled over presumed threats of AI. Advanced AI systems could end up misaligned with human values and interests, able to cause chaos and catastrophe either deliberately or (often) despite efforts to make them safe. And as they advance, the threats we face today will only expand as new systems learn to self-improve, collaborate and potentially resist human oversight.

If we can bring about an ecosystem of nimble, sophisticated, independent testing companies who continuously develop and improve their skill evaluating AI testing, we can help bring about a future in which society benefits from the incredible power of AI tools while maintaining meaningful safeguards against destructive outcomes.

It turns out that if you give everyone access to the Library of Congress, what they do is watch videos on TikTok

A chatbot is not an oracle; it’s a statistics engine that creates sentences that sound accurate. Right now my sense is that it’s like we’ve just invented transistors. We’ve got a couple of consumer products that people are starting to adopt, like the transistor radio, but we don’t yet know how the transistor will transform society

We’re in the transistor-radio stage of AI. I think a lot of the ferment that’s happening right now in the industry is venture capitalists putting money into business plans, and teams that are rapidly evaluating a whole lot of different things that could be done well. I’m sure that some things are going to emerge that I wouldn’t dare try to predict, because the results of the creative frenzy of millions of people is always more interesting than what a single person can think of.

the red-blue divide is perhaps less illuminating than the gap between engaged and disengaged Americans, in which an exhausted majority encounters the highly polarized activist wings of both parties and shrinks back from the fray

The wings aren’t changing each other’s minds — hard-core Democrats aren’t going to persuade hard-core Republicans — but they’re also not reaching sufficient numbers of persuadable voters to break America’s partisan deadlock.

In 2020, when I was doing research for my book about the growing danger of partisan division, I began to learn more about what extreme partisanship does not only to our hearts but also to our minds.

It can deeply and profoundly distort the way we view the world. We become so emotionally and spiritually invested in the outcome of a political contest that we can inadvertently become disconnected from reality.

Our heart connects with our mind in such a way that the heart demands that the mind conform to its deepest desires

When a partisan encounters negative information, it can often trigger the emotional equivalent of a fight-or-flight response. This applies not just to negative arguments but also to negative facts. To deal with the emotional response, we seek different arguments and alternative facts.

If you are a true partisan, you essentially become an unpaid lawyer for your side. Every “good” fact that bolsters your argument is magnified. Every “bad” fact is minimized or rationalized.

When partisanship reaches its worst point, every positive claim about your side is automatically believed, and every negative allegation is automatically disbelieved.

allegations of wrongdoing directed at your side are treated as acts of aggression — proof that “they” are trying to destroy “us.”

You see this reality most plainly in the daily Republican theatrics surrounding Trump’s criminal indictments. Rather than wrestle seriously with the profoundly troubling claims against him, they treat the criminal cases as proof of Democratic perfidy. They believe every claim against Hunter and Joe Biden and not a single claim against Trump.

ask why people are checking out, and one reason is that partisans make it so very difficult to engage.

The problem is most pronounced (and often overtly threatening) on the MAGA right, but it’s endemic to our partisan wings

as partisanship deepens, partisan subcultures can get increasingly weird. They become so convinced of the us-versus-them dynamic that they’ll eventually believe virtually anything, as long as it’s a claim against the other side.

If decades of partisanship have persuaded you that your opponents are evil, have no morals and want to destroy the country, then why wouldn’t they hack voting machines or recruit a pop star as a government asset?

I have some rules to help temper my worst partisan impulses.

Expose yourself to the best of the other side’s point of view — including the best essays, podcasts and books.

when you encounter a new idea, learn about it from its proponents before you read its opponents.

when you encounter bad news about a cause that you hold dear — whether it’s a presidential campaign, an international conflict or even a claim against a person you admire, take a close and careful look at the evidence

“People used to talk about politicians trying to control our bodies,” she said. “Now it’s, they have no business getting involved in these medical decisions, these politicians don’t have medical expertise, they’re making these laws, and they’re not basing it on health care or science.”

Seventy-three percent of independents who support abortion rights said stories about women almost dying because of bans would affect how they vote.

“Now it’s about pregnancy, and everybody knows someone who had a baby or wants to have a baby or might get pregnant,” she said. “It’s profoundly personal to a majority of the public.”

Anti-abortion groups have responded by trying to carve out a difference between “elective abortion” for unwanted pregnancies — which they want banned — and “maternal fetal separation” in medical emergencies. (The medical procedure is the same.)

Opponents have long stigmatized abortion as something irresponsible women use as birth control or because they care more about their careers than having children. “When the focus shifts to the dangers that abortion bans inflict on pregnant people,” said Reva Siegel, a constitutional law professor at Yale who has written extensively about the country’s abortion conflict, “it’s easier for Americans to talk about.”

Technology and criminal law have flipped the script, she said.

Before Roe legalized abortion nationally in 1973, the law allowed more leeway for what were considered “therapeutic abortions.” Doctors, often solo practitioners, could use their good faith judgment to provide them. Even the Southern Baptist Convention supported abortions in cases of fetal deformity or when a woman’s physical or mental health was at risk.

Now, the threat of prosecution, $100,000 fines and loss of their medical licenses have chilled doctors and hospital systems in treating women with pregnancy complications. More often than not in some states, lawyers are making the decisions.

In Georgia, she said, more people opposed the state’s ban on abortion after six weeks of pregnancy once they were told that this meant two weeks after the average woman misses her period — not, as her own partner believed, six weeks after conception. Some voters, she said, believed that six weeks meant six weeks after women found out they were pregnant.

In fact, the news from Wall Street lately has mostly been about retreat from climate goals, rather than recommitment. Banks and asset managers are withdrawing from international climate alliances and chafing at their rules. Regional banks are stepping up lending to fossil fuel producers. Sustainable investment funds have sustained crippling outflows, and many have collapsed.

In some cases, it’s a classic prisoner’s dilemma: If firms collectively shift to cleaner energy, a cooler climate benefits everyone more in the future

in the short term, each firm has an individual incentive to cash in on fossil fuels, making the transition much harder to achieve.

when it comes to avoiding climate damage to their own operations, the financial industry is genuinely struggling to comprehend what a warming future will mean.

A global compact of financial institutions made commitments worth $130 trillion to try to bring down emissions, confident that governments would create a regulatory and financial infrastructure to make those investments profitable. And in 2022, the Inflation Reduction Act passed.

What about the risk that climate change poses to the financial industry’s own investments, through more powerful hurricanes, heat waves that knock out power grids, wildfires that wipe out towns?

“If we think about what is going to be the best way to tilt your portfolios in the direction to benefit, it’s really difficult to do,”

“These will probably be great investments over 20 years, but when we’re judged over one to three years, it’s a little more challenging for us.”

Some firms cater to institutional clients, like public employee pension funds, that want combating climate change to be part of their investment strategy and are willing to take a short-term hit. But they aren’t a majority

And over the past couple of years, many banks and asset managers have shrunk from anything with a climate label for fear of losing business from states that frown on such concerns.

On top of that, the war in Ukraine scrambled the financial case for backing a rapid energy transition. Artificial intelligence and the movement toward greater electrification are adding demand for power, and renewables haven’t kept up

All of that is about the relative appeal of investments that would slow climate change

If you bought some of the largest solar-energy exchange-traded funds in early 2023, you would have lost about 20 percent of your money, while the rest of the stock market soared.

There is evidence that banks and investors price in some physical risk, but also that much of it still lurks, unheeded.

“I’m very, very worried about this, because insurance markets are this opaque weak link,” Dr. Sastry said. “There are parallels to some of the complex linkages that happened in 2008, where there is a weak and unregulated market that spills over to the banking system.”

Regulators worry that failing to understand those ripple effects could not just put a single bank in trouble but even become a contagion that would undermine the financial system.

But while the European Central Bank has made climate risk a consideration in its policy and oversight, the Federal Reserve has resisted taking a more active role, despite indications that extreme weather is feeding inflation and that high interest rates are slowing the transition to clean energy.

“The argument has been, ‘Unless we can convincingly show it’s part of our mandate, Congress should deal with it, it’s none of our business,’”

a much nearer-term uncertainty looms: the outcome of the U.S. election, which could determine whether further action is taken to address climate concerns or existing efforts are rolled back. An aggressive climate strategy might not fare as well during a second Trump administration, so it may seem wise to wait and see how it shakes out.

big companies are hesitating on climate-sensitive investments as November approaches, but says that “two things are misguided and quite dangerous about that hypothesis.”

One: States like California are establishing stricter rules for carbon-related financial disclosures and may step it up further if Republicans win

And two: Europe is phasing in a “carbon border adjustment mechanism,” which will punish polluting companies that want to do business there.

at the moment, even European financial institutions feel pressure from the United States, which — while providing some of the most generous subsidies so far for renewable-energy investment — has not imposed a price on carbon.

The global insurance company Allianz has set out a plan to align its investments in a way that would prevent warming above 1.5 degrees Celsius by the end of the century, if everyone else did the same. But it’s difficult to steer a portfolio to climate-friendly assets while other funds take on polluting companies and reap short-term profits for impatient clients.

“This is the main challenge for an asset manager, to really bring the customer along,” said Markus Zimmer, an Allianz economist. Asset managers don’t have sufficient tools on their own to move money out of polluting investments and into clean ones, if they want to stay in business,

“Of course it helps if the financial industry is somehow ambitious, but you cannot really substitute the lack of actions by policymakers,”

According to new research, the benefit is greater when decarbonization occurs faster, because the risks of extreme damage mount as time goes on. But without a uniform set of rules, someone is bound to scoop up the immediate profits, disadvantaging those that don’t — and the longer-term outcome is adverse for all.

Before the pandemic, a number of variables were statistically significant indicators for consumer sentiment in our model; in particular, the most salient variables appear to be vehicle sales, gas prices, median household income, the federal funds effective rate, personal savings and household expenditures (excluding food and energy).

During the pandemic, the personal savings rate soared. In April 2020, the metric was nearly double its previous high, recorded in May 1975.

All this taken together meant Americans were flush with cash but had nowhere to spend it. So despite the fact that the savings rate went way up, consumers still weren't feeling positively about the economy — contrary to the relationship between these two variables we saw in the decades before the pandemic.

Fast forward to 2024, and the personal savings rate has dropped to one of its lowest levels ever (the only time the savings rate was lower was in the years surrounding the Great Recession)

during and after the pandemic, Americans saw some of the highest rates of inflation the country has had in decades, and in a very short period of time. These sudden spikes naturally shocked many people who had been blissfully enjoying slow, steady price growth their entire adult lives. And it has taken a while for that shock to wear off, even as inflation has cre

the numbers align with our intuitive sense of how consumers process suddenly having their grocery store bill jump, as well as the findings from our model. In simple terms: Even if inflation is getting better, Americans aren't done being ticked off that it was bad to begin with.

surprisingly, our pre-pandemic model didn't find a notable relationship between housing prices and consumer sentiment

However, in our post-pandemic data, when we examined how correlated consumer sentiment was with each indicator we considered, consumer sentiment and median housing prices had the strongest correlation of all****** (a negative one, meaning higher prices were associated with lower consumer sentiment)

during the pandemic, low interest rates, high savings rates and changes in working patterns — namely, many workers' newfound ability to work from home — helped overheat the homebuying market, and buyers ran headlong into an enduring supply shortage. There simply weren't enough houses to buy, which drove up the costs of the ones that were for sale.

That's true even if a family has been able to save enough for a down payment, already a difficult task when rents remain high as well. Fewer people are able to cover their current housing costs while saving enough to make a down payment.

Low-income households are still the most likely to be burdened with high rents, but they're not the only ones affected anymore. High rents have also begun to affect those at middle-income levels as well.

In short, there was already a housing affordability crisis before the pandemic. Now it's worse, locking a wider array of people, at higher and higher income levels, out of the home-buying market

People who are renting but want to buy are stuck. People who live in starter homes and want to move to bigger homes are stuck. The conditions have frustrated a fundamental element of the American dream

In our pre-pandemic model, total vehicle sales had a strong positive relationship with consumer sentiment: If people were buying cars, you could pretty reasonably bet that they felt good about the economy. This feels intuitive — who buys a car if they think the economy

Cox Automotive also tracks vehicle affordability by calculating the estimated number of weeks' worth of median income needed to purchase the average new vehicle, and while that number has improved over the last two years, it remains high compared to pre-pandemic levels. In April, the most recent month with data, it took 37.7 weeks of median income to purchase a car, compared with fewer than 35 weeks at the end of 2019.

"Right before the pandemic, the typical average transaction price was around $38,000 for a new car. By 2023, it was $48,000," Schirmer said. This could all be contributing to the break in the relationship between car sales and sentiment, he noted. Basically, people might be buying cars, but they aren't necessarily happy about it.

Inspired by our model of economic indicators and sentiment from 1987 to 2019, we tried to train a similar linear regression model on the same data from 2021 to 2024 to more directly compare how things changed after the pandemic. While we were able to get a pretty good fit for this post-pandemic model,******* something interesting happened: Not a single variable showed up as a statistically significant predictor of consumer sentiment.

This suggests there's something much more complicated going on behind the scenes: Interactions between these variables are probably driving the prediction, and there's too much noise in this small post-pandemic data set for the model to disentangle i

Changes in the kinds of purchases we've discussed — homes, cars and everyday items like groceries — have fundamentally shifted the way Americans view how affordable their lives are and how they measure their quality of life.

Even though some indicators may be improving, Americans are simply weighing the factors differently than they used to, and that gives folks more than enough reason to have the economic blues.

AIs like ChatGPT rapidly got better in their early days, but what we’ve seen in the past 14-and-a-half months are only incremental gains, says Marcus. “The truth is, the core capabilities of these systems have either reached a plateau, or at least have slowed down in their improvement,” he adds.

the gaps between the performance of various AI models are closing. All of the best proprietary AI models are converging on about the same scores on tests of their abilities, and even free, open-source models, like those from Meta and Mistral, are catching up.

models work by digesting huge volumes of text, and it’s undeniable that up to now, simply adding more has led to better capabilities. But a major barrier to continuing down this path is that companies have already trained their AIs on more or less the entire internet, and are running out of additional data to hoover up. There aren’t 10 more internets’ worth of human-generated content for today’s AIs to inhale.

A mature technology is one where everyone knows how to build it. Absent profound breakthroughs—which become exceedingly rare—no one has an edge in performance

companies look for efficiencies, and whoever is winning shifts from who is in the lead to who can cut costs to the bone. The last major technology this happened with was electric vehicles, and now it appears to be happening to AI.

the future for AI startups—like OpenAI and Anthropic—could be dim.

Microsoft and Google will be able to entice enough users to make their AI investments worthwhile, doing so will require spending vast amounts of money over a long period of time, leaving even the best-funded AI startups—with their comparatively paltry warchests—unable to compete.

Many other AI startups, even well-funded ones, are apparently in talks to sell themselves.

the bottom line is that for a popular service that relies on generative AI, the costs of running it far exceed the already eye-watering cost of training it.

That difference is alarming, but what really matters to the long-term health of the industry is how much it costs to run AIs. 

Changing people’s mindsets and habits will be among the biggest barriers to swift adoption of AI. That is a remarkably consistent pattern across the rollout of all new technologies.

the industry spent $50 billion on chips from Nvidia to train AI in 2023, but brought in only $3 billion in revenue.

For an almost entirely ad-supported company like Google, which is now offering AI-generated summaries across billions of search results, analysts believe delivering AI answers on those searches will eat into the company’s margins

Google, Microsoft and others said their revenue from cloud services went up, which they attributed in part to those services powering other company’s AIs. But sustaining that revenue depends on other companies and startups getting enough value out of AI to justify continuing to fork over billions of dollars to train and run those systems

three in four white-collar workers now use AI at work. Another survey, from corporate expense-management and tracking company Ramp, shows about a third of companies pay for at least one AI tool, up from 21% a year ago.

OpenAI doesn’t disclose its annual revenue, but the Financial Times reported in December that it was at least $2 billion, and that the company thought it could double that amount by 2025. 

That is still a far cry from the revenue needed to justify OpenAI’s now nearly $90 billion valuation

the company excels at generating interest and attention, but it’s unclear how many of those users will stick around. 

AI isn’t nearly the productivity booster it has been touted as

While these systems can help some people do their jobs, they can’t actually replace them. This means they are unlikely to help companies save on payroll. He compares it to the way that self-driving trucks have been slow to arrive, in part because it turns out that driving a truck is just one part of a truck driver’s job.

Add in the myriad challenges of using AI at work. For example, AIs still make up fake information,

getting the most out of open-ended chatbots isn’t intuitive, and workers will need significant training and time to adjust.

That’s because AI has to think anew every single time something is asked of it, and the resources that AI uses when it generates an answer are far larger than what it takes to, say, return a conventional search result

None of this is to say that today’s AI won’t, in the long run, transform all sorts of jobs and industries. The problem is that the current level of investment—in startups and by big companies—seems to be predicated on the idea that AI is going to get so much better, so fast, and be adopted so quickly that its impact on our lives and the economy is hard to comprehend. 

Mounting evidence suggests that won’t be the case.