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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Altman and OpenAI have been candid on this front. The end goal of OpenAI has always been to build a so-called artificial general intelligence, or AGI, that would, in their imagining, alter the course of human history forever, ushering in an unthinkable revolution of productivity and prosperity—a utopian world where jobs disappear, replaced by some form of universal basic income, and humanity experiences quantum leaps in science and medicine. (Or, the machines cause life on Earth as we know it to end.) The stakes, in this hypothetical, are unimaginably high—all the more reason for OpenAI to accelerate progress by any means necessary.

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.

Part of Altman’s reasoning, he told Andersen, is that AI development is a geopolitical race against autocracies like China. “If you are a person of a liberal-democratic country, it is better for you to cheer on the success of OpenAI” rather than that of “authoritarian governments,” he said. He noted that, in an ideal world, AI should be a product of nations. But in this world, Altman seems to view his company as akin to its own nation-state.

In response to one question about AGI rendering jobs obsolete, Jeff Wu, an engineer for the company, confessed, “It’s kind of deeply unfair that, you know, a group of people can just build AI and take everyone’s jobs away, and in some sense, there’s nothing you can do to stop them right now.” He added, “I don’t know. Raise awareness, get governments to care, get other people to care. Yeah. Or join us and have one of the few remaining jobs. I don’t know; it’s rough.”

Wu’s colleague Daniel Kokotajlo jumped in with the justification. “To add to that,” he said, “AGI is going to create tremendous wealth. And if that wealth is distributed—even if it’s not equitably distributed, but the closer it is to equitable distribution, it’s going to make everyone incredibly wealthy.”

This is the unvarnished logic of OpenAI. It is cold, rationalist, and paternalistic. That such a small group of people should be anointed to build a civilization-changing technology is inherently unfair, they note. And yet they will carry on because they have both a vision for the future and the means to try to bring it to fruition

Wu’s proposition, which he offers with a resigned shrug in the video, is telling: You can try to fight this, but you can’t stop it. Your best bet is to get on board.

As a result, Trump’s threats of revenge and retribution have become the background noise of the election year—it’s just more blah-blah-blah from a master of it

CREW, a good-government group in Washington, D.C., which reviewed more than thirteen thousand of Trump’s Truth Social posts for a report released this week.

They found that Trump had threatened to unleash the powers of the federal government on Biden twenty-five times in the past two years. Other targets against whom Trump called for vengeance included senators, judges, and members of Biden’s family. “IF YOU GO AFTER ME, I’M COMING AFTER YOU!”—a blunt Trump social-media post from last year cited in the report

yet Congress, even when it was under full Democratic control in the first two years of Biden’s Presidency, has failed to pass measures that might insulate the Justice Department and other parts of the executive branch from efforts to politicize it during a second Trump term, such as reforming the Insurrection Act to make it harder to deploy the military on U.S. soil or passing legislation to make it more difficult for the White House to interfere in federal law-enforcement investigations.

A prosecutor may well have political motivation, but his motivation isn’t what determines a verdict; he must prove his charges in court, through an adversarial process. Despite the yelps that Trump was tried in a kangaroo court, his lawyers had every opportunity to challenge jurors, introduce evidence, question prosecution witnesses, and call their own.

Trump is also right to note that his business practices and records didn’t attract anywhere near as much attention before he was a politician. Trump was famous before he was president, but becoming the most famous person on Earth is something else entirely. With the perks of fame comes more scrutiny. (Just ask Hunter Biden.)

Supporters of the Trump prosecution should be honest about the possibility of political motive underlying the case. The danger of political bias is an inherent flaw in the system of elected district attorneys that most jurisdictions around the U.S. use.

Capone was a notorious gangster, involved in murder, bootlegging, and racketeering, so it seems ludicrous that he was nailed on something as procedural and dry and quotidian as evading taxes.

the Capone case. The mobster committed many crimes, but he did them in a way that made them hard to prosecute. Like many organized-crime bosses, he made sure to speak about things elliptically and keep his fingerprints (literal and metaphorical) off things. (Does this sound familiar?) But Capone couldn’t hide financial crimes as effectively. Prosecutors went after him for tax evasion because that’s what they could prove. It is not selective prosecution to go charge someone for a crime for which you have evidence, even if you don’t charge them for the other, more difficult-to-prove crimes. It is realism. It’s also justified and just.

Republican cries of political prosecution can also be understood in another, better way. Because Trump’s defenders are unwilling to argue that he didn’t falsify the records or that it shouldn’t be a crime, they’re actually arguing that he should get a pass on crimes they view as minor because he’s a political figure

“If they can do this to me, they can do this to anyone,” Trump said at a press conference this morning. Indeed, that’s the point of equal justice under the law.

Du Bois’s quote, “Either the United States will destroy ignorance or ignorance will destroy the United States,” serves as a poignant reminder of the importance of education, understanding and open discourse in addressing societal challenges.

Dr. Shi’s group was fascinated by how coronaviruses jump from species to species. To find viruses, they took samples from bats and other animals, as well as from sick people living near animals carrying these viruses or associated with the wildlife trade. Much of this work was conducted in partnership with the EcoHealth Alliance, a U.S.-based scientific organization that, since 2002, has been awarded over $80 million in federal funding to research the risks of emerging infectious diseases.

Their research showed that the viruses most similar to SARS‑CoV‑2, the virus that caused the pandemic, circulate in bats that live roughly 1,000 miles away from Wuhan. Scientists from Dr. Shi’s team traveled repeatedly to Yunnan province to collect these viruses and had expanded their search to Southeast Asia. Bats in other parts of China have not been found to carry viruses that are as closely related to SARS-CoV-2.

When the Covid-19 outbreak was detected, Dr. Shi initially wondered if the novel coronavirus had come from her laboratory, saying she had never expected such an outbreak to occur in Wuhan.

The SARS‑CoV‑2 virus is exceptionally contagious and can jump from species to species like wildfire. Yet it left no known trace of infection at its source or anywhere along what would have been a thousand-mile journey before emerging in Wuhan.

The year before the outbreak, the Wuhan institute, working with U.S. partners, had proposed creating viruses with SARS‑CoV‑2’s defining feature

The laboratory pursued risky research that resulted in viruses becoming more infectious: Coronaviruses were grown from samples from infected animals and genetically reconstructed and recombined to create new viruses unknown in nature. These new viruses were passed through cells from bats, pigs, primates and humans and were used to infect civets and humanized mice (mice modified with human genes). In essence, this process forced these viruses to adapt to new host species, and the viruses with mutations that allowed them to thrive emerged as victors.

Worse still, as the pandemic raged, their American collaborators failed to publicly reveal the existence of the Defuse proposal. The president of EcoHealth, Peter Daszak, recently admitted to Congress that he doesn’t know about virus samples collected by the Wuhan institute after 2015 and never asked the lab’s scientists if they had started the work described in Defuse.

By 2019, Dr. Shi’s group had published a database describing more than 22,000 collected wildlife samples. But external access was shut off in the fall of 2019, and the database was not shared with American collaborators even after the pandemic started, when such a rich virus collection would have been most useful in tracking the origin of SARS‑CoV‑2. It remains unclear whether the Wuhan institute possessed a precursor of the pandemic virus.

In 2021, The Intercept published a leaked 2018 grant proposal for a research project named Defuse, which had been written as a collaboration between EcoHealth, the Wuhan institute and Ralph Baric at the University of North Carolina, who had been on the cutting edge of coronavirus research for years. The proposal described plans to create viruses strikingly similar to SARS‑CoV‑2.

Coronaviruses bear their name because their surface is studded with protein spikes, like a spiky crown, which they use to enter animal cells. The Defuse project proposed to search for and create SARS-like viruses carrying spikes with a unique feature: a furin cleavage site — the same feature that enhances SARS‑CoV‑2’s infectiousness in humans, making it capable of causing a pandemic. Defuse was never funded by the United States.

owever, in his testimony on Monday, Dr. Fauci explained that the Wuhan institute would not need to rely on U.S. funding to pursue research independently.

While it’s possible that the furin cleavage site could have evolved naturally (as seen in some distantly related coronaviruses), out of the hundreds of SARS-like viruses cataloged by scientists, SARS‑CoV‑2 is the only one known to possess a furin cleavage site in its spike. And the genetic data suggest that the virus had only recently gained the furin cleavage site before it started the pandemic.

Ultimately, a never-before-seen SARS-like virus with a newly introduced furin cleavage site, matching the description in the Wuhan institute’s Defuse proposal, caused an outbreak in Wuhan less than two years after the proposal was drafted.

When the Wuhan scientists published their seminal paper about Covid-19 as the pandemic roared to life in 2020, they did not mention the virus’s furin cleavage site — a feature they should have been on the lookout for, according to their own grant proposal, and a feature quickly recognized by other scientists.

At the Wuhan Institute of Virology, a team of scientists had been hunting for SARS-like viruses for over a decade, led by Shi Zhengl

In May, citing failures in EcoHealth’s monitoring of risky experiments conducted at the Wuhan lab, the Biden administration suspended all federal funding for the organization and Dr. Daszak, and initiated proceedings to bar them from receiving future grants. In his testimony on Monday, Dr. Fauci said that he supported the decision to suspend and bar EcoHealth.

Separately, Dr. Baric described the competitive dynamic between his research group and the institute when he told Congress that the Wuhan scientists would probably not have shared their most interesting newly discovered viruses with him. Documents and email correspondence between the institute and Dr. Baric are still being withheld from the public while their release is fiercely contested in litigation.

In the end, American partners very likely knew of only a fraction of the research done in Wuhan. According to U.S. intelligence sources, some of the institute’s virus research was classified or conducted with or on behalf of the Chinese military.

In the congressional hearing on Monday, Dr. Fauci repeatedly acknowledged the lack of visibility into experiments conducted at the Wuhan institute, saying, “None of us can know everything that’s going on in China, or in Wuhan, or what have you. And that’s the reason why — I say today, and I’ve said at the T.I.,” referring to his transcribed interview with the subcommittee, “I keep an open mind as to what the origin is.”

The Wuhan lab pursued this type of work under low biosafety conditions that could not have contained an airborne virus as infectious as SARS‑CoV‑2.

Labs working with live viruses generally operate at one of four biosafety levels (known in ascending order of stringency as BSL-1, 2, 3 and 4) that describe the work practices that are considered sufficiently safe depending on the characteristics of each pathogen. The Wuhan institute’s scientists worked with SARS-like viruses under inappropriately low biosafety conditions.

​​Biosafety levels are not internationally standardized, and some countries use more permissive protocols than others.

In one experiment, Dr. Shi’s group genetically engineered an unexpectedly deadly SARS-like virus (not closely related to SARS‑CoV‑2) that exhibited a 10,000-fold increase in the quantity of virus in the lungs and brains of humanized mice. Wuhan institute scientists handled these live viruses at low biosafety levels, including BSL-2.

Even the much more stringent containment at BSL-3 cannot fully prevent SARS‑CoV‑2 from escaping. Two years into the pandemic, the virus infected a scientist in a BSL-3 laboratory in Taiwan, which was, at the time, a zero-Covid country. The scientist had been vaccinated and was tested only after losing the sense of smell. By then, more than 100 close contacts had been exposed. Human error is a source of exposure even at the highest biosafety levels, and the risks are much greater for scientists working with infectious pathogens at low biosafety.

An early draft of the Defuse proposal stated that the Wuhan lab would do their virus work at BSL-2 to make it “highly cost-effective.” Dr. Baric added a note to the draft highlighting the importance of using BSL-3 to contain SARS-like viruses that could infect human cells, writing that “U.S. researchers will likely freak out.”

Years later, after SARS‑CoV‑2 had killed millions, Dr. Baric wrote to Dr. Daszak: “I have no doubt that they followed state determined rules and did the work under BSL-2. Yes China has the right to set their own policy. You believe this was appropriate containment if you want but don’t expect me to believe it. Moreover, don’t insult my intelligence by trying to feed me this load of BS.”

SARS‑CoV‑2 is a stealthy virus that transmits effectively through the air, causes a range of symptoms similar to those of other common respiratory diseases and can be spread by infected people before symptoms even appear. If the virus had escaped from a BSL-2 laboratory in 2019, the leak most likely would have gone undetected until too late.

One alarming detail — leaked to The Wall Street Journal and confirmed by current and former U.S. government officials — is that scientists on Dr. Shi’s team fell ill with Covid-like symptoms in the fall of 2019. One of the scientists had been named in the Defuse proposal as the person in charge of virus discovery work. The scientists denied having been sick.

The hypothesis that Covid-19 came from an animal at the Huanan Seafood Market in Wuhan is not supported by strong evidence.

In December 2019, Chinese investigators assumed the outbreak had started at a centrally located market frequented by thousands of visitors daily. This bias in their search for early cases meant that cases unlinked to or located far away from the market would very likely have been missed

To make things worse, the Chinese authorities blocked the reporting of early cases not linked to the market and, claiming biosafety precautions, ordered the destruction of patient samples on January 3, 2020, making it nearly impossible to see the complete picture of the earliest Covid-19 cases. Information about dozens of early cases from November and December 2019 remains inaccessible.

A pair of papers published in Science in 2022 made the best case for SARS‑CoV‑2 having emerged naturally from human-animal contact at the Wuhan market by focusing on a map of the early cases and asserting that the virus had jumped from animals into humans twice at the market in 2019

More recently, the two papers have been countered by other virologists and scientists who convincingly demonstrate that the available market evidence does not distinguish between a human superspreader event and a natural spillover at the market.

Furthermore, the existing genetic and early case data show that all known Covid-19 cases probably stem from a single introduction of SARS‑CoV‑2 into people, and the outbreak at the Wuhan market probably happened after the virus had already been circulating in humans.

Not a single infected animal has ever been confirmed at the market or in its supply chain. Without good evidence that the pandemic started at the Huanan Seafood Market, the fact that the virus emerged in Wuhan points squarely at its unique SARS-like virus laboratory.

With today’s technology, scientists can detect how respiratory viruses — including SARS, MERS and the flu — circulate in animals while making repeated attempts to jump across species. Thankfully, these variants usually fail to transmit well after crossing over to a new species and tend to die off after a small number of infections

investigators have not reported finding any animals infected with SARS‑CoV‑2 that had not been infected by humans. Yet, infected animal sources and other connective pieces of evidence were found for the earlier SARS and MERS outbreaks as quickly as within a few days, despite the less advanced viral forensic technologies of two decades ago.

Even though Wuhan is the home base of virus hunters with world-leading expertise in tracking novel SARS-like viruses, investigators have either failed to collect or report key evidence that would be expected if Covid-19 emerged from the wildlife trade. For example, investigators have not determined that the earliest known cases had exposure to intermediate host animals before falling ill.

No antibody evidence shows that animal traders in Wuhan are regularly exposed to SARS-like viruses, as would be expected in such situations.

In previous outbreaks of coronaviruses, scientists were able to demonstrate natural origin by collecting multiple pieces of evidence linking infected humans to infected animals

In contrast, virologists and other scientists agree that SARS‑CoV‑2 required little to no adaptation to spread rapidly in humans and other animals. The virus appears to have succeeded in causing a pandemic upon its only detected jump into humans.

it was a SARS-like coronavirus with a unique furin cleavage site that emerged in Wuhan, less than two years after scientists, sometimes working under inadequate biosafety conditions, proposed collecting and creating viruses of that same design.

a laboratory accident is the most parsimonious explanation of how the pandemic began.

Given what we now know, investigators should follow their strongest leads and subpoena all exchanges between the Wuhan scientists and their international partners, including unpublished research proposals, manuscripts, data and commercial orders. In particular, exchanges from 2018 and 2019 — the critical two years before the emergence of Covid-19 — are very likely to be illuminating (and require no cooperation from the Chinese government to acquire), yet they remain beyond the public’s view more than four years after the pandemic began.

it is undeniable that U.S. federal funding helped to build an unprecedented collection of SARS-like viruses at the Wuhan institute, as well as contributing to research that enhanced them.

Advocates and funders of the institute’s research, including Dr. Fauci, should cooperate with the investigation to help identify and close the loopholes that allowed such dangerous work to occur. The world must not continue to bear the intolerable risks of research with the potential to cause pandemics.

A successful investigation of the pandemic’s root cause would have the power to break a decades-long scientific impasse on pathogen research safety, determining how governments will spend billions of dollars to prevent future pandemics. A credible investigation would also deter future acts of negligence and deceit by demonstrating that it is indeed possible to be held accountable for causing a viral pandemic

Last but not least, people of all nations need to see their leaders — and especially, their scientists — heading the charge to find out what caused this world-shaking event. Restoring public trust in science and government leadership requires it.

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.