Group items tagged

Yet the institutions and forces that once kept us from indulging the flagrantly untrue or absurd—media, academia, government, corporate America, professional associations, respectable opinion in the aggregate—have enabled and encouraged every species of fantasy over the past few decades.

Our whole social environment and each of its overlapping parts—cultural, religious, political, intellectual, psychological—have become conducive to spectacular fallacy and truthiness and make-believe. There are many slippery slopes, leading in various directions to other exciting nonsense. During the past several decades, those naturally slippery slopes have been turned into a colossal and permanent complex of interconnected, crisscrossing bobsled tracks, which Donald Trump slid down right into the White House.

Esalen is a mother church of a new American religion for people who think they don’t like churches or religions but who still want to believe in the supernatural. The institute wholly reinvented psychology, medicine, and philosophy, driven by a suspicion of science and reason and an embrace of magical thinking

The great unbalancing and descent into full Fantasyland was the product of two momentous changes. The first was a profound shift in thinking that swelled up in the ’60s; since then, Americans have had a new rule written into their mental operating systems: Do your own thing, find your own reality, it’s all relative.

The second change was the onset of the new era of information. Digital technology empowers real-seeming fictions of the ideological and religious and scientific kinds. Among the web’s 1 billion sites, believers in anything and everything can find thousands of fellow fantasists, with collages of facts and “facts” to support them

Today, each of us is freer than ever to custom-make reality, to believe whatever and pretend to be whoever we wish. Which makes all the lines between actual and fictional blur and disappear more easily. Truth in general becomes flexible, personal, subjective. And we like this new ultra-freedom, insist on it, even as we fear and loathe the ways so many of our wrongheaded fellow Americans use it.

we are the global crucible and epicenter. We invented the fantasy-industrial complex; almost nowhere outside poor or otherwise miserable countries are flamboyant supernatural beliefs so central to the identities of so many people.

We’re still rich and free, still more influential and powerful than any other nation, practically a synonym for developed country. But our drift toward credulity, toward doing our own thing, toward denying facts and having an altogether uncertain grip on reality, has overwhelmed our other exceptional national traits and turned us into a less developed country.

For most of our history, the impulses existed in a rough balance, a dynamic equilibrium between fantasy and reality, mania and moderation, credulity and skepticism.

It was a headquarters for a new religion of no religion, and for “science” containing next to no science. The idea was to be radically tolerant of therapeutic approaches and understandings of reality, especially if they came from Asian traditions or from American Indian or other shamanistic traditions. Invisible energies, past lives, astral projection, whatever—the more exotic and wondrous and unfalsifiable, the better.

These influential critiques helped make popular and respectable the idea that much of science is a sinister scheme concocted by a despotic conspiracy to oppress people. Mental illness, both Szasz and Laing said, is “a theory not a fact.”

The Greening of America may have been the mainstream’s single greatest act of pandering to the vanity and self-righteousness of the new youth. Its underlying theoretical scheme was simple and perfectly pitched to flatter young readers: There are three types of American “consciousness,” each of which “makes up an individual’s perception of reality … his ‘head,’ his way of life.” Consciousness I people were old-fashioned, self-reliant individualists rendered obsolete by the new “Corporate State”—essentially, your grandparents. Consciousness IIs were the fearful and conformist organization men and women whose rationalism was a tyrannizing trap laid by the Corporate State—your parents.

And then there was Consciousness III, which had “made its first appearance among the youth of America,” “spreading rapidly among wider and wider segments of youth, and by degrees to older people.” If you opposed the Vietnam War and dressed down and smoked pot, you were almost certainly a III. Simply by being young and casual and undisciplined, you were ushering in a new utopia.

Reich was half-right. An epochal change in American thinking was under way and “not, as far as anybody knows, reversible … There is no returning to an earlier consciousness.” His wishful error was believing that once the tidal surge of new sensibility brought down the flood walls, the waters would flow in only one direction, carving out a peaceful, cooperative, groovy new continental utopia, hearts and minds changed like his, all of America Berkeleyized and Vermontified. Instead, Consciousness III was just one early iteration of the anything-goes, post-reason, post-factual America enabled by the tsunami.

During the ’60s, large swaths of academia made a turn away from reason and rationalism as they’d been understood. Many of the pioneers were thoughtful, their work fine antidotes to postwar complacency. The problem was the nature and extent of their influence at that particular time, when all premises and paradigms seemed up for grabs. That is, they inspired half-baked and perverse followers in the academy, whose arguments filtered out into the world at large: All approximations of truth, science as much as any fable or religion, are mere stories devised to serve people’s needs or interests. Reality itself is a purely social construction, a tableau of useful or wishful myths that members of a society or tribe have been persuaded to believe. The borders between fiction and nonfiction are permeable, maybe nonexistent.

The delusions of the insane, superstitions, and magical thinking? Any of those may be as legitimate as the supposed truths contrived by Western reason and science. The takeaway: Believe whatever you want, because pretty much everything is equally true and false.

over in sociology, in 1966 a pair of professors published The Social Construction of Reality, one of the most influential works in their field. Not only were sanity and insanity and scientific truth somewhat dubious concoctions by elites, Peter Berger and Thomas Luckmann explained—so was everything else. The rulers of any tribe or society do not just dictate customs and laws; they are the masters of everyone’s perceptions, defining reality itself

Over in anthropology, where the exotic magical beliefs of traditional cultures were a main subject, the new paradigm took over completely—don’t judge, don’t disbelieve, don’t point your professorial finger.

then isn’t everyone able—no, isn’t everyone obliged—to construct their own reality? The book was timed perfectly to become a foundational text in academia and beyond.

To create the all-encompassing stage sets that everyone inhabits, rulers first use crude mythology, then more elaborate religion, and finally the “extreme step” of modern science. “Reality”? “Knowledge”? “If we were going to be meticulous,” Berger and Luckmann wrote, “we would put quotation marks around the two aforementioned terms every time we used them.” “What is ‘real’ to a Tibetan monk may not be ‘real’ to an American businessman.”

In the ’60s, anthropology decided that oracles, diviners, incantations, and magical objects should be not just respected, but considered equivalent to reason and science. If all understandings of reality are socially constructed, those of Kalabari tribesmen in Nigeria are no more arbitrary or faith-based than those of college professors.

Even the social critic Paul Goodman, beloved by young leftists in the ’60s, was flabbergasted by his own students by 1969. “There was no knowledge,” he wrote, “only the sociology of knowledge. They had so well learned that … research is subsidized and conducted for the benefit of the ruling class that they did not believe there was such a thing as simple truth.”

Ever since, the American right has insistently decried the spread of relativism, the idea that nothing is any more correct or true than anything else. Conservatives hated how relativism undercut various venerable and comfortable ruling ideas—certain notions of entitlement (according to race and gender) and aesthetic beauty and metaphysical and moral certaint

Conservatives are correct that the anything-goes relativism of college campuses wasn’t sequestered there, but when it flowed out across America it helped enable extreme Christianities and lunacies on the right—gun-rights hysteria, black-helicopter conspiracism, climate-change denial, and more.

Elaborate paranoia was an established tic of the Bircherite far right, but the left needed a little time to catch up. In 1964, a left-wing American writer published the first book about a JFK conspiracy, claiming that a Texas oilman had been the mastermind, and soon many books were arguing that the official government inquiry had ignored the hidden conspiracies.

Conspiracy became the high-end Hollywood dramatic premise—Chinatown, The Conversation, The Parallax View, and Three Days of the Condor came out in the same two-year period. Of course, real life made such stories plausible. The infiltration by the FBI and intelligence agencies of left-wing groups was then being revealed, and the Watergate break-in and its cover-up were an actual criminal conspiracy. Within a few decades, the belief that a web of villainous elites was covertly seeking to impose a malevolent global regime made its way from the lunatic right to the mainstream.

t more and more people on both sides would come to believe that an extraordinarily powerful cabal—international organizations and think tanks and big businesses and politicians—secretly ran America.

Each camp, conspiracists on the right and on the left, was ostensibly the enemy of the other, but they began operating as de facto allies. Relativist professors enabled science-denying Christians, and the antipsychiatry craze in the ’60s appealed simultaneously to left-wingers and libertarians (as well as to Scientologists). Conspiracy theories were more of a modern right-wing habit before people on the left signed on. However, the belief that the federal government had secret plans to open detention camps for dissidents sprouted in the ’70s on the paranoid left before it became a fixture on the right.

Extreme religious and quasi-religious beliefs and practices, Christian and New Age and otherwise, didn’t subside, but grew and thrived—and came to seem unexceptional.

Until we’d passed through the ’60s and half of the ’70s, I’m pretty sure we wouldn’t have given the presidency to some dude, especially a born-again Christian, who said he’d recently seen a huge, color-shifting, luminescent UFO hovering near him.

Starting in the ’80s, loving America and making money and having a family were no longer unfashionable.The sense of cultural and political upheaval and chaos dissipated—which lulled us into ignoring all the ways that everything had changed, that Fantasyland was now scaling and spreading and becoming the new normal. What had seemed strange and amazing in 1967 or 1972 became normal and ubiquitous.

For most of the 20th century, national news media had felt obliged to pursue and present some rough approximation of the truth rather than to promote a truth, let alone fictions. With the elimination of the Fairness Doctrine, a new American laissez-faire had been officially declared. If lots more incorrect and preposterous assertions circulated in our mass media, that was a price of freedom. If splenetic commentators could now, as never before, keep believers perpetually riled up and feeling the excitement of being in a mob, so be it.

Relativism became entrenched in academia—tenured, you could say

as he wrote in 1986, “the secret of theory”—this whole intellectual realm now called itself simply “theory”—“is that truth does not exist.”

After the ’60s, truth was relative, criticizing was equal to victimizing, individual liberty became absolute, and everyone was permitted to believe or disbelieve whatever they wished. The distinction between opinion and fact was crumbling on many fronts.

America didn’t seem as weird and crazy as it had around 1970. But that’s because Americans had stopped noticing the weirdness and craziness. We had defined every sort of deviancy down. And as the cultural critic Neil Postman put it in his 1985 jeremiad about how TV was replacing meaningful public discourse with entertainment, we were in the process of amusing ourselves to death.

In 1998, as soon as we learned that President Bill Clinton had been fellated by an intern in the West Wing, his popularity spiked. Which was baffling only to those who still thought of politics as an autonomous realm, existing apart from entertainment

Just before the Clintons arrived in Washington, the right had managed to do away with the federal Fairness Doctrine, which had been enacted to keep radio and TV shows from being ideologically one-sided. Until then, big-time conservative opinion media had consisted of two magazines, William F. Buckley Jr.’s biweekly National Review and the monthly American Spectator, both with small circulations. But absent a Fairness Doctrine, Rush Limbaugh’s national right-wing radio show, launched in 1988, was free to thrive, and others promptly appeared.

I’m pretty certain that the unprecedented surge of UFO reports in the ’70s was not evidence of extraterrestrials’ increasing presence but a symptom of Americans’ credulity and magical thinking suddenly unloosed. We wanted to believe in extraterrestrials, so we did.

Limbaugh’s virtuosic three hours of daily talk started bringing a sociopolitical alternate reality to a huge national audience. Instead of relying on an occasional magazine or newsletter to confirm your gnarly view of the world, now you had talk radio drilling it into your head for hours every day.

Fox News brought the Limbaughvian talk-radio version of the world to national TV, offering viewers an unending and immersive propaganda experience of a kind that had never existed before.

Over the course of the century, electronic mass media had come to serve an important democratic function: presenting Americans with a single shared set of facts. Now TV and radio were enabling a reversion to the narrower, factional, partisan discourse that had been normal in America’s earlier centuries.

there was also the internet, which eventually would have mooted the Fairness Doctrine anyhow. In 1994, the first modern spam message was sent, visible to everyone on Usenet: global alert for all: jesus is coming soon. Over the next year or two, the masses learned of the World Wide Web. The tinder had been gathered and stacked since the ’60s, and now the match was lit and thrown

After the ’60s and ’70s happened as they happened, the internet may have broken America’s dynamic balance between rational thinking and magical thinking for good.

Before the web, cockamamy ideas and outright falsehoods could not spread nearly as fast or as widely, so it was much easier for reason and reasonableness to prevail. Before the web, institutionalizing any one alternate reality required the long, hard work of hundreds of full-time militants. In the digital age, however, every tribe and fiefdom and principality and region of Fantasyland—every screwball with a computer and an internet connection—suddenly had an unprecedented way to instruct and rile up and mobilize believers

Why did Senator Daniel Patrick Moynihan begin remarking frequently during the ’80s and ’90s that people were entitled to their own opinions but not to their own facts? Because until then, that had not been necessary to say

Reason remains free to combat unreason, but the internet entitles and equips all the proponents of unreason and error to a previously unimaginable degree. Particularly for a people with our history and propensities, the downside of the internet seems at least as profound as the upside.

On the internet, the prominence granted to any factual assertion or belief or theory depends on the preferences of billions of individual searchers. Each click on a link is effectively a vote pushing that version of the truth toward the top of the pile of results.

Exciting falsehoods tend to do well in the perpetual referenda, and become self-validating. A search for almost any “alternative” theory or belief seems to generate more links to true believers’ pages and sites than to legitimate or skeptical ones, and those tend to dominate the first few pages of result

If more and more of a political party’s members hold more and more extreme and extravagantly supernatural beliefs, doesn’t it make sense that the party will be more and more open to make-believe in its politics?

an individual who enters the communications system pursuing one interest soon becomes aware of stigmatized material on a broad range of subjects. As a result, those who come across one form of stigmatized knowledge will learn of others, in connections that imply that stigmatized knowledge is a unified domain, an alternative worldview, rather than a collection of unrelated ideas.

Academic research shows that religious and supernatural thinking leads people to believe that almost no big life events are accidental or random. As the authors of some recent cognitive-science studies at Yale put it, “Individuals’ explicit religious and paranormal beliefs” are the best predictors of their “perception of purpose in life events”—their tendency “to view the world in terms of agency, purpose, and design.”

Americans have believed for centuries that the country was inspired and guided by an omniscient, omnipotent planner and interventionist manager. Since the ’60s, that exceptional religiosity has fed the tendency to believe in conspiracies.

Oliver and Wood found the single strongest driver of conspiracy belief to be belief in end-times prophecies.

People on the left are by no means all scrupulously reasonable. Many give themselves over to the appealingly dubious and the untrue. But fantastical politics have become highly asymmetrical. Starting in the 1990s, America’s unhinged right became much larger and more influential than its unhinged left. There is no real left-wing equivalent of Sean Hannity, let alone Alex Jones. Moreover, the far right now has unprecedented political power; it controls much of the U.S. government.

Why did the grown-ups and designated drivers on the political left manage to remain basically in charge of their followers, while the reality-based right lost out to fantasy-prone true believers?

One reason, I think, is religion. The GOP is now quite explicitly Christian

, as the Syracuse University professor Michael Barkun saw back in 2003 in A Culture of Conspiracy, “such subject-specific areas as crank science, conspiracist politics, and occultism are not isolated from one another,” but ratherthey are interconnected. Someone seeking information on UFOs, for example, can quickly find material on antigravity, free energy, Atlantis studies, alternative cancer cures, and conspiracy.

Religion aside, America simply has many more fervid conspiracists on the right, as research about belief in particular conspiracies confirms again and again. Only the American right has had a large and organized faction based on paranoid conspiracism for the past six decades.

The right has had three generations to steep in this, its taboo vapors wafting more and more into the main chambers of conservatism, becoming familiar, seeming less outlandish. Do you believe that “a secretive power elite with a globalist agenda is conspiring to eventually rule the world through an authoritarian world government”? Yes, say 34 percent of Republican voters, according to Public Policy Polling.

starting in the ’90s, the farthest-right quarter of Americans, let’s say, couldn’t and wouldn’t adjust their beliefs to comport with their side’s victories and the dramatically new and improved realities. They’d made a god out of Reagan, but they ignored or didn’t register that he was practical and reasonable, that he didn’t completely buy his own antigovernment rhetoric.

Another way the GOP got loopy was by overdoing libertarianism

Republicans are very selective, cherry-picking libertarians: Let business do whatever it wants and don’t spoil poor people with government handouts; let individuals have gun arsenals but not abortions or recreational drugs or marriage with whomever they wish

For a while, Republican leaders effectively encouraged and exploited the predispositions of their variously fantastical and extreme partisans

Karl Rove was stone-cold cynical, the Wizard of Oz’s evil twin coming out from behind the curtain for a candid chat shortly before he won a second term for George W. Bush, about how “judicious study of discernible reality [is] … not the way the world really works anymore.” These leaders were rational people who understood that a large fraction of citizens don’t bother with rationality when they vote, that a lot of voters resent the judicious study of discernible reality. Keeping those people angry and frightened won them elections.

But over the past few decades, a lot of the rabble they roused came to believe all the untruths. “The problem is that Republicans have purposefully torn down the validating institutions,”

“They have convinced voters that the media cannot be trusted; they have gotten them used to ignoring inconvenient facts about policy; and they have abolished standards of discourse.”

What had been the party’s fantastical fringe became its middle. Reasonable Republicanism was replaced by absolutism: no new taxes, virtually no regulation, abolish the EPA and the IRS and the Federal Reserve.

The Christian takeover happened gradually, but then quickly in the end, like a phase change from liquid to gas. In 2008, three-quarters of the major GOP presidential candidates said they believed in evolution, but in 2012 it was down to a third, and then in 2016, just one did

A two-to-one majority of Republicans say they “support establishing Christianity as the national religion,” according to Public Policy Polling.

Although constitutionally the U.S. can have no state religion, faith of some kind has always bordered on mandatory for politicians.

What connects them all, of course, is the new, total American embrace of admixtures of reality and fiction and of fame for fame’s sake. His reality was a reality show before that genre or term existed

When he entered political show business, after threatening to do so for most of his adult life, the character he created was unprecedented—presidential candidate as insult comic with an artificial tan and ridiculous hair, shamelessly unreal and whipped into shape as if by a pâtissier.

Republicans hated Trump’s ideological incoherence—they didn’t yet understand that his campaign logic was a new kind, blending exciting tales with a showmanship that transcends ideology.

Trump waited to run for president until he sensed that a critical mass of Americans had decided politics were all a show and a sham. If the whole thing is rigged, Trump’s brilliance was calling that out in the most impolitic ways possible, deriding his straight-arrow competitors as fakers and losers and liars—because that bullshit-calling was uniquely candid and authentic in the age of fake.

Trump took a key piece of cynical wisdom about show business—the most important thing is sincerity, and once you can fake that, you’ve got it made—to a new level: His actual thuggish sincerity is the opposite of the old-fashioned, goody-goody sanctimony that people hate in politicians.

Trump’s genius was to exploit the skeptical disillusion with politics—there’s too much equivocating; democracy’s a charade—but also to pander to Americans’ magical thinking about national greatness. Extreme credulity is a fraternal twin of extreme skepticism.

Trump launched his political career by embracing a brand-new conspiracy theory twisted around two American taproots—fear and loathing of foreigners and of nonwhites.

The fact-checking website PolitiFact looked at more than 400 of his statements as a candidate and as president and found that almost 50 percent were false and another 20 percent were mostly false.

He gets away with this as he wouldn’t have in the 1980s or ’90s, when he first talked about running for president, because now factual truth really is just one option. After Trump won the election, he began referring to all unflattering or inconvenient journalism as “fake news.”

indeed, their most honest defense of his false statements has been to cast them practically as matters of religious conviction—he deeply believes them, so … there. When White House Press Secretary Sean Spicer was asked at a press conference about the millions of people who the president insists voted illegally, he earnestly reminded reporters that Trump “has believed that for a while” and “does believe that” and it’s “been a long-standing belief that he’s maintained” and “it’s a belief that he has maintained for a while.”

Which is why nearly half of Americans subscribe to that preposterous belief themselves. And in Trump’s view, that overrides any requirement for facts.

he idea that progress has some kind of unstoppable momentum, as if powered by a Newtonian law, was always a very American belief. However, it’s really an article of faith, the Christian fantasy about history’s happy ending reconfigured during and after the Enlightenment as a set of modern secular fantasies

I really can imagine, for the first time in my life, that America has permanently tipped into irreversible decline, heading deeper into Fantasyland. I wonder whether it’s only America’s destiny, exceptional as ever, to unravel in this way. Or maybe we’re just early adopters, the canaries in the global mine

I do despair of our devolution into unreason and magical thinking, but not everything has gone wrong.

I think we can slow the flood, repair the levees, and maybe stop things from getting any worse. If we’re splitting into two different cultures, we in reality-based America—whether the blue part or the smaller red part—must try to keep our zone as large and robust and attractive as possible for ourselves and for future generations

We need to firmly commit to Moynihan’s aphorism about opinions versus facts. We must call out the dangerously untrue and unreal

do not give acquaintances and friends and family members free passes. If you have children or grandchildren, teach them to distinguish between true and untrue as fiercely as you do between right and wrong and between wise and foolish.

How many Americans now inhabit alternate realities?

reams of survey research from the past 20 years reveal a rough, useful census of American credulity and delusion. By my reckoning, the solidly reality-based are a minority, maybe a third of us but almost certainly fewer than half.

Only a third of us, for instance, don’t believe that the tale of creation in Genesis is the word of God. Only a third strongly disbelieve in telepathy and ghosts. Two-thirds of Americans believe that “angels and demons are active in the world.”

A third of us believe not only that global warming is no big deal but that it’s a hoax perpetrated by scientists, the government, and journalists. A third believe that our earliest ancestors were humans just like us; that the government has, in league with the pharmaceutical industry, hidden evidence of natural cancer cures; that extraterrestrials have visited or are visiting Earth.

During his first week in office, Mr. Trump reiterated the unfounded charge that millions of people had voted illegally. When challenged on the evident falsehood, Sean Spicer, the White House press secretary, seemed to argue that Mr. Trump’s belief that something was true qualified as evidence. The press secretary also declined to answer a straightforward question about the unemployment rate, suggesting that the number will henceforth be whatever the Trump administration wants it to be.

Unfortunately, that also means that the more the fact-based media tries to debunk the president’s falsehoods, the further it will entrench the battle lines.

The news media’s spectacular failure to get the election right has made it only easier for many conservatives to ignore anything that happens outside the right’s bubble and for the Trump White House to fabricate facts with little fear of alienating its base.

The relationship appears to be symbiotic, as Mr. Trump often seems to pick up on talking points from Fox News and has tweeted out links from websites notorious for their casual relationship to the truth, including sites like Gateway Pundit, a hoax-peddling site that announced, shortly after the inauguration, that it would have a White House correspondent.

In a stunning demonstration of the power and resiliency of our new post-factual political culture, Mr. Trump and his allies in the right media have already turned the term “fake news” against its critics, essentially draining it of any meaning

now any news deemed to be biased, annoying or negative can be labeled “fake news.”

Even as he continues to attack the “dishonest media,” Mr. Trump and his allies are empowering this alt-reality media, providing White House access to Breitbart and other post-factual outlets that are already morphing into fierce defenders of the administration.

He can do this because members of the Trump administration feel confident that the alternative-reality media will provide air cover, even if they are caught fabricating facts or twisting words (like claiming that the “ban” on Muslim immigrants wasn’t really a “ban”). Indeed, they believe they have shifted the paradigm of media coverage, replacing the traditional media with their own.

All administrations lie, but what we are seeing here is an attack on credibility itself.

The Russian dissident and chess grandmaster Garry Kasparov drew upon long familiarity with that process when he tweeted: “The point of modern propaganda isn’t only to misinform or push an agenda. It is to exhaust your critical thinking, to annihilate truth.”

the real threat is not merely that a large number of Americans have become accustomed to rejecting factual information, or even that they have become habituated to believing hoaxes. The real danger is that, inundated with “alternative facts,” many voters will simply shrug, asking, “What is truth?” — and not wait for an answer.

In that world, the leader becomes the only reliable source of truth; a familiar phenomenon in an authoritarian state, but a radical departure from the norms of a democratic society. The battle over truth is now central to our politics.

as George Orwell said: “The very concept of objective truth is fading out of the world. Lies will pass into history.”

Trump and company seem to be betting that much of the electorate will not care if the president tells demonstrable lies, and will pick and choose whatever “alternative facts” confirm their views.

If we want to restore respect for facts and break through the intellectual ghettos on both the right and left, the mainstream media will have to be aggressive without being hysterical and adversarial without being unduly oppositional.

it will be incumbent on conservative media outlets to push back as well. Conservatism should be a reality-based philosophy, and the movement will be better off if it recognizes that facts really do matter.

The conservative media ecosystem — like the rest of us — has to recognize how critical, but also how fragile, credibility is in the Orwellian age of Donald Trump.

This is largely a demand driven phenomenon. People want 'fake news' (news which maximally confirms their beliefs and excites their fears, regardless of its accuracy) and because of that people will pop up to provide it

The converse of this is what got so many people so frustrated during the election. There was no end of information, basically undisputed, that Donald Trump was manifestly unfit to be president: too corrupt, too dishonest, too impulsive. And yet it just didn't matter. The other forces - a mix of intense support and the partisan reinforcement that took effect after he was the nominee - were more powerful.

For people whose agenda is journalism I don't think there's anything to do but do more and better journalism. I don't think journalism's job is to make people believe factual information. It's to provide it and in a reasoned, empirically demonstrated way

For people whose agenda is politics and political change, it is important not to be fooled by the limited importance of factual detail can have on political beliefs. It's changing the beliefs themselves. And that comes from persuasion about deeper beliefs about political rights and wrongs, interests, at the end of the day, ideology.

The Derek Black story is actually an instructive one. It was a slow product of persuasion, about who people are, about what's right and wrong that shifted his thinking and led him to question the factual claims embedded in the racist ideology he'd been raised on. It wasn't exposing him to fact-checks of racist ideology

we shouldn't be obsessing about "fake news." The impact of truly "fake news" - completely made up stories - is likely less than we imagine. But the power of tendentious and misleading propaganda is driven by political beliefs that are not so easily changed and only do change by persuasion, activism and organizing.

Pence is just the most recent example of how the rubble of ruined reputations will become deeper before Nov. 8. It has been well said that “sooner or later, we all sit down to a banquet of consequences.” The Republican Party’s multicourse banquet has begun.

 virtual influencers can also be cost-effective and provide more flexibility than a human alternative. 

Two groups saw a post with an emotional endorsement where the influencer uses words like love and adore. The other two groups saw a more staid post, focusing on specific software features. In each scenario one group was told the influencer was human and one group was told the influencer was virtual.

In one part of the study, about 300 participants were shown a social-media post purported to be from an influencer about either ice cream or sunglasses. Then, roughly half were told the influencer was human and half were told she was virtual. Regardless of the product, participants perceived the virtual influencer to be less credible than its “human” counterpart. Participants who were told the influencer was virtual also had a less-positive attitude toward the brand behind the product.

When the influencers “can’t really use the brand they are promoting,” it’s hard to see them as trustworthy experts, say Ozdemir.

“When it comes to an endorsement by a virtual influencer, the followers start questioning the expertness of the influencer on the field of the endorsed product/service,” he says. “Pretending that the influencer has actual experience with the product backfires.”

For the emotional endorsement, participants found the human influencer to be more credible. Participants who were told the influencer was human also had a more positive view of the brand than those who were told the influencer was virtual.

For the more factual endorsement, however, there was no statistically significant difference between the two groups when it came to influencer credibility or brand perception.

“When it comes to delivering a more factual endorsement, highlighting features that could be found by doing an internet search, participants really didn’t seem to care if the influencer was human or not,”

Unfortunately, that also means that the more the fact-based media tries to debunk the president’s falsehoods, the further it will entrench the battle lines.

The press secretary also declined to answer a straightforward question about the unemployment rate, suggesting that the number will henceforth be whatever the Trump administration wants it to be.

In a stunning demonstration of the power and resiliency of our new post-factual political culture, Mr. Trump and his allies in the right media have already turned the term “fake news” against its critics, essentially draining it of any meaning. During the campaign, actual “fake news” — deliberate hoaxes — polluted political discourse and clogged social media timelines.

For years, the widely read Drudge Report has linked to the bizarre conspiracy theorist Alex Jones, who believes that both the attacks of Sept. 11 and the Sandy Hook shootings were government-inspired “false flag” operations.

But now any news deemed to be biased, annoying or negative can be labeled “fake news.” Erroneous reports that the bust of the Rev. Dr. Martin Luther King Jr. had been removed from the Oval Office or misleading reports that sanctions against Russia had been lifted will be seized on by Mr. Trump’s White House to reinforce his indictment.

In that world, the leader becomes the only reliable source of truth; a familiar phenomenon in an authoritarian state, but a radical departure from the norms of a democratic society. The battle over truth is now central to our politics.

By now, it ought to be evident that enemies are important to this administration, whether they are foreigners, refugees, international bankers or the press.

But discrediting independent sources of information also has two major advantages for Mr. Trump: It helps insulate him from criticism and it allows him to create his own narratives, metrics and “alternative facts.”

The Russian dissident and chess grandmaster Garry Kasparov drew upon long familiarity with that process when he tweeted: “The point of modern propaganda isn’t only to misinform or push an agenda. It is to exhaust your critical thinking, to annihilate truth.”

Mr. Trump and his allies are empowering this alt-reality media, providing White House access to Breitbart and other post-factual outlets that are already morphing into fierce defenders of the administration.

Scottie Nell Hughes, was asked to defend the clearly false statement by Mr. Trump that millions of votes had been cast illegally. She answered by explaining that everybody now had their own way of interpreting whether a fact was true or not.

Or as George Orwell said: “The very concept of objective truth is fading out of the world. Lies will pass into history.”

In the meantime, we must recognize the magnitude of the challenge. If we want to restore respect for facts and break through the intellectual ghettos on both the right and left, the mainstream media will have to be aggressive without being hysterical and adversarial without being unduly oppositional.

There may be short-term advantages to running headlines about millions of illegal immigrants voting or secret United Nations plots to steal your guns, but the longer the right enables such fabrications, the weaker it will be in the long run. As uncomfortable as it may be, it will fall to the conservative media to police its worst actors.

armchair epidemiology, which Slate described as “convincing but flawed epidemiological analyses.”

The prime example is a Medium blog post titled “Covid-19 — Evidence Over Hysteria” by Aaron Ginn, a Silicon Valley product manager and “growth hacker” who argued against the severity of the virus and condemned the mainstream media for hyping it.

Conservative pundits, who’d spent weeks downplaying the seriousness of the virus, were drawn to Mr. Ginn’s conclusion that “shuttering the local economy is a distraction and arbitrary with limited accretive gain outside of greatly annoying millions and bankrupting hundreds of businesses.”

The Fox News political analyst Brit Hume tweeted the post, and then so did the anchors Bret Baier and Laura Ingraham. Within hours, the blog post was amplified across conservative media.

“I am seeing this playbook more and more,” Dr. Bergstrom said. “Secondhand data showing a crisis narrative that feels just a bit too well crafted. Mixing the truth with the plausible and the plausible with that which seems plausibly true in a week.”

Epidemiologists disagreed, pointing to some of Mr. Ginn’s assumptions as “unsubstantiated” and ignorant of “first-chapter-of-the-epidemiology-textbook stuff.”

After a 31-tweet thread from the infectious-disease expert Carl Bergstrom debunking Mr. Ginn’s data as cherry-picked, Medium took the post down, prompting a backlash in conservative spheres. More than two million people had already viewed it.

Mr. Ginn’s post, which seems informed by his reflexive skepticism of the mainstream media, filled two needs for readers: It offered a scientific-seeming explanation that real scientists would not provide. And it provided a political foil, the media.

This, according to Dr. Bergstrom, is what makes armchair epidemiology so harmful. Posts like Mr. Ginn’s “deplete the critical resource you need to manage the pandemic, which is trust,

“When people are getting conflicting messages, it makes it very hard for state and local authorities to generate the political will to take strong actions downstream.”

At first glance, the piece looked quite convincing. Mr. Ginn drew heavily from charts from the C.D.C., Johns Hopkins and the Financial Times. “You don’t need a special degree to understand what the data says and doesn’t say,” he claimed.

Dr. Bergstrom argues that the advances in available data make it easier than ever for junk-science peddlers to appear legitimate.

“Statistical analysis is a black box to most of us,” Dr. Bergstrom said. “And it’s like, ‘I can’t challenge a multilinear statistical regression because I don’t know what that is,’

“And so a form of authority gets imposed on a reader and we tend not to challenge data the way we’ve learned to challenge words.”

Mr. Evans is concerned that ultimately important nuance will be lost and pro-Trump pundits will use the news to exclusively scapegoat China and divert blame away from domestic failings. “What’s scary is how smart the false stuff is,” he said.

Covid-19 and the immediate threat to public health means that networks like Facebook, Twitter and YouTube have been unusually decisive about taking down misinformation. “In a case of a pandemic like this, when we are seeing posts that are urging people not to get treatment,” Facebook’s chief executive, Mark Zuckerberg, said recently, “that’s a completely different class of content versus the back-and-forth of what candidates may say about each other.”

The Trump administration and right-wing media watchdogs will weaponize changing facts about the virus, pointing to them as proof of a deep state bent on damaging the president or a media apparatus trying to swing an election

Others will try to pin the blame for the pandemic solely on the Trump administration

What we don’t know about Covid-19 will degenerate into ever more intricate conspiracies — some almost believable, some outrageous but all dangerous.

“We’re in a stream of ever-evolving data, and it’s being shaped around cognitive biases, partisanship and preferences embedded in our cultural identities,”

I called Mr. Pomerantsev because the information vacuum around the virus made me think of the title of his earlier book on Russia — “Nothing Is True and Everything Is Possible.

In the absence of new, vetted information, reckless speculation takes its place, muddling our conception of the truth.

in crisis situations — especially early on — our desire for information exceeds our ability to accurately deliver it. Add to this the complexities of epidemiology: exponential growth; statistical modeling; and the slow, methodical nature of responsible science.

Together, they create the ideal conditions for distrust, bad-faith interpretations and political manipulation, the contours of which we’re only beginning to see.

“The really big question that haunts me is, ‘When do we return to reality?’” Mr. Pomerantsev mused over the phone from his own quarantine. “Or is it that in this partisan age absolutely everything is chopped, cut and edited to fit a different view? I’m waiting for society to finally hit up against a shared reality, like diving into the bottom of swimming pool. Instead we just go deeper.”

chological context.ADThe good news: Americans are no “crazier” — that is, no more paranoid or predisposed to conspiracy thinking — than in the past

“our political elites are amplifying the fringe more than we’ve seen” in modern times, while a president mounts a “grinding attack on factual evidence.” The result, he says, is “conspiracy theories and misinformation become yoked to partisanship in increasingly powerful ways.”

There has always been what the late historian Richard Hofstadter called the “paranoid style” in U.S. politics: witch hunts, Illuminati, Red Scares. William Jennings Bryan promoted conspiracy theories. Richard Nixon believed in them. But Trump is unique in promoting conspiracy thinking from the bully pulpit, and in building a system in which elites — Republican Party leaders — validate the paranoia.

Americans, by nature, are more distrustful of authority than citizens of other advanced democracies. “You always hear Americans say, ‘I know my rights,’ but you never hear an American say, ‘I know my responsibilities and obligations,’

The distrust is compounded by polarization of the political system: the collapse of local media (replaced by coastal national media); the growing tendency to live, work and worship among people of similar beliefs; the divisive effect of social media; and increased “sorting” of political parties into ideologically homogeneous blocs.

This has encouraged what Eitan Hersh of Tufts University describes as “political hobbyism,” in which partisans embrace political parties as they do hometown sports teams

Hersh explains the thinking: “I care about truth, but I care less about truth than about supporting the Patriots because the stakes are really low. … It’s a catharsis, camaraderie with our partisan peers.

“The science is very clear: People take cues from political leaders,” Nyhan says. Leaders typically rejected conspiracy theories, and the public followed. Now, Trump embraces them, and his followers concur — some out of partisan solidarity, others out of genuine belief.

“Human psychology has not changed,” Nyhan says. What’s changed is we’re discovering that “democratic systems don’t work well when political elites don’t deal in factual information.”

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?”

Divergence may result from an AI’s misapplication of its goal to increasingly novel situations as the world changes

Or the AI may grasp its mandate perfectly, but find it ill-suited to a being of its cognitive prowess. It might come to resent the people who want to train it to, say, cure diseases. “They want me to be a doctor,” Sutskever imagines an AI thinking. “I really want to be a YouTuber.”

If AIs get very good at making accurate models of the world, they may notice that they’re able to do dangerous things right after being booted up. They might understand that they are being red-teamed for risk, and hide the full extent of their capabilities.

hey may act one way when they are weak and another way when they are strong, Sutskever said

We would not even realize that we had created something that had decisively surpassed us, and we would have no sense for what it intended to do with its superhuman powers.

That’s why the effort to understand what is happening in the hidden layers of the largest, most powerful AIs is so urgent. You want to be able to “point to a concept,” Sutskever said. You want to be able to direct AI toward some value or cluster of values, and tell it to pursue them unerringly for as long as it exists.

we don’t know how to do that; indeed, part of his current strategy includes the development of an AI that can help with the research. If we are going to make it to the world of widely shared abundance that Altman and Sutskever imagine, we have to figure all this out.

This is why, for Sutskever, solving superintelligence is the great culminating challenge of our 3-million-year toolmaking tradition. He calls it “the final boss of humanity.”

“First of all, I think that whether the chance of existential calamity is 0.5 percent or 50 percent, we should still take it seriously,”

. “I don’t have an exact number, but I’m closer to the 0.5 than the 50.”

As to how it might happen, he seems most worried about AIs getting quite good at designing and manufacturing pathogens, and with reason: In June, an AI at MIT suggested four viruses that could ignite a pandemic, then pointed to specific research on genetic mutations that could make them rip through a city more quickly

Around the same time, a group of chemists connected a similar AI directly to a robotic chemical synthesizer, and it designed and synthesized a molecule on its own.

Altman worries that some misaligned future model will spin up a pathogen that spreads rapidly, incubates undetected for weeks, and kills half its victims. He worries that AI could one day hack into nuclear-weapons systems too. “There are a lot of things,” he said, and these are only the ones we can imagine.

Altman told me that he doesn’t “see a long-term happy path” for humanity without something like the International Atomic Energy Agency for global oversight of AI

In San Francisco, Agarwal had suggested the creation of a special license to operate any GPU cluster large enough to train a cutting-edge AI, along with mandatory incident reporting when an AI does something out of the ordinary

Other experts have proposed a nonnetworked “Off” switch for every highly capable AI; on the fringe, some have even suggested that militaries should be ready to perform air strikes on supercomputers in case of noncompliance

Sutskever thinks we will eventually want to surveil the largest, most powerful AIs continuously and in perpetuity, using a team of smaller overseer AIs.

Safety rules for a new technology usually accumulate over time, like a body of common law, in response to accidents or the mischief of bad actors. The scariest thing about genuinely powerful AI systems is that humanity may not be able to afford this accretive process of trial and error. We may have to get the rules exactly right at the outset.

Several years ago, Altman revealed a disturbingly specific evacuation plan he’d developed. He told The New Yorker that he had “guns, gold, potassium iodide, antibiotics, batteries, water, gas masks from the Israeli Defense Force, and a big patch of land in Big Sur” he could fly to in case AI attacks.

if the worst-possible AI future comes to pass, “no gas mask is helping anyone.”

but he told me that he can’t really be sure how AI will stack up. “I just have to build the thing,” he said. He is building fast

Altman insisted that they had not yet begun GPT-5’s training run. But when I visited OpenAI’s headquarters, both he and his researchers made it clear in 10 different ways that they pray to the god of scale. They want to keep going bigger, to see where this paradigm leads. After all, Google isn’t slackening its pace; it seems likely to unveil Gemini, a GPT-4 competitor, within months. “We are basically always prepping for a run,

To think that such a small group of people could jostle the pillars of civilization is unsettling. It’s fair to note that if Altman and his team weren’t racing to build an artificial general intelligence, others still would be

Altman’s views about the likelihood of AI triggering a global class war, or the prudence of experimenting with more autonomous agent AIs, or the overall wisdom of looking on the bright side, a view that seems to color all the rest—these are uniquely his

No single person, or single company, or cluster of companies residing in a particular California valley, should steer the kind of forces that Altman is imagining summoning.

AI may well be a bridge to a newly prosperous era of greatly reduced human suffering. But it will take more than a company’s founding charter—especially one that has already proved flexible—to make sure that we all share in its benefits and avoid its risks. It will take a vigorous new politics.

I don’t think the general public has quite awakened to what’s happening. A global race to the AI future has begun, and it is largely proceeding without oversight or restraint. If people in America want to have some say in what that future will be like, and how quickly it arrives, we would be wise to speak up soon.

Now, says Stengel, social media give everyone the opportunity to construct their own narrative of reality

, the problems of today’s information-saturated society would have been unimaginable for Marshall, who lived at a time when information was scarce and precious and when openness brought change.

Stengel argues that the U.S. government should sometimes protect citizens by exposing “weaponized information, false information” that is polluting the ecosystem. But ultimately, the defense of truth must be independent of a government that many people mistrust. “There are inherent dangers in having the government be the verifier of last resort,”

“They’re not trying to say that their version of events is the true one. They’re saying: ‘Everybody’s lying! Nobody’s telling you the truth!’ ”

For a Russian leadership schooled on KGB tactics, Pomerantsev argues, “It’s not an information war. It’s a war on information.”

“The central insight was that we’re not the best messenger for our message,” Stengel explains, “because in the post-truth world, the people we’re trying to reach automatically question anything from the U.S. government.

“They don’t have a candidate, per se. But they want to undermine faith in democracy, faith in the West.” In the cyber-propagandists’ atomized, construct-your-own-narrative world, agreement on a common framework of factual evidence can become almost impossible.

How should citizens who want a fact-based world combat this assault on truth? Stengel has approved State Department programs that teach investigative reporting and empower truth-tellers, but he’s right that this isn’t really a job for Uncle Sam

The best hope may be the global companies that have created the social-media platforms. “They see this information war as an existential threat,” says Stengel. The tech companies have made a start: He says Twitter has removed more than 400,000 accounts, and YouTube daily deletes extremist videos.

The real challenge for global tech giants is to restore the currency of truth.

Perhaps “machine learning” can identify falsehoods and expose every argument that uses them. Perhaps someday, a human-machine process will create what Stengel describes as a “global ombudsman for information.”

right now, the truth is losing.

Which side will America’s next president take in the war on information?

what the administration should have been doing in January to prepare us for today.

She cites the delay on tests, without which “cases go undetected and people continue to circulate” as a leading issue along with other missed federal government responses—many of which are still not fully operational

The prescient recommendations from experts across disciplines in the period before COVID-19 reached American shores—about testing, equipment, and distancing—make clear that more than any single factor, it was Trump’s squandering of out lead-time which should have been used to prepare for the pandemic that has exacerbated this crisis.

What follows is an annotated timeline revealing the warning signs the administration received and showing how slow the administration was to act on these recommendations.

The Early Years: Warnings Ignored

2017: Trump administrations officials are briefed on an intelligence document titled “Playbook for Early Response to High-Consequence Emerging Infectious Disease Threats and Biological Incidents.” That’s right. The administration literally had an actual playbook for what to do in the early stages of a pandemic

February 2018: The Washington Post writes “CDC to cut by 80 percent efforts to prevent global disease outbreak.” The meat of the story is “Countries where the CDC is planning to scale back include some of the world’s hot spots for emerging infectious disease, such as China, Pakistan, Haiti, Rwanda and Congo.”

May 2018: At an event marking the 100 year anniversary of the 1918 pandemic, Borio says “pandemic flu” is the “number 1 health security issue” and that the U.S. is not ready to respond.

One day later her boss, Rear Adm. Timothy Ziemer is pushed out of the administration and the global health security team is disbanded

Beth Cameron, former senior director for global health security on the National Security Council adds: “It is unclear in his absence who at the White House would be in charge of a pandemic,” Cameron said, calling it “a situation that should be immediately rectified.” Note: It was not

January 2019: The director of National Intelligence issues the U.S. Intelligence Community’s assessment of threats to national security. Among its findings:

A novel strain of a virulent microbe that is easily transmissible between humans continues to be a major threat, with pathogens such as H5N1 and H7N9 influenza and Middle East Respiratory Syndrome Coronavirus having pandemic potential if they were to acquire efficient human-to-human transmissibility.”

Page 21: “We assess that the United States and the world will remain vulnerable to the next flu pandemic or large scale outbreak of a contagious disease that could lead to massive rates of death and disability, severely affect the world economy, strain international resources, and increase calls on the United States for support.”

September, 2019: The Trump Administration ended the pandemic early warning program, PREDICT, which trained scientists in China and other countries to identify viruses that had the potential to turn into pandemics. According to the Los Angeles Times, “field work ceased when funding ran out in September,” two months before COVID-19 emerged in Wuhan Province, China.

2020: COVID-19 Arrives

anuary 3, 2020: The CDC is first alerted to a public health event in Wuhan, China

January 6, 2020: The CDC issues a travel notice for Wuhan due to the spreading coronavirus

Note: The Trump campaign claims that this marks the beginning of the federal government disease control experts becoming aware of the virus. It was 10 weeks from this point until the week of March 16 when Trump began to change his tone on the threat.

January 10, 2020: Former Trump Homeland Security Advisor Tom Bossert warns that we shouldn’t “jerk around with ego politics” because “we face a global health threat…Coordinate!”

January 18, 2020: After two weeks of attempts, HHS Secretary Alex Azar finally gets the chance to speak to Trump about the virus. The president redirects the conversation to vaping, according to the Washington Post. 

January 21, 2020: Dr. Nancy Messonnier, the director of the National Center for Immunization and Respiratory Disease at the CDC tells reporters, “We do expect additional cases in the United States.”

January 27, 2020: Top White House aides meet with Chief of Staff Mick Mulvaney to encourage greater focus on the threat from the virus. Joe Grogan, head of the White House Domestic Policy Council warns that “dealing with the virus was likely to dominate life in the United States for many months.”

January 28, 2020: Two former Trump administration officials—Gottlieb and Borio—publish an op-ed in the Wall Street Journal imploring the president to “Act Now to Prevent an American Epidemic.” They advocate a 4-point plan to address the coming crisis:

(1) Expand testing to identify and isolate cases. Note: This did not happen for many weeks. The first time more than 2,000 tests were deployed in a single day was not until almost six weeks later, on March 11.

(3) Prepare hospital units for isolation with more gowns and masks. Note: There was no dramatic ramp-up in the production of critical supplies undertaken. As a result, many hospitals quickly experienced shortages of critical PPE materials. Federal agencies waited until Mid-March to begin bulk orders of N95 masks.

January 29, 2020: Trump trade advisor Peter Navarro circulates an internal memo warning that America is “defenseless” in the face of an outbreak which “elevates the risk of the coronavirus evolving into a full-blown pandemic, imperiling the lives of millions of Americans.”

January 30, 2020: Dr. James Hamblin publishes another warning about critical PPE materials in the Atlantic, titled “We Don’t Have Enough Masks.”

January 29, 2020: Republican Senator Tom Cotton reaches out to President Trump in private to encourage him to take the virus seriously.