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The S&P 500 closed Tuesday at the highest level in nearly three months. The Nasdaq has spiked 40% since March 23, fueled by the resilience of Big Tech, and is now within striking distance of all-time highs.

unprecedented stimulus from the Federal Reserve, and investors not wanting to miss out on monster returns once the economy recovers.

That means that while Main Street is still grappling with coronavirus, racial crisis and the impacts of both, Wall Street is doing just fine. Fed policy has allowed markets to decouple from economic reality.

Although the unrest was initially sparked by the killing of George Floyd, the continued broader economic discontent is an undercurrent.

that the American dream is not alive and well.

The divide between rich and poor was worsened by the Great Recession and its aftermath. The US government's response relied heavily on easy money from the Fed, rather than the kind of fiscal stimulus that can help lower-income Americans.

First, the coronavirus pandemic disproportionately hit poorer Americans, many of whom work in the hospitality and service sectors rocked by the pandemic. Nearly 40% of low-income workers lost their jobs in March alone, according to the Fed.

Mnuchin also commented on US markets, saying that they will remain open for the time being instead of being closed as they were after the terrorist attacks in September 2001."We believe in keeping the markets open," Mnuchin told reporters during the press conference. "Everyone wants them open." 

Trump's administration also spoke about other "big" plans to aid workers and the economy in the midst of the fallout. Those plans include help for industries such as airlines that have been hit hard by the coronavirus pandemic.

The Fed said it will support American households and businesses, but it acknowledged "our economy will face severe disruptions."

The social distancing policies imposed to fight the coronavirus crisis have brought the American economy to its knees. Malls are empty. Factories have been shut down. Casinos have gone dark. And countless flights have been suspended. The economic toll is massive.

Aided by extremely low interest rates, US businesses have borrowed heavily over the past decade to hire workers, build factories, research new products and pay for share buybacks. That debt now looks especially treacherous as the economy goes into a tailspin.

Over the subsequent four decades, Thatcherites at think tanks like the Institute of Economic Affairs and the Centre for Policy Studies (which Margaret Thatcher helped set up) described the struggle against both the Labour Party and the broader persistence of Socialism in the Communist and non-Communist world as a “war of ideas.”

Thatcherites, known collectively as the ultras, gained fresh blood in the 2010s as a group of Gen Xers too young to experience Thatcherism in its insurgent early years — including the former home secretary Priti Patel, the former foreign secretary Dominic Raab, the former minister of state for universities Chris Skidmore, Mr. Kwarteng and Ms. Truss — attempted to reboot her ideology for the new millennium.

They followed their idol not only in her antagonism to organized labor but also in her less-known fascination with Asian capitalism. In 2012’s “Britannia Unchained,” a book co-written by the group that remains a Rosetta Stone for the policy surprises of the last month, they slammed the Britons for their eroded work ethic and “culture of excuses” and the “cosseted” public sector unions. They praised China, South Korea, Singapore and Hong Kon

“Britannia Unchained” expressed a desire to go back to the future by restoring Victorian values of hard work, self-improvement and bootstrapping.

While the Gen X Thatcherites didn’t scrimp on data, they also saw something ineffable at the root of British malaise. “Beyond the statistics and economic theories,” they wrote, “there remains a sense in which many of Britain’s problems lie in the sphere of cultural values and mind-set.”

As Thatcher herself put it, “Economics are the method; the object is to change the heart and soul.” Britain needed a leap of faith to restore itself.

Ms. Truss and Mr. Kwarteng seemed to have believed that by patching together all of the most radical policies of Thatcherism (while conveniently dropping the need for spending cuts), they would be incanting a kind of magic spell, an “Open sesame” for “global Britain.” This was their Reagan moment, their moment when, as their favorite metaphors put it, a primordial repressed force would be “unchained,” “unleashed” or “unshackled.”But as a leap of faith, it broke the diver’s neck.

the money markets were not waiting for an act of faith in Laffer Curve fundamentalism after all. This was “Reaganism without the dollar.” Without the confidence afforded to the global reserve currency, the pound went into free fall.

ince the 1970s, the world of think tanks had embraced a framing of the world in terms of discrete spaces that could become what they called laboratories for new policies

The mini-budget subjected the entire economy to experimental treatment. This was put in explicit terms in a celebratory post by a Tory journalist and think tanker claiming that Ms. Truss and Mr. Kwarteng had been “incubated” by the Institute of Economic Affairs in their early years and “Britain is now their laboratory.”

The scientists at the bench discovered that the money markets would not only punish left-wing experiments in changing the balance between states and markets, but they were also sensitive to experiments that pushed too far to the right. A cowed Ms. Truss apologized, and Mr. Kwarteng’s successor has reversed almost all of the planned cuts and limited the term for energy supports.

“I think the differences are still greater than the similarities,” he says. “Hitler was not only more intelligent, but craftier. He was not just a powerful orator, but a talented actor who succeeded in winning over various social milieus. So not just the economically threatened lower middle classes which Trump targeted, but also the upper middle classes. Hitler had many supporters in the German aristocracy.”

Trump was also democratically elected, while Hitler never had a majority vote. “He was appointed by the president of the German Reich.” Then there’s the fact that Trump does not lead a party “which is unconditionally committed to him”.

“A further obvious difference is that Trump doesn’t have a private militia, as Hitler did with the SA, which he used in his first months after coming to power to settle scores with his opponents, like the Communists and Social Democrats. You can’t possibly imagine something similar with Trump – that he’ll be locking Democrats up into concentration camps

“Finally, the American constitution is based on a system of checks and balances. It remains to be seen how far Congress will really limit Trump or if, as is feared, he can override it. It was different with Hitler, who, as we know, managed to eliminate all resistance in the shortest space of time and effectively establish himself as an all-powerful dictator. Within a few months, there was effectively no longer any opposition.”

“Hitler profited from the fact that his opponents always underestimated him,” Ullrich explains. “His conservative allies in government assumed they could tame or ‘civilise’ him – that once he became chancellor he’d become vernünftig (meaning sensible, reasonable). Very quickly it became clear that was an illusion.”

“There were many situations where he could have been stopped. For example in 1923 after the failed Munich putsch – if he’d served his full prison sentence of several years, he wouldn’t have made a political comeback. Instead, he only spent a few months behind bars, [having been released after political pressure] and could rebuild his movement.”

The western powers made the same mistake with their appeasement politics, indecision and indulgence. “In the 1930s Hitler strengthened, rather than weakened, his aggressive intentions,” Ullrich says. “So you could learn from this that you have to react faster and much more vigorously than was the case at the time.”

llrich also contends that if Hindenburg, the president of the Reich, had allowed Chancellor Brüning, of the Centre party, to remain chancellor to the end of 1934, rather than responding to pressure from conservatives to dismiss him in 1932, “then the peak of the economic crisis would have passed and it would have been very questionable whether Hitler could still have come to power”.

At the same time, Hitler’s ascent was no mere fluke. “There were powerful forces in the big industries, but also in the landowning class and the armed forces, which approved of a fascist solution to the crisis.”

If fascism “now just means aggressive nationalism, racism, patriarchy and authoritarianism, then maybe it is back on the agenda,” Bosworth continues. But today’s context is fundamentally different

Today’s “alt-right” agitators “live in a neoliberal global order where the slogan, ‘all for the market, nothing outside the market, no one against the market’ is far more unquestionably accepted than the old fascist slogan of ‘all for the state, nothing outside the state, no one against the state’”.

“What is that if it’s not racially authoritarian?” asks Schama. “If you want to call it fascist, fine. I don’t really care if it’s called that or not. It’s authoritarian, you know, ferociously authoritarian.”

Schama also points to deeply worrying messaging, such as “the parallel universe of lies which are habitual, massive, cumulative”; the criminalization of political opponents; the threat to change the libel laws against the press and the demonization of different racial and ethnic groups, going as far as proposing a Muslim registry.

Schama is clear: Trump is obviously not Hitler. “But, you know, if you like, he’s an entertainment fascist, which may be less sinister but is actually in the end more dangerous. If you’re not looking for jackboots and swastikas – although swastikas are indeed appearing – there’s a kind of laundry list of things which are truly sinister and authoritarian and not business as usual.”

Don’t ignore what people vote fo

f you’re of German heritage, it’s hard to understand how so many people could have bought Mein Kampf and gone on to vote for Hitler. Maybe no one really read it, or got beyond the first few pages of bluster, or took antisemitism seriously, you tell yourself. “Or they liked what he said,

“I think one of the mistakes this time around would be not to think that the people who voted for Trump were serious. They may have been serious for different reasons, but it would be a big mistake not to try and figure out what their reasons were.

Hitler presented himself as a “messiah” offering the public “salvation”, Ullrich points out. With austerity and hostility to the EU and to immigrants riding high, there is fertile ground for European populists next year to seduce with equally simplistic, sweeping “solutions”.

The problem, in Mazower’s view, is that establishment politicians currently have no response

“The Gestapo was piddling compared with the size and reach of surveillance equipment and operations today,

“Very belatedly, everyone is waking up to the fact that there was a general assumption that no government in the west would fall into the wrong hands, that it was safe to acquiesce in this huge expansion of surveillance capabilities, and the debate wasn’t as vigorous as it could have been.”

“Now, there is a lot of discussion about allowing this kind of surveillance apparatus in the wrong hands,” he adds. “And we’ve woken up to this a bit late in the day.”

Ullrich calls crises, “the elixir of rightwing populists”, and urges that politicians “do everything they can to correct the inequalities and social injustice which have arisen in the course of extreme financial capitalism in western countries”

Jane Caplan, a history professor at Oxford University who has written about Trump and fascism, highlights the want of “dissenting voices against marketisation and neoliberalism

The failure to resist the incursion of the market as the only criterion for political utility, or economic utility, has been pretty comprehensive.

Paranoia, bullying and intimidation are a hallmark of authoritarian regimes. They are also alive and well in our culture today, where online trolls, violent thugs at rallies, threats of expensive libel action and of course terrorist acts are equally effective in getting individuals and the press to self-censor.

He shorted oil and, as a proxy for oil, the Canadian dollar. (That is, he bet against both.) Finally, he shorted U.S. equities.

Last October, he listened to an audiobook by the Hardcore History podcaster, Dan Carlin, called “The End Is Always Near.” “So I had pandemics and plagues in my head,” the Australian said. “In December, I started seeing the first articles about this wet-market thing going on in China, and then in early January there was a lot on Twitter about the shit in Wuhan.” He was in Switzerland on a ski holiday with his family, and he bought all the surgical masks and gloves he could find.

The Australian, who spoke on the condition that his name not be used, is a voluble redhead just shy of fifty.

The problem, he said, was that, perhaps more now than ever, Americans lack what he called “social cohesion,” and thus the collective will, to commit to such a path.

perhaps the government should reward each citizen who strictly observed the quarantine with fifty thousand dollars. “The virus would burn out after four weeks,” he said. The U.S. had all the food and water and fuel it would need to survive months, if not years, of total isolation from the world. “If you don’t trade with China, they’re screwed,” he said. “You’d win this war. Let the rest of the world burn.

I’d been eavesdropping for a week on the friend’s WhatsApp conversation with dozens of his acquaintances and colleagues (he called them the Fokkers, for an acronym involving his name), all of them men, most of them expensively educated financial professionals, some of them very rich, a few with connections in high places. The general disposition of the participants, with exceptions, was the opposite of the Australian’s

they expressed the belief, with a conviction that occasionally tipped into stridency or mockery, that the media, the modellers, and the markets were overreacting to the threat of the coronavirus

They mocked Jim Cramer, the host of the market program “Mad Money,” on CNBC, for predicting a great depression and wondering if anyone would ever board an airplane again. Anecdotes, hyperbole: the talking chuckleheads sowing and selling fear.

it’s hard for a coldhearted capitalist to know just how cold the heart must go. Public-health professionals make a cost-benefit calculation, too, with different weightings.

This brutal shock is attacking a body that was already vulnerable. In the event of a global depression, a postmortem might identify COVID-19 as the cause of death, but, as with so many of the virus’s victims, the economy had a preëxisting condition—debt, instead of pulmonary disease.

“It’s as if the virus is almost beside the point,” a trader I know told me. “This was all set up to happen.”

the “smart money,” like the giant asset-management firms Blackstone and the Carlyle Group, was now telling companies to draw down their bank lines, and borrow as much as they could, in case the lenders went out of business or found ways to say no. Sure enough, by March’s end, corporations had reportedly tapped a record two hundred and eight billion dollars from their revolving-credit lines

In a world where we talk, suddenly, of trillions, two hundred billion may not seem like a lot, but it is: in 2007, the subprime-mortgage lender Countrywide Financial, in drawing down “just” $11.5 billion, helped bring the system to its knees.

It is hard to navigate out of the debt trap. Creditors can forgive debtors, but that process, especially at this level, would be almost impossibly laborious and fraught. Meanwhile, defaults flood the market with collateral, be it buildings, stocks, or aircraft. The price of that collateral collapses—haircuts for baldheads—leading to more defaults.

In New York State, where nearly half a million new claims had been filed in two weeks, the unemployment-insurance trust began to teeter toward insolvency. Come summer, there would be no money left to pay unemployment benefits.

As April arrived, businesses, large and small, decided not to pay rent, either because they didn’t have the cash on hand or because, with a recession looming, they wanted to preserve what cash they had. Furloughed or fired employees, meanwhile, faced similar decisions

On March 20th, Goldman Sachs spooked the world, by predicting a twenty-four-per-cent decline in G.D.P. in the second quarter, a falloff in activity that seemed at once both unthinkable and inevitable. Subsequent predictions grew even more disma

But the headline-grabbing differences between the findings of these Nobel laureates are less significant than the profound agreement in their scientific approach to economic questions, which is characterized by formulating and testing precise hypotheses

I’m troubled by the sense among skeptics that disagreements about the answers to certain questions suggest that economics is a confused discipline, a fake science whose findings cannot be a useful basis for making policy decisions.

It is true that the answers to many “big picture” macroeconomic questions — like the causes of recessions or the determinants of growth — remain elusive.

As is the case with epidemiologists, the fundamental challenge faced by economists — and a root cause of many disagreements in the field — is our limited ability to run experiments

economists have recently begun to overcome these challenges by developing tools that approximate scientific experiments to obtain compelling answer

Other economic studies have taken advantage of the constraints inherent in a particular policy to obtain scientific evidence

Even when such experiments are unfeasible, there are ways to use “big data” to help answer policy questions

it also explained why committees have so much trouble coming to consistent conclusions and why, with an increasingly polarized electorate, democracy can become increasingly dysfunctional.

until Kenneth drew on the techniques of topology (that is, the study of geometric properties and spatial relations), no one had ever been able to establish precise conditions under which there would be prices that would clear all markets, or under which one could assume that the market outcome was optimal

in the early 1950s, he clarified the very specific conditions under which market outcomes were for the best and, of equal importance, the far more general conditions under which public interventions in markets had the potential to make things better.

By the end of World War II, however, American corporations were facing severe talent shortages. Their senior executives were growing old, and a dearth of hiring from the Depression through the war had resulted in a shortfall of able, well-trained managers. Finding people who had the potential to rise quickly through the ranks became an overriding preoccupation of American businesses. They began to devise a formal hiring-and-management system based in part on new studies of human behavior, and in part on military techniques developed during both world wars, when huge mobilization efforts and mass casualties created the need to get the right people into the right roles as efficiently as possible. By the 1950s, it was not unusual for companies to spend days with young applicants for professional jobs, conducting a battery of tests, all with an eye toward corner-office potential.

But companies abandoned their hard-edged practices for another important reason: many of their methods of evaluation turned out not to be very scientific.

this regime, so widespread in corporate America at mid-century, had almost disappeared by 1990. “I think an HR person from the late 1970s would be stunned to see how casually companies hire now,”

Many factors explain the change, he said, and then he ticked off a number of them: Increased job-switching has made it less important and less economical for companies to test so thoroughly. A heightened focus on short-term financial results has led to deep cuts in corporate functions that bear fruit only in the long term. The Civil Rights Act of 1964, which exposed companies to legal liability for discriminatory hiring practices, has made HR departments wary of any broadly applied and clearly scored test that might later be shown to be systematically biased.

about a quarter of the country’s corporations were using similar tests to evaluate managers and junior executives, usually to assess whether they were ready for bigger roles.

He has encouraged the company’s HR executives to think about applying the games to the recruitment and evaluation of all professional workers.

Knack makes app-based video games, among them Dungeon Scrawl, a quest game requiring the player to navigate a maze and solve puzzles, and Wasabi Waiter, which involves delivering the right sushi to the right customer at an increasingly crowded happy hour. These games aren’t just for play: they’ve been designed by a team of neuroscientists, psychologists, and data scientists to suss out human potential. Play one of them for just 20 minutes, says Guy Halfteck, Knack’s founder, and you’ll generate several megabytes of data, exponentially more than what’s collected by the SAT or a personality test. How long you hesitate before taking every action, the sequence of actions you take, how you solve problems—all of these factors and many more are logged as you play, and then are used to analyze your creativity, your persistence, your capacity to learn quickly from mistakes, your ability to prioritize, and even your social intelligence and personality. The end result, Halfteck says, is a high-resolution portrait of your psyche and intellect, and an assessment of your potential as a leader or an innovator.

When the results came back, Haringa recalled, his heart began to beat a little faster. Without ever seeing the ideas, without meeting or interviewing the people who’d proposed them, without knowing their title or background or academic pedigree, Knack’s algorithm had identified the people whose ideas had panned out. The top 10 percent of the idea generators as predicted by Knack were in fact those who’d gone furthest in the process.

What Knack is doing, Haringa told me, “is almost like a paradigm shift.” It offers a way for his GameChanger unit to avoid wasting time on the 80 people out of 100—nearly all of whom look smart, well-trained, and plausible on paper—whose ideas just aren’t likely to work out.

Aptitude, skills, personal history, psychological stability, discretion, loyalty—companies at the time felt they had a need (and the right) to look into them all. That ambit is expanding once again, and this is undeniably unsettling. Should the ideas of scientists be dismissed because of the way they play a game? Should job candidates be ranked by what their Web habits say about them? Should the “data signature” of natural leaders play a role in promotion? These are all live questions today, and they prompt heavy concerns: that we will cede one of the most subtle and human of skills, the evaluation of the gifts and promise of other people, to machines; that the models will get it wrong; that some people will never get a shot in the new workforce.

scoring distance from work could violate equal-employment-opportunity standards. Marital status? Motherhood? Church membership? “Stuff like that,” Meyerle said, “we just don’t touch”—at least not in the U.S., where the legal environment is strict. Meyerle told me that Evolv has looked into these sorts of factors in its work for clients abroad, and that some of them produce “startling results.”

consider the alternative. A mountain of scholarly literature has shown that the intuitive way we now judge professional potential is rife with snap judgments and hidden biases, rooted in our upbringing or in deep neurological connections that doubtless served us well on the savanna but would seem to have less bearing on the world of work.

We may like to think that society has become more enlightened since those days, and in many ways it has, but our biases are mostly unconscious, and they can run surprisingly deep. Consider race. For a 2004 study called “Are Emily and Greg More Employable Than Lakisha and Jamal?,” the economists Sendhil Mullainathan and Marianne Bertrand put white-sounding names (Emily Walsh, Greg Baker) or black-sounding names (Lakisha Washington, Jamal Jones) on similar fictitious résumés, which they then sent out to a variety of companies in Boston and Chicago. To get the same number of callbacks, they learned, they needed to either send out half again as many résumés with black names as those with white names, or add eight extra years of relevant work experience to the résumés with black names.

a sociologist at Northwestern, spent parts of the three years from 2006 to 2008 interviewing professionals from elite investment banks, consultancies, and law firms about how they recruited, interviewed, and evaluated candidates, and concluded that among the most important factors driving their hiring recommendations were—wait for it—shared leisure interests.

Lacking “reliable predictors of future performance,” Rivera writes, “assessors purposefully used their own experiences as models of merit.” Former college athletes “typically prized participation in varsity sports above all other types of involvement.” People who’d majored in engineering gave engineers a leg up, believing they were better prepared.

the prevailing system of hiring and management in this country involves a level of dysfunction that should be inconceivable in an economy as sophisticated as ours. Recent survey data collected by the Corporate Executive Board, for example, indicate that nearly a quarter of all new hires leave their company within a year of their start date, and that hiring managers wish they’d never extended an offer to one out of every five members on their team

In the late 1990s, as these assessments shifted from paper to digital formats and proliferated, data scientists started doing massive tests of what makes for a successful customer-support technician or salesperson. This has unquestionably improved the quality of the workers at many firms.

In 2010, however, Xerox switched to an online evaluation that incorporates personality testing, cognitive-skill assessment, and multiple-choice questions about how the applicant would handle specific scenarios that he or she might encounter on the job. An algorithm behind the evaluation analyzes the responses, along with factual information gleaned from the candidate’s application, and spits out a color-coded rating: red (poor candidate), yellow (middling), or green (hire away). Those candidates who score best, I learned, tend to exhibit a creative but not overly inquisitive personality, and participate in at least one but not more than four social networks, among many other factors. (Previous experience, one of the few criteria that Xerox had explicitly screened for in the past, turns out to have no bearing on either productivity or retention

When Xerox started using the score in its hiring decisions, the quality of its hires immediately improved. The rate of attrition fell by 20 percent in the initial pilot period, and over time, the number of promotions rose. Xerox still interviews all candidates in person before deciding to hire them, Morse told me, but, she added, “We’re getting to the point where some of our hiring managers don’t even want to interview anymore”

Gone are the days, Ostberg told me, when, say, a small survey of college students would be used to predict the statistical validity of an evaluation tool. “We’ve got a data set of 347,000 actual employees who have gone through these different types of assessments or tools,” he told me, “and now we have performance-outcome data, and we can split those and slice and dice by industry and location.”

Evolv’s tests allow companies to capture data about everybody who applies for work, and everybody who gets hired—a complete data set from which sample bias, long a major vexation for industrial-organization psychologists, simply disappears. The sheer number of observations that this approach makes possible allows Evolv to say with precision which attributes matter more to the success of retail-sales workers (decisiveness, spatial orientation, persuasiveness) or customer-service personnel at call centers (rapport-building)

There are some data that Evolv simply won’t use, out of a concern that the information might lead to systematic bias against whole classes of people

the idea that hiring was a science fell out of favor. But now it’s coming back, thanks to new technologies and methods of analysis that are cheaper, faster, and much-wider-ranging than what we had before

what most excites him are the possibilities that arise from monitoring the entire life cycle of a worker at any given company.

Now the two companies are working together to marry pre-hire assessments to an increasing array of post-hire data: about not only performance and duration of service but also who trained the employees; who has managed them; whether they were promoted to a supervisory role, and how quickly; how they performed in that role; and why they eventually left.

What begins with an online screening test for entry-level workers ends with the transformation of nearly every aspect of hiring, performance assessment, and management.

I turned to Sandy Pentland, the director of the Human Dynamics Laboratory at MIT. In recent years, Pentland has pioneered the use of specialized electronic “badges” that transmit data about employees’ interactions as they go about their days. The badges capture all sorts of information about formal and informal conversations: their length; the tone of voice and gestures of the people involved; how much those people talk, listen, and interrupt; the degree to which they demonstrate empathy and extroversion; and more. Each badge generates about 100 data points a minute.

he tried the badges out on about 2,500 people, in 21 different organizations, and learned a number of interesting lessons. About a third of team performance, he discovered, can usually be predicted merely by the number of face-to-face exchanges among team members. (Too many is as much of a problem as too few.) Using data gathered by the badges, he was able to predict which teams would win a business-plan contest, and which workers would (rightly) say they’d had a “productive” or “creative” day. Not only that, but he claimed that his researchers had discovered the “data signature” of natural leaders, whom he called “charismatic connectors” and all of whom, he reported, circulate actively, give their time democratically to others, engage in brief but energetic conversations, and listen at least as much as they talk.

His group is developing apps to allow team members to view their own metrics more or less in real time, so that they can see, relative to the benchmarks of highly successful employees, whether they’re getting out of their offices enough, or listening enough, or spending enough time with people outside their own team.

Torrents of data are routinely collected by American companies and now sit on corporate servers, or in the cloud, awaiting analysis. Bloomberg reportedly logs every keystroke of every employee, along with their comings and goings in the office. The Las Vegas casino Harrah’s tracks the smiles of the card dealers and waitstaff on the floor (its analytics team has quantified the impact of smiling on customer satisfaction). E‑mail, of course, presents an especially rich vein to be mined for insights about our productivity, our treatment of co-workers, our willingness to collaborate or lend a hand, our patterns of written language, and what those patterns reveal about our intelligence, social skills, and behavior.

people analytics will ultimately have a vastly larger impact on the economy than the algorithms that now trade on Wall Street or figure out which ads to show us. He reminded me that we’ve witnessed this kind of transformation before in the history of management science. Near the turn of the 20th century, both Frederick Taylor and Henry Ford famously paced the factory floor with stopwatches, to improve worker efficiency.

“The quantities of data that those earlier generations were working with,” he said, “were infinitesimal compared to what’s available now. There’s been a real sea change in the past five years, where the quantities have just grown so large—petabytes, exabytes, zetta—that you start to be able to do things you never could before.”

People analytics will unquestionably provide many workers with more options and more power. Gild, for example, helps companies find undervalued software programmers, working indirectly to raise those people’s pay. Other companies are doing similar work. One called Entelo, for instance, specializes in using algorithms to identify potentially unhappy programmers who might be receptive to a phone cal

He sees it not only as a boon to a business’s productivity and overall health but also as an important new tool that individual employees can use for self-improvement: a sort of radically expanded The 7 Habits of Highly Effective People, custom-written for each of us, or at least each type of job, in the workforce.

the most exotic development in people analytics today is the creation of algorithms to assess the potential of all workers, across all companies, all the time.

The way Gild arrives at these scores is not simple. The company’s algorithms begin by scouring the Web for any and all open-source code, and for the coders who wrote it. They evaluate the code for its simplicity, elegance, documentation, and several other factors, including the frequency with which it’s been adopted by other programmers. For code that was written for paid projects, they look at completion times and other measures of productivity. Then they look at questions and answers on social forums such as Stack Overflow, a popular destination for programmers seeking advice on challenging projects. They consider how popular a given coder’s advice is, and how widely that advice ranges.

The algorithms go further still. They assess the way coders use language on social networks from LinkedIn to Twitter; the company has determined that certain phrases and words used in association with one another can distinguish expert programmers from less skilled ones. Gild knows these phrases and words are associated with good coding because it can correlate them with its evaluation of open-source code, and with the language and online behavior of programmers in good positions at prestigious companies.

having made those correlations, Gild can then score programmers who haven’t written open-source code at all, by analyzing the host of clues embedded in their online histories. They’re not all obvious, or easy to explain. Vivienne Ming, Gild’s chief scientist, told me that one solid predictor of strong coding is an affinity for a particular Japanese manga site.

Gild’s CEO, Sheeroy Desai, told me he believes his company’s approach can be applied to any occupation characterized by large, active online communities, where people post and cite individual work, ask and answer professional questions, and get feedback on projects. Graphic design is one field that the company is now looking at, and many scientific, technical, and engineering roles might also fit the bill. Regardless of their occupation, most people leave “data exhaust” in their wake, a kind of digital aura that can reveal a lot about a potential hire.

professionally relevant personality traits can be judged effectively merely by scanning Facebook feeds and photos. LinkedIn, of course, captures an enormous amount of professional data and network information, across just about every profession. A controversial start-up called Klout has made its mission the measurement and public scoring of people’s online social influence.

Mullainathan expressed amazement at how little most creative and professional workers (himself included) know about what makes them effective or ineffective in the office. Most of us can’t even say with any certainty how long we’ve spent gathering information for a given project, or our pattern of information-gathering, never mind know which parts of the pattern should be reinforced, and which jettisoned. As Mullainathan put it, we don’t know our own “production function.”

Over time, better job-matching technologies are likely to begin serving people directly, helping them see more clearly which jobs might suit them and which companies could use their skills. In the future, Gild plans to let programmers see their own profiles and take skills challenges to try to improve their scores. It intends to show them its estimates of their market value, too, and to recommend coursework that might allow them to raise their scores even more. Not least, it plans to make accessible the scores of typical hires at specific companies, so that software engineers can better see the profile they’d need to land a particular job

Knack, for its part, is making some of its video games available to anyone with a smartphone, so people can get a better sense of their strengths, and of the fields in which their strengths would be most valued. (Palo Alto High School recently adopted the games to help students assess careers.) Ultimately, the company hopes to act as matchmaker between a large network of people who play its games (or have ever played its games) and a widening roster of corporate clients, each with its own specific profile for any given type of job.

When I began my reporting for this story, I was worried that people analytics, if it worked at all, would only widen the divergent arcs of our professional lives, further gilding the path of the meritocratic elite from cradle to grave, and shutting out some workers more definitively. But I now believe the opposite is likely to happen, and that we’re headed toward a labor market that’s fairer to people at every stage of their careers

For decades, as we’ve assessed people’s potential in the professional workforce, the most important piece of data—the one that launches careers or keeps them grounded—has been educational background: typically, whether and where people went to college, and how they did there. Over the past couple of generations, colleges and universities have become the gatekeepers to a prosperous life. A degree has become a signal of intelligence and conscientiousness, one that grows stronger the more selective the school and the higher a student’s GPA, that is easily understood by employers, and that, until the advent of people analytics, was probably unrivaled in its predictive powers.

the limitations of that signal—the way it degrades with age, its overall imprecision, its many inherent biases, its extraordinary cost—are obvious. “Academic environments are artificial environments,” Laszlo Bock, Google’s senior vice president of people operations, told The New York Times in June. “People who succeed there are sort of finely trained, they’re conditioned to succeed in that environment,” which is often quite different from the workplace.

because one’s college history is such a crucial signal in our labor market, perfectly able people who simply couldn’t sit still in a classroom at the age of 16, or who didn’t have their act together at 18, or who chose not to go to graduate school at 22, routinely get left behind for good. That such early factors so profoundly affect career arcs and hiring decisions made two or three decades later is, on its face, absurd.

I spoke with managers at a lot of companies who are using advanced analytics to reevaluate and reshape their hiring, and nearly all of them told me that their research is leading them toward pools of candidates who didn’t attend college—for tech jobs, for high-end sales positions, for some managerial roles. In some limited cases, this is because their analytics revealed no benefit whatsoever to hiring people with college degrees; in other cases, and more often, it’s because they revealed signals that function far better than college history,

Google, too, is hiring a growing number of nongraduates. Many of the people I talked with reported that when it comes to high-paying and fast-track jobs, they’re reducing their preference for Ivy Leaguers and graduates of other highly selective schools.

This process is just beginning. Online courses are proliferating, and so are online markets that involve crowd-sourcing. Both arenas offer new opportunities for workers to build skills and showcase competence. Neither produces the kind of instantly recognizable signals of potential that a degree from a selective college, or a first job at a prestigious firm, might. That’s a problem for traditional hiring managers, because sifting through lots of small signals is so difficult and time-consuming.

all of these new developments raise philosophical questions. As professional performance becomes easier to measure and see, will we become slaves to our own status and potential, ever-focused on the metrics that tell us how and whether we are measuring up? Will too much knowledge about our limitations hinder achievement and stifle our dreams? All I can offer in response to these questions, ironically, is my own gut sense, which leads me to feel cautiously optimistic.

Google’s understanding of the promise of analytics is probably better than anybody else’s, and the company has been changing its hiring and management practices as a result of its ongoing analyses. (Brainteasers are no longer used in interviews, because they do not correlate with job success; GPA is not considered for anyone more than two years out of school, for the same reason—the list goes on.) But for all of Google’s technological enthusiasm, these same practices are still deeply human. A real, live person looks at every résumé the company receives. Hiring decisions are made by committee and are based in no small part on opinions formed during structured interviews.

Investment is usually the result of forgoing consumption. In a purely agrarian society, early humans had to choose how much grain to eat after the harvest and how much to save for future planting. The latter was investment.

In a more modern society, we allocate our productive capacity to producing pure consumer goods such as hamburgers and hot dogs, and investment goods such as semiconductor foundries. If we create one dollar worth of hamburgers today, then our gross national product is higher by one dollar.

Investment need not always take the form of a privately owned physical product. The most common example of nonphysical investment is investment in human capital.

In an economy that is closed to the outside world, investment can come only from the forgone consumption—the saving—of private individuals, private firms, or government.

In an open economy, however, investment can surge at the same time that a nation’s saving is low because a country can borrow the resources necessary to invest from neighboring countries.

That economists have a fairly strong understanding of firms’ investment behavior makes sense. A firm that maximizes its profits must address investment using the framework discussed in this article.

This method of financing investment has been very important in the United States. The industrial base of the United States in the nineteenth century—railroads, factories, and so on—was built on foreign finance, especially from Britain. More recently, the United States has repeatedly posted significant investment growth and very low savings.

Investment fluctuates a lot because the fundamentals that drive investment—output prices, interest rates, and taxes—also fluctuate. But economists do not fully understand fluctuations in investment. Indeed, the sharp swings in investment that occur might require an extension to the Jorgenson theory.

In Jorgenson’s user cost model, firms will purchase a machine if the extra revenue the machine generates is a smidgen more than its cost.

The general conclusion is that there is a gain to waiting if there is uncertainty and if the installation of the machine entails sunk costs, that is, costs that cannot be recovered once spent.

Although quantifying this gain exactly is a highly mathematical exercise, the reasoning is straightforward. That would explain why firms typically want to invest only in projects that have a high expected profit.

The fact of irreversibility might explain the large fluctuations in investment that we observe.

The theory of investment dates back to the giants of economics. irving fisher, arthur cecil pigou, and alfred marshall all made contributions; as did john maynard keynes, whose Marshallian user cost theory is a central feature in his General Theory.

Consumer behavior is harder to study than firms’ behavior. Market forces that drive irrational people out of the marketplace are much weaker than market forces that drive bad companies from the market.

Because the saving response of consumers must be known if one is to fully understand the impact of any investment policy, and because saving behavior is so poorly understood, much work remains to be done.

Rajan’s real aim seems to be movement “toward one borderless world,” with stronger communities a perhaps helpful means to that end

On one page, Rajan recommends that “powers should stay at the most decentralized level consistent with their effective use,” but on the next he declares that “when inclusiveness goes up against localism, inclusiveness should always triumph.”

When genuine community does make a return in the book’s section on prescriptions, Rajan sacrifices it willingly.

Facebook shut down the team in late 2019.

“We have a role to play, which is why we’ve built tools and controls to help people manage when and how they use our services,” she said in the statement. “Furthermore, we have a dedicated team working across our platforms to better understand these issues and ensure people are using our apps in ways that are meaningful to them.”

They wrote that they don’t consider the behavior to be a clinical addiction because it doesn’t affect the brain in the same way as gambling or substance abuse. In one document, they noted that “activities like shopping, sex and Facebook use, when repetitive and excessive, may cause problems for some people.”

In March 2020, several months after the well-being team was dissolved, researchers who had been on the team shared a slide deck internally with some of the findings and encouraged other teams to pick up the work.

The researchers estimated these issues affect about 12.5% of the flagship app’s more than 2.9 billion users, or more than 360 million people. About 10% of users in the U.S., one of Facebook’s most lucrative markets, exhibit this behavior

In the Philippines and in India, which is the company’s largest market, the employees put the figure higher, at around 25%.

“Why should we care?” the researchers wrote in the slide deck. “People perceive the impact. In a comparative study with competitors, people perceived lower well-being and higher problematic use on Facebook compared to any other service.

Facebook’s findings are consistent with what many external researchers have observed for years,

said Brian Primack, a professor of public health and medicine and dean of the College of Education and Health Professions at the University of Arkansas

His research group followed about a thousand people over six months in a nationally representative survey and found that the amount of social media that a person used was the No. 1 predictor of the variables they measured for who became depressed.

In late 2017, a Facebook executive and a researcher wrote a public blog post that outlined some of the issues with social-media addiction. According to the post, the company had found that while passive consumption of social media could make you feel worse, the opposite was true of more active social-media use.

Inside Facebook, the researchers registered concern about the direction of Facebook’s focus on certain metrics, including the number of times a person logs into the app, which the company calls a session. “One of the worries with using sessions as a north star is we want to be extra careful not to game them by creating bad experiences for vulnerable populations,” a researcher wrote, referring to elements designed to draw people back to Facebook frequently, such as push notifications.

Facebook then made a switch to more heavily weigh “meaningful social interactions” in its news feed as a way to combat passive consumption. One side effect of that change, as outlined in a previous Journal article in The Facebook Files, was that the company’s algorithms rewarded content that was angry or sensational, because those posts increased engagement from users.

Facebook said any algorithm can promote objectionable or harmful content and that the company is doing its best to mitigate the problem.

“Every second that I wasn’t occupied by something I had to do I was fooling around on my phone scrolling through Facebook,” Ms. Gandy said. “Facebook took over my brain.”

“Actively interacting with people—especially sharing messages, posts and comments with close friends and reminiscing about past interactions—is linked to improvements in well-being,” the company said.

The well-being team, according to people familiar with the matter, was reshuffled at least twice since late 2017 before it was disbanded, and could get only about half of the resources the team requested to do its work.

In 2018, Facebook’s researchers surveyed 20,000 U.S. users and paired their answers with data about their behavior on Facebook. The researchers found about 3% of these users said they experienced “serious problems” in their sleep, work or relationships related to their time on Facebook that they found difficult to change. Some of the researchers’ work was published in a 2019 paper.

According to that study, the researchers also said that a liberal interpretation of the results would be that 14% of respondents spent “a lot more time on Facebook than they want to,” although they didn’t label this group problematic users.

In 2019, the researchers had come to a new figure: What they called problematic use affects 12.5% of people on Facebook, they said. This survey used a broader definition for the issue, including users who reported negative results on key aspects of their life as well as feelings of guilt or a loss of control, according to the documents.

The researchers also asked Facebook users what aspects of Facebook triggered them most. The users said the app’s many notifications sucked them in. “Red dots are toxic on the home screen,” a male young adult in the U.S. told the researchers, referring to the symbol that alerts a user to new content.

One entrepreneur came up with his own solution to some of these issues. In 2016, software developer Louis Barclay manually unfollowed all the people, pages and groups he saw on Facebook in an attempt to be more deliberate about how he used technology. The process, which isn’t the same as unfriending, took him days, but he was happy with the result: an empty newsfeed that no longer sucked him in for hours. He could still visit the profile pages of everyone he wanted to connect with on Facebook, but their content would no longer appear in the never-ending scroll of posts.

Thinking other people might benefit from a similar experience on Facebook, he built a tool that would enable anyone to automate the process. He created it as a piece of add-on software called a browser extension that anyone could download. He called it Unfollow Everything and made it available on Chrome’s web store free of charge.

In July, Facebook sent Mr. Barclay a cease-and-desist letter, which the inventor earlier wrote about for Slate, saying his tool was a breach of its terms of service for automating user interactions. It also permanently disabled Mr. Barclay’s personal Facebook and Instagram accounts.

Ms. Lever, the company spokeswoman, said Mr. Barclay’s extension could pose risks if abused, and said Facebook offers its own unfollow tool that allows users to manually unfollow accounts.

We haven’t seen this combination of economic anxiety and high inflation before. Economists are weighing the risks of a U.S. recession, and companies in many industries are worried that their businesses are slowing.

Fast growing start-ups in particular need the faith of investors, customers and employees to keep the momentum going.

If within a few months, stock prices bounce back, investors start putting money into start-ups again and the market for initial public offerings unfreezes, the industry might be fine. But if investors stay skittish for many months or years, that could lead to a major shake-up.

he forces his employees to bridge the enormous gap between technological reality and his dreams. This disconnect fosters a negligent and sometimes cruel workplace, to disastrous effect.

That fully self-driving announcement that so delighted his fans came as a far more jarring revelation to the project’s engineers, who found out about their staggering new mission when Mr. Musk tweeted about it.

This is the fundamental weakness of every organization run as a cult of personality: The dear leader can’t be everywhere or make every decision but often fails to provide the clear code of values that allows managers to independently shape their decisions around common goals.

Lawsuits by workers and California’s Department of Fair Employment and Housing allege that Black workers were tasked with menial physical labor in parts of the factory nicknamed “the plantation,” where they were subjected to racist slurs and graffiti.

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Female workers have sued, alleging a pervasive culture of sexual harassment and groping by supervisors. Mr. Musk was indifferent, emailing workers who experienced abuse that “it is important to be thick-skinned.”

He ultimately gave up and cobbled together a manual-labor-intensive production line in an open-air tent.

Mr. Musk’s reliance on hype is especially jarring.

By moving to buy Twitter, Mr. Musk has not only added another distraction to his long list but has also already shown the same drive to announce sweeping decisions in public.

Ultimately Mr. Musk’s goals for Twitter, as they are for Tesla, are not about making the right decisions for his companies or the people who make them possible.

They are about playing to the crowd and burnishing the legend that keeps fresh bodies and minds moving through the businesses that chew them up and spit them out.

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.’

Microsoft leaders felt “enormous urgency” for it to be the company to bring this technology to market, because others around the world are working on similar tech but might not have the resources or inclination to build it as responsibly, says Sarah Bird, a leader on Microsoft’s responsible AI team.

One common starting point for such models is what is essentially a download or “scrape” of most of the internet. In the past, these language models were used to try to understand text, but the new generation of them, part of the revolution in “generative” AI, uses those same models to create texts by trying to guess, one word at a time, the most likely word to come next in any given sequence.

Wide-scale testing gives Microsoft and OpenAI a big competitive edge by enabling them to gather huge amounts of data about how people actually use such chatbots. Both the prompts users input into their systems, and the results their AIs spit out, can then be fed back into a complicated system—which includes human content moderators paid by the companies—to improve it.

, being first to market with a chat-based AI gives these companies a huge initial lead over companies that have been slower to release their own chat-based AIs, such as Google.

rarely has an experiment like Microsoft and OpenAI’s been rolled out so quickly, and at such a broad scale.

Among those who build and study these kinds of AIs, Mr. Altman’s case for experimenting on the global public has inspired responses ranging from raised eyebrows to condemnation.

The fact that we’re all guinea pigs in this experiment doesn’t mean it shouldn’t be conducted, says Nathan Lambert, a research scientist at the AI startup Huggingface.

“I would kind of be happier with Microsoft doing this experiment than a startup, because Microsoft will at least address these issues when the press cycle gets really bad,” says Dr. Lambert. “I think there are going to be a lot of harms from this kind of AI, and it’s better people know they are coming,” he adds.

Others, particularly those who study and advocate for the concept of “ethical AI” or “responsible AI,” argue that the global experiment Microsoft and OpenAI are conducting is downright dangerous

Celeste Kidd, a professor of psychology at University of California, Berkeley, studies how people acquire knowledge

Her research has shown that people learning about new things have a narrow window in which they form a lasting opinion. Seeing misinformation during this critical initial period of exposure to a new concept—such as the kind of misinformation that chat-based AIs can confidently dispense—can do lasting harm, she says.

Dr. Kidd likens OpenAI’s experimentation with AI to exposing the public to possibly dangerous chemicals. “Imagine you put something carcinogenic in the drinking water and you were like, ‘We’ll see if it’s carcinogenic.’ After, you can’t take it back—people have cancer now,”

Part of the challenge with AI chatbots is that they can sometimes simply make things up. Numerous examples of this tendency have been documented by users of both ChatGPT and OpenA

These models also tend to be riddled with biases that may not be immediately apparent to users. For example, they can express opinions gleaned from the internet as if they were verified facts

When millions are exposed to these biases across billions of interactions, this AI has the potential to refashion humanity’s views, at a global scale, says Dr. Kidd.

OpenAI has talked publicly about the problems with these systems, and how it is trying to address them. In a recent blog post, the company said that in the future, users might be able to select AIs whose “values” align with their own.

“We believe that AI should be a useful tool for individual people, and thus customizable by each user up to limits defined by society,” the post said.

Eliminating made-up information and bias from chat-based search engines is impossible given the current state of the technology, says Mark Riedl, a professor at Georgia Institute of Technology who studies artificial intelligence

He believes the release of these technologies to the public by Microsoft and OpenAI is premature. “We are putting out products that are still being actively researched at this moment,” he adds. 

in other areas of human endeavor—from new drugs and new modes of transportation to advertising and broadcast media—we have standards for what can and cannot be unleashed on the public. No such standards exist for AI, says Dr. Riedl.

To modify these AIs so that they produce outputs that humans find both useful and not-offensive, engineers often use a process called “reinforcement learning through human feedback.

that’s a fancy way of saying that humans provide input to the raw AI algorithm, often by simply saying which of its potential responses to a query are better—and also which are not acceptable at all.

Microsoft’s and OpenAI’s globe-spanning experiments on millions of people are yielding a fire hose of data for both companies. User-entered prompts and the AI-generated results are fed back through a network of paid human AI trainers to further fine-tune the models,

Huggingface’s Dr. Lambert says that any company, including his own, that doesn’t have this river of real-world usage data helping it improve its AI is at a huge disadvantage

In chatbots, in some autonomous-driving systems, in the unaccountable AIs that decide what we see on social media, and now, in the latest applications of AI, again and again we are the guinea pigs on which tech companies are testing new technology.

It may be the case that there is no other way to roll out this latest iteration of AI—which is already showing promise in some areas—at scale. But we should always be asking, at times like these: At what price?

We’ve gone from advertizing supporting journalism to journalism supporting corporate propaganda. At the rate we’re going, as the line between church and state is deliberately blurred by desperate media companies, we may end up with a handful of actual independent online magazines and newspapers and a vast industry of corporate propaganda designed to look like the real thing. If we’re lucky.