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economic output must flow either to owners of capital, in the form of profits, dividends and rents, or to labour, as wages, salaries and perks. Economists refer to this as the “capital” or “labour” share of GDP. Who has the upper hand in the post-lockdown economy?

First we calculate a high-frequency measure of the capital-labour share across 30 mostly rich countries.

In 2020 the aggregate labour share across this group soared (see chart 1). This was largely because firms continued to pay people’s wages—helped, in large part, by government-stimulus programmes—even as GDP collapsed. Advantage, labour.

More recently, however, the battle seems to have shifted in favour of capital.

most economists anyway argue that labour’s share is not a perfect gauge of economic fairness, since it is so hard to measure.

In the first camp is Britain. There, underlying wage growth is in the region of 5% a year, unusually fast by rich-world standards.

But corporations seem not to have much pricing power, meaning that they are struggling to fully offset higher costs in the form of higher prices.

Labour seems to be winning out at the expense of capital.

The second group consists of most other rich countries outside America.

There, neither labour nor capital seems able to triumph. After correcting for pandemic-related distortions Japan’s pay growth appears to be slowing to below 1% a year

Pay settlements in Italy and Spain are treading water, while wage growth in Australia, France and Germany remains well below where it was before the pandemic. Workers in these places are not really joining in with the inflationary party.

In Europe pre-tax profit margins, as measured in the national accounts, have risen in recent months but remain below where they were just before the pandemic.

In Japan the “recurring” profits before tax of large and medium-sized firms recently returned to pre-pandemic levels. The profits of smaller firms remain well below, however.

A series of unusually large stimulus payments may mean that households are able to absorb the higher prices that companies impose.

Wages are rising, but nonetheless markups are responsible for more than 70% of inflation since late 2019,

In a recent report, analysts at Bank of America argue that greater pricing power helps explain why American equities have a higher price-earnings ratio than European ones.

Some economists wonder if workers will before long demand even higher wages to compensate for higher shop prices.

With the Ukraine war entering its second month, few Indian newspapers and mainstream commentators have bluntly questioned the government’s decision to refrain from condemning Russia, except Subramanian Swamy, a senior member of Modi’s BJP who sometimes criticizes his own party’s foreign policy.

This week, Swamy wrote an unusual op-ed in the Hindu newspaper condemning India’s neutrality as “tragic” and urging his government not to “crawl for the goodwill of Russia.” Even if the Indian right felt a “growing resentment” about liberal American lecturing on everything from the government’s promotion of Hinduism to its Ukraine policy, it was India’s duty to side with the West, Swamy said in an interview. “Whether we like the Russians or not, invading a sovereign nation in the 21st century in a 19th-century-style war is outrageous,”

This month on IndiaTV, a pro-government Hindi-language channel, the celebrity astrologer Acharya Indu Prakash presented an hour-long Ukraine special in which he predicted 96 percent good fortune for Biden and 99 percent for Putin. The likelihood of nuclear war, he calculated, stood at 37 percent.

After interpreting the divine probabilities, Prakash analyzed the earthly politics at play.

The invasion “was the last resort for Mr. Putin, he was left with no options,” Prakash told viewers. “Even now, attempts are being made to create this narrative that Putin is engaging in a bad war.”

Putin was acting with restraint even in the face of NATO expansionism, Prakash said. “Russia gave Ukraine warnings, Russia provided a safe humanitarian corridor for evacuation, Russia observed cease-fires and Russia tried its best to act with humanity,” he said. “This is what the movement of the planets say.”

“My first and biggest wish is that we had known early that Covid-19 was airborne,”

, “Once you’ve realized that, it informs an entirely different strategy for protection.” Masking, ventilation and air cleaning become key, as well as avoiding high-risk encounters with strangers, he says.

Instead of washing our produce and wearing hand-sewn cloth masks, we could have made sure to avoid superspreader events and worn more-effective N95 masks or their equivalent. “We could have made more of an effort to develop and distribute N95s to everyone,” says Dr. Volckens. “We could have had an Operation Warp Speed for masks.”

We didn’t realize how important clear, straight talk would be to maintaining public trust. If we had, we could have explained the biological nature of a virus and warned that Covid-19 would change in unpredictable ways.  

In the face of a pandemic, he says, the public needs an early basic and blunt lesson in virology

“The science is really important, but if you don’t get the trust and communication right, it can only take you so far,”

and mutates, and since we’ve never seen this particular virus before, we will need to take unprecedented actions and we will make mistakes, he says.

Since the public wasn’t prepared, “people weren’t able to pivot when the knowledge changed,”

By the time the vaccines became available, public trust had been eroded by myriad contradictory messages—about the usefulness of masks, the ways in which the virus could be spread, and whether the virus would have an end date.

, the absence of a single, trusted source of clear information meant that many people gave up on trying to stay current or dismissed the different points of advice as partisan and untrustworthy.

We didn’t know how difficult it would be to get the basic data needed to make good public-health and medical decisions. If we’d had the data, we could have more effectively allocated scarce resources

For much of the pandemic, doctors, epidemiologists, and state and local governments had no way to find out in real time how many people were contracting Covid-19, getting hospitalized and dying

Doctors didn’t know what medicines worked. Governors and mayors didn’t have the information they needed to know whether to require masks. School officials lacked the information needed to know whether it was safe to open schools.

people didn’t know whether it was OK to visit elderly relatives or go to a dinner party.

just months before the outbreak of the pandemic, the Council of State and Territorial Epidemiologists released a white paper detailing the urgent need to modernize the nation’s public-health system still reliant on manual data collection methods—paper records, phone calls, spreadsheets and faxes.

While the U.K. and Israel were collecting and disseminating Covid case data promptly, in the U.S. the CDC couldn’t. It didn’t have a centralized health-data collection system like those countries did, but rather relied on voluntary reporting by underfunded state and local public-health systems and hospitals.

doctors and scientists say they had to depend on information from Israel, the U.K. and South Africa to understand the nature of new variants and the effectiveness of treatments and vaccines. They relied heavily on private data collection efforts such as a dashboard at Johns Hopkins University’s Coronavirus Resource Center that tallied cases, deaths and vaccine rates globally.

With good data, Dr. Ranney says, she could have better managed staffing and taken steps to alleviate the strain on doctors and nurses by arranging child care for them.

To solve the data problem, Dr. Ranney says, we need to build a public-health system that can collect and disseminate data and acts like an electrical grid. The power company sees a storm coming and lines up repair crews.

If we’d known how damaging lockdowns would be to mental health, physical health and the economy, we could have taken a more strategic approach to closing businesses and keeping people at home.

t many doctors say they were crucial at the start of the pandemic to give doctors and hospitals a chance to figure out how to accommodate and treat the avalanche of very sick patients.

The measures reduced deaths, according to many studies—but at a steep cost.

The lockdowns didn’t have to be so harmful, some scientists say. They could have been more carefully tailored to protect the most vulnerable, such as those in nursing homes and retirement communities, and to minimize widespread disruption.

Lockdowns could, during Covid-19 surges, close places such as bars and restaurants where the virus is most likely to spread, while allowing other businesses to stay open with safety precautions like masking and ventilation in place.  

If England’s March 23, 2020, lockdown had begun one week earlier, the measure would have nearly halved the estimated 48,600 deaths in the first wave of England’s pandemic

If the lockdown had begun a week later, deaths in the same period would have more than doubled

The key isn’t to have the lockdowns last a long time, but that they are deployed earlier,

It is possible to avoid lockdowns altogether. Taiwan, South Korea and Hong Kong—all countries experienced at handling disease outbreaks such as SARS in 2003 and MERS—avoided lockdowns by widespread masking, tracking the spread of the virus through testing and contact tracing and quarantining infected individuals.

Had we known that even a mild case of Covid-19 could result in long Covid and other serious chronic health problems, we might have calculated our own personal risk differently and taken more care.

Early in the pandemic, public-health officials were clear: The people at increased risk for severe Covid-19 illness were older, immunocompromised, had chronic kidney disease, Type 2 diabetes or serious heart conditions

t had the unfortunate effect of giving a false sense of security to people who weren’t in those high-risk categories. Once case rates dropped, vaccines became available and fear of the virus wore off, many people let their guard down, ditching masks, spending time in crowded indoor places.

it has become clear that even people with mild cases of Covid-19 can develop long-term serious and debilitating diseases. Long Covid, whose symptoms include months of persistent fatigue, shortness of breath, muscle aches and brain fog, hasn’t been the virus’s only nasty surprise

In February 2022, a study found that, for at least a year, people who had Covid-19 had a substantially increased risk of heart disease—even people who were younger and had not been hospitalized

respiratory conditions.

Some scientists now suspect that Covid-19 might be capable of affecting nearly every organ system in the body. It may play a role in the activation of dormant viruses and latent autoimmune conditions people didn’t know they had

 A blood test, he says, would tell people if they are at higher risk of long Covid and whether they should have antivirals on hand to take right away should they contract Covid-19.

If the risks of long Covid had been known, would people have reacted differently, especially given the confusion over masks and lockdowns and variants? Perhaps. At the least, many people might not have assumed they were out of the woods just because they didn’t have any of the risk factors.

titled “Tightness-Looseness Across the 50 United States,” the study calculated a catalog of measures for each state, including the incidence of natural disasters, disease prevalence, residents’ levels of openness and conscientiousness, drug and alcohol use, homelessness and incarceration rates.

Gelfand said:Some groups have much stronger norms than others; they’re tight. Others have much weaker norms; they’re loose. Of course, all cultures have areas in which they are tight and loose — but cultures vary in the degree to which they emphasize norms and compliance with them.

states in New England and on the West Coast were the loosest: California, Oregon, Washington, Maine, Massachusetts, Connecticut, New Hampshire and Vermont.

In both 2016 and 2020, Donald Trump carried all 10 of the top “tight” states; Hillary Clinton and Joe Biden carried all 10 of the top “loose” states.

“Rule Makers, Rule Breakers: How Tight and Loose Cultures Wire the World” in 2018, in which she described the results of a 2016 pre-election survey she and two colleagues had commissioned

The results were telling: People who felt the country was facing greater threats desired greater tightness. This desire, in turn, correctly predicted their support for Trump. In fact, desired tightness predicted support for Trump far better than other measures. For example, a desire for tightness predicted a vote for Trump with 44 times more accuracy than other popular measures of authoritarianism.

The 2016 election, Gelfand continued, “turned largely on primal cultural reflexes — ones that had been conditioned not only by cultural forces, but by a candidate who was able to exploit them.”

Along the same lines, if liberals and conservatives hold differing moral visions, not just about what makes a good government but about what makes a good life, what turned the relationship between left and right from competitive to mutually destructive?

Cultural differences, Gelfand continued, “have a certain logic — a rationale that makes good sense,” noting that “cultures that have threats need rules to coordinate to survive (think about how incredibly coordinated Japan is in response to natural disasters).

cultures that don’t have a lot of threat can afford to be more permissive and loose.”

The tight-loose concept, Gelfand argued,is an important framework to understand the rise of President Donald Trump and other leaders in Poland, Hungary, Italy, and France,

The gist is this: when people perceive threat — whether real or imagined, they want strong rules and autocratic leaders to help them survive

My research has found that within minutes of exposing study participants to false information about terrorist incidents, overpopulation, pathogen outbreaks and natural disasters, their minds tightened. They wanted stronger rules and punishments.

The South dominated the tight states: Mississippi, Alabama Arkansas, Oklahoma, Tennessee, Texas, Louisiana, Kentucky, South Carolina and North Carolina

Looseness, Gelfand posits, fosters tolerance, creativity and adaptability, along with such liabilities as social disorder, a lack of coordination and impulsive behavior.

If liberalism and conservatism have historically played a complementary role, each checking the other to constrain extremism, why are the left and right so destructively hostile to each other now, and why is the contemporary political system so polarized?

Gelfand writes that tightness encourages conscientiousness, social order and self-control on the plus side, along with close-mindedness, conventional thinking and cultural inertia on the minus side.

Niemi contended that sensitivity to various types of threat is a key factor in driving differences between the far left and far right.

She cited research thatfound 47 percent of the most extreme conservatives strongly endorsed the view that “The world is becoming a more and more dangerous place,” compared to 19 percent of the most extreme liberals

Conservatives and liberals, Niemi continued,see different things as threats — the nature of the threat and how it happens to stir one’s moral values (and their associated emotions) is a better clue to why liberals and conservatives react differently.

Unlike liberals, conservatives strongly endorse the binding moral values aimed at protecting groups and relationships. They judge transgressions involving personal and national betrayal, disobedience to authority, and disgusting or impure acts such as sexually or spiritually unchaste behavior as morally relevant and wrong.

Underlying these differences are competing sets of liberal and conservative moral priorities, with liberals placing more stress than conservatives on caring, kindness, fairness and rights — known among scholars as “individualizing values

conservatives focus more on loyalty, hierarchy, deference to authority, sanctity and a higher standard of disgust, known as “binding values.”

As a set, Niemi wrote, conservative binding values encompassthe values oriented around group preservation, are associated with judgments, decisions, and interpersonal orientations that sacrifice the welfare of individuals

Just as ecological factors differing from region to region over the globe produced different cultural values, ecological factors differed throughout the U.S. historically and today, producing our regional and state-level dimensions of culture and political patterns.

Niemi cited a paper she and Liane Young, a professor of psychology at Boston College, published in 2016, “When and Why We See Victims as Responsible: The Impact of Ideology on Attitudes Toward Victims,” which tested responses of men and women to descriptions of crimes including sexual assaults and robberies.

We measured moral values associated with unconditionally prohibiting harm (“individualizing values”) versus moral values associated with prohibiting behavior that destabilizes groups and relationships (“binding values”: loyalty, obedience to authority, and purity)

Increased endorsement of binding values predicted increased ratings of victims as contaminated, increased blame and responsibility attributed to victims, increased perceptions of victims’ (versus perpetrators’) behaviors as contributing to the outcome, and decreased focus on perpetrators.

For example, binding values are associated with Machiavellianism (e.g., status-seeking and lying, getting ahead by any means, 2013); victim derogation, blame, and beliefs that victims were causal contributors for a variety of harmful acts (2016, 2020); and a tendency to excuse transgressions of ingroup members with attributions to the situation rather than the person (2023).

What happened to people ecologically affected social-political developments, including the content of the rules people made and how they enforced them

Numerous factors potentially influence the evolution of liberalism and conservatism and other social-cultural differences, including geography, topography, catastrophic events, and subsistence styles

Joshua Hartshorne, who is also a professor of psychology at Boston College, took issue with the binding versus individualizing values theory as an explanation for the tendency of conservatives to blame victims:

I would guess that the reason conservatives are more likely to blame the victim has less to do with binding values and more to do with the just-world bias (the belief that good things happen to good people and bad things happen to bad people, therefore if a bad thing happened to you, you must be a bad person).

Belief in a just world, Hartshorne argued, is crucial for those seeking to protect the status quo:It seems psychologically necessary for anyone who wants to advocate for keeping things the way they are that the haves should keep on having, and the have-nots have got as much as they deserve. I don’t see how you could advocate for such a position while simultaneously viewing yourself as moral (and almost everyone believes that they themselves are moral) without also believing in the just world

Conversely, if you generally believe the world is not just, and you view yourself as a moral person, then you are likely to feel like you have an obligation to change things.

I asked Lene Aaroe, a political scientist at Aarhus University in Denmark, why the contemporary American political system is as polarized as it is now, given that the liberal-conservative schism is longstanding. What has happened to produce such intense hostility between left and right?

There is variation across countries in hostility between left and right. The United States is a particularly polarized case which calls for a contextual explanatio

A central explanation typically offered for the current situation in American politics is that partisanship and political ideology have developed into strong social identities where the mass public is increasingly sorted — along social, partisan, and ideological lines.

I then asked Aaroe why surveys find that conservatives are happier than liberals. “Some research,” she replied, “suggests that experiences of inequality constitute a larger psychological burden to liberals because it is more difficult for liberals to rationalize inequality as a phenomenon with positive consequences.”

Steven Pinker, a professor of psychology at Harvard, elaborated in an email on the link between conservatism and happiness:

t’s a combination of factors. Conservatives are likelier to be married, patriotic, and religious, all of which make people happier

They may be less aggrieved by the status quo, whereas liberals take on society’s problems as part of their own personal burdens. Liberals also place politics closer to their identity and striving for meaning and purpose, which is a recipe for frustration.

Some features of the woke faction of liberalism may make people unhappier: as Jon Haidt and Greg Lukianoff have suggested, wokeism is Cognitive Behavioral Therapy in reverse, urging upon people maladaptive mental habits such as catastrophizing, feeling like a victim of forces beyond one’s control, prioritizing emotions of hurt and anger over rational analysis, and dividing the world into allies and villains.

Why, I asked Pinker, would liberals and conservatives react differently — often very differently — to messages that highlight threat?

It may be liberals (or at least the social-justice wing) who are more sensitive to threats, such as white supremacy, climate change, and patriarchy; who may be likelier to moralize, seeing racism and transphobia in messages that others perceive as neutral; and being likelier to surrender to emotions like “harm” and “hurt.”

The authors used neural imaging to follow changes in the dorsomedial prefrontal cortex (known as DMPFC) as conservatives and liberals watched videos presenting strong positions, left and right, on immigration.

there are ways to persuade conservatives to support liberal initiatives and to persuade liberals to back conservative proposals:

While liberals tend to be more concerned with protecting vulnerable groups from harm and more concerned with equality and social justice than conservatives, conservatives tend to be more concerned with moral issues like group loyalty, respect for authority, purity and religious sanctity than liberals are. Because of these different moral commitments, we find that liberals and conservatives can be persuaded by quite different moral arguments

For example, we find that conservatives are more persuaded by a same-sex marriage appeal articulated in terms of group loyalty and patriotism, rather than equality and social justice.

“political arguments reframed to appeal to the moral values of those holding the opposing political position are typically more effective

We find support for these claims across six studies involving diverse political issues, including same-sex marriage, universal health care, military spending, and adopting English as the nation’s official language.”

In one test of persuadability on the right, Feinberg and Willer assigned some conservatives to read an editorial supporting universal health care as a matter of “fairness (health coverage is a basic human right)” or to read an editorial supporting health care as a matter of “purity (uninsured people means more unclean, infected, and diseased Americans).”

Conservatives who read the purity argument were much more supportive of health care than those who read the fairness case.

Liberals who read the fairness argument were substantially more supportive of military spending than those who read the loyalty and authority argument.

In “Conservative and Liberal Attitudes Drive Polarized Neural Responses to Political Content,” Willer, Yuan Chang Leong of the University of Chicago, Janice Chen of Johns Hopkins and Jamil Zaki of Stanford address the question of how partisan biases are encoded in the brain:

society. How do such biases arise in the brain? We measured the neural activity of participants watching videos related to immigration policy. Despite watching the same videos, conservative and liberal participants exhibited divergent neural responses. This “neural polarization” between groups occurred in a brain area associated with the interpretation of narrative content and intensified in response to language associated with risk, emotion, and morality. Furthermore, polarized neural responses predicted attitude change in response to the videos.

The four authors argue that their “findings suggest that biased processing in the brain drives divergent interpretations of political information and subsequent attitude polarization.” These results, they continue, “shed light on the psychological and neural underpinnings of how identical information is interpreted differently by conservatives and liberals.”

While liberals and conservatives, guided by different sets of moral values, may make agreement on specific policies difficult, that does not necessarily preclude consensus.

or each video,” they write,participants with DMPFC activity time courses more similar to that of conservative-leaning participants became more likely to support the conservative positio

Conversely, those with DMPFC activity time courses more similar to that of liberal-leaning participants became more likely to support the liberal position. These results suggest that divergent interpretations of the same information are associated with increased attitude polarizatio

Together, our findings describe a neural basis for partisan biases in processing political information and their effects on attitude change.

Describing their neuroimaging method, the authors point out that theysearched for evidence of “neural polarization” activity in the brain that diverges between people who hold liberal versus conservative political attitudes. Neural polarization was observed in the dorsomedial prefrontal cortex (DMPFC), a brain region associated with the interpretation of narrative content.

The question is whether the political polarization that we are witnessing now proves to be a core, encoded aspect of the human mind, difficult to overcome — as Leong, Chen, Zaki and Willer sugges

— or whether, with our increased knowledge of the neural basis of partisan and other biases, we will find more effective ways to manage these most dangerous of human predispositions.

second, the people inventing them think they are potentially incredibly dangerous: ten percent of them, in fact, think they might extinguish the human species. They don’t know exactly how, but think Sorcerer’s Apprentice (or google ‘paper clip maximizer.’)

One pundit after another explains that an AI program called Deep Mind worked far faster than scientists doing experiments to uncover the basic structure of all the different proteins, which will allow quicker drug development. It’s regarded as ipso facto better because it’s faster, and hence—implicitly—worth taking the risks that come with AI.

That is, it seems to me, a dumb answer from smart people—the answer not of people who have thought hard about ethics or even outcomes, but the answer that would be supplied by a kind of cultist.

(Probably the kind with stock options).

it does go, fairly neatly, with the default modern assumption that if we can do something we should do it, which is what I want to talk about. The question that I think very few have bothered to answer is, why?

But why? The sun won’t blow up for a few billion years, meaning that if we don’t manage to drive ourselves to extinction, we’ve got all the time in the world. If it takes a generation or two for normal intelligence to come up with the structure of all the proteins, some people may die because a drug isn’t developed in time for their particular disease, but erring on the side of avoiding extinction seems mathematically sound

Allowing that we’re already good enough—indeed that our limitations are intrinsic to us, define us, and make us human—should guide us towards trying to shut down this technology before it does deep damage.

The other challenge that people cite, over and over again, to justify running the risks of AI is to “combat climate change,

As it happens, regular old intelligence has already give us most of what we need: engineers have cut the cost of solar power and windpower and the batteries to store the energy they produce so dramatically that they’re now the cheapest power on earth

We don’t actually need artificial intelligence in this case; we need natural compassion, so that we work with the necessary speed to deploy these technologies.

Beyond those, the cases become trivial, or worse

All of this is a way of saying something we don’t say as often as we should: humans are good enough. We don’t require improvement. We can solve the challenges we face, as humans.

It may take us longer than if we can employ some “new form of intelligence,” but slow and steady is the whole point of the race.

Unless, of course, you’re trying to make money, in which case “first-mover advantage” is the point

I find they often answer from something that sounds like the A.I.’s perspective. Many — not all, but enough that I feel comfortable in this characterization — feel that they have a responsibility to usher this new form of intelligence into the world.”

here’s the thing: pausing, slowing down, stopping calls on the one human gift shared by no other creature, and perhaps by no machine. We are the animal that can, if we want to, decide not to do something we’re capable of doing.

n individual terms, that ability forms the core of our ethical and religious systems; in societal terms it’s been crucial as technology has developed over the last century. We’ve, so far, reined in nuclear and biological weapons, designer babies, and a few other maximally dangerous new inventions

It’s time to say do it again, and fast—faster than the next iteration of this tech.

The basic driver is this: “Europe has an aging population that values its free time and social benefits over work and productivity. (This reduces labor force participation, innovation potential, and the economic growth of the continent.)

The eurozone economy grew about 6% over the past 15 years, measured in dollars, compared with 82% for the US, according to International Monetary Fund data. That has left the average EU country poorer per head than every US state except Idaho and Mississippi

If the current trend continues, by 2035 the gap between economic output per capita in the US and EU will be as large as that between Japan and Ecuador today

even in Smith’s figures, there is no Northern European economy that outperforms the US.

The US economy has grown 82% in fifteen years! Barring anything more than a mild recession, that means that we can expect the US economy to more than double by the time we hit 20 years from 2008. Isn’t that wonderful?

It’s not just a case of doubling the overall size of the economy. The increase in wealth has been widely distributed.

I was struck by a calculation by George Washington University’s Stephen Rose that he describes at a center-left newsletter called The Liberal Patriot

Deciding what is “middle class” versus “lower middle class” versus “upper middle class” is difficult, and every analysis sets up different cutoffs between these categories. But Rose sets a reasonable level, describing “upper middle class” as an income between $100,000 and $350,000

Using this measure, there was real growth in every rung of the economic ladder over the period from 1979 to 2019, with each ascending step having slightly higher percentage gain….

In brief, economic growth from 1979 to 2019 led more of the population to move up to higher social classes. As Table 1 shows, the bottom two categories—poor and near-poor plus lower middle class—went from a combined 49 percent to 29 percent

The size of the [core middle class] also declined, down from 39 percent to 31 percent over these years

These declines manifest themselves in a massive—and massively under-covered—growth of the [upper middle class], spiking from 13 percent in 1979 to 37 percent in 2019.

America has always thought of itself as a middle-class country. But we are rapidly becoming an upper-middle-class country

This is now the largest category, and at the rate we’re going, it will soon be an outright majority.

upper-middle-class people can afford more welfare-state spending, and they also have more access to education and, frankly, the luxury of agonizing over something other than our pocketbooks. It has been a long time since most Americans were concerned about how to put a roof over our heads, so we have moved on from “kitchen table” issues to concerns about values and status and self-image.

in this context, the Old Left welfare-state programs look, not merely unnecessary, but callous and cruel

the incentives created by welfare programs discourage work for the poor. But in a growing and thriving upper-middle-class country, this looks like a way to create a permanent underclass who are kept in poverty so we can congratulate ourselves on our compassion and generosity

some of this may also explain the right’s belligerent opposition to immigration. If we are becoming an upper-middle-class country, perhaps we are taking on some of the attitudes of a gated community that wants to keep out the riff-raff.

But only a handful of cancers—of the breast, lung, colon and cervix—have screening tests recommended by the U.S. Preventive Services Task Force

Many companies are developing blood tests that can detect cancer signals before symptoms occur, and Grail’s is the most advanced. A study found it can identify more than 50 types of cancer 52% of the time and the 12 deadliest cancers in Stages I through III 68% of the time.

There’s a hitch. The test costs $949 and isn’t covered by Medicare or most private insurance.

The trouble is that this cancer is almost never caught early. There’s no routine screening for it, and symptoms don’t develop until it is advanced. Mr. Royse, 64, had no idea he was sick until he took a blood test called Galleri, produced by the Menlo Park, Calif., startup Grail. He had surgery and chemotherapy and is now cancer-free.

Mr. Royse visited Grail’s website, which referred him to a telemedicine provider who ordered a test. Another telemedicine doctor walked him through his results, which showed a cancer signal likely emanating from the pancreas, gallbladder, stomach or esophagus.

An MRI revealed a suspicious mass on his pancreas, which a biopsy confirmed was cancerous. Mr. Royse had three months of chemotherapy, surgery and another three months of chemotherapy, which ended last February. Because pancreatic cancer often recurs, he gets CT and MRI scans every three months. In addition, he has signed up for startup Natera’s Signatera customized blood test, which checks DNA specific to the patient’s cancer and can signal its return before signs are visible on the scans

Grail’s test likewise looks for DNA shed by cancer cells, which is tagged by molecules called methyl groups that are specific to a cancer’s origin. Grail uses genetic sequencing and machine learning to recognize links between DNA methyl groups and particular cancers

The test “is based on how much DNA is being shed by tumor,” Grail’s president, Josh Ofman, says. “Some tumors shed a lot of DNA. Some shed almost none.

ut slow-growing tumors typically aren’t shedding a lot of DNA.” That reduces the probability that Grail’s test will identify indolent cancers that pose no immediate danger.

Grail’s test has a roughly 0.5% false-positive rate, meaning 1 in 200 patients who don’t have cancer will get a positive signal

Its positive predictive value is 43%, so that of every 100 patients with a positive signal, 43 actually have cancer

the legislation’s price tag could reduce political support. According to one private company’s estimate, the test could cost the government $39 billion to $145 billion over a decade. Mr. Goldman counters that analysts usually overestimate the costs and underestimate the benefits of medical interventions.

Because Grail uses machine learning to detect DNA-methylation cancer linkages, the Grail test’s accuracy should improve as more tests and patient data are collected

regulators may balk at approving the test, and insurers at covering it, until it becomes cheaper and more reliable.

How would the FDA weigh the risk that a false positive on a test like Grail’s could require invasive follow-up testing against the dire but hard-to-quantify risk that a deadly cancer wouldn’t be caught until it’s much harder to treat? It’s unclear.

some experts urge the FDA to require large randomized controlled trials before approving blood cancer tests. “Multicancer screening would entail tremendous costs and potentially substantial harms,” H. Gilbert Welch and Tanujit Dey of Brigham and Women’s Hospital wrote

Dr. Welch and Mr. Dey also suggested that companies should be required to prove their tests reduce overall mortality, even though the FDA doesn’t require drugmakers to prove their products reduce deaths or extend life. Clinical trials for the mRNA Covid vaccines didn’t show they reduced deaths.

One alternative is to rely on real-world studies, which Grail is already doing. One study of patients 50 and older without signs of cancer showed that the test doubled the number of cancers detected.

One recurring problem he has seen: “Epidemiologists are always getting cancer wrong,” he says. “Epidemiologists a decade ago said U.S. overtreats cancers. Well, no, the EU undertreats cancer.”

A 2012 study that he co-authored found that the higher U.S. spending on cancer care relative to Europe between 1983 and 1999 resulted in significantly higher survival rates for American patients than for those in Europe

By his study’s calculation, U.S. spending on cancer treatments during that period resulted in $556 billion in net benefits owing to reduced mortality.

He expects Galleri and other multicancer early-detection tests to reduce deaths and produce public-health and economic benefits that exceed their monetary costs

Expanding access to multicancer early-detection tests could also help solve the chicken-and-egg problem of drug development. Because few patients are diagnosed at early stages of some cancers, it’s hard to develop treatments for them

the positive predictive value for some recommended cancer screenings is far lower. Fewer than 1 in 10 women with an abnormal finding on a mammogram are diagnosed with breast cancer.

Mr. Royse makes the same point with personal force. “I would be dead right now if not for multicancer early-detection testing,” Mr. Royse told an FDA advisory committee last fall. “The longer the FDA waits, the more people are going to die. It’s that simple.”

Two sources said that during the early weeks of the war they were permitted to kill 15 or 20 civilians during airstrikes on low-ranking militants. Attacks on such targets were typically carried out using unguided munitions known as “dumb bombs”, the sources said, destroying entire homes and killing all their occupants.

“You don’t want to waste expensive bombs on unimportant people – it’s very expensive for the country and there’s a shortage [of those bombs],” one intelligence officer said. Another said the principal question they were faced with was whether the “collateral damage” to civilians allowed for an attack.

“Because we usually carried out the attacks with dumb bombs, and that meant literally dropping the whole house on its occupants. But even if an attack is averted, you don’t care – you immediately move on to the next target. Because of the system, the targets never end. You have another 36,000 waiting.”

ccording to conflict experts, if Israel has been using dumb bombs to flatten the homes of thousands of Palestinians who were linked, with the assistance of AI, to militant groups in Gaza, that could help explain the shockingly high death toll in the war.

Details about the specific kinds of data used to train Lavender’s algorithm, or how the programme reached its conclusions, are not included in the accounts published by +972 or Local Call. However, the sources said that during the first few weeks of the war, Unit 8200 refined Lavender’s algorithm and tweaked its search parameters.

Responding to the publication of the testimonies in +972 and Local Call, the IDF said in a statement that its operations were carried out in accordance with the rules of proportionality under international law. It said dumb bombs are “standard weaponry” that are used by IDF pilots in a manner that ensures “a high level of precision”.

“The IDF does not use an artificial intelligence system that identifies terrorist operatives or tries to predict whether a person is a terrorist,” it added. “Information systems are merely tools for analysts in the target identification process.”

In earlier military operations conducted by the IDF, producing human targets was often a more labour-intensive process. Multiple sources who described target development in previous wars to the Guardian, said the decision to “incriminate” an individual, or identify them as a legitimate target, would be discussed and then signed off by a legal adviser.

n the weeks and months after 7 October, this model for approving strikes on human targets was dramatically accelerated, according to the sources. As the IDF’s bombardment of Gaza intensified, they said, commanders demanded a continuous pipeline of targets.

“We were constantly being pressured: ‘Bring us more targets.’ They really shouted at us,” said one intelligence officer. “We were told: now we have to fuck up Hamas, no matter what the cost. Whatever you can, you bomb.”

Lavender was developed by the Israel Defense Forces’ elite intelligence division, Unit 8200, which is comparable to the US’s National Security Agency or GCHQ in the UK.

After randomly sampling and cross-checking its predictions, the unit concluded Lavender had achieved a 90% accuracy rate, the sources said, leading the IDF to approve its sweeping use as a target recommendation tool.

Lavender created a database of tens of thousands of individuals who were marked as predominantly low-ranking members of Hamas’s military wing, they added. This was used alongside another AI-based decision support system, called the Gospel, which recommended buildings and structures as targets rather than individuals.

The accounts include first-hand testimony of how intelligence officers worked with Lavender and how the reach of its dragnet could be adjusted. “At its peak, the system managed to generate 37,000 people as potential human targets,” one of the sources said. “But the numbers changed all the time, because it depends on where you set the bar of what a Hamas operative is.”

broadly, and then the machine started bringing us all kinds of civil defence personnel, police officers, on whom it would be a shame to waste bombs. They help the Hamas government, but they don’t really endanger soldiers.”

Before the war, US and Israeli estimated membership of Hamas’s military wing at approximately 25-30,000 people.

there was a decision to treat Palestinian men linked to Hamas’s military wing as potential targets, regardless of their rank or importance.

According to +972 and Local Call, the IDF judged it permissible to kill more than 100 civilians in attacks on a top-ranking Hamas officials. “We had a calculation for how many [civilians could be killed] for the brigade commander, how many [civilians] for a battalion commander, and so on,” one source said.

Another source, who justified the use of Lavender to help identify low-ranking targets, said that “when it comes to a junior militant, you don’t want to invest manpower and time in it”. They said that in wartime there was insufficient time to carefully “incriminate every target”

So you’re willing to take the margin of error of using artificial intelligence, risking collateral damage and civilians dying, and risking attacking by mistake, and to live with it,” they added.

When it came to targeting low-ranking Hamas and PIJ suspects, they said, the preference was to attack when they were believed to be at home. “We were not interested in killing [Hamas] operatives only when they were in a military building or engaged in a military activity,” one said. “It’s much easier to bomb a family’s home. The system is built to look for them in these situations.”

Such a strategy risked higher numbers of civilian casualties, and the sources said the IDF imposed pre-authorised limits on the number of civilians it deemed acceptable to kill in a strike aimed at a single Hamas militant. The ratio was said to have changed over time, and varied according to the seniority of the target.

The IDF’s targeting processes in the most intensive phase of the bombardment were also relaxed, they said. “There was a completely permissive policy regarding the casualties of [bombing] operations,” one source said. “A policy so permissive that in my opinion it had an element of revenge.”

“There were regulations, but they were just very lenient,” another added. “We’ve killed people with collateral damage in the high double digits, if not low triple digits. These are things that haven’t happened before.” There appears to have been significant fluctuations in the figure that military commanders would tolerate at different stages of the war

One source said that the limit on permitted civilian casualties “went up and down” over time, and at one point was as low as five. During the first week of the conflict, the source said, permission was given to kill 15 non-combatants to take out junior militants in Gaza

at one stage earlier in the war they were authorised to kill up to “20 uninvolved civilians” for a single operative, regardless of their rank, military importance, or age.

“It’s not just that you can kill any person who is a Hamas soldier, which is clearly permitted and legitimate in terms of international law,” they said. “But they directly tell you: ‘You are allowed to kill them along with many civilians.’ … In practice, the proportionality criterion did not exist.”

Experts in international humanitarian law who spoke to the Guardian expressed alarm at accounts of the IDF accepting and pre-authorising collateral damage ratios as high as 20 civilians, particularly for lower-ranking militants. They said militaries must assess proportionality for each individual strike.

An international law expert at the US state department said they had “never remotely heard of a one to 15 ratio being deemed acceptable, especially for lower-level combatants. There’s a lot of leeway, but that strikes me as extreme”.

Sarah Harrison, a former lawyer at the US Department of Defense, now an analyst at Crisis Group, said: “While there may be certain occasions where 15 collateral civilian deaths could be proportionate, there are other times where it definitely wouldn’t be. You can’t just set a tolerable number for a category of targets and say that it’ll be lawfully proportionate in each case.”

Whatever the legal or moral justification for Israel’s bombing strategy, some of its intelligence officers appear now to be questioning the approach set by their commanders. “No one thought about what to do afterward, when the war is over, or how it will be possible to live in Gaza,” one said.

Another said that after the 7 October attacks by Hamas, the atmosphere in the IDF was “painful and vindictive”. “There was a dissonance: on the one hand, people here were frustrated that we were not attacking enough. On the other hand, you see at the end of the day that another thousand Gazans have died, most of them civilians.”

the model’s calculations took into account trends in pollution levels, population growth, the amount of natural resources and the overall quality of life on Earth. The model’s predictions for the worsening quality of life and the dwindling natural resources have so far been unnervingly on target.

“At around 2020, the condition of the planet becomes highly critical. If we do nothing about it, the quality of life goes down to zero. Pollution becomes so seriously it will start to kill people, which in turn will cause the population to diminish, lower than it was in the 1900. At this stage, around 2040 to 2050, civilised life as we know it on this planet will cease to exist.”

Alexander King, the then-leader of the Club of Rome, evaluated the program’s results to also mean that nation-states will lose their sovereignty, forecasting a New World Order with corporations managing everything.

“Sovereignty of nations is no longer absolute,” King told ABC. “There is a gradual diminishing of sovereignty, little bit by little bit. Even in the big nations, this will happen.”

Their reasoning is straightforward: These companies have decided that the best way to make generative AI better is to build bigger AI models. And that is really, really expensive, requiring resources on the scale of moon missions and the interstate-highway system to fund the data centers and related infrastructure that generative AI depends on

“If we’re going to justify a trillion or more dollars of investment, [AI] needs to solve complex problems and enable us to do things we haven’t been able to do before.” Today’s flagship AI models, he said, largely cannot.

Now a number of voices in the finance world are beginning to ask whether all of this investment can pay off. OpenAI, for its part, may lose up to $5 billion this year, almost 10 times more than what the company lost in 2022,

Over the past few weeks, analysts and investors at some of the world’s most influential financial institutions—including Goldman Sachs, Sequoia Capital, Moody’s, and Barclays—have issued reports that raise doubts about whether the enormous investments in generative AI will be profitable.

Dario Amodei, the CEO of the rival start-up Anthropic, has predicted that a single AI model (such as, say, GPT-6) could cost $100 billion to train by 2027. The global data-center buildup over the next few years could require trillions of dollars from tech companies, utilities, and other industries, according to a July report from Moody’s Ratings.

generative AI has already done extraordinary things, of course—advancing drug development, solving challenging math problems, generating stunning video clips. But exactly what uses of the technology can actually make money remains unclear

At present, AI is generally good at doing existing tasks—writing blog posts, coding, translating—faster and cheaper than humans can. But efficiency gains can provide only so much value, boosting the current economy but not creating a new one.

Right now, Silicon Valley might just functionally be replacing some jobs, such as customer service and form-processing work, with historically expensive software, which is not a recipe for widespread economic transformation.

McKinsey has estimated that generative AI could eventually add almost $8 trillion to the global economy every year

“Here, we can manufacture intelligence.”

Tony Kim, the head of technology investment at BlackRock, the world’s largest money manager, told me he believes that AI will trigger one of the most significant technological upheavals ever. “Prior industrial revolutions were never about intelligence,”

this future is not guaranteed. Many of the productivity gains expected from AI could be both greatly overestimated and very premature, Daron Acemoglu, an economist at MIT, has found

AI products’ key flaws, such as a tendency to invent false information, could make them unusable, or deployable only under strict human oversight, in certain settings—courts, hospitals, government agencies, schools

AI as a truly epoch-shifting technology, it may well be more akin to blockchain, a very expensive tool destined to fall short of promises to fundamentally transform society and the economy.

Researchers at Barclays recently calculated that tech companies are collectively paying for enough AI-computing infrastructure to eventually power 12,000 different ChatGPTs. Silicon Valley could very well produce a whole host of hit generative-AI products like ChatGPT, “but probably not 12,000 of them,

even if it did, there would be nowhere enough demand to use all those apps and actually turn a profit.

Some of the largest tech companies’ current spending on AI data centers will require roughly $600 billion of annual revenue to break even, of which they are currently about $500 billion short.

Tech proponents have responded to the criticism that the industry is spending too much, too fast, with something like religious dogma. “I don’t care” how much we spend, Altman has said. “I genuinely don’t.

the industry is asking the world to engage in something like a trillion-dollar tautology: AI’s world-transformative potential justifies spending any amount of resources, because its evangelists will spend any amount to make AI transform the world.

in the AI era in particular, a lack of clear evidence for a healthy return on investment may not even matter. Unlike the companies that went bust in the dot-com bubble in the early 2000s, Big Tech can spend exorbitant sums of money and be largely fine

perhaps even more important in Silicon Valley than a messianic belief in AI is a terrible fear of missing out. “In the tech industry, what drives part of this is nobody wants to be left behind. Nobody wants to be seen as lagging,

Go all in on AI, the thinking goes, or someone else will. Their actions evince “a sense of desperation,” Cahn writes. “If you do not move now, you will never get another chance.” Enormous sums of money are likely to continue flowing into AI for the foreseeable future, driven by a mix of unshakable confidence and all-consuming fear.

What is A.I. even for?

“A.I. slop”: often uncanny, frequently misleading material, now flooding web browsers and social-media platforms like spam in old inboxes. Years deep into national hysteria over the threat of internet misinformation pushed on us by bad actors, we’ve sleepwalked into a new internet in which meaningless, nonfactual slop is casually mass-produced and force-fed to us by A.I.

It has already helped drive down the cost and drive up the performance of next-gen batteries and solar photovoltaic cells, whose performance can also be improved, even after the panels have been manufactured and installed on your roof, by as much as 25 percent

while the internet was never perfectly trustworthy, one epoch-defining breakthrough of Google was that it got us pretty close. Now the company’s chief executive acknowledges that hallucinations are “inherent” to the technology it has celebrated as a kind of successor for ranked-order search results, which are now often found far below not just the A.I. summary but a whole stack of “sponsored” results as well.

Where not long ago we used to find the very best results for Google searches, we can now find instead potentially plagiarized and often inaccurate paragraph summaries of answers to our queries

Machine learning may help make our electricity grid as much as 40 percent more efficient at delivering power as it is today, when many of its routing decisions are made by individual humans on the basis of experience and intuition

This month, KoBold Metals announced the largest discovery of new copper deposits in a decade — a green-energy gold mine, so to speak, delivered with the help of its own proprietary A.I., which integrated information about subatomic particles detected underground with century-old mining reports and radar imagery to make predictions about where minerals critical for the green transition might be found.

.I. is designing new proteins, rapidly accelerating drug discovery and speeding up clinical trials testing new medicines and therapies.

perhaps that a more optimistic perspective can be drawn by analogy to what economists call the “environmental Kuznets curve,” which suggests that, as nations develop, they tend to first pollute a lot more and then, over time, as they grow richer, they ultimately pollute less.

Even in describing regular old pollution, this framework has its shortcomings, especially because it treats as automatic eventual progress that has always required tooth-and-nail fights against some very stubborn bad actors

A.I. is generating an awful lot of genuine pollution, too — both Google and Microsoft, which each pledged in 2019 to reach zero emissions by 2030, have instead expanded their carbon footprints by nearly 50 percent in the interim.

Before the pandemic, a number of variables were statistically significant indicators for consumer sentiment in our model; in particular, the most salient variables appear to be vehicle sales, gas prices, median household income, the federal funds effective rate, personal savings and household expenditures (excluding food and energy).

surprisingly, our pre-pandemic model didn't find a notable relationship between housing prices and consumer sentiment

All this taken together meant Americans were flush with cash but had nowhere to spend it. So despite the fact that the savings rate went way up, consumers still weren't feeling positively about the economy — contrary to the relationship between these two variables we saw in the decades before the pandemic.

Fast forward to 2024, and the personal savings rate has dropped to one of its lowest levels ever (the only time the savings rate was lower was in the years surrounding the Great Recession)

during and after the pandemic, Americans saw some of the highest rates of inflation the country has had in decades, and in a very short period of time. These sudden spikes naturally shocked many people who had been blissfully enjoying slow, steady price growth their entire adult lives. And it has taken a while for that shock to wear off, even as inflation has cre

the numbers align with our intuitive sense of how consumers process suddenly having their grocery store bill jump, as well as the findings from our model. In simple terms: Even if inflation is getting better, Americans aren't done being ticked off that it was bad to begin with.

During the pandemic, the personal savings rate soared. In April 2020, the metric was nearly double its previous high, recorded in May 1975.

However, in our post-pandemic data, when we examined how correlated consumer sentiment was with each indicator we considered, consumer sentiment and median housing prices had the strongest correlation of all****** (a negative one, meaning higher prices were associated with lower consumer sentiment)

"Right before the pandemic, the typical average transaction price was around $38,000 for a new car. By 2023, it was $48,000," Schirmer said. This could all be contributing to the break in the relationship between car sales and sentiment, he noted. Basically, people might be buying cars, but they aren't necessarily happy about it.

That's true even if a family has been able to save enough for a down payment, already a difficult task when rents remain high as well. Fewer people are able to cover their current housing costs while saving enough to make a down payment.

Low-income households are still the most likely to be burdened with high rents, but they're not the only ones affected anymore. High rents have also begun to affect those at middle-income levels as well.

In short, there was already a housing affordability crisis before the pandemic. Now it's worse, locking a wider array of people, at higher and higher income levels, out of the home-buying market

People who are renting but want to buy are stuck. People who live in starter homes and want to move to bigger homes are stuck. The conditions have frustrated a fundamental element of the American dream

In our pre-pandemic model, total vehicle sales had a strong positive relationship with consumer sentiment: If people were buying cars, you could pretty reasonably bet that they felt good about the economy. This feels intuitive — who buys a car if they think the economy

Cox Automotive also tracks vehicle affordability by calculating the estimated number of weeks' worth of median income needed to purchase the average new vehicle, and while that number has improved over the last two years, it remains high compared to pre-pandemic levels. In April, the most recent month with data, it took 37.7 weeks of median income to purchase a car, compared with fewer than 35 weeks at the end of 2019.

during the pandemic, low interest rates, high savings rates and changes in working patterns — namely, many workers' newfound ability to work from home — helped overheat the homebuying market, and buyers ran headlong into an enduring supply shortage. There simply weren't enough houses to buy, which drove up the costs of the ones that were for sale.

Inspired by our model of economic indicators and sentiment from 1987 to 2019, we tried to train a similar linear regression model on the same data from 2021 to 2024 to more directly compare how things changed after the pandemic. While we were able to get a pretty good fit for this post-pandemic model,******* something interesting happened: Not a single variable showed up as a statistically significant predictor of consumer sentiment.

This suggests there's something much more complicated going on behind the scenes: Interactions between these variables are probably driving the prediction, and there's too much noise in this small post-pandemic data set for the model to disentangle i

Changes in the kinds of purchases we've discussed — homes, cars and everyday items like groceries — have fundamentally shifted the way Americans view how affordable their lives are and how they measure their quality of life.

Even though some indicators may be improving, Americans are simply weighing the factors differently than they used to, and that gives folks more than enough reason to have the economic blues.

The Israeli military relies on reservist soldiers, some of whom have described growing exhaustion as Israel manages conflicts for months on end on multiple fronts, including the border with Lebanon and in the West Bank. An end to fighting in Gaza would give Israeli forces a respite that analysts say is needed, especially if fighting with Hezbollah escalates further.

Israel Ziv, a retired Israeli general and veteran of multiple wars, said tensions between the Israeli military and security establishment and Netanyahu are at a record high.“The IDF feels and the security echelon feels that we exhausted the purpose of the war. We reached the maximum tactical peak that we can achieve,” he said. “As long as Rafah was there, they could say finish the job. OK it’s finished now.”

Netanyahu has rejected a series of proposals for possible alternatives to Hamas, including an American plan to bring in the Palestinian Authority and Arab calls for a Palestinian unity government that would include Hamas. Some military analysts and former Israeli officials have questioned whether installing a new government in Gaza was ever possible, given that Hamas has managed to survive the Israeli military assault.

“We need to make a decision,” said Ziv. “Even a bad decision, that’s OK. Let’s say [we] occupy Gaza in the next few years because we need to clear up the last few terrorists. OK, it’s a bad decision, but it’s a decision. The military needs to know.”

The dispute between Netanyahu and the military centers in part on how officials define a defeat of Hamas. An Israeli military official said the army considers a battalion “dismantled” not when all its fighters are killed, but when its command structure and ability to carry out organized attacks are eliminated. 

Military analysts say that Hamas’s militia forces are likely to survive the Israeli military operation even in Rafah, in part because the Israeli army’s approach leaves many lower-ranking Hamas fighters in place. Hamas’s top leadership in the enclave, including its leader, Yahya Sinwar, have also eluded Israeli forces throughout the war.

“Hamas is preserving its forces in Rafah rather than engaging the Israel Defense Forces, likely because Hamas does not believe Israel’s Rafah operation will be decisive,” said an assessment this week from the Institute for the Study of War and the American Enterprise Institute’s Critical Threats Project.