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They were able to reverse engineer the ancient climate by analyzing temperature-sensitive structures of lipids produced by fossil bacteria and archaea living in these bygone wetlands, and preserved for all time in the coal. The team found that, under this past regime of high CO2, in the ancient U.K., Germany, and New Zealand, life endured mean annual temperatures of 23–29 degrees Celsius (73–84 degrees Fahrenheit) or 10–15 degrees Celsius (18–27 degrees Fahrenheit) warmer than modern times.

“These wetlands looked exactly how only tropical wetlands look at present, like the Everglades or the Amazon,” Naafs says. “So Europe would look like the Everglades and a heat wave like we’re currently experiencing in Europe would be completely normal. That is, it would be the everyday climate.”

But over 50 million years ago this would have been the baseline from about 45 to 60 degrees latitude. Under this broiling regime, with unprecedented heat as the norm, actual heat waves might have begun to take on an unearthly quality.

closer to the equator in this global sweat lodge, the heat might have been even more outrageous, shattering the limits of complex life. To see exactly how hot, Naafs’ team also analyzed ancient lignite samples from India, which would have been in the tropics at the time—that subcontinent still drifting across the Indian Ocean toward its eventual mountain-raising rendezvous with Asia. But unfortunately, the temperatures from these samples were maxed out. That is, they were too hot for his team to measure by the new methods they had developed.

“Some climate models suggest that the tropics just became a dead zone with temperatures over 50 degrees Celsius (122 degrees Fahrenheit) like in Africa and South America,” says Naafs. “But we have no data so we don’t know.”

“Basically every type of paleoclimate research that’s being done shows that high CO2 means that it’s very warm. And when it gets very warm, it can be really, really, really warm.

“You start really looking into them and you go, ‘Wow. We are dealing with a rainforest.’”

“You’ve got alligators, giant tortoises, primates, things like that. We have these big hippo-like animals called Coryphodon. You have tapirs—so you’ve got tapirs living pretty close to the North Pole in the early Eocene, which today—clearly tapirs are not at the North Pole,” she says, laughing.

One obvious way to reconcile this disparity is by noticing that the changes to the ancient earth took place over hundreds-of-thousands to millions of years and (IPCC graphs notwithstanding) that time won’t stop at the end of the 21st century. The changes that we’ve already set in motion, unless we act rapidly to countervail them, will similarly take millennia to fully unfold

we’re clearly not content to stop at just 400 ppm. If we do, in fact, push CO2 up to around 1,000 ppm by the end of the century, the warming will persist and the earth will continue to change for what, to humans, is a practical eternity

Most worryingly, the climate models that we depend on as a species to predict our future have largely failed to predict our sultry ancient past.

we know methane can actually amplify high-latitude warming, so maybe that’s some of the missing feedback.

Though there are no trees here at the top of the world, there are tree stumps. And they are around 50 million years old.

Clearly we are missing something, and Naafs thinks that one of the missing ingredients in the models is methane, a powerful greenhouse gas which might help close the divide between model worlds and fossil worlds.

We know tropical wetlands pump much more methane into the atmosphere compared to [cooler] wetlands.

The last time CO2 was at 400 ppm (as it is today) was 3 million years ago during the Pliocene epoch, when sea levels were perhaps 80 feet higher than today.

Naafs thinks that many of the wildest features of the early age of mammals could be recreated.

COVID-19 testing has been an unmitigated failure in this country.

Rather than growing rapidly—as all experts think is absolutely necessary—the daily number of tests administered in some jurisdictions has even decreased. In New York, for instance, 10,241 tests were performed on April 6, but supply limits forced a huge drop a few days later to 25 total tests.

Quest Diagnostics, one of the two biggest firms that run tests, just furloughed 9 percent of its workforce. In addition, news reports suggest that, as of last week, 90 percent of the 15-minute tests developed by Abbott Laboratories are idle due to a lack of necessary reagents and qualified personnel

How many tests do we need in order to safely relax social-distancing measures, reopen nonessential businesses and schools, and allow large gatherings

we should be conducting a minimum of 500,000 tests a day.

Paul Romer, has called for the capacity to run 20 million to 30 million tests a day

Even this has been criticized as insufficient for the task of identifying enough of the asymptomatic spreaders to keep the pandemic in check.

Current guidelines from the Centers for Disease Control and Prevention give priority first to hospitalized patients and symptomatic health-care workers, then to high-risk patients

ptomatic individuals are not tested, even if they had contact with people who tested positive.

This is an enormous mistake. If we want to control the spread of COVID-19, the United States must adopt a new testing policy that prioritizes people who, although asymptomatic, may have the virus and infect many others.

We should target four groups. First, all health-care workers and other first responders who directly interact with many people

The fourth group would include all those who are planning to return to the workplace. These are precisely the individuals without symptoms whom the CDC recommends against testing.

The next group would be potential “super-spreaders”—asymptomatic individuals who could come into contact with many people. This third group would include people in large families and those who must interact with many vulnerable people, such as employees of long-term-care facilities

Second, workers who maintain our supply chains and crucial infrastructure, including grocery-store workers, police officers, public-transit workers, and sanitation personnel.

Not testing suspected COVID-19 patients will not harm those patients

Symptomatic patients should be tested only in the rare case where a positive test would meaningfully change what type of care is delivered.

To shift the focus of testing away from the sickest patients and toward the people most likely to spread the coronavirus, we will have to conduct millions of tests a day.

How can we close this gap between our needs and current capacity? We need a national strategy over the next 10 weeks, one that draws on the many strengths of our research system

We also need to encourage rapid adoption of the saliva test that now has an emergency approval from the FDA and expedite the approval of tests that require fewer reagents and staff.

Another promising pathway is to pool many tests and run them together. If a pooled sample tests negative, everyone in the pool is negative. If it is positive, the members of the pool can be tested individually

A more sophisticated version of this approach uses genetic “bar codes” that make it possible to trace back which of the many samples in a pool was the one that had RNA from the virus, without any retesting.

How can we get this testing capacity up and running? One idea is for Congress to award in the next stimulus bill, say, $150 million in unrestricted research funds to the first five universities that can process 10 million tests in a week or less

Another catalyst could be to subsidize businesses that agree to test all their employees as they return to work

When someone tests positive, officials should identify close contacts, find them, and test them. To do the tracing, we may need to hire 100,000 to 200,000 additional public-health workers.

This type of voluntary contact tracing is labor-intensive and requires some training, but it does not require highly specialized skills

If we adopt and follow a coherent plan, we can have a testing regime that keeps us safe without compromising our freedoms

Further analysis, using radiocarbon dating of charcoal or shell fragments within samples from 31 of the sites, reveal human occupation dating as far back as 10,850 years ago – shortly after the end of the last ice age, when cultivation of plants began independently in various regions of the world.

Among other discoveries, the researchers found phytoliths from maize that date as far back as 6,850 years ago.

with Germany now carrying out about 50,000 coronavirus tests per day. Britain’s chief medical officer Chris Whitty on Tuesday said the United Kingdom had “a lot to learn” from Germany, which had “got ahead” in mass testing.

European governments are mulling the introduction of what have been dubbed “immunity passports” to let individuals who have had the virus return to a more normal life.

German scientists caution that this stage of the response is still a long way off. There remain crucial questions over how long someone’s immunity might last and at what level, and there are concerns about tests producing false positives and insufficient capacity for widespread testing

there needs to be more validation of the test and there needs to be science on how long immunity lasts.”

“This is one of many tools to address coronavirus, but it may be a very important one,”

antibody testing studies could provide an indication of how many people have been infected and thus help governments assess the risk of easing restrictions.

Knowing the level of infection among the population and particular risk groups could also let the government determine whether it can be “a little bit more relaxed” during a potential second wave of infections

In Germany, studies already underway or planned over the coming months involve a sampling of roughly 100,000 people

One by the University of Bonn will study 1,000 people from the hard-hit town of Heinsberg. Another in Munich will repeat tests on the same sample group regularly to monitor the spread.

Merkel, who has been tight-lipped on Germany’s strategy for lifting restrictions, says she doesn’t want to raise the population’s hopes prematurely. “We would be a bad government if we weren’t thinking about an exit strategy,”

it might take two years for the population to achieve herd immunity — meaning that so many people have been infected that the number of those still susceptible is no longer enough to sustain further spread of the virus.

the number of people infected could be as much as 10 times greater than now thought.

“If you want to assess the real danger and the real mortality, you have to know the number of people who have had the disease,”

knowing who is immune could be “extremely effective,” he said, for instance by helping identify which front-line workers are immune and do not need protective gear.

Testing accuracy also remains a concern. Some tests are overly sensitive, reacting to other types of coronaviruses and producing false positives. Errors could be particularly problematic when testing those who have not shown symptoms

Another concern is that tests are most effective several weeks after infection. So the timing of wide-scale studies is important,

“The question is, if someone tests for the antibody, what does that mean? Immunity? Part immunity? For how long?” he said. “If someone asked me, ‘What can I do with the results?’ I wouldn’t be able to answer.”

City officials say it is too soon to relax some of the restrictions that have helped slow the spread of the virus, especially those around fitness classes.

Several epidemiologists agreed that caution was important.

Dr. Wafaa el-Sadr, an epidemiologist at the Mailman School of Public Health at Columbia University, said she was dubious of claims that reopening different businesses and activities had not resulted in more infection

ut two of the variants — B.1.1.7, which was first discovered in Britain and is highly transmissible; and B.1.526, which was first detected in New York City and shares characteristics with variants first found in South Africa and Brazil that seem more resistant to existing vaccines — appear to be increasing steadily in the samples that have been sequenced (a sample size, the city cautions, that is still very small and may not convey the full extent of the problem).

On top of that, Holmes says, the bat species carrying these viruses are common across most of Southeast Asia. "So imagine if you ran our experiment across the whole of Southeast Asia. You'd find an amazing diversity of coronaviruses," Holmes says. "And there's just an enormous number of them."

And depending on how you define a virus species, Holmes says, there are likely thousands of different coronaviruses all around the world. "We're only just starting to scratch the surface," he says. "The virusphere of coronaviruses is just immense."

This past year, Holmes and his colleagues trapped several hundred bats in a tiny section of the Yunnan province in southern China — an area about the size of Los Angeles International Airport. They took samples of the bats' saliva, urine and feces. Then they looked for coronavirus genes inside the samples. What they found surprised him.

In one area, they found that nearly 3% of people had been infected with an unknown coronavirus in the past few years. "

If you expand those findings to all parts of Southeast Asia where people are exposed to these bats, Daszak estimates that more than a million people are infected with unknown coronaviruses each year.

"It's happening every day," Daszak says. "I look at the spillover event a bit like rain or snow. These viruses are getting into and trickling across our populations."

But each one gives the virus the opportunity to adapt and spread more easily from person to person. Every once in a while, a contagious virus infects a person who finds their way to a dense city, such as Wuhan.

"Coronavirus pandemics are not a once in a hundred year event. "The next one could come at any time. It could come in 50 years or in 10 years. Or it could be next year."

Both cities have also banned celebrations of the upcoming Hindu festival of Holi, where people gather in large groups and smear colored powder on each other. Authorities have also expressed concern about another Hindu gathering, the Kumbh Mela, held in the northern Indian town of Haridwar. It is expected to attract tens of millions of pilgrims from across the country and the government is urging local officials to ramp up testing.

Meanwhile, India has relaxed eligibility rules for coronavirus vaccinations. Starting in April, anyone who is 45 years of age or older will be allowed to get a coronavirus shot. India began its massive immunization campaign in mid-January starting with healthcare and frontline workers, followed by elderly people and people above 45 years of age with certain health conditions. Calls to open up the vaccination program to all adults have been growing, especially as cases rise.

India's second wave comes one year after Prime Minister Narendra Modi announced a strict lockdown with barely four hours' notice that left poor migrant workers stranded and pushed the economy into a recession. Infections began climbing when the lockdown was lifted in June and reached a peak of nearly 100,000 cases a day last September. India has the third-highest coronavirus caseload in the world, after the United States and Brazil.

On Saturday, Britain reported eight cases of one of the variants found in Brazil, hours after the British authorities imposed a travel ban from Latin American countries and Portugal, which is linked to Brazil by its colonial history and by current travel and trade ties. Italy also suspended flights from Brazil, its health minister, Roberto Speranza, announced on Facebook.

The study reveals, for the first time, the genetic sequence of the virus that may have infected Mr. Trump and dozens of others, researchers said.

Viruses constantly mutate, picking up tiny, accidental alterations to their genetic material as they reproduce. Few mutations alter how a virus functions. But by comparing patterns of mutations across many genetic sequences, scientists can construct family trees of a virus, illuminating how it spreads.

The genomes believed by these researchers to be connected to the White House outbreak do not identify a recent geographic source, in part because they are unusual.

The results show that even weeks after it was identified, the White House outbreak would be better understood by sequencing samples of more people who were infected.

In a study released on Thursday, the C.D.C. cited genetic sequencing and intensive contact tracing that documented an super-spreading event at a high school retreat in Wisconsin.

But the Trump administration is not known to have conducted its own genetic analysis of people infected in the outbreak. The White House declined to respond to questions on genetic sequencing of Mr. Trump and the cluster of aides and officials who tested positive or became ill.

Scientists not involved in the research who reviewed the results agreed with the conclusion that the two samples sharing rare mutations strongly suggested they are part of the same outbreak.

“These genomes are probably going to be identical or nearly identical to the genome that infected the president,” said Michael Worobey, head of the department of ecology and evolutionary biology at the University of Arizona.

For months, the White House minimized the threat of the virus and eschewed basic safety precautions at official events, like wearing a mask or keeping people six feet apart.

At least 11 people who attended a Rose Garden celebration on Sept. 26 for Judge Barrett, which included an indoor event without masks, became infected with the coronavirus, including Mr. Trump.

The work is convincing, and it is the best way to piece together the progression of such an outbreak, said David Engelthaler, head of the infectious disease branch of the Translational Genomics Research Institute in Arizona, where he and colleagues have sequenced thousands of genomes to track the spread of the coronavirus, including devastating outbreaks at Native American reservations in the state.

The number who favor Trump’s re-election lags his 2016 vote share in each state. 

Biden’s leads are not insurmountable.

However, few voters are up for grabs.  In each of the four states, fewer than 10 percent are undecided or support a third-party candidate.  Plus, roughly equal majorities of Biden and Trump supporters, about 8 in 10, are extremely committed to their candidate. 

It’s tough to overstate how important women voters are to Biden.  They prefer him by 19 points in Michigan, 6 in Ohio, 12 in Pennsylvania, and 17 in Wisconsin. And he trounces Trump among suburban women:  Michigan +35 points, Ohio +18, Pennsylvania +29, and Wisconsin +21.

Trump is the choice among rural voters in each state -- by wide margins in Ohio (+27 points) and Pennsylvania (+21), and smaller spreads in Michigan (+11) and Wisconsin (+6).   In 2016, he won rural voters nationally by 25 points, according to Pew Research Center validated voter data.

White men without a college degree were an important constituency for Trump four years ago, and they are still big supporters.  He leads by double-digit margins among this group in all four states

“Trump is polling behind his 2016 support, but remains competitive across these crucial rust-belt states,” says Democratic pollster Chris Anderson, who conducts the Fox News survey with Republican Daron Shaw.

In the Michigan Senate race, incumbent Democrat Gary Peters has a 49-41 percent edge over Republican John James.  Three percent back a third-party candidate and five percent are undecided.

Seniors in Ohio (+6 points) and Pennsylvania (+1) favor Trump, while they pick Biden in Michigan (+13 points) and Wisconsin (+14). 

Voters under age 35 back the Democrat:  Michigan (by 34 points), Ohio (+17), Pennsylvania (+33), and Wisconsin (+25).

In Michigan, by 8 points, more Democrats support Biden (94 percent) than Republicans back Trump (86 percent).  The loyalty gap is 9 points in Wisconsin, with 96 percent of Democrats for Biden compared to 87 percent of Republicans for Trump. 

On the economy, more trust Trump to do a better job than Biden in Ohio (by 11 points), Pennsylvania (+5), and Wisconsin (+7), while the two tie in Michigan. 

By larger spreads, voters prefer Biden to handle coronavirus in all four states:  Michigan (by 17 points), Ohio (+6), Pennsylvania (+11), and Wisconsin (+13).

“The economy is still the key to success for Trump,” says Shaw. “Voters don’t rate him very favorably on handling the pandemic and that’s a big drag on his re-election chances right now.”

Biden is more popular than Trump.  The former vice president gets net positive favorable ratings

Trump’s ratings are net negative

Trump’s 2016 victory in Ohio was by a wider 8-point margin, which is higher than his current 3-point edge in the Buckeye State (45 percent Biden to 48 percent Trump).  That’s a reversal since last month, when Biden was ahead by 5 points in Ohio (50-45 percent). 

More voters disapprove than approve of President Trump’s job performance in Michigan, Pennsylvania, and Wisconsin.  In Ohio, they split: 50 approve vs. 49 disapprove.

Voters in each state give their governor better ratings than the president. 

Across these rust-belt states, most voters casting their ballot by mail support Biden (between 61-73 percent), while over half of those voting in person go for Trump (between 55-59 percent).

Conducted October 17-20, 2020 under the joint direction of Beacon Research (D) and Shaw & Company (R), these Fox News surveys include interviews with likely voters in Michigan (1,032), Ohio (1,018), Pennsylvania (1,045), and Wisconsin (1,037) randomly selected from statewide voter files, who spoke with live interviewers on landlines and cellphones.  In each state, the margin of sampling error is plus or minus 3 percentage points for the total sample of likely voters. 

The shortfall in testing isn’t just a problem for individual patients and their doctors. It is also holding back large-scale surveys of seemingly healthy populations, in workplaces and elsewhere, and scientific research into fundamental properties of the virus and the disease it causes.

there is an escalating need to test much larger groups repeatedly—to track the spread of the virus as restrictions ease—and to carry out population-based studies that will reveal more about how this virus behaves.

in determining whether an individual is safe to enter a workplace or school on a given morning. Ideally, for the later purposes, tests would be conducted swiftly and at high volume at the places where samples are taken

One such approach, still in development, would exploit the ability of the well-known bacterial gene-editing system known as CRISPR to recognize coronavirus RNA.

While the need for greatly expanded testing in the next phase of this pandemic is widely acknowledged, the United States has no coordinated plan for how to achieve it. The technical building blocks are in hand, but how to put them together is not yet clear. Moreover, major regulatory hurdles limit the use of the results from novel tests in patient care, especially in certain states such as New York. And the logistics of deploying enough personnel to track samples and deliver results are daunting. Because of the complexity and importance of such testing, a centralized program, run by a strong scientific leader and paid for with federal dollars, may be the only solution.

rmed with efficient and accurate tests to detect the virus (indicating active infection) and reliable tests to measure antibodies against it (implying prior exposure and possible immunity), public-health programs could paint an accurate picture of the current pandemic. Small and large businesses, schools, health-care facilities, and other organizations could track the outcomes of their attempts to restore normal activities, and scientists could answer key questions about viral transmission and host immunity.

decisive answers will come only from studying human beings who are exposed to the virus under real-life conditions. Such studies may be feasible only under circumstances in which natural transmission is occurring at significant rates, as it currently is. Therefore, if we are to get answers to the following questions, we must act now.

tudies to answer these questions require identifying enough people who have recovered, then testing them repeatedly for the appearance of a new infection. Such people are relatively easy to find. They include doctors and nurses in hospitals in hard-hit metropolitan areas such as New York City; staff and residents at nursing homes with high rates of infection; and crews of U.S. Navy ships that have experienced outbreaks of COVID-19.

identify asymptomatic infections. Following up on those cases will shed light on how many asymptomatic people ultimately develop symptoms; how long it takes for them to do so; whether asymptomatic people who ultimately develop symptoms have higher viral loads than those who don’t get sick; whether symptomatic and asymptomatic people have different immune responses; whether other, simpler procedures (such as tests for some chemical abnormality in the blood) might be used to screen for infection; and how large a contribution asymptomatic people make to the ongoing transmission of the virus.

Despite repeated warnings after prior epidemics about the likelihood of new ones caused by novel microbes, the United States and many other countries failed to respond efficiently to this one. Scientists might have detected the new coronavirus much earlier with the better tools for microbial surveillance that already exist; prevented the pathogen’s worldwide spread by more aggressive testing and contact tracing; and supported better and safer health care with larger stockpiles and pipelines for procurement of medical equipment. Humanity should never be this unprepared again.

There are three hundred and twenty-one testing locations in the city, and the system is so extensive that in June, when Beijing suffered an outbreak, Wuhan hospitals sent seventy-two staffers to the capital to help with tests.

When Wuhan was sealed, the strategy of isolation was replicated throughout the city. Housing compounds were closed and monitored by neighborhood committees, with residents going out only for necessities.

Toward the end of the first month, the guidelines were tightened further, until virtually all goods were delivered. On February 17th, Fang Fang wrote, “Everyone is now required to remain inside their homes at all times.”

Meanwhile, approximately ten thousand contact tracers were working in the city, in order to cut off chains of infection, and hospitals were developing large-scale testing systems. But isolation remained crucial: patients were isolated; suspected exposures were isolated; medical workers were isolated.

Zhang said the experience of working through the pandemic had left him calmer and more patient. He drove more carefully now; he wasn’t in such a rush.

I often asked Wuhan residents how they had been personally changed by the spring, and there was no standard response. Some expressed less trust in government information; others said they had increased faith in the national leadership.

Wuhan had most recently reported a locally transmitted symptomatic case on May 18th. It’s the most thoroughly tested city in China: at the end of May, in part to boost confidence, the government tried to test every resident, a total of eleven million.

I never met a cabdriver who had been swab-tested less than twice, and a couple had been tested five times. Most of the cabbies had no relatives or friends who had been infected; swabbing was simply required by the city and by their cab companies.

“I tend to take a charitable view of countries that are at the beginning stage of epidemics,” Jennifer Nuzzo, an epidemiologist at the Johns Hopkins Center for Health Security, told me, in a phone conversation. According to her, it’s unrealistic to expect that any country could have stopped this particular virus at its source. “I’ve always believed that this thing was going to spread,” she said

The physician who handled testing told me that, on average, his hospital still recorded one positive for every forty thousand exams. Most of these positives were repeat patients: after having been infected during the initial run of the virus, they recovered fully, and then for some reason, months later, showed evidence of the virus again. So far, most of the positives had been asymptomatic, and the physician saw no indication that the virus was spreading in the city.

In town, there were few propaganda signs about the epidemic, and Wuhan newspapers ran upbeat headlines every morning (Yangtze Daily, August 29th, front page: “STUDENTS DO NOT HAVE TO WEAR MASKS IN SCHOOLS”). Movie theatres were open; restaurants and bars had no seating restrictions. At the Hanyang Renxinghui Mall, I saw barefaced kids playing in what may have been one of the last fully functioning ball pits on earth, a sight that seemed worthy of other headlines (“CHILDREN DO NOT HAVE TO WEAR MASKS IN WUHAN BALL PITS”).

Across town, colleges and universities were in the process of bringing back more than a million students. Wuhan has the second-highest number of students of any city in China, after Guangzhou.

Wuhan memories remained fresh, and the materials of documentation were also close at hand. People sometimes handed over manuscripts, and they took out their phones and pulled up photographs and messages from January and February. But I wondered how much of this material would dissipate over time.

In town, I met two Chinese journalists in their twenties who were visiting from out of town. They had been posted during the period of the sealed city: back then, anybody sent to cover events in Wuhan had to stay for the long haul.

One was a director of streaming media whom I’ll call Han, and he had found that government-run outlets generally wanted footage that emphasized the victory over the disease, not the suffering of Wuhan residents. Han hoped that eventually he’d find other ways to use the material. “It will be in the hard drive,” he said, tapping his camera.

After that, Yin reported on a number of issues that couldn’t be published or completed, and she often talked with scientists and officials who didn’t want to say too much. “One person said, ‘Ten years later, if the climate has changed, I’ll tell you my story,’ ” Yin told me. “He knew that he would be judged by history.” She continued, “These people are inside the system, but they also know that they are inside history.”

Such fare is much more popular in Guangdong, in the far south. It’s possible that the disease arrived from somewhere else and then spread in the wet, cool conditions of the fish stalls. A few Wuhan residents told me that a considerable amount of their seafood comes from Guangdong, and they suggested that perhaps a southerner had unwittingly imported the disease,

When I spoke with scientists outside China, they weren’t focussed on the government’s early missteps

In time, we will learn more, but the delay is important to the Communist Party. It handles history the same way that it handles the pandemic—a period of isolation is crucial. Throughout the Communist era, there have been many moments of quarantined history: the Great Leap Forward, the Cultural Revolution, the massacre around Tiananmen Square. In every case, an initial silencing has been followed by sporadic outbreaks of leaked information. Wuhan will eventually follow the same pattern, but for the time being many memories will remain in the sealed city.

Wafaa El-Sadr, the director of ICAP, a global-health center at Columbia University, pointed out that Chinese scientists had quickly sequenced the virus’s genome, which was made available to researchers worldwide on January 11th. “I honestly think that they had a horrific situation in Wuhan and they were able to contain it,” she said. “There were mistakes early on, but they did act, and they shared fast.”

For much of El-Sadr’s career, she has worked on issues related to AIDS in the United States, Africa, and elsewhere. After years of research, scientists eventually came to the consensus that H.I.V. most likely started through the bushmeat trade—the first human was probably infected after coming into contact with a primate or primate meat.

El-Sadr views the coronavirus as another inevitable outcome of people’s encroachment on the natural world. “We are now living through two concomitant massive pandemics that are the result of spillover from animal to human hosts, the H.I.V. and the COVID pandemics,” she wrote to me, in an e-mail. “Never in history has humanity experienced something along this scale and scope.”

There’s a tendency to believe that we would know the source of the coronavirus if the Chinese had been more forthcoming, or if they hadn’t cleaned out the Huanan market before stalls and animals could be studied properly.

Yiwu He, the chief innovation officer at the University of Hong Kong, told me that the C.N.B.G. vaccine has already been given to a number of Chinese government officials, under an emergency-use approval granted by the authorities. “I know a few government officials personally, and they told me that they took the vaccine,” he said, in a phone conversation. He thought that the total number was probably around a hundred. “It’s middle-level officials,” he said. “Vice-ministers, mayors, vice-mayors.”

Daszak believes the virus probably circulated for weeks before the Wuhan outbreak, and he doubts that the city was the source. “There are bats in Wuhan, but it was the wrong time of year,” he told me. “It was winter, and bats are not out as much.”

His research has indicated that, across Southeast Asia, more than a million people each year are infected by bat coronaviruses. Some individuals trap, deal, or raise animals that might serve as intermediary hosts. “But generally it’s people who live near bat caves,”

Daszak said that he had always thought that such an outbreak was most likely to occur in Kunming or Guangzhou, southern cities that are close to many bat caves and that also have an intensive wildlife trade.

He thinks that Chinese scientists are probably now searching hospital freezers for lab samples of people who died of pneumonia shortly before the outbreak. “You would take those samples and look for the virus,” he said. “They’ll find something eventually. These things just don’t happen overnight; it requires a lot of work. We’ve seen this repeatedly with every disease. It turns out that it was already trickling through the population.”

Daszak is the president of EcoHealth Alliance, a nonprofit research organization based in New York. EcoHealth has become the target of conspiracy theorists, including some who claim that the virus was man-made. Daszak and many prominent virologists say that anything created in a lab would show clear signs of manipulation.

There’s also speculation that the outbreak started when researchers accidentally released a coronavirus they were studying at the Wuhan Institute of Virology. But there’s no evidence of a leak, or even that the institute has ever studied a virus that could cause a COVID-19 outbreak.

“Scientists in China are under incredible pressure to publish,” Daszak said. “It really drives openness and transparency.”

He has spent a good deal of time in Wuhan, and co-authored more than a dozen papers with Chinese colleagues. “If we had found a virus that infected human cells and spread within a cell culture, we would have put the information out there,” he said. “In sixteen years, I’ve never come across the slightest hint of subterfuge. They’ve never hidden data. I’ve never had a situation where one lab person tells me one thing and the other says something else. If you were doing things that you didn’t want people to know about, why would you invite foreigners into the lab?”

In April, President Trump told reporters that the U.S. should stop funding research connected to the Wuhan Institute of Virology. Shortly after Trump’s comments, the National Institutes of Health cancelled a $3.7-million grant to EcoHealth, which had been studying how bat coronaviruses are transmitted to people.

I asked Daszak why, if he has such faith in the openness of his Wuhan colleagues, the Chinese government has been so closed about other aspects of the outbreak. He said that science is one thing, and politics something else; he thinks that officials were embarrassed about the early mistakes, and in response they simply shut down all information.

At the beginning of July, China National Biotec Group, a subsidiary of a state-owned pharmaceutical company called Sinopharm, completed construction of a vaccine-manufacturing plant in Wuhan. The project began while the city was still sealed. “That’s the politically correct thing to do,” a Shanghai-based biotech entrepreneur told me. “To show the world that the heroic people of Wuhan have come back.”

But Peter Daszak, a British disease ecologist who has collaborated with the Wuhan Institute of Virology for sixteen years on research on bat coronaviruses, told me that it’s typical to fail to gather good data from the site of an initial outbreak. Once people get sick, local authorities inevitably focus on the public-health emergency. “You send in the human doctors, not the veterinarians,” he said, in a phone conversation. “And the doctors’ response is to clean out the market. They want to stop the infections.”

Pharmaceutical executives have also been expected to lead the way, like the construction manager who donned P.P.E. in order to escort his workers into the patient ward. “Every senior executive at Sinopharm and C.N.B.G. has been vaccinated,” He said. “Including the C.E.O. of Sinopharm, the chairman of the board, every vice-president—everyone.” The Chinese press has reported that vaccinations have also been administered to hundreds of thousands of citizens in high-risk areas around the world.

In the West, China’s image has been badly damaged by the pandemic and by other recent events. The country has tightened political crackdowns in Hong Kong and Xinjiang, and, in May, after Australia called for an investigation into the origins of the virus, China responded furiously, placing new tariffs and restrictions on Australian goods ranging from barley to beef.

But He believes that the situation is fluid. “All of these feelings can turn around quickly,” he told me. “I think that once China has a vaccine, and if they can help other countries, it can make a huge difference.”

There’s also a competitive element. “China wants to beat America,” He said. He believes that the C.N.B.G. vaccine will receive some level of approval for public use by the end of October. “Chinese officials are thinking that Donald Trump might approve a U.S. vaccine before the election,” he said. “So their goal is to have a vaccine approved before that.”

No matter how quickly the Chinese develop a vaccine, or how effectively they have handled the pandemic since January, it’s unlikely to make Westerners forget the mistakes and misinformation during the pandemic’s earliest phase.

Some of this is due to a cultural difference—the Chinese response to errors is often to look forward, not back. On January 31st, Fang Fang commented in her diary, “The Chinese people have never been fond of admitting their own mistakes, nor do they have a very strong sense of repentance.” It’s often hard for them to understand why this quality is so frustrating for Westerners. In this regard, the pandemic is truly a mirror—it doesn’t allow the Chinese to look out and see themselves through the eyes of others.

The pandemic illuminates both the weaknesses and the strengths of the Chinese system, as well as the relationship between the government and the people. They know each other well: officials never felt the need to tell citizens exactly what happened in Wuhan, but they understood that American-level casualties would have been shocking—given China’s population, the tally would have been more than a million and counting.

In order to avoid death on that scale, the government also knew that people would be willing to accept strict lockdowns and contribute their own efforts toward fighting the virus.

In turn, citizens were skilled at reading their government. People often held two apparently contradictory ideas: that the Party lied about some things but gave good guidance about others. More often than not, citizens could discern the difference. During the pandemic, it was striking that, when the Chinese indulged in conspiracy theories, these ideas rarely resulted in personally risky behavior, as they often did in the U.S.

Perhaps the Chinese have been inoculated by decades of censorship and misinformation: in such an environment, people develop strong instincts for self-preservation, and they don’t seem as disoriented by social media as many Americans are.

Early in the year, I corresponded by WeChat with a Wuhan pharmacist who worked in a hospital where many were infected. On February 26th, he expressed anger about the early coverup. “My personal opinion is that the government has always been careless and suppressed dissent,” he wrote. “Because of this, they lost a golden opportunity to control the virus.”

In Wuhan, we met a few times, and during one of our conversations I showed him what he had written in February. I asked what he would do now if he found himself in Li Wenliang’s position, aware of an outbreak of some unknown disease. Would he post a warning online? Contact a health official? Alert a journalist?The pharmacist thought for a moment. “I would tell my close friends in person,” he said. “But I wouldn’t put anything online. Nothing in writing.”

I asked if such an event would turn out differently now.“It would be the same,” he said. “It’s a problem with the system.”

He explained that, with an authoritarian government, local officials are afraid of alarming superiors, which makes them inclined to cover things up. But, once higher-level leaders finally grasp the truth, they can act quickly and effectively.

“Security and privacy have long been top companywide priorities at Twitter,” said Twitter spokeswoman Rebecca Hahn. She said that Zatko’s allegations appeared to be “riddled with inaccuracies” and that Zatko “now appears to be opportunistically seeking to inflict harm on Twitter, its customers, and its shareholders.” Hahn said that Twitter fired Zatko after 15 months “for poor performance and leadership.” Attorneys for Zatko confirmed he was fired but denied it was for performance or leadership.

the whistleblower document alleges the company prioritized user growth over reducing spam, though unwanted content made the user experience worse. Executives stood to win individual bonuses of as much as $10 million tied to increases in daily users, the complaint asserts, and nothing explicitly for cutting spam.

Chief executive Parag Agrawal was “lying” when he tweeted in May that the company was “strongly incentivized to detect and remove as much spam as we possibly can,” the complaint alleges.

Zatko described his decision to go public as an extension of his previous work exposing flaws in specific pieces of software and broader systemic failings in cybersecurity. He was hired at Twitter by former CEO Jack Dorsey in late 2020 after a major hack of the company’s systems.

“I felt ethically bound. This is not a light step to take,” said Zatko, who was fired by Agrawal in January. He declined to discuss what happened at Twitter, except to stand by the formal complaint. Under SEC whistleblower rules, he is entitled to legal protection against retaliation, as well as potential monetary rewards.

A person familiar with Zatko’s tenure said the company investigated Zatko’s security claims during his time there and concluded they were sensationalistic and without merit. Four people familiar with Twitter’s efforts to fight spam said the company deploys extensive manual and automated tools to both measure the extent of spam across the service and reduce it.

In 1998, Zatko had testified to Congress that the internet was so fragile that he and others could take it down with a half-hour of concentrated effort. He later served as the head of cyber grants at the Defense Advanced Research Projects Agency, the Pentagon innovation unit that had backed the internet’s invention.

Overall, Zatko wrote in a February analysis for the company attached as an exhibit to the SEC complaint, “Twitter is grossly negligent in several areas of information security. If these problems are not corrected, regulators, media and users of the platform will be shocked when they inevitably learn about Twitter’s severe lack of security basics.”

Zatko’s complaint says strong security should have been much more important to Twitter, which holds vast amounts of sensitive personal data about users. Twitter has the email addresses and phone numbers of many public figures, as well as dissidents who communicate over the service at great personal risk.

This month, an ex-Twitter employee was convicted of using his position at the company to spy on Saudi dissidents and government critics, passing their information to a close aide of Crown Prince Mohammed bin Salman in exchange for cash and gifts.

Zatko’s complaint says he believed the Indian government had forced Twitter to put one of its agents on the payroll, with access to user data at a time of intense protests in the country. The complaint said supporting information for that claim has gone to the National Security Division of the Justice Department and the Senate Select Committee on Intelligence. Another person familiar with the matter agreed that the employee was probably an agent.

“Take a tech platform that collects massive amounts of user data, combine it with what appears to be an incredibly weak security infrastructure and infuse it with foreign state actors with an agenda, and you’ve got a recipe for disaster,” Charles E. Grassley (R-Iowa), the top Republican on the Senate Judiciary Committee,

Many government leaders and other trusted voices use Twitter to spread important messages quickly, so a hijacked account could drive panic or violence. In 2013, a captured Associated Press handle falsely tweeted about explosions at the White House, sending the Dow Jones industrial average briefly plunging more than 140 points.

After a teenager managed to hijack the verified accounts of Obama, then-candidate Joe Biden, Musk and others in 2020, Twitter’s chief executive at the time, Jack Dorsey, asked Zatko to join him, saying that he could help the world by fixing Twitter’s security and improving the public conversation, Zatko asserts in the complaint.

The complaint — filed last month with the Securities and Exchange Commission and the Department of Justice, as well as the FTC — says thousands of employees still had wide-ranging and poorly tracked internal access to core company software, a situation that for years had led to embarrassing hacks, including the commandeering of accounts held by such high-profile users as Elon Musk and former presidents Barack Obama and Donald Trump.

But at Twitter Zatko encountered problems more widespread than he realized and leadership that didn’t act on his concerns, according to the complaint.

Twitter’s difficulties with weak security stretches back more than a decade before Zatko’s arrival at the company in November 2020. In a pair of 2009 incidents, hackers gained administrative control of the social network, allowing them to reset passwords and access user data. In the first, beginning around January of that year, hackers sent tweets from the accounts of high-profile users, including Fox News and Obama.

Several months later, a hacker was able to guess an employee’s administrative password after gaining access to similar passwords in their personal email account. That hacker was able to reset at least one user’s password and obtain private information about any Twitter user.

Twitter continued to suffer high-profile hacks and security violations, including in 2017, when a contract worker briefly took over Trump’s account, and in the 2020 hack, in which a Florida teen tricked Twitter employees and won access to verified accounts. Twitter then said it put additional safeguards in place.

This year, the Justice Department accused Twitter of asking users for their phone numbers in the name of increased security, then using the numbers for marketing. Twitter agreed to pay a $150 million fine for allegedly breaking the 2011 order, which barred the company from making misrepresentations about the security of personal data.

After Zatko joined the company, he found it had made little progress since the 2011 settlement, the complaint says. The complaint alleges that he was able to reduce the backlog of safety cases, including harassment and threats, from 1 million to 200,000, add staff and push to measure results.

But Zatko saw major gaps in what the company was doing to satisfy its obligations to the FTC, according to the complaint. In Zatko’s interpretation, according to the complaint, the 2011 order required Twitter to implement a Software Development Life Cycle program, a standard process for making sure new code is free of dangerous bugs. The complaint alleges that other employees had been telling the board and the FTC that they were making progress in rolling out that program to Twitter’s systems. But Zatko alleges that he discovered that it had been sent to only a tenth of the company’s projects, and even then treated as optional.

“If all of that is true, I don’t think there’s any doubt that there are order violations,” Vladeck, who is now a Georgetown Law professor, said in an interview. “It is possible that the kinds of problems that Twitter faced eleven years ago are still running through the company.”

“Agrawal’s Tweets and Twitter’s previous blog posts misleadingly imply that Twitter employs proactive, sophisticated systems to measure and block spam bots,” the complaint says. “The reality: mostly outdated, unmonitored, simple scripts plus overworked, inefficient, understaffed, and reactive human teams.”

One current and one former employee recalled that incident, when failures at two Twitter data centers drove concerns that the service could have collapsed for an extended period. “I wondered if the company would exist in a few days,” one of them said.

The current and former employees also agreed with the complaint’s assertion that past reports to various privacy regulators were “misleading at best.”

For example, they said the company implied that it had destroyed all data on users who asked, but the material had spread so widely inside Twitter’s networks, it was impossible to know for sure

As the head of security, Zatko says he also was in charge of a division that investigated users’ complaints about accounts, which meant that he oversaw the removal of some bots, according to the complaint. Spam bots — computer programs that tweet automatically — have long vexed Twitter. Unlike its social media counterparts, Twitter allows users to program bots to be used on its service: For example, the Twitter account @big_ben_clock is programmed to tweet “Bong Bong Bong” every hour in time with Big Ben in London. Twitter also allows people to create accounts without using their real identities, making it harder for the company to distinguish between authentic, duplicate and automated accounts.

In the complaint, Zatko alleges he could not get a straight answer when he sought what he viewed as an important data point: the prevalence of spam and bots across all of Twitter, not just among monetizable users.

Zatko cites a “sensitive source” who said Twitter was afraid to determine that number because it “would harm the image and valuation of the company.” He says the company’s tools for detecting spam are far less robust than implied in various statements.

The complaint also alleges that Zatko warned the board early in his tenure that overlapping outages in the company’s data centers could leave it unable to correctly restart its servers. That could have left the service down for months, or even have caused all of its data to be lost. That came close to happening in 2021, when an “impending catastrophic” crisis threatened the platform’s survival before engineers were able to save the day, the complaint says, without providing further details.

The four people familiar with Twitter’s spam and bot efforts said the engineering and integrity teams run software that samples thousands of tweets per day, and 100 accounts are sampled manually.

Some employees charged with executing the fight agreed that they had been short of staff. One said top executives showed “apathy” toward the issue.

Zatko’s complaint likewise depicts leadership dysfunction, starting with the CEO. Dorsey was largely absent during the pandemic, which made it hard for Zatko to get rulings on who should be in charge of what in areas of overlap and easier for rival executives to avoid collaborating, three current and former employees said.

For example, Zatko would encounter disinformation as part of his mandate to handle complaints, according to the complaint. To that end, he commissioned an outside report that found one of the disinformation teams had unfilled positions, yawning language deficiencies, and a lack of technical tools or the engineers to craft them. The authors said Twitter had no effective means of dealing with consistent spreaders of falsehoods.

Dorsey made little effort to integrate Zatko at the company, according to the three employees as well as two others familiar with the process who spoke on the condition of anonymity to describe sensitive dynamics. In 12 months, Zatko could manage only six one-on-one calls, all less than 30 minutes, with his direct boss Dorsey, who also served as CEO of payments company Square, now known as Block, according to the complaint. Zatko allegedly did almost all of the talking, and Dorsey said perhaps 50 words in the entire year to him. “A couple dozen text messages” rounded out their electronic communication, the complaint alleges.

Faced with such inertia, Zatko asserts that he was unable to solve some of the most serious issues, according to the complaint.

Some 30 percent of company laptops blocked automatic software updates carrying security fixes, and thousands of laptops had complete copies of Twitter’s source code, making them a rich target for hackers, it alleges.

A successful hacker takeover of one of those machines would have been able to sabotage the product with relative ease, because the engineers pushed out changes without being forced to test them first in a simulated environment, current and former employees said.

“It’s near-incredible that for something of that scale there would not be a development test environment separate from production and there would not be a more controlled source-code management process,” said Tony Sager, former chief operating officer at the cyberdefense wing of the National Security Agency, the Information Assurance divisio

Sager is currently senior vice president at the nonprofit Center for Internet Security, where he leads a consensus effort to establish best security practices.

The complaint says that about half of Twitter’s roughly 7,000 full-time employees had wide access to the company’s internal software and that access was not closely monitored, giving them the ability to tap into sensitive data and alter how the service worked. Three current and former employees agreed that these were issues.

“A best practice is that you should only be authorized to see and access what you need to do your job, and nothing else,” said former U.S. chief information security officer Gregory Touhill. “If half the company has access to and can make configuration changes to the production environment, that exposes the company and its customers to significant risk.”

The complaint says Dorsey never encouraged anyone to mislead the board about the shortcomings, but that others deliberately left out bad news.

When Dorsey left in November 2021, a difficult situation worsened under Agrawal, who had been responsible for security decisions as chief technology officer before Zatko’s hiring, the complaint says.

An unnamed executive had prepared a presentation for the new CEO’s first full board meeting, according to the complaint. Zatko’s complaint calls the presentation deeply misleading.

The presentation showed that 92 percent of employee computers had security software installed — without mentioning that those installations determined that a third of the machines were insecure, according to the complaint.

Another graphic implied a downward trend in the number of people with overly broad access, based on the small subset of people who had access to the highest administrative powers, known internally as “God mode.” That number was in the hundreds. But the number of people with broad access to core systems, which Zatko had called out as a big problem after joining, had actually grown slightly and remained in the thousands.

The presentation included only a subset of serious intrusions or other security incidents, from a total Zatko estimated as one per week, and it said that the uncontrolled internal access to core systems was responsible for just 7 percent of incidents, when Zatko calculated the real proportion as 60 percent.

Zatko stopped the material from being presented at the Dec. 9, 2021 meeting, the complaint said. But over his continued objections, Agrawal let it go to the board’s smaller Risk Committee a week later.

Agrawal didn’t respond to requests for comment. In an email to employees after publication of this article, obtained by The Post, he said that privacy and security continues to be a top priority for the company, and he added that the narrative is “riddled with inconsistences” and “presented without important context.”

On Jan. 4, Zatko reported internally that the Risk Committee meeting might have been fraudulent, which triggered an Audit Committee investigation.

Agarwal fired him two weeks later. But Zatko complied with the company’s request to spell out his concerns in writing, even without access to his work email and documents, according to the complaint.

Since Zatko’s departure, Twitter has plunged further into chaos with Musk’s takeover, which the two parties agreed to in May. The stock price has fallen, many employees have quit, and Agrawal has dismissed executives and frozen big projects.

Zatko said he hoped that by bringing new scrutiny and accountability, he could improve the company from the outside.

“I still believe that this is a tremendous platform, and there is huge value and huge risk, and I hope that looking back at this, the world will be a better place, in part because of this.”

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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