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some activities, including dangerous ones, provide negative feedback only rarely. When I am in a rush, I often cross the street at a red light. I understand intellectually that this is stupid, but I’ve never once seen evidence of my stupidity.

Exposure to COVID-19 works the same way. Every time you engage in a risky activity—like meeting up with your friends indoors—the world is likely to send you a signal that you made the right choice. I saw my pal and didn’t get sick. Clearly, I shouldn’t have worried so much about socializing!

Let’s assume, for example, that going to a large indoor gathering gives you a one in 20 chance of contracting COVID-19—a significant risk. Most likely, you’ll get away with it the first time. You’ll then infer that taking part in such gatherings is pretty safe, and will do so again. Eventually, you are highly likely to fall sick.

2. Individually Rational, Collectively DisastrousWe tend to think behavior that is justifiable on the individual level is also justifiable on the collective level, and vice versa. If eating the occasional sugary treat is fine for me it’s fine for all of us. And if smoking indoors is bad for me, it’s bad for all of us.

The dynamics of contagion in a pandemic do not work like that

if everyone who isn’t at especially high risk held similar dinner parties, some percentage of these events would lead to additional infections. And because each newly infected person might spread the virus to others, everyone’s decision to hold a one-off dinner party would quickly lead to a significant spike in transmissions.

The dynamic here is reminiscent of classic collective-action problems. If you go to one dinner, you’ll likely be fine. But if everyone goes to one dinner, the virus will spread with such speed that your own chances of contracting COVID-19 will also rise precipitously.

3. Dangers Are Hard to Recognize and Avoid

Many of the dangers we face in life are easy to spot—and we have, over many millennia, developed biological instincts and social conventions to avoid them

When we deal with an unaccustomed danger, such as a new airborne virus, we can’t rely on any of these protective mechanisms.

The virus is invisible. This makes it hard to spot or anticipate. We don’t see little viral particles floating through the air

In time, we can overcome these biases (at least to some extent).

Social disapprobation can help

We all should do what we can to identify the biases from which we suffer—and try to stop them from influencing our behavior.

The WHO, CDC and other bureaucracies initially failed to quickly realize that the benefits of masks compound, simply because two people are wearing them and you have to look at the interaction.

Let us say (to simplify) that masks reduce both transmission and reception to p. What effect on the R0(that is, the rate of spreading of the infection)?

Simply the naive approach (used by the CDC/WHO bureaucrats and other imbeciles) is to say if masks reduce the transmission probability to ¼, one would think it would then drop from, say R0= 5, to R0=1 ¼. Yuuge, but there is better.

For one should count both sides. Under our simplification, with p=1/4 we get R0'= p² R0 . The drop in R becomes 93.75%! You divide R by 16! Even with masks working at 50% we get a 75% drop in R0.

Second error: Missing the Nonlinearity of the Risk of Infection

we are in the convex part of the curve. For example, to use the case above, a reduction of viral load by 75% for a short exposure could reduce the probability of infection by 95% or more!

Third Error: Mistaking Absence of Evidence for Evidence of Absence

“There is no evidence that masks work”, I kept hearing repeated to me by the usual idiots calling themselves “evidence based” scientists. The point is that there is no evidence that locking the door tonight will prevent me from being burglarized. But everything that may block transmission could help.

Unlike school, real life is not about certainties. When in doubt, use what protection you can

Fourth Error: Misunderstanding the Market and PeoplePaternalistic bureaucrats resisted inviting the general public to use masks on grounds that the supply was limited and would be needed by health professionals — hence they lied to us saying “masks are not effective”

Fifth Error: Missing Extremely Strong Statistical Signals

they fear to be presenting “anecdotes”, and fail to grasp the broader notion of statistical signals where you look at the whole story, not the body parts.

evidence compounds.

We have a) the salon story where two infected stylists failed to infect all their 140 clients (making the probability of infection for bilateral mask wearing safely below 1% for a salon-style exposure)

plus b) the rate of infection of countries where masks were mandatory

plus c) tons of papers with more or less flawed methodologies, etc.

Sixth Error: The Non-Aggression Principle

“Libertarians” (in brackets) are resisting mask wearing on grounds that it constrains their freedom. Yet the entire concept of liberty lies in the Non-Aggression Principle, the equivalent of the Silver Rule: do not harm others; they in turn should not harm you.

Even more insulting is the demand by pseudolibertarians that Costco should banned from forcing customers to wear mask — but libertarianism allows you to set the rules on your own property. Costco should be able to force visitors to wear pink shirts and purple glasses if they wished.

Note that by infecting another person you are not infecting just another person. You are infecting many many more and causing systemic risk.

First, as Pew’s software made choices, the system selected longer videos. It’s as if the software recognizes that the user is going to be around for a while, and starts to serve up longer fare. Second, it also began to recommend more popular videos regardless of how popular the starting video was.

more than 70 percent of the videos that YouTube recommended showed up on the list only once. It’s impossible to examine how hundreds of thousands of videos connect to each first random video when there are such limited data about each one.

People want to know if YouTube regularly radicalizes people with its recommendations, as the scholar Zeynep Tufekci has suggested. This study suggests that YouTube pushes an anonymous user toward more popular, not more fringe, content.

For my November magazine story about children’s YouTube, the company’s answer to these kinds of troubling suggestions was that YouTube isn’t for kids. Children, they told me, should be using only the YouTube Kids app, which has been built as a safe space for them

nd that group is growing.

“Even as Americans who primarily turn to social media for political news are less aware and knowledgeable about a wide range of events and issues in the news, they are more likely than other Americans to have heard about a number of false or unproven claims.”

Specifically, they’ve been far more exposed to the conspiracy theory that powerful people intentionally planned the pandemic. Yet this group, says Pew, is also less concerned about the impact of made-up news like this than the rest of the U.S. population.

They’re absorbing fake news, but they don’t see it as a problem. In a society that depends on an informed citizenry to make reasonably intelligent decisions about self-governance, this is the worst kind of trouble.

In a sweeping piece on disinformation and the 2020 campaign in February — in the pre-pandemic era — the Atlantic’s McKay Coppins concluded with a telling quote from the political theorist Hannah Arendt that bears repetition now. Through an onslaught of lies, which may be debunked before the cycle is repeated, totalitarian leaders are able to instill in their followers “a mixture of gullibility and cynicism,” she warned.

Over time, people are conditioned to “believe everything and nothing, think that everything was possible and that nothing was true.” And then such leaders can do pretty much whatever they wish

one of the leading modelers, Gabriela Gomes, suggested the entire area of research was being effectively blackballed out of fear it might encourage a relaxation of pandemic vigilance. “This is the very sad reason for the absence of more optimistic projections on the development of this pandemic in the scientific literature,” she wrote on Twitter. “Our analysis suggests that herd-immunity thresholds are being achieved despite strict social-distancing measures.”

Gomes suggested, herd immunity could happen with as little as one quarter of the population of a community exposed — or perhaps just 20 percent. “We just keep running the models, and it keeps coming back at less than 20 percent,” she told Hamblin. “It’s very striking.” Such findings, if they held up, would be very instructive, as Hamblin writes: “It would mean, for instance, that at 25 percent antibody prevalence, New York City could continue its careful reopening without fear of another major surge in cases.”

But for those hoping that 25 percent represents a true ceiling for pandemic spread in a given community, well, it almost certainly does not, considering that recent serological surveys have shown that perhaps 93 percent of the population of Iquitos, Peru, has contracted the disease; as have more than half of those living in Indian slums; and as many as 68 percent in particular neighborhoods of New York City

overshoot of that scale would seem unlikely if the “true” threshold were as low as 20 or 25 percent.

But, of course, that threshold may not be the same in all places, across all populations, and is surely affected, to some degree, by the social behavior taken to protect against the spread of the disease.

we probably err when we conceive of group immunity in simplistically binary terms. While herd immunity is a technical term referring to a particular threshold at which point the disease can no longer spread, some amount of community protection against that spread begins almost as soon as the first people are exposed, with each case reducing the number of unexposed and vulnerable potential cases in the community by one

you would not expect a disease to spread in a purely exponential way until the point of herd immunity, at which time the spread would suddenly stop. Instead, you would expect that growth to slow as more people in the community were exposed to the disease, with most of them emerging relatively quickly with some immune response. Add to that the effects of even modest, commonplace protections — intuitive social distancing, some amount of mask-wearing — and you could expect to get an infection curve that tapers off well shy of 60 percent exposure.

Looking at the data, we see that transmissions in many severely impacted states began to slow down in July, despite limited interventions. This is especially notable in states like Arizona, Florida, and Texas. While we believe that changes in human behavior and changes in policy (such as mask mandates and closing of bars/nightclubs) certainly contributed to the decrease in transmission, it seems unlikely that these were the primary drivers behind the decrease. We believe that many regions obtained a certain degree of temporary herd immunity after reaching 10-35 percent prevalence under the current conditions. We call this 10-35 percent threshold the effective herd immunity threshold.

Indeed, that is more or less what was recently found by Youyang Gu, to date the best modeler of pandemic spread in the U.S

he cautioned again that he did not mean to imply that the natural herd-immunity level was as low as 10 percent, or even 35 percent. Instead, he suggested it was a plateau determined in part by better collective understanding of the disease and what precautions to take

Gu estimates national prevalence as just below 20 percent (i.e., right in the middle of his range of effective herd immunity), it still counts, I think, as encouraging — even if people in hard-hit communities won’t truly breathe a sigh of relief until vaccines arrive.

If you can get real protection starting at 35 percent, it means that even a mediocre vaccine, administered much more haphazardly to a population with some meaningful share of vaccination skeptics, could still achieve community protection pretty quickly. And that is really significant — making both the total lack of national coordination on rollout and the likely “vaccine wars” much less consequential.

At least 20 percent of the public, and perhaps 50 percent, had some preexisting, cross-protective T-cell response to SARS-CoV-2, according to one much-discussed recent paper. An earlier paper had put the figure at between 40 and 60 percent. And a third had found an even higher prevalence: 81 percent.

The T-cell story is similarly encouraging in its big-picture implications without being necessarily paradigm-changing

These numbers suggest their own heterogeneity — that different populations, with different demographics, would likely exhibit different levels of cross-reactive T-cell immune response

The most optimistic interpretation of the data was given to me by Francois Balloux, a somewhat contrarian disease geneticist and the director of the University College of London’s Genetics Institute

According to him, a cross-reactive T-cell response wouldn’t prevent infection, but would probably mean a faster immune response, a shorter period of infection, and a “massively” reduced risk of severe illness — meaning, he guessed, that somewhere between a third and three-quarters of the population carried into the epidemic significant protection against its scariest outcomes

the distribution of this T-cell response could explain at least some, and perhaps quite a lot, of COVID-19’s age skew when it comes to disease severity and mortality, since the young are the most exposed to other coronaviruses, and the protection tapers as you get older and spend less time in environments, like schools, where these viruses spread so promiscuously.

Balloux told me he believed it was also possible that the heterogeneous distribution of T-cell protection also explains some amount of the apparent decline in disease severity over time within countries on different pandemic timelines — a phenomenon that is more conventionally attributed to infection spreading more among the young, better treatment, and more effective protection of the most vulnerable (especially the old).

Going back to Youyang Gu’s analysis, what he calls the “implied infection fatality rate” — essentially an estimated ratio based on his modeling of untested cases — has fallen for the country as a whole from about one percent in March to about 0.8 percent in mid-April, 0.6 percent in May, and down to about 0.25 percent today.

even as we have seemed to reach a second peak of coronavirus deaths, the rate of death from COVID-19 infection has continued to decline — total deaths have gone up, but much less than the number of cases

In other words, at the population level, the lethality of the disease in America has fallen by about three-quarters since its peak. This is, despite everything that is genuinely horrible about the pandemic and the American response to it, rather fantastic.

there may be some possible “mortality displacement,” whereby the most severe cases show up first, in the most susceptible people, leaving behind a relatively protected population whose experience overall would be more mild, and that T-cell response may play a significant role in determining that susceptibility.

That, again, is Balloux’s interpretation — the most expansive assessment of the T-cell data offered to me

The most conservative assessment came from Sarah Fortune, the chair of Harvard’s Department of Immunology

Fortune cautioned not to assume that cross-protection was playing a significant role in determining severity of illness in a given patient. Those with such a T-cell response, she told me, would likely see a faster onset of robust response, yes, but that may or may not yield a shorter period of infection and viral shedding

Most of the scientists, doctors, epidemiologists, and immunologists I spoke to fell between those two poles, suggesting the T-cell cross-immunity findings were significant without necessarily being determinative — that they may help explain some of the shape of pandemic spread through particular populations, but only some of the dynamics of that spread.

he told me he believed, in the absence of that data, that T-cell cross-immunity from exposure to previous coronaviruses “might explain different disease severity in different people,” and “could certainly be part of the explanation for the age skew, especially for why the very young fare so well.”

the headline finding was quite clear and explicitly stated: that preexisting T-cell response came primarily via the variety of T-cells called CD4 T-cells, and that this dynamic was consistent with the hypothesis that the mechanism was inherited from previous exposure to a few different “common cold” coronaviruses

“This potential preexisting cross-reactive T-cell immunity to SARS-CoV-2 has broad implications,” the authors wrote, “as it could explain aspects of differential COVID-19 clinical outcomes, influence epidemiological models of herd immunity, or affect the performance of COVID-19 candidate vaccines.”

“This is at present highly speculative,” they cautioned.

Mr. Obama starts where most lawmakers are stuck: Section 230 of the Communications Decency Act, which gives platforms immunity from legal liability for most third-party posts. He suggested a “higher standard of care” for ads than for so-called organic content that everyday users post. This would strike a sensible balance between eviscerating Section 230, making sites accountable for everything they host, and doing nothing.

On top of that, “an instant, 24/7 global information stream,” from which audiences can pick and choose material that confirms their biases, has deepened the social divides that bad actors seek to exploit.

Mr. Obama identified another problem with the Section 230 talk: homing in on what material platforms do and don’t take down risks missing how the “very design” of these sites privileges polarizing, inflammatory posts.

With this, Mr. Obama adds something vital to the mainstream debate over social media regulation, shifting attention away from a debate about whack-a-mole content removal and toward the sites’ underlying structures. His specific suggestions, while fuzzy, also have promise — from slowing down viral material to imposing transparency obligations that would subject social media companies’ algorithms to scrutiny from researchers and regulators.

Mr. Obama calls this “democratic oversight.” But the material companies reveal could be highly technical. Ideally, it would get translated into layman’s terms so that everyday people, too, can understand how decisions so significant in their daily lives and the life of the country are made.

Hours after the attack, a post on the fringe online message board 4chan circulated claiming that the gunman was transgender.

where people falsely claimed that the shooting was a result of hormone therapy undertaken by the gunman.

“There is an overwhelming number of individuals who are posting images of this person, who was the shooter, and information about the nature of them being transgender,”

On Tuesday, a transgender artist said on Reddit that people online “just took my photos and used it to spread misinformation.”

False claims that the gunman was born outside the United States began to circulate within hours of the shooting. Spread largely on white nationalist Telegram channels and Gab accounts, the claims alleged that he was an undocumented immigrant in the United States, even after authorities including Roland Gutierrez, a Texas state senator, confirmed that the gunman was born in North Dakota.

“Did he cross the border illegally?” Code of Vets, a veterans organization, posted on Twitter. “Our nation has a serious national security crisis evolving.”

It also comes amid an ongoing, global battle over the future of platform moderation, with officials in Europe seeking to heighten standards surrounding tech companies' content decision-making and lawmakers in many US states seeking to force platforms to moderate less.

It is easy to understand the appeal of such one-sided tales. As human beings, we seem to prefer to impose clear and linear narratives on a chaotic and unpredictable reality; ambiguity and contradiction are much harder to live with.

To truly grasp the complex nature of our current time, we need first of all to embrace its most terrifying aspect: its fundamental open-endedness. It is precisely this radical uncertainty — not knowing where we are and what lies ahead — that gives rise to such existential anxiety.

Anthropologists have a name for this disturbing type of experience: liminality

liminality originally referred to the sense of disorientation that arises during a rite of passage. In a traditional coming-of-age ritual, for instance, it marks the point at which the adolescent is no longer considered a child but is not yet recognized as an adult — betwixt and between

We are ourselves in the midst of a painful transition, a sort of interregnum, as the Italian political theorist Antonio Gramsci famously called it, between an old world that is dying and a new one that is struggling to be born. Such epochal shifts are inevitably fraught with danger

the great upheavals in world history can equally be seen “as genuine signs of vitality” that “clear the ground” of discredited ideas and decaying institutions. “The crisis,” he wrote, “is to be regarded as a new nexus of growth.”

Once we embrace this Janus-faced nature of our times, at once frightening yet generative, a very different vision of the future emerges.

we see phases of relative calm punctuated every so often by periods of great upheaval. These crises can be devastating, but they are also the drivers of history.

even the collapse of modern civilization — but it may also open up possibilities for transformative change

a “web 2.0” revolution in “user-generated content,” offering easy-to-use, easily adopted tools on websites and then mobile apps. They were built for creating and sharing “content,”

As the original name suggested, social networking involved connecting, not publishing. By connecting your personal network of trusted contacts (or “strong ties,” as sociologists call them) to others’ such networks (via “weak ties”), you could surface a larger network of trusted contacts

The whole idea of social networks was networking: building or deepening relationships, mostly with people you knew. How and why that deepening happened was largely left to the users to decide.

That changed when social networking became social media around 2009, between the introduction of the smartphone and the launch of Instagram. Instead of connection—forging latent ties to people and organizations we would mostly ignore—social media offered platforms through which people could publish content as widely as possible, well beyond their networks of immediate contacts.

Social media turned you, me, and everyone into broadcasters (if aspirational ones). The results have been disastrous but also highly pleasurable, not to mention massively profitable—a catastrophic combination.

A social network is an idle, inactive system—a Rolodex of contacts, a notebook of sales targets, a yearbook of possible soul mates. But social media is active—hyperactive, really—spewing material across those networks instead of leaving them alone until needed.

The authors propose social media as a system in which users participate in “information exchange.” The network, which had previously been used to establish and maintain relationships, becomes reinterpreted as a channel through which to broadcast.

The toxicity of social media makes it easy to forget how truly magical this innovation felt when it was new. From 2004 to 2009, you could join Facebook and everyone you’d ever known—including people you’d definitely lost track of—was right there, ready to connect or reconnect. The posts and photos I saw characterized my friends’ changing lives, not the conspiracy theories that their unhinged friends had shared with them

Twitter, which launched in 2006, was probably the first true social-media site, even if nobody called it that at the time. Instead of focusing on connecting people, the site amounted to a giant, asynchronous chat room for the world. Twitter was for talking to everyone—which is perhaps one of the reasons journalists have flocked to it

on Twitter, anything anybody posted could be seen instantly by anyone else. And furthermore, unlike posts on blogs or images on Flickr or videos on YouTube, tweets were short and low-effort, making it easy to post many of them a week or even a day.

soon enough, all social networks became social media first and foremost. When groups, pages, and the News Feed launched, Facebook began encouraging users to share content published by others in order to increase engagement on the service, rather than to provide updates to friends. LinkedIn launched a program to publish content across the platform, too. Twitter, already principally a publishing platform, added a dedicated “retweet” feature, making it far easier to spread content virally across user networks.

When we look back at this moment, social media had already arrived in spirit if not by name. RSS readers offered a feed of blog posts to catch up on, complete with unread counts. MySpace fused music and chatter; YouTube did it with video (“Broadcast Yourself”)

From being asked to review every product you buy to believing that every tweet or Instagram image warrants likes or comments or follows, social media produced a positively unhinged, sociopathic rendition of human sociality.

Other services arrived or evolved in this vein, among them Reddit, Snapchat, and WhatsApp, all far more popular than Twitter. Social networks, once latent routes for possible contact, became superhighways of constant content

Although you can connect the app to your contacts and follow specific users, on TikTok, you are more likely to simply plug into a continuous flow of video content that has oozed to the surface via algorithm.

In the social-networking era, the connections were essential, driving both content creation and consumption. But the social-media era seeks the thinnest, most soluble connections possible, just enough to allow the content to flow.

This is also why journalists became so dependent on Twitter: It’s a constant stream of sources, events, and reactions—a reporting automat, not to mention an outbound vector for media tastemakers to make tastes.

“influencer” became an aspirational role, especially for young people for whom Instagram fame seemed more achievable than traditional celebrity—or perhaps employment of any kind.

social-media operators discovered that the more emotionally charged the content, the better it spread across its users’ networks. Polarizing, offensive, or just plain fraudulent information was optimized for distribution. By the time the platforms realized and the public revolted, it was too late to turn off these feedback loops.

The ensuing disaster was multipar

Rounding up friends or business contacts into a pen in your online profile for possible future use was never a healthy way to understand social relationships.

when social networking evolved into social media, user expectations escalated. Driven by venture capitalists’ expectations and then Wall Street’s demands, the tech companies—Google and Facebook and all the rest—became addicted to massive scale

Social media showed that everyone has the potential to reach a massive audience at low cost and high gain—and that potential gave many people the impression that they deserve such an audience.

On social media, everyone believes that anyone to whom they have access owes them an audience: a writer who posted a take, a celebrity who announced a project, a pretty girl just trying to live her life, that anon who said something afflictive

When network connections become activated for any reason or no reason, then every connection seems worthy of traversing.

people just aren’t meant to talk to one another this much. They shouldn’t have that much to say, they shouldn’t expect to receive such a large audience for that expression, and they shouldn’t suppose a right to comment or rejoinder for every thought or notion either.

Facebook and all the rest enjoyed a massive rise in engagement and the associated data-driven advertising profits that the attention-driven content economy created. The same phenomenon also created the influencer economy, in which individual social-media users became valuable as channels for distributing marketing messages or product sponsorships by means of their posts’ real or imagined reach

That’s no surprise, I guess, given that the model was forged in the fires of Big Tech companies such as Facebook, where sociopathy is a design philosophy.

If change is possible, carrying it out will be difficult, because we have adapted our lives to conform to social media’s pleasures and torments. It’s seemingly as hard to give up on social media as it was to give up smoking en masse

Quitting that habit took decades of regulatory intervention, public-relations campaigning, social shaming, and aesthetic shifts. At a cultural level, we didn’t stop smoking just because the habit was unpleasant or uncool or even because it might kill us. We did so slowly and over time, by forcing social life to suffocate the practice. That process must now begin in earnest for social media.

Something may yet survive the fire that would burn it down: social networks, the services’ overlooked, molten core. It was never a terrible idea, at least, to use computers to connect to one another on occasion, for justified reasons, and in moderation

The problem came from doing so all the time, as a lifestyle, an aspiration, an obsession. The offer was always too good to be true, but it’s taken us two decades to realize the Faustian nature of the bargain.

when I first wrote about downscale, the ambition seemed necessary but impossible. It still feels unlikely—but perhaps newly plausible.

To win the soul of social life, we must learn to muzzle it again, across the globe, among billions of people. To speak less, to fewer people and less often–and for them to do the same to you, and everyone else as well

We cannot make social media good, because it is fundamentally bad, deep in its very structure. All we can do is hope that it withers away, and play our small part in helping abandon it.

It’s a lesson to remember as we watch the crypto space melt down, with ex-billionaire Sam Bankman-Fried

It’s not like perfidy in crypto was some hidden secret. At the top of the market, back in December 2021, I wrote a piece very explicitly saying that crypto was a set of Ponzi schemes. It went viral, and I got a huge amount of hate mail from crypto types

one of the more bizarre aspects of the crypto meltdown is the deep anger not just at those who perpetrated it, but at those who were trying to stop the scam from going on. For instance, here’s crypto exchange Coinbase CEO Brian Armstrong, who just a year ago was fighting regulators vehemently, blaming the cops for allowing gambling in the casino he helps run.

FTX.com was an offshore exchange not regulated by the SEC. The problem is that the SEC failed to create regulatory clarity here in the US, so many American investors (and 95% of trading activity) went offshore. Punishing US companies for this makes no sense.

many crypto ‘enthusiasts’ watching Gensler discuss regulation with his predecessor “called for their incarceration or worse.”

Cryptocurrencies are securities, and should fit under securities law, which would have imposed rules that would foster a de facto ban of the entire space. But since regulators had not actually treated them as securities for the last ten years, a whole new gray area of fake law had emerged

Almost as soon as he took office, Gensler sought to fix this situation, and treat them as securities. He began investigating important players

But the legal wrangling to just get the courts to treat crypto as a set of speculative instruments regulated under securities law made the law moot

In May of 2022, a year after Gensler began trying to do something about Terra/Luna, Kwon’s scheme blew up. In a comically-too-late-to-matter gesture, an appeals court then said that the SEC had the right to compel information from Kwon’s now-bankrupt scheme. It is absolute lunacy that well-settled law, like the ability for the SEC to investigate those in the securities business, is now being re-litigated.

Securities and Exchange Commission Chair Gary Gensler, who took office in April of 2021 with a deep background in Wall Street, regulatory policy, and crypto, which he had taught at MIT years before joining the SEC. Gensler came in with the goal of implementing the rule of law in the crypto space, which he knew was full of scams and based on unproven technology. Yesterday, on CNBC, he was again confronted with Andrew Ross Sorkin essentially asking, “Why were you going after minor players when this Ponzi scheme was so flagrant?”

it wasn’t just the courts who were an impediment. Gensler wasn’t the only cop on the beat. Other regulators, like those at the Commodities Futures Trading Commission, the Federal Reserve, or the Office of Comptroller of the Currency, not only refused to take action, but actively defended their regulatory turf against an attempt from the SEC to stop the scams.

Behind this was the fist of political power. Everyone saw the incentives the Senate laid down when every single Republican, plus a smattering of Democrats, defeated the nomination of crypto-skeptic Saule Omarova in becoming the powerful bank regulator at the Comptroller of the Currency

Instead of strong figures like Omarova, we had a weakling acting Comptroller Michael Hsu at the OCC, put there by the excessively cautious Treasury Secretary Janet Yellen. Hsu refused to stop bank interactions with crypto or fintech because, as he told Congress in 2021, “These trends cannot be stopped.”

It’s not just these regulators; everyone wanted a piece of the bureaucratic pie. In March of 2022, before it all unraveled, the Biden administration issued an executive order on crypto. In it, Biden said that virtually every single government agency would have a hand in the space.

That’s… insane. If everyone’s in charge, no one is.

And behind all of these fights was the money and political prestige of some most powerful people in Silicon Valley, who were funding a large political fight to write the rules for crypto, with everyone from former Treasury Secretary Larry Summers to former SEC Chair Mary Jo White on the payroll.

(Even now, even after it was all revealed as a Ponzi scheme, Congress is still trying to write rules favorable to the industry. It’s like, guys, stop it. There’s no more bribe money!)

Moreover, the institution Gensler took over was deeply weakened. Since the Reagan administration, wave after wave of political leader at the SEC has gutted the place and dumbed down the enforcers. Courts have tied up the commission in knots, and Congress has defanged it

Under Trump crypto exploded, because his SEC chair Jay Clayton had no real policy on crypto (and then immediately went into the industry after leaving.) The SEC was so dormant that when Gensler came into office, some senior lawyers actually revolted over his attempt to make them do work.

In other words, the regulators were tied up in the courts, they were against an immensely powerful set of venture capitalists who have poured money into Congress and D.C., they had feeble legal levers, and they had to deal with ‘crypto enthusiasts' who thought they should be jailed or harmed for trying to impose basic rules around market manipulation.

The bottom line is, Gensler is just one regulator, up against a lot of massed power, money, and bad institutional habits. And we as a society simply made the choice through our elected leaders to have little meaningful law enforcement in financial markets, which first became blindingly obvious in 2008 during the financial crisis, and then became comical ten years later when a sector whose only real use cases were money laundering

, Ponzi scheming or buying drugs on the internet, managed to rack up enough political power to bring Tony Blair and Bill Clinton to a conference held in a tax haven billed as ‘the future.’

Pandu Nayak, a vice president for search who leads the company’s search-quality teams, told me that snippets are designed to be helpful to the user, to surface relevant and high-caliber results. He argued that they are “usually an invitation to learn more” about a subject

“It’s a strange world where these massive companies think they’re just going to slap this generative slop at the top of search results and expect that they’re going to maintain quality of the experience,” Nicholas Diakopoulos, a professor of communication studies and computer science at Northwestern University, told me. “I’ve caught myself starting to read the generative results, and then I stop myself halfway through. I’m like, Wait, Nick. You can’t trust this.”

Nayak said the team focuses on the bigger underlying problem, and whether its algorithm can be trained to address it.

If Nayak is right, and people do still follow links even when presented with a snippet, anyone who wants to gain clicks or money through search has an incentive to capitalize on that—perhaps even by flooding the zone with AI-written content.

Nayak told me that Google plans to fight AI-generated spam as aggressively as it fights regular spam, and claimed that the company keeps about 99 percent of spam out of search results.

The result is a world that feels more confused, not less, as a result of new technology.

The Kenya result still pops up on Google, despite viral posts about it. This is a strategic choice, not an error. If a snippet violates Google policy (for example, if it includes hate speech) the company manually intervenes and suppresses it, Nayak said. However, if the snippet is untrue but doesn’t violate any policy or cause harm, the company will not intervene.

experts I spoke with had several ideas for how tech companies might mitigate the potential harms of relying on AI in search

For starters, tech companies could become more transparent about generative AI. Diakopoulos suggested that they could publish information about the quality of facts provided when people ask questions about important topics

They can use a coding technique known as “retrieval-augmented generation,” or RAG, which instructs the bot to cross-check its answer with what is published elsewhere, essentially helping it self-fact-check. (A spokesperson for Google said the company uses similar techniques to improve its output.) They could open up their tools to researchers to stress-test it. Or they could add more human oversight to their outputs, maybe investing in fact-checking efforts.

Fact-checking, however, is a fraught proposition. In January, Google’s parent company, Alphabet, laid off roughly 6 percent of its workers, and last month, the company cut at least 40 jobs in its Google News division. This is the team that, in the past, has worked with professional fact-checking organizations to add fact-checks into search results

Alex Heath, at The Verge, reported that top leaders were among those laid off, and Google declined to give me more information. It certainly suggests that Google is not investing more in its fact-checking partnerships as it builds its generative-AI tool.

Nayak acknowledged how daunting a task human-based fact-checking is for a platform of Google’s extraordinary scale. Fifteen percent of daily searches are ones the search engine hasn’t seen before, Nayak told me. “With this kind of scale and this kind of novelty, there’s no sense in which we can manually curate results.”

Creating an infinite, largely automated, and still accurate encyclopedia seems impossible. And yet that seems to be the strategic direction Google is taking.

A representative for Google told me that this was an example of a “false premise” search, a type that is known to trip up the algorithm. If she were trying to date me, she argued, she wouldn’t just stop at the AI-generated response given by the search engine, but would click the link to fact-check it.

“Very long chat sessions can confuse the underlying chat model in the new Bing,” Microsoft said in a blog on Friday. “To address these issues, we have implemented some changes to help focus the chat sessions.”

Microsoft said in the Wednesday blog that Bing seems to start coming up with strange answers following chat sessions of 15 or more questions after which it can become repetitive or respond in ways that don’t align with its designed tone.

The company said it was trying to train the technology to be more reliable. It is also considering adding a toggle switch, which would allow users to decide whether they want Bing to be more or less creative with its responses.

that a coming storm will sweep elites from power

and that violence might be necessary to save the country.

In October 2021, 17 percent of Americans believed in the conspiracy theory, up from 14 percent in March

the percentage of people who rejected QAnon falsehoods shrank to 34 percent in October from 40 percent in March

After Mr. Trump lost the 2020 presidential election, QAnon was expected to be hobbled without him. But it has persisted despite that and despite efforts by tech platforms to staunch its spread. Forensic linguists have also tried to unmask and defang the anonymous author who signed online messages as Q.

Robert P. Jones, the founder and chief executive of the research group and a social science researcher with decades of experience, said he never expected to be dealing with serious survey questions about whether powerful American institutions were controlled by devil-worshiping, sex-trafficking pedophiles. To have so many Americans agree with such a question, he said, was “stunning.”

Believers are “racially, religiously and politically diverse,”

Among Republicans, 25 percent found QAnon to be valid, compared with 14 percent of independents and 9 percent of Democrats.

Media preferences were a major predictor of QAnon susceptibility, with people who trust far-right news sources such as One America News Network and Newsmax nearly five times more likely to be believers than those who trust mainstream news

Fox News viewers were twice as likely to back QAnon ideas

Most QAnon believers associated Christianity with being American and said that the United States risked losing its culture and identity and must be protected from foreign influence

seven in 10 believers agreed with the lie that the 2020 election was stolen from Mr. Trump.

More than half of QAnon supporters are white, while 20 percent are Hispanic and 13 percent are Black.

They were most likely to have household incomes of less than $50,000 a year, hold at most a high school degree, hail from the South and reside in a suburb.

many corporate technology chiefs have taken a wait-and-see approach to the technology, which has developed a reputation for producing false, misleading and unintelligible results—dubbed AI ‘hallucinations’. 

Though ChatGPT, which is available free online, is considered a consumer app, OpenAI has encouraged companies and startups to build apps on top of its language models—in part by providing access to the underlying computer code for a fee.

here are “certain spaces where it’s clearly directly applicable,” such as summarizing documents or responding to customer queries. Many startups are racing to apply the technology to a wider set of enterprise use case

“I think it’s going to creep into our lives in ways we least expect it,” Mr. Casado said.

the popular suffix “maxxing” for “maximizing” (e.g., “I’m burritomaxxing” instead of “I’m eating a lot of burritos”) is drawn from the incel idea of “looksmaxxing,” or “maximizing attractiveness” through surgical or cosmetic techniques.

Then there’s the word “cucked” for “weakened” or “emasculated.” If the taqueria is out of burritos, you might be “tacocucked,” drawing on the incel idea of being sexually emasculated by more attractive “chads.

These slang terms developed on 4chan precisely because of the site’s anonymity. Since users don’t have identifiable aliases, they signal their in-group status through performative fluency in shared slang

there’s a dark side to the site as well — certain boards, like /r9k/, are known breeding grounds for incel discussion, and the source of the incel words being used today.

finally, we have the word “sigma” for “assertive male,” which comes from an incel’s desired position outside the social hierarchy.

Memes and niche vocabulary become a form of cultural currency, fueling their proliferation.

From there, those words filter out to more mainstream websites such as Reddit and eventually become popularized by viral memes and TikTok trends. Social media algorithms do the rest of the work by curating recommended content for viewers.

Because these terms often spread in ironic contexts, people find them funny, engage with them and are eventually rewarded with more memes featuring incel vocabulary.

Creators are not just aware of this process — they are directly incentivized to abet it. We know that using trending audio helps our videos perform better and that incorporating popular metadata with hashtags or captions will help us reach wider audiences

kids aren’t actually saying “cucked” because they’re “blackpilled”; they’re using it for the same reason all kids use slang: It helps them bond as a group. And what are they bonding over? A shared mockery of incel ideas.

These words capture an important piece of the Gen Z zeitgeist. We should therefore be aware of them, keeping in mind that they’re being used ironically.

Because right-wing commentators aren’t the only ones who think Biden’s mind is basically gone—lots of voters think so too

Of course, a major reason why the public thinks this is because the entirety of the right-wing information superstructure is devoted, on a daily basis, to depicting Biden as severely cognitively compromised

By contrast, most of the news sources the right sees as hyperpartisan Biden spin machines actually strain at being fair-minded and objective, which disinclines them toward producing any sort of muscular pushback against the right’s relentless mischaracterizations.

Since mainstream media venues by and large epistemically rely on the views of the masses to supply journalists with their coverage frames, news operations end up treating popular concerns about Biden’s age as a kind of sacrosanct window into reality rather than as a hype cycle perpetually fed into the ambient collective consciousness by anti-Biden voices intending to sink his reelection chances.

even if we grant every single concern that Klein and others have voiced, it is indisputably true that Joe Biden remains an intellectual giant next to Donald Trump