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Some 87 percent of the meat the researchers collected contained either normal or antibiotic-resistant enterococcus, suggesting that most of the meat came in contact with fecal material at some point.

More stark was the proportion of microbes identified that were resistant. Of all the salmonella found on raw chicken pieces sampled in 2011, 74 percent were antibiotic-resistant, while less than 50 percent of the salmonella found on chicken tested in 2002 was of a superbug variety.

The planet has multiple morbidities, some of which will make this coronavirus look, by comparison, easy to treat

how will we feed ourselves?

A large body of evidence is beginning to accumulate showing how climate breakdown is likely to affect our food supply

In his forthcoming book, Our Final Warning, Mark Lynas explains what is likely to happen to our food supply with every extra degree of global heating. He finds that extreme danger kicks in somewhere between 3C and 4C above pre-industrial levels.

At this point, a series of interlocking impacts threatens to send food production into a death spiral. Outdoor temperatures become too high for humans to tolerate, making subsistence farming impossible across Africa and South Asia. Livestock die from heat stress. Temperatures start to exceed the lethal thresholds for crop plants across much of the world, and major food producing regions turn into dust bowls.

Simultaneous global harvest failure – something that has never happened in the modern world – becomes highly likely.

Every food-producing sector claims that its own current practices are sustainable and don’t need to change

even if every nation keeps its promises under the Paris agreement, which currently seems unlikely, global heating will amount to between 3C and 4C.

Thanks to our illusion of security, we are doing almost nothing to anticipate this catastrophe, let alone prevent it. This existential issue scarcely seems to impinge on our consciousness

A food deficit could result in billions starving. Hoarding will happen, as it always has, at the global level, as powerful people snatch food from the mouths of the poor.

But this is just one of our impending crises. Antibiotic resistance is, potentially, as deadly as any new disease. One of the causes is the astonishingly profligate way in which these precious medicines are used on many livestock farms

Pharmaceutical companies are failing to invest sufficiently in the search for new drugs. If antibiotics cease to be effective, surgery becomes almost impossible

Childbirth becomes a mortal hazard once more. Chemotherapy can no longer be safely practised. Infectious diseases we have comfortably forgotten become deadly threats. We should discuss this issue as often as we talk about football. But again, it scarcely registers.

Sunk costs within the fossil fuel industry, farming, banking, private healthcare and other sectors prevent the rapid transformations we need. Money becomes more important than life.

this could be the moment when we begin to see ourselves, once more, as governed by biology and physics, and dependent on a habitable planet. Never again should we listen to the liars and the deniers. Never again should we allow a comforting falsehood to trounce a painful truth. No longer can we afford to be dominated by those who put money ahead of life. This coronavirus reminds us that we belong to the material world.

Gloves and soap have vanished from some hospitals. Often, cancer medicines are found only on the black market. There is so little electricity that the government works only two days a week to save what energy is left.

At the University of the Andes Hospital in the mountain city of Mérida, there was not enough water to wash blood from the operating table. Doctors preparing for surgery cleaned their hands with bottles of seltzer water.

The hospital has no fully functioning X-ray or kidney dialysis machines because they broke long ago. And because there are no open beds, some patients lie on the floor in pools of their blood.

This nation has the largest oil reserves in the world, yet the government saved little money for hard times when oil prices were high.

So without water, gloves, soap or antibiotics, a group of surgeons prepared to remove an appendix that was about to burst, even though the operating room was still covered in another patient’s blood.

Ms. Parucho, a diabetic, was unable to receive kidney dialysis because the machines were broken. An infection had spread to her feet, which were black that night. She was going into septic shock.

In a supply room, cockroaches fled as the door swung open.

In April, the authorities arrested its director, Aquiles Martínez, and removed him from his post. Local news reports said he was accused of stealing equipment meant for the hospital, including machines to treat people with respiratory illnesses, as well as intravenous solutions and 127 boxes of medicine.

A holiday had been declared by the government to save electricity, and the blood bank took donations only on workdays.

For the past two and a half months, the hospital has not had a way to print X-rays. So patients must use a smartphone to take a picture of their scans and take them to the proper doctor.

Near him, a handwritten sign read, “We sell antibiotics — negotiable.” A black-market seller’s number was listed.

The ninth floor of the hospital is the maternity ward, where the seven babies had died the day before. A room at the end of the hall was filled with broken incubators.

The day of the power blackout, Dr. Rodríguez said, the hospital staff tried turning on the generator, but it did not work.

“why not go whole hog” (forgive me) “and advocate a strictly vegan diet?” Isn’t being a part-time vegan, the more strident demand, like being a little bit pregnant?

You might think of patterns of eating as falling on one point or another along a spectrum, and moving toward the plant-based end of that spectrum — as opposed to the end represented by Morgan Spurlock’s “super-sized” diet — is almost always beneficial.[7] It is about eating better, or well, not perfectly, and it must be said that “perfectly” has not yet been defined.

This much is known, now: We produce most animal products in deplorable conditions, and some of our health and environmental problems can be traced both to dominant production methods and our overconsumption. But we like to eat them, and they’re a pleasurable and even healthy part of many traditional diets and even sound agricultural practices.

The low-income, middle-income and high-income health systems have extremely different problems. You know, in low-income countries, getting to a health post is hard. It’s very expensive. Whereas in rich countries, yes, you can get to your doctor. In low-income countries, the main problems you have is infectious diseases. We’re dealing with countries that in the worst case where kids have death rates of 20 percent and that’s all infectious disease. And nothing else. In the U.S., in terms of kids under 5, other than premature birth, you really don’t have big problems. Kids just don’t die of infectious disease.

in the U.S., what do people die of? From age 5 till age 50, you’ve got suicide, you’ve got traffic accidents. There’s very little cancer and heart disease before age 50.

what’s a year of life worth? They call it a disability-adjusted life year (DALY). When you’re running a poor country health-care system, you can’t treat a year of life as being worth more than, say, $200, $300 or else you’ll bankrupt your health system immediately. So, with very few exceptions, you do nothing for cancer. If you get cancer, you’re going to die. And so none of the stuff that’s going on in the U.S. about $300,000 a year chemotherapy drugs is relevant.

If you spend the less than 2 percent of what the rich countries spend, but you spend it on vaccinations and antibiotics, you get over half of all that healthcare does to extend life. So you spend 2 percent and you get 50 percent. If you spend another 80 percent you’re at over 90 percent.

in rich-world health, innovation is both your friend and your enemy. Innovation is inventing organ replacement, joint replacement. We’re inventing ways of doing new things that cost $300,000 and take people in their 70s and, on average, give them an extra, say, two or three years of life. And then you have to say, given finite resources, should we fire two or three teachers to do this operation? And with chemotherapies, we’ve got things where we’ll spend our dollars on treatments where you’re valuing a life here at over $10 to $20 million. Really big, big numbers, which if you were infinitely rich, of course that would be fine. So most innovations, unfortunately, actually increase the net costs of the healthcare system. There’s a few, particularly having to do with chronic diseases, that are an exception. If you could cure Alzheimer’s, if you could avoid diabetes — those are gigantic in terms of saving money. But the incentive regime doesn’t favor them.

We’re very uncomfortable putting a value on human life. The way I see our health system is we’ve chosen to pay a huge premium in order to avoid these questions. A prerequisite for the kind of cost-cutting innovations you’re talking about it is being willing to make judgments about what a human life is worth, or even what a few months of a human life are worth. Because if you can’t decide that, then of course you just pay for everything. But if you start trying to make those choices, or even get people to think about those choices, people cry “death panels!”

BG. Yes, someone in the society has to deal with the reality that there are finite resources and we’re making trade-offs, and be explicit about that. When the car companies were found to have a memo that actually said, “This safety feature costs X and saved Y lives,” the very existence of that memo was considered damning. It was “Oh, you think human life is only a bank account.” Or when you made it reimbursable for a doctor to ask, “Do you want heroic care at the end-of-life,” that was a death panel. No, it wasn’t a death panel! It was asking somebody to make a decision.

So equal opportunities might mean a meritocracy. A society in which the best people succeed. Sort of like what we see in the world of distance running.

It turns out that the genetic predispositions for successful long-distance running are much more prevalent in Kenya than in Kansas or Korea. And as far as that goes, it’s fine. Distance running isn’t that big a deal. But would we want all of society to look like that?

The concept necessarily veers between opportunity-as-randomness and opportunity-as-hereditary-aristocracy, and neither comes across as particularly appealing.

, an opportunity to climb is no real answer for people at the bottom. A perfectly fair race is, in at least one important way, the same as a rigged race: Both have a first-place finisher and a last-place finisher

The question of what happens to the person at the bottom genuinely matters. Whether you want to phrase that in terms of the gap between the bottom and the top—inequality, as such—or simply look at the absolute condition of the people at the bottom, you can’t escape the conclusion that outcomes matter, and not just in terms of procedural fairness

Today, even poor people are able to take advantage of things like electricity and antibiotics that were rare or nonexistent 100 years ago. That’s the kind of opportunity that matters—the opportunity for everyone to enjoy a better life. But over the past generation, progress has been slow for the nonrich. And over the past 10 years, it’s been essentially absent.

three different factors that might be making harder for adults today to stay thin.

First, people are exposed to more chemicals that might be weight-gain inducing. Pesticides, flame retardants, and the substances in food packaging might all be altering our hormonal processes and tweaking the way our bodies put on and maintain weight.

Second, the use of prescription drugs has risen dramatically since the ‘70s and ‘80s. Prozac, the first blockbuster SSRI, came out in 1988. Antidepressants are now one of the most commonly prescribed drugs in the U.S., and many of them have been linked to weight gain.

the microbiomes of Americans might have somehow changed between the 1980s and now. It’s well known that some types of gut bacteria make a person more prone to weight gain and obesity. Americans are eating more meat than they were a few decades ago, and many animal products are treated with hormones and antibiotics in order to promote growth. All that meat might be changing gut bacteria in ways that are subtle, at first, but add up over time.

Kuk believes the proliferation of artificial sweeteners could also be playing a role.

“There's a huge weight bias against people with obesity,” she said. “They're judged as lazy and self-indulgent. That's really not the case

If our research is correct, you need to eat even less and exercise even more” just to be same weight as your parents were at your age.

I’m sorry, too, that more progress hasn’t been made in reducing the marketing of junk food to kids and on limiting the ease with which children can buy soda

Here are the things you don’t think about when you’re watching something like “The Forsyte Saga.” The choking air pollution from all the coal fires. The atrophy of a woman’s stomach and chest muscles from years of relying on a corset for shape and posture. The fact that a country that saw its population almost triple within a few decades had no real sewage system, which meant that by 1858, the Thames was overflowing with human waste.

the potato blight meant that huge numbers of English citizens were also starving. Goodman notes that the poor were markedly shorter than the wealthy, and several inches shorter than the average Londoner today. “It takes a lot of hunger to do that to people,” she adds. But even when money wasn’t an issue, self-abnegation was. In many homes children were sent to bed without dinner not as a punishmen

Before antibiotics, and with the new crowding and population explosion brought on by the Industrial Revolution, cholera, measles, diphtheria, whooping cough, tuberculosis and typhoid were looming threats. And with no regulation of the advertising industry, manufacturers could claim pretty much anything.

drugs that did nothing might have been preferable to the “tonics” that did work, which often contained laudanum or mercury. It was speculated that as much as a third of the infant mortality rate in Manchester had to do not with disease but with drugging children. Popular tonics like Mrs. Winslow’s Soothing Syrup and Street’s Infant Quietness did indeed stop children from being pesky, as they were filled with opiates. Unfortunately they also stopped children from wanting to eat,

How will you and the CDC help ensure a well-executed program?

Right, and one of the things that will help tuberculosis control is the test and treat approach, and increasing the proportion of HIV-positive people that are treated. The majority of TB cases currently occur before people are started on anti-HIV medicines.

Partly it's the characteristic of the bacteria: You require long-term treatment for many months, we don't have a vaccine as we do with measles or polio, but partly it's the nature of the control program.

Fundamentally, what happens with tuberculosis will depend on two things. First, how well we implement what we know today, and second, how quickly we get better tools to stop tuberculosis.

If we could figure out which of them are going to develop active TB and provide shorter, more effective treatments for them, then we might really be able to knock down tuberculosis cases.

Such an approach gives a clear picture of a vaccine's effectiveness without having to immunise thousands of people. It showed the vaccine was up to 87% effective.

Before antibiotics, typhoid killed one-in-five people infected. Now there is growing levels of typhoid that is resistant to drugs.

There is intriguing neuroscience research that suggests that it may be possible to boost the efficacy of psychotherapy with drugs that share some of ketamine’s effects, somewhat the way athletes can enhance their performance with steroids

How might this work? D-cycloserine is an antibiotic that, like ketamine, increases the activity of glutamate in key brain regions, which promotes connections between neurons. It seems to amp up the molecular machinery of learning. And psychotherapy is all about learning — to overcome fear and to better handle stress, among other lessons.

For decades, psychoanalysts were against medicating anxiety at all, because they believed it would interfere with the therapeutic process. Fortunately, those days are largely over. Many are now comfortable giving patients anti-anxiety benzodiazepines like Klonopin if they are having a hard time grappling with issues that arise in therapy.

If farmers must grow twice as much food to feed the 10 billion, following the ecosystem-conserving rules of Sir Albert Howard ties their hands.

Prophets smite their brows at this logic. To their minds, evaluating farm systems wholly in terms of calories per acre is folly. It doesn’t include the sort of costs identified by Vogt: fertilizer runoff, watershed degradation, soil erosion and compaction, and pesticide and antibiotic overuse.

To Borlaugians, farming is a kind of useful drudgery that should be eased and reduced as much as possible to maximize individual liberty. To Vogtians, agriculture is about maintaining a set of communities, ecological and human, that have cradled life since the first agricultural revolution, 10,000-plus years ago. It can be drudgery, but it is also work that reinforces the human connection to the Earth. The two arguments are like skew lines, not on the same plane.

Preparation is the key to a good plan.

Hopefully, you've been following standard hygiene practices. These are behaviors we should be doing daily, automatically, to protect ourselves from germs, colds and flu:

Avoid touching your eyes, nose and mouth with unwashed hands; cough and sneeze into elbows or tissues that you immediately throw away, and regularly wash, wash, wash those hands with warm water and lots of soapy bubbles.

Parents and guardians should plan well in advance by setting up a structure in which all kids and potential caregivers know their roles and expectations, said pediatrician Dr. Tanya Altmann, Editor-in-Chief of the American Academy of Pediatrics' book "Caring for Your Baby and Young Child: Birth to Age 5 and The Wonder Years."

A working thermometer to monitor fever, which is considered to be 100 degrees Fahrenheit (37.7 degrees Celsius), and a method to clean it, such as Isopropyl alcohol.

Fever reducing medications, such as acetaminophen.

Regular soap and 70% alcohol-based hand sanitizer (antibacterial soap isn't necessary if you wash properly, and that way you won't will contribute to the world's growing antibiotic-resistant superbugs).

Tissues to cover sneezes and coughs. But there's really no need to hoard toilet paper -- this is a respiratory disease.

Regular cleaning supplies, kitchen cleaning gloves and trash can liners.

Disinfectant cleaning supplies -- the CDC suggests picking from a list that meets the virus-fighting standards of the US Environmental Protection Agency, but says you can also make your own version by using 1/3 cup unexpired bleach per gallon of water or 4 teaspoons bleach per quart of water. Never mix bleach with ammonia or any other cleanser -- it produces toxic gases.

If you live alone, that's not difficult. Your challenge is to monitor your symptoms and care for yourself when you're not feeling well. Be sure to have a plan in place to deliver food and medications, and find someone who can be responsible for virtually checking in on you on a regular basis.

The rest of the family should practice isolation as well, Radesky added.

If you are running short on face masks, or don't have any because of hoarding, try to protect the caregiver as best you can, the CDC says.

Altmann stresses maximizing isolation and protective actions.

"You can have a healthy person leave the sick one food and drinks at the door, and then go wash their hands," Altmann explained. "Wear gloves to pick up the empty plates, take them back to the kitchen and wash them in hot water with soap, or preferably with a dishwasher, and wash your hands again."

One last, very important thing: Call 911 immediately if you or your loved ones have any of these symptoms: increased or sudden difficulty breathing or shortness of breath; a persistent pain or pressure in the chest; and any sign of oxygen deprivation, such as new confusion, bluish lips or face, or you can't arouse the sick person.

To be clear: Everyone in the house needs to isolate themselves from the outside world as much as possible.

what happens when an extraordinarily rich society can’t find enough new ideas that justify investing all its stockpiled wealth. We inflate bubbles and then pop them, invest in Theranos and then repent, and the supposed cutting edge of capitalism is increasingly defined by technologies that have almost arrived, business models that are on their way to profitability, by runways that go on and on without the plane achieving takeoff.

what this tells us, unfortunately, is that 21st-century growth and innovation are not at all that we were promised they would be.

slowly compounding growth is not the same as dynamism. American entrepreneurship has been declining since the 1970s: Early in the Jimmy Carter presidency, 17 percent of all United States businesses had been founded in the previous year; by the start of Barack Obama’s second term, that rate was about 10 percent. In the late 1980s, almost half of United States companies were “young,” meaning less than five years old; by the Great Recession, that share was down to only 39 percent, and the share of “old” firms (founded more than 15 years ago) rose from 22 percent to 34 percent over a similar period

From World War II through the 1980s, according to a recent report from Senator Marco Rubio’s office, private domestic investment often approached 10 percent of G.D.P.; in 2019, despite a corporate tax cut intended to get money off the sidelines, the investment-to-G.D.P. ratio was less than half of that.

This suggests that the people with the most experience starting businesses look around at their investment opportunities and see many more start-ups that resemble Theranos than resemble Amazon, let alone the behemoths of the old economy.

the dearth of corporate investment also means that the steady climb of the stock market has boosted the wealth of a rentier class — basically, already-rich investors getting richer off dividends — rather than reflecting surging prosperity in general.

In 2017 a group of economists published a paper asking, “Are Ideas Getting Harder to Find?” The answer was a clear yes: “We present a wide range of evidence from various industries, products, and firms showing that research effort is rising substantially while research productivity is declining sharply.”

In his 2011 book “The Great Stagnation,” Tyler Cowen cited an analysis from the Pentagon physicist Jonathan Huebner, who modeled an innovations-to-population ratio for the last 600 years: It shows a slowly ascending arc through the late 19th century, when major inventions were rather easy to conceive and adopt, and a steepening decline ever since, as rich countries spend more and more on research to diminishing returns.

the trends reveal a slowdown, a mounting difficulty in achieving breakthroughs — a bottleneck if you’re optimistic, a ceiling if you aren’t

the relative exception, the internet and all its wonders, highlights the general pattern.

The Northwestern University economist Robert Gordon, one of the most persuasive theorists of stagnation, points out that the period from 1840 to 1970 featured dramatic growth and innovation across multiple arenas — energy and transportation and medicine and agriculture and communication and the built environment.

in the last two generations, progress has become increasingly monodimensional — all tech and nothing else.

We used to travel faster, build bigger, live longer; now we communicate faster, chatter more, snap more selfies.

Take a single one of the great breakthroughs of the industrial age — planes and trains and automobiles, antibiotics and indoor plumbing — and it still looms larger in our everyday existence than all of the contributions of the tech revolution combined.

With this stagnation comes social torpor. America is a more peaceable country than it was in 1970 or 1990, with lower crime rates and safer streets and better-behaved kids

it’s also a country where that supposedly most American of qualities, wanderlust, has markedly declined: Americans no longer “go west” (or east or north or south) in search of opportunity the way they did 50 years ago; the rate at which people move between states has fallen from 3.5 percent in the early 1970s to 1.4 percent in 2010. Nor do Americans change jobs as often as they once did.

Meanwhile, those well-behaved young people are more depressed than prior cohorts, less likely to drive drunk or get pregnant but more tempted toward self-harm

once we crossed over into permanent below-replacement territory, the birth dearth began undercutting the very forces (youth, risk -taking, dynamism) necessary for continued growth, meaning that any further gains to individual welfare are coming at the future’s expense.

then there is the opioid epidemic, whose spread across the unhappiest parts of white America passed almost unnoticed in elite circles for a while because the drug itself quiets rather than inflames, supplying a gentle euphoria that lets its users simply slip away, day by day and bit by bit, without causing anyone any trouble

In the land of the lotus eaters, people are also less likely to invest in the future in the most literal of ways. The United States birthrate was once an outlier among developed countries, but since the Great Recession, it has descended rapidly, converging with the wealthy world’s general below-replacement norm.

This demographic decline worsens economic stagnation; economists reckoning with its impact keep finding stark effects. A 2016 analysis found that a 10 percent increase in the fraction of the population over 60 decreased the growth rate of states’ per capita G.D.P. by 5.5 percent.

This doesn’t excuse the grifting or the rage stoking, especially presidential grifting and rage stoking, and it doesn’t make the mass shootings, when they come, any less horrific. But it’s important context for thinking about whether online politics is really carrying our society downward into civil strife

This feedback loop — in which sterility feeds stagnation, which further discourages childbearing, which sinks society ever-deeper into old age — makes demographic decline a clear example of how decadence overtakes a civilization

Both populism and socialism, Trump and Bernie Sanders, represent expressions of discontent with decadence, rebellions against the technocratic management of stagnation that defined the Obama era.

in practical terms the populist era has mostly delivered a new and deeper stalemate. From Trump’s Washington to the capitals of Europe, Western politics is now polarized between anti-establishment forces that are unprepared to competently govern and an establishment that’s too disliked to effectively rule.

The hysteria with which we’re experiencing them may represent nothing more than the way that a decadent society manages its political passions, by encouraging people to playact extremism, to re-enact the 1930s or 1968 on social media, to approach radical politics as a sport, a hobby, a kick to the body chemistry, that doesn’t put anything in their relatively comfortable late-modern lives at risk.

The terrorist in 21st-century America isn’t the guy who sees more deeply than the rest; he’s the guy who doesn’t get it, who takes the stuff he reads on the internet literally in a way that most of the people posting don’t

For adults, the increasingly legal drug of choice is marijuana, whose prototypical user is a relaxed and harmless figure — comfortably numb, experiencing stagnation as a chill good time.

It suggests that the virtual realm might make our battles more ferocious but also more performative and empty; and that online rage is a steam-venting technology for a society that is misgoverned, stagnant and yet, ultimately, far more stable than it looks on Twitter

in the real world, it’s possible that Western society is leaning back in an easy chair, hooked up to a drip of something soothing, playing and replaying an ideological greatest-hits tape from its wild and crazy youth.

Human beings can still live vigorously amid a general stagnation, be fruitful amid sterility, be creative amid repetition. And the decadent society, unlike the full dystopia, allows those signs of contradictions to exist

The last hundred-odd years of Western history offer plenty of examples of how the attempt to throw off decadence can bring in far worse evils, from the craving for Meaning and Action that piled corpses at Verdun and Passchendaele, to the nostalgic yearning for the Cold War that inspired post-9/11 crusading and led to a military quagmire in the Middle East.

So you can even build a case for decadence, not as a falling-off or disappointing end, but as a healthy balance between the misery of poverty and the dangers of growth for growth’s sake

A sustainable decadence, if you will, in which the crucial task for 21st-century humanity would be making the most of a prosperous stagnation: learning to temper our expectations and live within limits; making sure existing resources are distributed more justly; using education to lift people into the sunlit uplands of the creative class; and doing everything we can to help poorer countries transition successfully into our current position

this argument carries you only so far. Even if the dystopia never quite arrives, the longer a period of stagnation continues, the narrower the space for fecundity and piety, memory and invention, creativity and daring.

So decadence must be critiqued and resisted

by the hope that where there’s stability, there also might eventually be renewal,

The next renaissance will be necessarily different, but realism about our own situation should make us more inclined, not less, to look and hope for one — for the day when our culture feels more fruitful, our politics less futile and the frontiers that seem closed today are opened once again.

According to Cirium, an aviation-industry consulting firm, more than 200,000 flights in and out of China have been canceled, a 60 percent decline. In 2003, in the midst of SARS, global air travel was down 25 percent.

Fear dissipates eventually, replaced by a more realistic sense of the risks. An epidemic, even one of a disease as seemingly easy to transmit as COVID-19, while burdening public-health systems and potentially deadly for the elderly and those with compromised immune systems, is eminently survivable by the majority of the population

Which brings us to the last stage of epidemic grief: rational response. After denial, panic, and fear, we can finally get down to the business of basic sanitary measures and infection protocols

there are only four things you need to know about a virus: “What is it? What does it do? Where does it come from? And how do you kill it?”

We never did develop a vaccine. With SARS, infections peaked sometime in May 2003, at about 9,000 cases. By then, the daily rate of new infections had dipped below the number declared cured or dead

That is the inflection point of any outbreak, the point at which the worst is over.

Why did the rate of transmission slow? Part of the answer is seasonality: The Northern Hemisphere’s virus season tends to run from winter to mid-spring, perhaps because people aren’t clustered indoors and so are less contagious, or because viruses might weaken in lower relative humidity or direct sunlight. (Nobody actually knows.)

In the antibiotic era, infection control has been largely delegated to IV drips rather than sanitary cordons. With respiratory diseases like SARS, MERS, and COVID-19, 19th-century medical techniques and equipment—masks, gloves, galoshes, sealed wards, quarantines, and ventilation—are what comprise a rational response.

Security concerns, moreover, keep most information about stockpile purchases under wraps. Details about the contracts and inventory are rarely made public, and even the storage locations are secret.

Former Emergent employees, government contractors, members of Congress, biodefense experts and current and former officials from agencies that oversee the stockpile described a deeply dysfunctional system that contributed to the shocking shortages last year.

Purchases are supposed to be based on careful assessments by government officials of how best to save lives, but many have also been influenced by Emergent’s bottom line

The stockpile has long been the company’s biggest and most reliable customer for its anthrax vaccines, which expire and need to be replaced every few years.

In the two decades since the repository was created, Emergent’s aggressive tactics, broad political connections and penchant for undercutting competitors have given it remarkable sway over the government’s purchasing decisions related to the vaccines

While national security officials still consider anthrax a threat, it has not received specific mention since 2012 in the intelligence community’s annual public assessment of dangers facing the country, a report that has repeatedly warned of pandemics.

Emergent bought the license for the country’s only approved anthrax vaccine in 1998 from the State of Michigan. Over time, the price per dose the government agreed to pay Emergent increased nearly sixfold, accounting for inflation, contributing to record revenues last year that topped $1.5 billion

The company, whose board is stocked with former federal officials, has deployed a lobbying budget more typical of some big pharmaceutical companies

Competing efforts to develop a better and cheaper anthrax vaccine, for example, collapsed after Emergent outmaneuvered its rivals, the documents and interviews show.

preparations for an outbreak like Covid-19 almost always took a back seat to Emergent’s anthrax vaccines

the government approved a plan in 2015 to buy tens of millions of N95 respirators — lifesaving equipment for medical workers that has been in short supply because of Covid-19 — but the masks repeatedly lost out in the competition for funding over the years leading up to the pandemic

After Dr. Frieden and others in the Obama administration tried but failed to lessen Emergent’s dominance over stockpile purchases, the company’s fortunes rose under Mr. Trump, who appointed a former Emergent consultant with a background in bioterrorism to run the office that now oversees the stockpile

“If I could spend less on anthrax replenishment, I could buy more N95s,” Dr. Kadlec said in an interview shortly after leaving office. “I could buy more ventilators. I could buy more of other things that quite frankly I didn’t have the money to buy.”

And now, as some members of Congress push for larger reserves of ventilators, masks and other equipment needed in a pandemic, a trade group led in part by a top Emergent lobbyist has warned that the purchases could endanger companies focused on threats like anthrax and smallpox by drawing down limited funds.

Last year, as the pandemic raced across the country, the government paid Emergent $626 million for products that included vaccines to fight an entirely different threat: a terrorist attack using anthrax.

“I think it’s pretty clear that the benefit of the vaccine is marginal,” he said in an interview

“They’re very vicious in their behavior toward anybody they perceive as having a different point of view,” said Dr. Tara O’Toole, a former Homeland Security official who says she ran afoul of Emergent in 2010 after telling Congress that the nation needed a newer and better anthrax vaccine.

That year, the company that would become Emergent — then known as BioPort — paid Michigan $25 million to buy the license for a government-developed anthrax vaccine and an aging manufacturing plant.

The company opened its doors with one product, called BioThrax, and one customer, the Defense Department, which required the vaccine for service members.

Emergent’s anthrax vaccine was not the government’s first choice. It was more than 30 years old and plagued by manufacturing challenges and complaints about side effects. Officials instead backed a company named VaxGen, which was developing a vaccine using newer technology licensed from the military.

Emergent’s successful campaign against VaxGen — deploying a battalion of lobbyists, publicly attacking its rival and warning that it might cease production of its own vaccine if the government didn’t buy it — established its formidable reputation. By 2006, VaxGen had lost its contract and the government had turned to Emergent to supply BioThrax.

“They were totally feared by everybody,” Dr. Philip Russell, a top health official in the administration of President George W. Bush, said in an interview. He said that he clashed with Emergent when he backed VaxGen, and that his reputation came under attack, which was documented by The Times in 2006. (Dr. Russell died this January.)

the group of federal officials who make decisions about the stockpile and other emergency preparations — known as the Phemce, for the Public Health Emergency Medical Countermeasures Enterprise — ordered up a study. It found in 2010 that the government could not afford to devote so much of its budget to a single threat.

Instead, the review concluded, the government should invest more in products with multiple applications, like diagnostic tests, ventilators, reusable respirator masks and “plug and play” platforms that can rapidly develop vaccines for a range of outbreaks.

from 2010 through 2018, the anthrax vaccine consumed more than 40 percent of the stockpile’s budget, which averaged $560 million during those years.

Emergent and the government have withheld details of the stockpile contracts, including how much the company has charged for each dose of BioThrax, but executives have shared some of the missing information with investors.

The company in 1998 agreed to charge the government an average of about $3.35 per dose, documents show. By 2010, the price had risen to about $28, according to financial disclosures and statements by Emergent executives, and now it is about $30

Over the past 15 years, the company recorded a gross profit margin of about 75 percent for the vaccine, in an arrangement that one Emergent vice president called a “monopoly.”

Emergent’s rise is the stuff of lore in biodefense circles — a tale of savvy dealings, fortuitous timing and tough, competitive tactics.

One afternoon in October 2010, Wall Street investors gathered at the Millennium Broadway Hotel in Manhattan for a presentation by Mr. Burrows. He shared with them a secret number: 75 million.That was how many BioThrax doses the government had committed to stockpiling, and it was the backbone of Emergent’s thriving business. In pursuit of that goal, the government had already spent more than $900 million, and it continued to buy virtually every dose Emergent could produce. It had even awarded the company more than $100 million to expand its Michigan factory.

“The best approach toward anthrax is antimicrobial therapy,” Dr. Anthony S. Fauci, the government’s top infectious-disease expert, told Congress as early as 2007.

In an analysis published in 2007, the firm determined that giving antibiotics immediately after a large outdoor anthrax attack was likely to reduce serious illnesses by more than 80 percent. Administering the vaccine would then cut serious illnesses only by an additional 4 percent.

Dr. Ali S. Khan, who ran the C.D.C. office managing the stockpile until 2014, said bluntly: “We overpaid.”

“A bunch of people, including myself, were sitting in a room and asking what kind of attack might happen,” said Dr. Kenneth Bernard, a top biodefense adviser to Mr. Bush, recalling a meeting in the months after the 2001 attacks.

“And somebody said, ‘Well, I can’t imagine anyone attacking more than three cities at once,’” he said. “So we took the population of a major U.S. city and multiplied by three.”

A team of Homeland Security and health officials began doing just that in 2013. The group determined, in a previously undisclosed analysis, that the government could stockpile less BioThrax and still be prepared for a range of plausible attacks, according to two people involved in the assessment. Separately, government researchers concluded that two doses of BioThrax provided virtually the same protection as three.

the National Intelligence Council, which helped draft the assessments during Mr. Obama’s second term, said in an interview that the idea of a three-city attack affecting 25 million people was “straining credulity.”

“If you talk to the head of the House Intelligence Committee,” Don Elsey, Emergent’s chief financial officer, told investors in 2011, “and you say, ‘What are you most worried about?’ he’ll say, ‘Let me see: Number one, anthrax; number two, anthrax; number three, anthrax.’”

Emergent’s sales strategy was to address that fear by promising the federal government peace of mind with its vaccine.

“There’s a political element involved,” Mr. Burrows, the company’s vice president of investor relations, said at an industry conference in 2016. “I don’t have a marketing expense. I have lobbying expense.”

Since 2010, the company has spent an average of $3 million a year on lobbying — far outspending similarly sized biotech firms, and roughly matching the outlays of two pharmaceutical companies with annual revenues at least 17 times greater, AstraZeneca and Bristol Myers Squibb

In 2015, as stockpile managers questioned the large purchases of BioThrax, the spending topped $4 million

“They were pouring it on — how poor they were and how this was going to ruin the company, and they’d have to close down factories, and America was going to be left without anthrax vaccine,”

“Their revolving door is moving at 60 miles per hour,” said former Senator Claire McCaskill, a Democrat from Missouri who had questioned spending on the vaccine while in the Senate. “There is really a lot of incestuousness because it’s such a specialized field.”

Ms. DeLorenzo, the Emergent spokeswoman, said the lobbying was necessary because government investment “in biodefense and other public health threats has not been as strongly prioritized as it should be.”

Over the past 10 years, Emergent’s political action committee has spread almost $1.4 million in campaign contributions among members of both partie

The move followed a yearslong pattern of retaining a bipartisan lobbying corps of former agency officials, staff members and congressmen, including Pete Hoekstra of Michigan, Tom Latham of Iowa and Jim Saxton of New Jersey.

“You have people coming and saying, ‘There’s no market for this — nobody’s going to produce this unless you buy enough of it to keep the production line open,’” he said. “It’s an absolutely appropriate argument to make.”

Emergent’s campaign proved effective. Despite the 2015 recommendation by the stockpile managers, Senate overseers made clear they opposed the reduction, and the government went ahead and bought $300 million worth of BioThrax.

Emergent executives, meanwhile, warned that there could be job losses at the factory in Lansing, Mich. — the capital of a swing state at the center of a contentious presidential campaign between Mr. Trump and Hillary Clinton.

Because Emergent was the sole manufacturer of a product deemed critical to national security, the company has played what one former executive described to The Times as “the we’re-going-to-go-bankrupt card.”

Dr. Hatchett said the idea gave him pause. But, he explained in an interview, “if there’s only one partner that can provide a product and only one customer for that product, the customer needs the partner to survive.”

Just a year later, Emergent spent about $200 million in cash, and made other financial commitments, to acquire Sanofi’s smallpox vaccine and GlaxoSmithKline’s anthrax treatment, two products with established pipelines to the stockpile. The purchases expanded Emergent’s hold over the reserve.

Ms. DeLorenzo said the acquisitions did not suggest the company was better off than it had claimed, but Dr. Bright said he and others involved in the bailout felt used.

a plan five years earlier to create an emergency supply of N95 respirators was simply not funded. A team of experts had proposed buying tens of millions of the masks to fill the gap during an outbreak until domestic manufacturing could ramp up, according to five officials involved in the assessment, which has not been previously disclosed.

By the time the novel coronavirus emerged, the stockpile had only 12 million of the respirators. The stockpile has since set a goal of amassing 300 million.

Dr. Kadlec, the Trump administration official overseeing the stockpile, said he used the previous administration’s mask recommendation to raise alarms as early as 2018.

Dr. Annie De Groot, chief executive of the small vaccine company EpiVax, spoke about the need to break Emergent’s lock on research dollars at a biodefense forum in 2015.

“Politicians want to look like they’ve addressed the problem,” she said. “But we need to actually listen to the scientists.”

Over the last five years, Emergent has received nearly a half-billion dollars in federal research and development funding, the company said in its financial disclosures.

“We know ahead of time when funding opportunities are going to come out,” Barbara Solow, a senior vice president, told investors in 2017. “When we talk to the government, we know how to speak the government’s language around contracting.”

The company used federal money to make improvements to BioThrax, and also found a way to earn government money from a competing anthrax vaccine it had excoriated. After the demise of VaxGen in 2006, Emergent bought the company’s unfinished vaccine and in 2010 persuaded the federal government to continue paying for research on it

By the time the research contract was canceled in 2016, Emergent had collected about $85 million, records show. The company then shelved the vaccine. “If the U.S. government withdraws funding, we re-evaluate whether there is any business case for continuing,” Ms. DeLorenzo said.

For more than 30 years, the government had been encouraging the development of a BioThrax replacement. In 2002, the Institute of Medicine had concluded that an alternative based on more modern technology was “urgently needed.” By 2019, there were three leading candidates, including one made by Emergent, known as AV7909.

Emergent’s candidate was hardly the breakthrough the government was seeking, former health officials said. AV7909 was essentially an enhanced version of BioThrax. The competitors were using more modern technology that could produce doses more rapidly and consistently, and were promising significant cost savings for the stockpile.

To qualify for emergency authorization, a vaccine must be at an advanced stage of development with no approved alternatives. Emergent acknowledged in its financial disclosures that there was “considerable uncertainty” whether the new vaccine met those requirements.

The election of Mr. Trump as president was good news for Emergent.

Dr. Lurie, the senior health official in the Obama administration who had tried to scale back BioThrax purchases, was out. Mr. Trump’s pick to replace her was Dr. Kadlec, a career Air Force physician and top biodefense official in the Bush administration who was fixated on bioterrorism threats, especially anthrax, current and former officials said

Soon after entering the Trump administration in 2017, Dr. Kadlec took a series of actions that he characterized as streamlining a cumbersome bureaucracy but that had the effect of benefiting Emergent.

He assumed greater control of purchasing decisions, diminishing the authority of the Phemce, the oversight group that had proposed buying less BioThrax. And in 2018, he backed a decision to move control of the stockpile to his office in the Department of Health and Human Services and away from the C.D.C., which is based in Atlanta and prides itself on being insulated from the influence of lobbyists.

Dr. Frieden, the former C.D.C. director, was strongly opposed. The move, he said, “had almost as an explicit goal to give the lobbyists more say in what got purchased.”

That July, the government made the announcement Emergent had been banking on, committing to buying millions of doses. Separately, it said it would stop funding Emergent’s competitors.

The decision to side with Emergent did not surprise Dr. Khan, the former C.D.C. official overseeing the stockpile.“Again and again, we seem unable to move past an old technology that’s bankrupting the stockpile,” he said.

Last month, as the death toll from Covid-19 neared a half-million, Mr. Kramer, the company’s chief executive, told analysts there had been no “evidence of a slowdown or a delay or a deprioritization,” and echoed a statement he had made in April when asked whether the pandemic might interrupt Emergent’s sales to the stockpile.“It’s pretty much business as usual,” he said then.

In March of 2020, for example, he forwarded a correspondence about the possible Covid-19 immunity of indigenous people harvesting guano (bat excrement), as well as an idea from a psychiatry professor about using the antibiotic minocycline to slow viral replication. He commented on an email from a Swedish psychiatrist, "There may be nothing to this, but we should at least be aware." And in April of that year, he told a persistent doctor with multiple ideas for possible antiviral agents, "You are not being ignored."

I find the emails about the funding of Fauci's agency particularly fascinating. Over the course of the messages, budget discussions transition from small funding increases to tremendous week-on-week growths in expenditures -- particularly when the US National Institute of Allergy and Infectious Diseases was empowered by Congress to dream bigger thanks to $1.532 billion in supplemental appropriations. To put a finer point on this group of emails: it was these bigger dreams and bigger trials that provided us with important answers and, ultimately, with the vaccines.

At other times, it feels like looking at the celebrity photos on the front pages of People magazine: "He felt that way, too?!"

Finally, in between the lines, we watch him manage this pandemic as a human. There are emails where he is clearly overwhelmed, forwarding media request after speaking request to his assistants at the Office of the Chief of Staff. (One has to wonder: who wouldn't be overwhelmed by this number of requests?)

There are emails where he is tremendously kind, thanking people for their service, telling staff to "stay well and safe" or complimenting folks on well-written papers and columns.

Again and again, he responds to concerned citizens, scientists and journalists with: "Thank you for your note." He pays attention both to people he knows -- apologetically telling Ralph Nader at 7 p.m. on a Sunday, "I receive over 1000 e-mails per day and even with staff screening, I do not see them for days." -- and to those he probably doesn't. There are times when he's funny, times when he's frustrated and times when he's clearly exhausted, admitting that he's simply too tired to make sense of something.

He is just like us -- or, at least, he's how most of us like to imagine ourselves to be, on our best days.

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.”