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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.

“Over 80 technical papers published since January 2022,” LeCun responded. “What about you?”

To which Musk posted: “That’s nothing, you’re going soft. Try harder!

At stake are the hearts and minds of AI experts—academic and otherwise—needed to usher in the technology

“Join xAI,” LeCun wrote, “if you can stand a boss who:– claims that what you are working on will be solved next year (no pressure).– claims that what you are working on will kill everyone and must be stopped or paused (yay, vacation for 6 months!).– claims to want a ‘maximally rigorous pursuit of the truth’ but spews crazy-ass conspiracy theories on his own social platform.”

Some read Musk’s “science” dig as dismissing the role research has played for a generation of AI experts. For years, the Metas and Googles of the world have hired the top minds in AI from universities, indulging their desires to keep a foot in both worlds by allowing them to release their research publicly, while also trying to deploy products. 

For an academic such as LeCun, published research, whether peer-reviewed or not, allowed ideas to flourish and reputations to be built, which in turn helped build stars in the system.

LeCun has been at Meta since 2013 while serving as an NYU professor since 2003. His tweets suggest he subscribes to the philosophy that one’s work needs to be published—put through the rigors of being shown to be correct and reproducible—to really be considered science. 

“If you do research and don’t publish, it’s not Science,” he posted in a lengthy tweet Tuesday rebutting Musk. “If you never published your research but somehow developed it into a product, you might die rich,” he concluded. “But you’ll still be a bit bitter and largely forgotten.” 

After pushback, he later clarified in another post: “What I *AM* saying is that science progresses through the collision of ideas, verification, analysis, reproduction, and improvements. If you don’t publish your research *in some way* your research will likely have no impact.”

The spat inspired debate throughout the scientific community. “What is science?” Nature, a scientific journal, asked in a headline about the dust-up.

Others, such as Palmer Luckey, a former Facebook executive and founder of Anduril Industries, a defense startup, took issue with LeCun’s definition of science. “The extreme arrogance and elitism is what people have a problem with,” he tweeted.

For Musk, who prides himself on his physics-based viewpoint and likes to tout how he once aspired to work at a particle accelerator in pursuit of the universe’s big questions, LeCun’s definition of science might sound too ivory-tower. 

Musk has blamed universities for helping promote what he sees as overly liberal thinking and other symptoms of what he calls the Woke Mind Virus. 

Over the years, an appeal of working for Musk has been the impression that his companies move quickly, filled with engineers attracted to tackling hard problems and seeing their ideas put into practice.

“I’ve teamed up with Elon to see if we can actually apply these new technologies to really make a dent in our understanding of the universe,” Igor Babuschkin, an AI expert who worked at OpenAI and Google’s DeepMind, said last year as part of announcing xAI’s mission. 

The creation of xAI quickly sent ripples through the AI labor market, with one rival complaining it was hard to compete for potential candidates attracted to Musk and his reputation for creating value

that was before xAI’s latest round raised billions of dollars, putting its valuation at $24 billion, kicking off a new recruiting drive. 

It was already a seller’s market for AI talent, with estimates that there might be only a couple hundred people out there qualified to deal with certain pressing challenges in the industry and that top candidates can easily earn compensation packages worth $1 million or more

Since the launch, Musk has been quick to criticize competitors for what he perceived as liberal biases in rival AI chatbots. His pitch of xAI being the anti-woke bastion seems to have worked to attract some like-minded engineers.

As for Musk’s final response to LeCun’s defense of research, he posted a meme featuring Pepé Le Pew that read: “my honest reaction.”

Those are revolutionary aspirations in that they reject not a policy consensus, but social and governing compacts that date to the Founding. (Or at least the end of the Civil War.)

Most revolutions are borne of dissatisfaction.

The Trumpian revolution, on the other hand, seems to be the product of decadent boredom commingled with casual nihilism.

Circumstances for our revolutionaries have never been better. They are so flush that they parade on their boats. And fly upside-down flags outside of their million-dollar suburban homes. And put stickers depicting a hogtied president on their $75,000 pickup trucks. All while posting angry memes to Facebook on their $1,000 iPhones.

Unlike normal revolutionaries, the Trumpist revolutionaries risk nothing. If their gambit succeeds, then they overturn the Constitutional order. And if it fails? They go back to their boats, and trucks, and good-paying jobs, and iPhones.

What’s more, this revolution has discovered that it gets as many bites at the apple as it likes. All defeats and setback are temporary. The movement lives to fight again. They can lose a dozen times—they only have to win once more.

Trumpist revolutionaries get to tell themselves that they are part of a historic, final battle—but also that if they lose, they get to keep their normal, pampered lives. And four years from now they can try again.

In sum: While the revolutionaries get to have their glamorous Götterdämmerung, over and over, the forces of the status quo have to defend against wave after wave of challenges. And it doesn’t matter how many authoritarian attempts are beaten back. There’s always another one looming.That is why you’re exhausted.

let’s be honest about human nature: Breaking things is fun. Especially when you don’t experience any consequences. But running around putting out fires, and cleaning up broken glass, and asking people to stop breaking things? That is not fun. It is enervating.

So while the revolutionary feels like a hero, you feel like a scold.

To paraphrase Mr. Cobb, once an idea has taken hold in society, it’s almost impossible to eradicate.

the Trumpist revolution’s weakness is that it has no ideas. It has goals, but these are motivated by nothing more than will-to-power. There is no logic—not even a faulty logic—behind them.7

How do we fight the exhaustion?First, we try to have some fun while we are scolding the twits and defending the imperfect status quo.Second, we remain fearless about the fight and clear eyed about reality.

Third, we organize and build communities to rally normal people to the cause of democracy.

MAGA has replaced Reaganism as the chief operating system of the Republican Party.

If Democrats hope to win in the near future they have to take the MAGA worldview seriously, and respectfully make the case, especially to working-class voters, for something better.

In a volatile world, MAGA offers people security. It promises secure borders and secure neighborhoods. It offers protection from globalization, from the creative destruction of modern capitalism. It offers protection from an educated class that looks down on you and indoctrinates your children in school. It offers you protection from corporate predators.

the problem with MAGA — and here is where the Democratic opportunity lies — is that it emerges from a mode of consciousness that is very different from the traditional American consciousness.

Americans have a zeal for continual self-improvement, a “need tirelessly to tinker, improve everything and everybody, never leave anything alone.”

“the Spirit of America is best known in Europe by one of its qualities — energy.”

we saw ourselves, as the dynamic nation par excellence. We didn’t have a common past, but we dreamed of a common futur

Americans can’t be secure if the world is in flames. That’s why America has to be active abroad in places like Ukraine, keeping wolves like Vladimir Putin at bay.

Through most of our history, we were not known for our profundity or culture but for living at full throttle.

MAGA, on the other hand, emerges from a scarcity consciousness, a zero-sum mentality: If we let in tons of immigrants they will take all our jobs; if America gets browner, “they” will replace “us.”

MAGA is based on a series of victim stories: The elites are out to screw us. Our allies are freeloading off us. Secular America is oppressing Christian America.

MAGA looks less like an American brand of conservatism and more like a European brand of conservatism. It resembles all those generations of Russian chauvinists who argued that the Russian masses embody all that is good but they are threatened by aliens from the outside

MAGA looks like a kind of right-wing Marxism, which assumes that class struggle is the permanent defining feature of politics.

MAGA is a fortress mentality, but America has traditionally been defined by a pioneering mentality. MAGA offers a strong shell, but not much in the way of wings needed to soar.

If Democrats are to thrive, they need to tap into America’s dynamic cultural roots and show how they can be applied to the 21st century

My favorite definition of dynamism is adapted from the psychologist John Bowlby: All of life is a series of daring explorations from a secure base. If Democrats are to thrive, they need to offer people a vision both of the secure base and of the daring explorations.

The American consciousness has traditionally been an abundance consciousness.

Americans can’t be secure if the border is in chaos. Popular support for continued immigration depends on a sense that the government has things under control.

Americans can’t be secure if a single setback will send people to the depths of crushing poverty. That’s why the social insurance programs that Democrats largely built are so important.

offer people a vision of the daring explorations that await them. That’s where the pessimistic post-Reagan Republicans can’t compete

champion the abundance agenda that people like Derek Thompson and my colleague Ezra Klein have been writing about. We need to build things. Lots of new homes. Supersonic airplanes and high-speed trains.

If Republicans are going to double down on class war rhetoric — elites versus masses — Democrats need to get out of that business

They need to stand up to protectionism, not join the stampede.

Democrats need to throttle back the regulators who have been given such free rein that they’ve stifled innovation.

Democrats need to take on their teachers’ unions and commit to dynamism in the field of education.

tap back into the more traditional American aspiration: We are not sentenced to a permanent class-riven future but can create a fluid, mobile society.

The economist Michael Strain of the American Enterprise Institute has offered a telling psychic critique of MAGA economic thinking: “The economics of grievance is ineffective, counterproductive and corrosive, eroding the foundations of prosperity. Messages matter. Tell people that the system is rigged, and they will aspire to less

Champion personal responsibility, and they will lift their aspirations. Promoting an optimistic vision of economic life can increase risk tolerance, ambition, effort and dynamism.”

t aspiration is not like a brick that just sits there. Aspiration is more like a flame that can be fed or dampened

“The problem is desire. We need to *want* these things. The problem is inertia. We need to want these things more than we want to prevent these things.”