Group items tagged

Operation Warp Speed — the federal effort to accelerate vaccine development — set as a goal producing 300 million doses this year, but fell far short of that, reflecting just how difficult and unpredictable the manufacturing process has been.

The more realistic timelines for manufacturing the vaccines are no surprise to those who closely follow the industry

“They always used the same four words: ‘in a perfect world,’” said Steve Brozak

“That world does not exist,” Mr. Brozak said. “Instead, we have a Covid world.”

“Manufacturing a biological product is a science and an art in some ways,” said Prashant Yadav

Moderna has received more than $2 billion from the federal government to develop and manufacture 100 million doses, and the company has said it can supply about 20 million doses before January.

Pfizer’s chief executive, has said the company can produce up to 50 million doses by the end of the year, and half of that supply will go to other countries

the vaccine, which requires two doses, could be approved. “As soon as you turn this vaccine into vials, the shelf life starts ticking,” he said.

The global race for a vaccine, as well as the pandemic’s disruption of factories and transportation, has severely taxed supply chains

His chief of staff, Mark Meadows, said in September that 100 million doses would be available by October. At the same time, Mr. Trump slapped down some of his top health officials who laid out more realistic timelines.

if several vaccines are authorized by the Food and Drug Administration in the next few months, the United States may have “several hundred million” doses available by the end of March, and then a few months after that, there may be enough for every American to receive a vaccine who wants one.

“Let’s not lose sight of the fact that never in the history of vaccine development has a vaccine been developed at scale in this period of time,”

it is easy to forget how much we have in common as people. I can’t think of any major nation after the United States with more of a Protestant work ethic and naturally capitalist population than China.

These days, it is extremely difficult for a visiting columnist to get anyone — a senior official or a Starbucks barista — to speak on the record. It was not that way a decade ago.

The Communist Party’s hold is also a product of all the hard work and savings of the Chinese people, which have enabled the party and the state to build world-class infrastructure and public goods that make life for China’s middle and lower classes steadily better.

Beijing and Shanghai, in particular, have become very livable cities, with the air pollution largely erased and lots of new, walkable green spaces.

some 900 cities and towns in China are now served by high-speed rail, which makes travel to even remote communities incredibly cheap, easy and comfortable

Just when trust has become more important than ever between the U.S. and China, it also has become scarcer than ever. Bad trend.

China’s stability is a product of both an increasingly pervasive police state and a government that has steadily raised standards of living. It’s a regime that takes both absolute control and relentless nation-building seriously.

For an American to fly from New York’s Kennedy Airport into Beijing Capital International Airport today is to fly from an overcrowded bus terminal to a Disney-like Tomorrowland.

China got an early jump on A.I. in two realms — facial recognition technology and health records — because there are virtually no privacy restrictions on the government’s ability to build huge data sets for machine learning algorithms to find patterns.

“ChatGPT is prompting some people to ask if the U.S. is rising again, like in the 1990s,”

“I understand your feeling: You have been in the first place for a century, and now China is rising, and we have the potential to become the first — and that is not easy for you,” Hu said to me. But “you should not try to stop China’s development. You can’t contain China in the end. We are quite smart. And very diligent. We work very hard. And we have 1.4 billion people.”

Before the Trump presidency, he added: “We never thought China-U.S. relations would ever become so bad. Now we gradually accept the situation, and most Chinese people think there is no hope for better relations. We think the relationship will be worse and worse and hope that war will not break out between our two countries.”

A lot of people hesitated when I asked. Indeed, many would answer with some version of “I’m not sure, I just know that it’s THEIR fault.”

t was repeated conversations like these that got me started asking American, Chinese and Taiwanese investors, analysts and officials a question that has been nagging at me for a while: What exactly are America and China fighting about?

the real answer is so much deeper and more complex than just the usual one-word response — “Taiwan” — or the usual three-word response — “autocracy versus democracy.”

Let me try to peel back the layers. The erosion in U.S.-China relations is a result of something old and obvious — a traditional great-power rivalry between an incumbent power (us) and a rising power (China) — but with lots of new twists

One of the twists, though, is that this standard-issue great-power rivalry is occurring between nations that have become as economically intertwined as the strands of a DNA molecule. As a result, neither China nor America has ever had a rival quite like the other.

in modern times, China, like America, has never had to deal with a true economic and military peer with which it was also totally intertwined through trade and investment.

Another new twist, and a reason it’s hard to define exactly what we’re fighting about, has a lot to do with how this elusive issue of trust and the absence of it have suddenly assumed much greater importance in international affairs.

This is a byproduct of our new technological ecosystem in which more and more devices and services that we both use and trade are driven by microchips and software, and connected through data centers in the cloud and high-speed internet

so many more things became “dual use.” That is, technologies that can easily be converted from civilian tools to military weapons, or vice versa.

no one country or company can own the whole supply chain. You need the best from everywhere, and that supply chain is so tightly intertwined that each company has to trust the others intimately.

when we install the ability to sense, digitize, connect, process, learn, share and act into more and more things — from your GPS-enabled phone to your car to your toaster to your favorite app — they all become dual use, either weapons or tools depending on who controls the software running them and who owns the data that they spin off.

As long as most of what China sold us was shallow goods, we did not care as much about its political system — doubly so because it seemed for a while as if China was slowly but steadily becoming more and more integrated with the world and slightly more open and transparent every year. So, it was both easy and convenient to set aside some of our worries about the dark sides of its political system.

when you want to sell us ‘deep goods’ — goods that are dual use and will go deep into our homes, bedrooms, industries, chatbots and urban infrastructure — we don’t have enough trust to buy them. So, we are going to ban Huawei and instead pay more to buy our 5G telecom systems from Scandinavian companies we do trust: Ericsson and Nokia.”

as we’ve seen in Ukraine, a smartphone can be used by Grandma to call the grandkids or to call a Ukrainian rocket-launching unit and give it the GPS coordinates of a Russian tank in her backyard.

So today, the country or countries that can make the fastest, most powerful and most energy efficient microchips can make the biggest A.I. computers and dominate in economics and military affairs.

As more and more products and services became digitized and electrified, the microchips that powered everything became the new oil. What crude oil was to powering 19th- and 20th-century economies, microchips are for powering 21st-century economies.

When you ask them what is the secret that enables TSMC to make 90 percent of the world’s most advanced logic chips — while China, which speaks the same language and shares the same recent cultural history, makes zero — their answer is simple: “trust.”

TSMC is a semiconductor foundry, meaning it takes the designs of the most advanced computer companies in the world — Apple, Qualcomm, Nvidia, AMD and others — and turns the designs into chips that perform different processing functions

TSMC makes two solemn oaths to its customers: TSMC will never compete against them by designing its own chips and it will never share the designs of one of its customers with another.

“Our business is to serve multiple competitive clients,” Kevin Zhang, senior vice president for business development at TSMC, explained to me. “We are committed not to compete with any of them, and internally our people who serve customer A will never leak their information to customer C.”

But by working with so many trusted partners, TSMC leverages the partners’ steadily more complex designs to make itself better — and the better it gets, the more advanced designs it can master for its customers. This not only requires incredibly tight collaboration between TSMC and its customers, but also between TSMC and its roughly 1,000 critical local and global suppliers.

As the physics of chip making gets more and more extreme, “the investment from customers is getting bigger and bigger, so they have to work with us more closely to make sure they harvest as much [computing power] as they can. They have to trust you.”

China also has a foundry, Semiconductor Manufacturing International Corporation, which is partly state-owned. But guess what? Because no global chip designers trust SMIC with their most advanced designs, it is at least a decade behind TSMC.

It’s for these reasons that the erosion in U.S.-China relations goes beyond our increasingly sharp disagreements over Taiwan. It is rooted in the fact that just when trust, and its absence, became much bigger factors in international affairs and commerce, China changed its trajectory. It made itself a less trusted partner right when the most important technology for the 21st century — semiconductors — required unprecedented degrees of trust to manufacture and more and more devices and services became deep and dual use.

when American trade officials said: “Hey, you need to live up to your W.T.O. commitments to restrict state-funding of industries,” China basically said: “Why should we live by your interpretation of the rules? We are now big enough to make our own interpretations. We’re too big; you’re too late.”

Combined with China’s failure to come clean on what it knew about the origins of Covid-19, its crackdown on democratic freedoms in Hong Kong and on the Uyghur Muslim minority in Xinjiang, its aggressive moves to lay claim to the South China Sea, its increasing saber rattling toward Taiwan, its cozying up to Vladimir Putin (despite his savaging of Ukraine), Xi’s moves toward making himself president for life, his kneecapping of China’s own tech entrepreneurs, his tighter restrictions on speech and the occasional abduction of a leading Chinese businessman — all of these added up to one very big thing: Whatever trust that China had built up with the West since the late 1970s evaporated at the exact moment in history when trust, and shared values, became more important than ever in a world of deep, dual-use products driven by software, connectivity and microchips.

it started to matter a lot more to Western nations generally and the United States in particular that this rising power — which we were now selling to or buying from all sorts of dual-use digital devices or apps — was authoritarian.

eijing, for its part, argues that as China became a stronger global competitor to America — in deep goods like Huawei 5G — the United States simply could not handle it and decided to use its control over advanced semiconductor manufacturing and other high-tech exports from America, as well as from our allies, to ensure China always remained in our rearview mirror

Beijing came up with a new strategy, called “dual circulation.” It said: We will use state-led investments to make everything we possibly can at home, to become independent of the world. And we will use our manufacturing prowess to make the world dependent on our exports.

Chinese officials also argue that a lot of American politicians — led by Trump but echoed by many in Congress — suddenly seemed to find it very convenient to put the blame for economic troubles in the U.S.’s middle class not on any educational deficiencies, or a poor work ethic, or automation or the 2008 looting by financial elites, and the crisis that followed, but on China’s exports to the United States.

As Beijing sees it, China not only became America’s go-to boogeyman, but in their frenzy to blame Beijing for everything, members of Congress started to more recklessly promote Taiwan’s independence.

Xi told President Biden at their summit in Bali in November, in essence: I will not be the president of China who loses Taiwan. If you force my hand, there will be war. You don’t understand how important this is to the Chinese people. You’re playing with fire.

at some level Chinese officials now understand that, as a result of their own aggressive actions in recent years on all the fronts I’ve listed, they have frightened both the world and their own innovators at precisely the wrong time.

I don’t buy the argument that we are destined for war. I believe that we are doomed to compete with each other, doomed to cooperate with each other and doomed to find some way to balance the two. Otherwise we are both going to have a very bad 21st century.

I have to say, though, Americans and Chinese remind me of Israelis and Palestinians in one respect: They are both expert at aggravating the other’s deepest insecurities.

China’s Communist Party is now convinced that America wants to bring it down, which some U.S. politicians are actually no longer shy about suggesting. So, Beijing is ready to crawl into bed with Putin, a war criminal, if that is what it takes to keep the Americans at bay.

Americans are now worried that Communist China, which got rich by taking advantage of a global market shaped by American rules, will use its newfound market power to unilaterally change those rules entirely to its advantage. So we’ve decided to focus our waning strength vis-à-vis Beijing on ensuring the Chinese will always be a decade behind us on microchips.

I don’t know what is sufficient to reverse these trends, but I think I know what is necessary.

If it is not the goal of U.S. foreign policy to topple the Communist regime in China, the United States needs to make that crystal clear, because I found a lot more people than ever before in Beijing think otherwise.

As for China, it can tell itself all it wants that it has not taken a U-turn in recent years. But no one is buying it. China will never realize its full potential — in a hyper-connected, digitized, deep, dual-use, semiconductor-powered world — unless it understands that establishing and maintaining trust is now the single most important competitive advantage any country or company can have. And Beijing is failing in that endeavor.

In his splendid biography of the great American statesman George Shultz, Philip Taubman quotes one of Shultz’s cardinal rules of diplomacy and life: “Trust is the coin of the realm.”

“I could not have done it sooner,” he says. “I don’t think anybody could have done it sooner. Because I was the first one.” 

By the late 1960s, he was managing TI’s integrated-circuit division. Before long, he was running the entire semiconductor group. 

He transferred to the Massachusetts Institute of Technology, where he studied mechanical engineering, earned his master’s degree and would have stayed for his Ph.D. if he hadn’t failed the qualifying exam. Instead, he got his first job in semiconductors and moved to Texas Instruments in 1958

he came along as the integrated circuit was being invented, and his timing couldn’t have been any better, as Chang belonged to the first generation of semiconductor geeks. He developed a reputation as a tenacious manager who could wring every possible improvement out of production lines, which put his career on the fast track.

Chang grew up dreaming of being a writer—a novelist, maybe a journalist—and he planned to major in English literature at Harvard University. But after his freshman year, he decided that what he actually wanted was a good job

“They talk about life-work balance,” he says. “That’s a term I didn’t even know when I was their age. Work-life balance. When I was their age, if there was no work, there was no life.” 

These days, TSMC is investing $40 billion to build plants in Arizona, but the project has been stymied by delays, setbacks and labor shortages, and Chang told me that some of TSMC’s young employees in the U.S. have attitudes toward work that he struggles to understand. 

Chang says he wouldn’t have taken the risk of moving to Taiwan if he weren’t financially secure. In fact, he didn’t take that same risk the first time he could have.

“The closer the industry match,” they wrote, “the greater the success rate.” 

By then, Chang knew that he wasn’t long for Texas Instruments. But his stock options hadn’t vested, so he turned down the invitation to Taiwan. “I was not financially secure yet,” he says. “I was never after great wealth. I was only after financial security.” For this corporate executive in the middle of the 1980s, financial security equated to $200,000 a year. “After tax, of course,” he says. 

Chang’s situation had changed by the time Li called again three years later. He’d exercised a few million dollars of stock options and bought tax-exempt municipal bonds that paid enough for him to be financially secure by his living standards. Once he’d achieved that goal, he was ready to pursue another one. 

“There was no certainty at all that Taiwan would give me the chance to build a great semiconductor company, but the possibility existed, and it was the only possibility for me,” Chang says. “That’s why I went to Taiwan.” 

Not long ago, a team of economists investigated whether older entrepreneurs are more successful than younger ones. By scrutinizing Census Bureau records and freshly available Internal Revenue Service data, they were able to identify 2.7 million founders in the U.S. who started companies between 2007 and 2014. Then they looked at their ages.

The average age of those entrepreneurs at the founding of their companies was 41.9. For the fastest-growing companies, that number was 45. The economists also determined that 50-year-old founders were almost twice as likely to achieve major success as 30-year-old founders, while the founders with the lowest chance of success were the ones in their early 20s

“Successful entrepreneurs are middle-aged, not young,” they wrote in their 2020 paper.  

Silicon Valley’s venture capitalists throw money at talented young entrepreneurs in the hopes they will start the next trillion-dollar company. They have plentiful energy, insatiable ambition and the vision to peek around corners and see the future. What they don’t typically have are mortgages, family obligations and other adult responsibilities to distract them or diminish their appetite for risk. Chang himself says that younger people are more innovative when it comes to science and technical subjects. 

But in business, older is better. Entrepreneurs in their 40s and 50s may not have the exuberance to believe they will change the world, but they have the experience to know how they actually can. Some need years of specialized training before they can start a company. In biotechnology, for example, founders are more likely to be college professors than college dropouts. Others require the lessons and connections they accumulate over the course of their careers. 

one more finding from their study of U.S. companies that helps explain the success of a chip maker in Taiwan. It was that prior employment in the area of their startups—both the general sector and specific industry—predicted “a vastly higher probability” of success.

Chang was such a workaholic that he made sales calls on his honeymoon and had no patience for those who didn’t share his drive

Morris Chang had 30 years of experience in his industry when he decided to uproot his life and move to another continent. He knew more about semiconductors than just about anyone on earth—and certainly more than anyone in Taiwan. As soon as he started his job at the Industrial Technology Research Institute, Chang was summoned to K.T. Li’s office and given a second job. “He felt I should start a semiconductor company in Taiwan,”

“I decided right away that this could not be the kind of great company that I wanted to build at either Texas Instruments or General Instrument,”

TI handled every part of chip production, but what worked in Texas would not translate to Taiwan. The only way that he could build a great company in his new home was to make a new sort of company altogether, one with a business model that would exploit the country’s strengths and mitigate its many weaknesses.

Chang determined that Taiwan had precisely one strength in the chip supply chain. The research firm that he was now running had been experimenting with semiconductors for the previous 10 years. When he studied that decade of data, Chang was pleasantly surprised by Taiwan’s yields, the percentage of working chips on silicon wafers. They were almost twice as high in Taiwan as they were in the U.S., he said. 

“People were ingrained in thinking the secret sauce of a successful semiconductor company was in the wafer fab,” Campbell told me. “The transition to the fabless semiconductor model was actually pretty obvious when you thought about it. But it was so against the prevailing wisdom that many people didn’t think about it.” 

Taiwan’s government took a 48% stake, with the rest of the funding coming from the Dutch electronics giant Philips and Taiwan’s private sector, but Chang was the driving force behind the company. The insight to build TSMC around such an unconventional business model was born from his experience, contacts and expertise. He understood his industry deeply enough to disrupt it. 

“TSMC was a business-model innovation,” Chang says. “For innovations of that kind, I think people of a more advanced age are perhaps even more capable than people of a younger age.”

the personal philosophy that he’d developed over the course of his long career. “To be a partner to our customers,” he says. That founding principle from 1987 is the bedrock of the foundry business to this day, as TSMC says the key to its success has always been enabling the success of its customers.  

TSMC manufactures chips in iPhones, iPads and Mac computers for Apple, which manufactures a quarter of TSMC’s net revenue. Nvidia is often called a chip maker, which is curious, because it doesn’t make chips. TSMC does. 

Churning out identical copies of a single chip for an iPhone requires one TSMC fab to produce more than a quintillion transistors—that is, one million trillions—every few months. In a year, the entire semiconductor industry produces “more transistors than the combined quantity of all goods produced by all other companies, in all other industries, in all human history,” Miller writes. 

I asked how he thought about success when he moved to Taiwan. “The highest degree of success in 1985, according to me, was to build a great company. A lower degree of success was at least to do something that I liked to do and I wanted to do,” he says. “I happened to achieve the highest degree of success that I had in mind.” 

Intel, founded by Gordon Moore and Robert Noyce, both physicists, began by building memory chips that were twice as fast as old ones. Sun Microsystems introduced a new kind of modular computer system, built by one of its founders, Andy Bechtolsheim. Their “big ideas” were expressed in physical products and grew out of their own technical expertise. In that light, Meraki, which came from Biswas’s work at M.I.T., can be seen as having its origins in the old guard. And it followed what was for decades the highway that connected academia to industry: Grad students researched technology, powerful advisers brokered deals, students dropped out to parlay their technologies into proprietary solutions, everyone reaped the profits. That implicit guarantee of academia’s place in entrepreneurship has since disappeared. Graduate students still drop out, but to start bike-sharing apps and become data scientists. That is, if they even make it to graduate school. The success of self-educated savants like Sean Parker, who founded Napster and became Facebook’s first president with no college education to speak of, set the template. Enstitute, a two-year apprenticeship, embeds high-school graduates in plum tech positions. Thiel Fellowships, financed by the PayPal co-founder and Facebook investor Peter Thiel, give $100,000 to people under 20 to forgo college and work on projects of their choosing.

Much of this precocity — or dilettantism, depending on your point of view — has been enabled by web technologies, by easy-to-use programming frameworks like Ruby on Rails and Node.js and by the explosion of application programming interfaces (A.P.I.s) that supply off-the-shelf solutions to entrepreneurs who used to have to write all their own code for features like a login system or an embedded map. Now anyone can do it, thanks to the Facebook login A.P.I. or the Google Maps A.P.I.

One of the more enterprising examples of these kinds of interfaces is the start-up Stripe, which sells A.P.I.s that enable businesses to process online payments. When Meraki first looked into taking credit cards online, according to Biswas, it was a monthslong project fraught with decisions about security and cryptography. “Now, with Stripe, it takes five minutes,” he said. “When you combine that with the ability to get a server in five minutes, with Rails and Twitter Bootstrap, you see that it has become infinitely easier for four people to get a start-up off the ground.”

The sense that it is no longer necessary to have particularly deep domain knowledge before founding your own start-up is real; that and the willingness of venture capitalists to finance Mark Zuckerberg look-alikes are changing the landscape of tech products. There are more platforms, more websites, more pat solutions to serious problems

There’s a glass-half-full way of looking at this, of course: Tech hasn’t been pedestrianized — it’s been democratized. The doors to start-up-dom have been thrown wide open. At Harvard, enrollment in the introductory computer-science course, CS50, has soared

many of the hottest web start-ups are not novel, at least not in the sense that Apple’s Macintosh or Intel’s 4004 microprocessor were. The arc of tech parallels the arc from manufacturing to services. The Macintosh and the microprocessor were manufactured products. Some of the most celebrated innovations in technology have been manufactured products — the router, the graphics card, the floppy disk

One of Stripe’s founders rowed five seat in the boat I coxed freshman year in college; the other is his older brother. Among the employee profiles posted on its website, I count three of my former teaching fellows, a hiking leader, two crushes. Silicon Valley is an order of magnitude bigger than it was 30 years ago, but still, the start-up world is intimate and clubby, with top talent marshaled at elite universities and behemoths like Facebook and Google.

Part of the answer, I think, lies in the excitement I’ve been hinting at. Another part is prestige. Smart kids want to work for a sexting app because other smart kids want to work for the same sexting app. “Highly concentrated pools of top talent are one of the rarest things you can find,” Biswas told me, “and I think people are really attracted to those environments.

The latter source of frustration is the phenomenon of “the 10X engineer,” an engineer who is 10 times more productive than average. It’s a term that in its cockiness captures much of what’s good, bad and impossible about the valley. At the start-ups I visit, Friday afternoons devolve into bouts of boozing and Nerf-gun wars. Signing bonuses at Facebook are rumored to reach the six digits. In a landscape where a product may morph several times over the course of a funding round, talent — and the ability to attract it — has become one of the few stable metrics.

there is a surprising amount of angst in Silicon Valley. Which is probably inevitable when you put thousands of ambitious, talented young people together and tell them they’re god’s gift to technology. It’s the angst of an early hire at a start-up that only he realizes is failing; the angst of a founder who raises $5 million for his company and then finds out an acquaintance from college raised $10 million; the angst of someone who makes $100,000 at 22 but is still afraid that he may not be able to afford a house like the one he grew up in.

San Francisco, which is steadily stealing the South Bay’s thunder. (“Sometime in the last two years, the epicenter of consumer technology in Silicon Valley has moved from University Ave. to SoMa,” Terrence Rohan, a venture capitalist at Index Ventures, told me

Both the geographic shift north and the increasingly short product cycles are things Jim attributes to the rise of Amazon Web Services (A.W.S.), a collection of servers owned and managed by Amazon that hosts data for nearly every start-up in the latest web ecosystem.Continue reading the main story

now, every start-up is A.W.S. only, so there are no servers to kick, no fabs to be near. You can work anywhere. The idea that all you need is your laptop and Wi-Fi, and you can be doing anything — that’s an A.W.S.-driven invention.”

This same freedom from a physical location or, for that matter, physical products has led to new work structures. There are no longer hectic six-week stretches that culminate in a release day followed by a lull. Every day is release day. You roll out new code continuously, and it’s this cycle that enables companies like Facebook, as its motto goes, to “move fast and break things.”

A few weeks ago, a programmer friend and I were talking about unhappiness, in particular the kind of unhappiness that arises when you are 21 and lavishly educated with the world at your feet. In the valley, it’s generally brought on by one of two causes: coming to the realization either that your start-up is completely trivial or that there are people your own age so knowledgeable and skilled that you may never catch up.

These days, a new college graduate arriving in the valley is merely stepping into his existing network. He will have friends from summer internships, friends from school, friends from the ever-increasing collection of incubators and fellowships.

As tech valuations rise to truly crazy levels, the ramifications, financial and otherwise, of a job at a pre-I.P.O. company like Dropbox or even post-I.P.O. companies like Twitter are frequently life-changing. Getting these job offers depends almost exclusively on the candidate’s performance in a series of technical interviews, where you are asked, in front of frowning hiring managers, to whip up correct and efficient code.

Moreover, a majority of questions seem to be pulled from undergraduate algorithms and data-structures textbooks, which older engineers may have not laid eyes on for years.

This failing in the West’s economies is also a failing of economics

many people have long felt the desire to do something with their lives besides consuming goods and having leisure. They desire to participate in a community in which they can interact and develop.

Our prevailing political economy is blind to the very concept of inclusion; it does not map out any remedy for the deficiency

injustice of another sort. Workers in decent jobs view the economy as unjust if they or their children have virtually no chance of climbing to a higher rung in the socioeconomic ladder

though the injustices in the West’s economies are egregious, they ought not to be seen as a major cause of the productivity slowdowns and globalization. (For one thing, a slowdown of productivity started in the US in the mid-1960s and the sharp loss of manufacturing jobs to poorer countries occurred much later—from the late 1970s to the early 1990s.) Deeper causes must be at work.

justice is not everything that people need from their economy. They need an economy that is good as well as just. And for some decades, the Western economies have fallen short of any conception of a “good economy”—an economy offering a “good life,” or a life of “richness,” as some humanists call it

The good life as it is popularly conceived typically involves acquiring mastery in one’s work, thus gaining for oneself better terms—or means to rewards, whether material, like wealth, or nonmaterial—an experience we may call “prospering.”

As humanists and philosophers have conceived it, the good life involves using one’s imagination, exercising one’s creativity, taking fascinating journeys into the unknown, and acting on the world—an experience I call “flourishing.”

“Money is like blood. You need it to live but it isn’t the point of life.”4

prospering and flourishing became prevalent in the nineteenth century when, in Europe and America, economies emerged with the dynamism to generate their own innovation.

today’s standard economics. This economics, despite its sophistication in some respects, makes no room for economies in which people are imagining new products and using their creativity to build them. What is most fundamentally “wrong with economics” is that it takes such an economy to be the norm—to be “as good as it gets.”

In nineteenth-century Britain and America, and later Germany and France, a culture of exploration, experimentation, and ultimately innovation grew out of the individualism of the Renaissance, the vitalism of the Baroque era, and the expressionism of the Romantic period.

What made innovating so powerful in these economies was that it was not limited to elites. It permeated society from the less advantaged parts of the population on up.

High-enough wages, low-enough unemployment, and wide-enough access to engaging work are necessary for a “good-enough” economy—though far from sufficient. The material possibilities of the economy must be adequate for the nonmaterial possibilities to be widespread—the satisfactions of prospering and of flourishing through adventurous, creative, and even imaginative work.

prospering

ince around 1970, or earlier in some cases, most of the continental Western European economies have come to resemble more completely the mechanical model of standard economics. Most companies are highly efficient. Households, apart from the very low-paid or unemployed, have gone on saving

In most of Western Europe, economic dynamism is now at lows not seen, I would judge, since the advent of dynamism in the nineteenth century. Imagining and creating new products has almost disappeared from the continent

The bleak levels of both unemployment and job satisfaction in Europe are testimony to its dreary economies.

a recent survey of household attitudes found that, in “happiness,” the median scores in Spain (54), France (51), Italy (48), and Greece (37) are all below those in the upper half of the nations labeled “emerging”—Mexico (79), Venezuela (74), Brazil (73), Argentina (66), Vietnam (64), Colombia (64), China (59), Indonesia (58), Chile (58), and Malaysia (56)

The US economy is not much better. Two economists, Stanley Fischer and Assar Lindbeck, wrote of a “Great Productivity Slowdown,” which they saw as beginning in the late 1960s.11 The slowdown in the growth of capital and labor combined—what is called “total factor productivity”—is star

What is the mechanism of the slowdown in productivity

The plausible explanation of the syndrome in America—the productivity slowdown and the decline of job satisfaction, among other things—is a critical loss of indigenous innovation in the established industries like traditional manufacturing and services that was not nearly offset by the innovation that flowered in a few new industries

hat then caused this narrowing of innovation? No single explanation is persuasive. Yet two classes of explanations have the ring of truth. One points to suppression of innovation by vested interests

some professions, such as those in education and medicine, have instituted regulation and licensing to curb experimentation and change, thus dampening innovation

established corporations—their owners and stakeholders—and entire industries, using their lobbyists, have obtained regulations and patents that make it harder for new firms to gain entry into the market and to compete with incumbents.

The second explanation points to a new repression of potential innovators by families and schools. As the corporatist values of control, solidarity, and protection are invoked to prohibit innovation, traditional values of conservatism and materialism are often invoked to inhibit a young person from undertaking an innovation.

ow might Western nations gain—or regain—widespread prospering and flourishing? Taking concrete actions will not help much without fresh thinking: people must first grasp that standard economics is not a guide to flourishing—it is a tool only for efficiency.

Widespread flourishing in a nation requires an economy energized by its own homegrown innovation from the grassroots on up. For such innovation a nation must possess the dynamism to imagine and create the new—economic freedoms are not sufficient. And dynamism needs to be nourished with strong human values.

a reform of education stands out. The problem here is not a perceived mismatch between skills taught and skills in demand

The problem is that young people are not taught to see the economy as a place where participants may imagine new things, where entrepreneurs may want to build them and investors may venture to back some of them. It is essential to educate young people to this image of the economy.

It will also be essential that high schools and colleges expose students to the human values expressed in the masterpieces of Western literature, so that young people will want to seek economies offering imaginative and creative careers. Education systems must put students in touch with the humanities in order to fuel the human desire to conceive the new and perchance to achieve innovations

This reorientation of general education will have to be supported by a similar reorientation of economic education.

Even if trade flows begin to balance and there is less job loss in the future, the integration of the U.S. economy with those of its low-wage trading partners will pull down wages for many American workers, and will contribute to the ever rising inequality of incomes in the U.S. economy.

The mounting pressure applied by organized labor can be seen if you compare the votes of California House Democrats in 1993 on the North American Free Trade Agreement with votes on granting approval this year to fast track the TPP.

Nafta was approved in 1993, 234 to 200 by the full House, with Republicans in favor 132-43 and Democrats opposed 156-102. On June 18 of this year, the House passed fast-track legislation, but the partisan differences were much sharper: Republicans voted in favor, 190 to 50; Democrats opposed the legislation 158-28.

In between the votes on Nafta in 1993 and this year’s TPP, there was one major development pushing the union movement to the wall: the decline in manufacturing employment as trade with China accelerated in the 2000s.

from 2000 (the year that Congress granted China permanent normal trade relations status) to 2010, the number of manufacturing jobs fell by 34 percent, from just over 17 million to 11.5 million.

Trade always implies job destruction — this is the whole point of trade, namely to destroy jobs in import competing sectors to create them in exporting sectors, thus allowing for reallocation to sectors with comparative advantage, leading to an increased efficiency, the basis for the gains from trade.

Trade was linked to job losses in manufacturing that happened in the 1980s and then again in 2001-2004; it’s not happening now, because those jobs that could be shifted have been.” The bigger problem, Shapiro wrote, “is incomes – a majority of households saw their incomes decline from 2002-2013, even as they aged – i.e., people earned less at 40 than they had at 29.

In addition, one of “the ripple effects of globalization” is an intensification of competition, which, in turn, forces businesses to cut jobs and wages. “When people are feeling squeezed or worse economically, they retreat and see trade as a threat.”

The Democratic opposition to free trade has intensified in large part because of the current prominence of liberal-populist strength and the liberal tilt of individual – as opposed to special interest — donors.

The conflict between a pro-free-trade presidential wing of the Democratic Party and an anti-free-trade congressional wing has the potential to become a permanent fixture of the center-left. The conflict gives expression to both the downscale forces in the party that lean against trade and the pro-trade upscale forces.

The upstairs-downstairs character of the Democratic coalition proved to be the party’s strength in Obama’s two elections, and its continued viability is crucial to Clinton’s prospects next year. Trade, taxes and social spending are subjects that have not yet achieved progressive consensus and retain the potential to fracture the party

The real question, then, is how long will left and right within the Democratic coalition tolerate an inherently unstable posture on such a core issue as how this country does business abroad, and how it provides a livelihood for its citizens.

The workers I interviewed after the recession for my book on unemployment—less-educated factory workers—offer some tentative clues about what might be driving the disquieting trends described by the Case and Deaton study.

This is one of the groups hit hardest by the rising inequality and greater risk of unemployment and financial insecurity that have become features of today’s economy, and their experiences put in concrete terms how the economy and culture have become more hostile to workers not lucky enough to be working in posh offices on Wall Street or in Silicon Valley.

When it comes to explaining American economic trends, it is important to remember how critical a role manufacturing and unions have played in the building—and now dismantling—of a strong middle class.

or generations, factories provided good jobs to people who never went to college, allowing families—first white ethnic immigrants, and then others—to be upwardly mobile.

But in the late ’90s—the beginning of the crisis period that Case and Deaton identify—the number of manufacturing jobs in the U.S. dropped dramatically.

Political leaders, bankrolled by the wealthy, rolled back the interventionist policies of the New Deal and postwar period.

Corporations, once relatively tolerant of unions, tapped a cottage industry of anti-union consultants and adopted unseemly tactics to crush any organizing drives in their workplaces.

the two-thirds of Americans over the age of 25 who don’t have a bachelor’s degree

Certainly, it cannot be said enough that African Americans and Latinos continue to fare significantly worse than whites in terms of their overall rates of death and disease, even if the racial gap has narrowed.

. In the decades after World War II, racial minorities were denied many of the jobs, loans, and other resources that allowed the white majority to buy homes and accrue wealth.

If the gains of economic growth have gone largely to the rich in recent years, in that earlier period the white working class could count on hefty rises in living standards from generation to generation, and they grew accustomed to that upward trajectory of growing prosperity.

For example, while the disappearance of high-paying jobs for those with little education is a large part of the overall story of a shrinking middle class, it can’t wholly account for the uptick of mortality identified in the Case and Deaton study.

Likewise, the groups that have been affected most viciously by these market trends in the U.S., African Americans and Latinos, have not suffered the dramatic increases in death by suicide or substance abuse that whites have.

hite Americans, for instance, are more likely to see success as the result of individual effort than African Americans are (though not Hispanics). The less educated, particularly less-educated whites, also share this view to a disproportionate degree.

As scholars of family life as politically distinct as Andrew Cherlin and Charles Murray have stressed, college graduates and the less educated have greatly diverged in terms of when and how they partner up and have kids.

Nowadays, well-educated couples are much more likely to marry, stay married, and have children within marriage than those with less schooling.

A large part of the explanation for this must be that society’s attitudes about the sanctity and permanence of marriage have changed. But it’s important to note that there is an economic dimension to these trends, too—as the frequent separations and divorces I saw among the long-term unemployed made plain to me.

Those struggling financially are less likely to follow the traditional path of first comes marriage, then comes a baby.

The waning of religious belief may be another trend aggravating the modern malaise of the white working class. Since the ’90s, the number of Americans who declare no religious preference on surveys has almost tripled—from 8 percent at the beginning of that decade, to 21 percent in 2014.

Many said their faith was helping them get through their ongoing troubles, yet they rarely or never went to church. Some felt ashamed to be around people because they were out of work.

For others, their religious belief was somewhat a source of self-help, rather than a source of community.

The larger context of this isolation and alienation is America’s culture of individualism. It, too, can worsen the despair. Taken to an extreme, self-reliance becomes a cudgel: Those who falter and fail have only themselves to blame.

America’s frontier spirit of rugged individualism is strong, and it manifests itself differently by race and education level, too.

When asked in national surveys about the people with whom they discussed “important matters” in the past six months, those with just a high-school education or less are likelier to say no one (this percentage has risen over the years for college graduates, too).

To this day, the supreme value of education remains one of the few things that Americans of all persuasions (presidential candidates included) can agree on.

Some of the analysis of the Case and Deaton article has focused rightly on recent developments in this country’s drug crisis—namely, the surge in abuse of prescription opioids, and the resurgence in heroin use, notably among whites.

There is clearly a pressing need to deal more vigorously with this drug problem and the epidemic of fatal overdoses and liver disease that has affected the poor and working class in particular.

At the same time, it should be said that risky individual behaviors are shaped by broader social conditions. As the researchers Bruce Link and Jo Phelan have argued, effective health interventions need to consider the underlying factors that put people “at risk of risks”—specifically, socioeconomic status and social support.

One parting observation, then, is that policies to keep people from sinking into poverty and long-term unemployment can make a huge difference.

economists are reassessing their belief that technological progress lifts all boats, and are beginning to worry about the new configuration of work.

“We automate the pieces that can be automated,” said Paul Hart, a senior vice president running the radio-frequency power business at NXP’s plant in Chandler. “The work force grows but we need A.I. and automation to increase the throughput.”

“The view that we should not worry about any of these things and follow technology to wherever it will go is insane,”

But the industry doesn’t generate that many jobs

Because it pushes workers to the less productive parts of the economy, automation also helps explain one of the economy’s thorniest paradoxes: Despite the spread of information technology, robots and artificial intelligence breakthroughs, overall productivity growth remains sluggish.

Axon, which makes the Taser as well as body cameras used by police forces, is also automating whatever it can. Today, robots make four times as many Taser cartridges as 80 workers once did less than 10 years ago

The same is true across the high-tech landscape. Aircraft manufacturing employed 4,234 people in 2017, compared to 4,028 in 2010. Computer systems design services employed 11,000 people in 2017, up from 7,000 in 2010.

To find the bulk of jobs in Phoenix, you have to look on the other side of the economy: where productivity is low. Building services, like janitors and gardeners, employed nearly 35,000 people in the area in 2017, and health care and social services accounted for 254,000 workers. Restaurants and other eateries employed 136,000 workers, 24,000 more than at the trough of the recession in 2010. They made less than $450 a week.

While Banner invests heavily in technology, the machines do not generally reduce demand for workers. “There are not huge opportunities to increase productivity, but technology has a significant impact on quality,” said Banner’s chief operating officer, Becky Kuhn

The 58 most productive industries in Phoenix — where productivity ranges from $210,000 to $30 million per worker, according to Mr. Muro’s and Mr. Whiton’s analysis — employed only 162,000 people in 2017, 14,000 more than in 2010

Employment in the 58 industries with the lowest productivity, where it tops out at $65,000 per worker, grew 10 times as much over the period, to 673,000.

The same is true across the national economy. Jobs grow in health care, social assistance, accommodation, food services, building administration and waste services

On the other end of the spectrum, the employment footprint of highly productive industries, like finance, manufacturing, information services and wholesale trade, has shrunk over the last 30 years

“In the standard economic canon, the proposition that you can increase productivity and harm labor is bunkum,” Mr. Acemoglu said

By reducing prices and improving quality, technology was expected to raise demand, which would require more jobs. What’s more, economists thought, more productive workers would have higher incomes. This would create demand for new, unheard-of things that somebody would have to make

To prove their case, economists pointed confidently to one of the greatest technological leaps of the last few hundred years, when the rural economy gave way to the industrial era.

In 1900, agriculture employed 12 million Americans. By 2014, tractors, combines and other equipment had flushed 10 million people out of the sector. But as farm labor declined, the industrial economy added jobs even faster. What happened? As the new farm machines boosted food production and made produce cheaper, demand for agricultural products grew. And farmers used their higher incomes to purchase newfangled industrial goods.

The new industries were highly productive and also subject to furious technological advancement. Weavers lost their jobs to automated looms; secretaries lost their jobs to Microsoft Windows. But each new spin of the technological wheel, from plastic toys to televisions to computers, yielded higher incomes for workers and more sophisticated products and services for them to buy.

In a new study, David Autor of the Massachusetts Institute of Technology and Anna Salomons of Utrecht University found that over the last 40 years, jobs have fallen in every single industry that introduced technologies to enhance productivity.

The only reason employment didn’t fall across the entire economy is that other industries, with less productivity growth, picked up the slack. “The challenge is not the quantity of jobs,” they wrote. “The challenge is the quality of jobs available to low- and medium-skill workers.”

the economy today resembles what would have happened if farmers had spent their extra income from the use of tractors and combines on domestic servants. Productivity in domestic work doesn’t grow quickly. As more and more workers were bumped out of agriculture into servitude, productivity growth across the economy would have stagnated.

The growing awareness of robots’ impact on the working class raises anew a very old question: Could automation go too far? Mr. Acemoglu and Pascual Restrepo of Boston University argue that businesses are not even reaping large rewards for the money they are spending to replace their workers with machines.

the cost of automation to workers and society could be substantial. “It may well be that,” Mr. Summers said, “some categories of labor will not be able to earn a subsistence income.” And this could exacerbate social ills, from workers dropping out of jobs and getting hooked on painkillers, to mass incarceration and families falling apart.

Silicon Valley’s dream of an economy without workers may be implausible. But an economy where most people toil exclusively in the lowliest of jobs might be little better.

The question is what liability gun manufacturers or sellers have in making, marketing a selling a product that kills thousands every year, and whether, for example, they are negligent in selling weapons, like the AR-15, that many argue have no place outside of a combat zone and are predictable weapons of mass death.

And yet they seem awfully nervous about having to defend themselves in court. Yes, lawsuits are expensive and time-consuming, but gun manufacturers aren’t exactly vulnerable little guys. The American legal system has mechanisms in place for tossing out baseless lawsuits, and surely some brought against gun makers and sellers will be summarily tossed.

We don’t know a whole lot about how the gun industry deals with the violence, death and destruction its products bring about because the whole industry operates under a Congress-approved veil of secrecy. We do know that victims have been unjustly stymied, denied even the chance to have their voices heard in court. No good happens in the shadows, and no industry does the right thing if it’s permitted to operate partly outside of the law. Getting rid of the PLCAA won’t end gun violence. But it would let victims be heard, and it would bring a little transparency to the shadowy, coddled gun industry.

To meet its old Paris target, the United States had to cut its annual carbon emissions by 1.3 percentage points every year from 2016 to 2025. To meet the 2050 goal, it must cut at more than double that rate—2.9 percentage points—for each of the next 31 years.

This program would go hand in hand with her also just debuted “Green Manufacturing Plan,” which promises to allocate $1.5 trillion in federal spending for climate-friendly technology. She would also use federal power to encourage other countries to purchase this new American gear.

Warren calls this issue “economic patriotism.” Under its banner, the senator from Massachusetts and presidential candidate proposes a huge new program of climate-friendly manufacturing investment, meant to turn the United States back into a major industrial exporter.

Inslee earlier outlined his aim to decarbonize some parts of the U.S. economy by the 2030s, and he has endorsed some aspects of the Green New Deal. Representative Alexandria Ocasio-Cortez, the Green New Deal’s champion, told a reporter yesterday that Inslee’s plan is the “golden standard.” (Inslee’s plan is also untainted by plagiarism accusations.)

Essentially, Warren wants to bring Germany or South Korea’s mixed-economy model to the United States and then point it at the challenge of climate change.

they show how the window of political possibility has already moved significantly, such that Biden’s $1.5 trillion in climate-focused federal spending can start to seem moderate to right-wing observers.

In the Washington Examiner yesterday, the conservative writer Tiana Lowe paid relatively high praise to Biden’s plan. Unlike the Green New Deal, she said, Biden’s proposal is “not insane,” but a “legitimate, big-boy climate change plan” in its own right.

“If nothing were executed into action here except for the international aspect, nuclear research and development, and the infrastructure developments that [Biden] details, it would do more to decrease greenhouse gas emissions in real life than any $93 trillion Green New Deal,” Lowe wrote.

actual supporters of a Green New Deal, who know a victory when they see one. In the opinion of the youth-led Sunrise Movement, Biden’s plan is far better than the “middle ground” proposal he was considering last month. “We forced [Biden] to backtrack and today, he put out a comprehensive climate plan that praises the Green New Deal,” it tweeted yesterday

China’s vast manufacturing machine has moved into overdrive to supply the country and the world with masks, testing kits, respirators and other gear to fight the global coronavirus pandemic.

Companies big and small that once manufactured other items are now in the business of making anti-coronavirus gear — and regulators in China are struggling to enforce standards while encouraging the flow.

Spanish health officials said they bought the tests from an unnamed third-party distributor but argued that they did not need to wait for a certified list from China

Still, the news was unwelcome for Spaniards who have waited for several days for their government’s promised rollout of hundreds of thousands of tests to help track the spread of the virus

In one Chinese province, authorities seized more than seven million masks that were substandard, mislabeled or counterfeited.

As it has in other industries, from cars to electronics, China has become essential to the medical supply business and has challenged outdated notions that it primarily makes cheap, shoddy goods. Even before the coronavirus struck it made roughly half the world’s protective masks and has become a major force in the manufacturing of day-to-day medical gear.

But even as it encourages production, the Chinese government has also had to step up enforcement efforts to stop defective and uncertified products

The problem isn’t confined to China.

In the United States, an initial coronavirus test kit rolled out by government officials contained a faulty indicator

Regulators have shut down tens of thousands of shops making fake masks,

Earlier this month, Chinese regulators vowed to step up cooperation with local law enforcement to stamp out counterfeits and shoddy goods

At the same time, local officials have worked to streamline the certification process to bring new capacity and new devices online.

In Inslee’s telling, this has resulted in a double whammy: a shortage of supplies and a lack of coordination of availability of parts. This could be mitigated by a much more robust and coordinated response via the Defense Production Act

As coronavirus cases recede in the coming months, if anything, more testing will be required. That’s because when people reassemble, it will be urgent to jump on cases in which people again show symptoms, and test them, to avoid a second wave.

“As we want to reopen our schools, as we want to reopen our industries, the amount of testing we need will actually increase,” Inslee said. “In the second wave, we have to have testing, a resource base, and a contact-tracing base that is so much more scaled up than right now. It’s an enormous challenge.”

“What we need now, what won World War II, was a quartermaster,” Inslee said. “That’s how you win wars. That’s what we need — a quartermaster.

Recently, Trump was directly confronted with glaring evidence of this. His own administration released a report documenting urgent shortages faced by hospital administrators around the country, who offered constructive suggestions on how the federal government can help save American lives.

In response, Trump lashed out in a rage, and pretended those problems are simply nonexistent.

The danger is that richer nations will buy up the supply for their own use or prevent exports of vaccines developed within their borders as countries scramble to protect their citizens or stockpile for future outbreaks

Another concern is that manufacturers might restrict sales to the highest bidder.

the Coalition for Epidemic Preparedness Innovations is trying to expedite the development of vaccines

The coalition has kick-started the development of at least nine coronavirus vaccines but needs to raise $2 billion to help up to three of these candidates make it through the regulatory and quality requirements and to complete efficacy trials. The world would then need to ensure funds to guarantee manufacturing capacity for global production to make billions of doses available within the next 18 to 24 months.

it is encouraging that the health ministers of the Group of 20 wealthy nations called for accelerating the development, manufacturing and equitable distribution of vaccines against the coronavirus at its April 19 meeting and that last Friday, more than 15 heads of state, the president of the European Union, the director general of the World Health Organization and leaders of other international institutions committed to join together to do the same.

to keep the global population safe, it must be done. Innovative financing mechanisms exist that can translate long-term government commitments into immediate funding by issuing bonds on capital markets. The Ebola vaccine we have today was made possible by such an approach.

Estimates put the cost of Covid-19 to the global economy at up to $2 trillion this year.

If we can advance the mass distribution of an effective vaccine or vaccines by as little as two weeks, not only would we would save countless lives, but the effort would almost pay for itself by getting the world back to work.

Another diagnostic test under development would work much like an at-home pregnancy test. You would swab your nose, but instead of sending it into a processing center, you’d put it in a liquid and then pour that liquid onto a strip of paper, which would change color if the virus was present. This test may be available in a few months.

We need one other advance in testing, but it’s social, not technical: consistent standards about who can get tested. If the country doesn’t test the right people — essential workers, people who are symptomatic and those who have been in contact with someone who tested positive — then we’re wasting a precious resource and potentially missing big reserves of the virus.

identifying the antibodies that are most effective against the novel coronavirus, and then manufacturing them in a lab. If this works, it is not yet clear how many doses could be produced; it depends on how much antibody material is needed per dose. In 2021, manufacturers may be able to make as few as 100,000 treatments or many millions.

An even better solution would be the broad, voluntary adoption of digital tools. For example, there are apps that will help you remember where you have been

treatment options

giving the plasma (and the antibodies it contains) to sick people. Several major companies are working together to see whether this succeeds.

The second area where we need innovation is contact tracing

Unfortunately, based on the evidence I’ve seen, they’ll likely find a good treatment, but not one that virtually guarantees you’ll recover.

making a vaccine.

The new approach I’m most excited about is known as an RNA vaccine.

an RNA vaccine gives your body the genetic code needed to produce viral fragments on its own.

n RNA vaccine essentially turns your body into its own vaccine manufacturing unit.

World War II was the defining moment of my parents’ generation. Similarly, the coronavirus pandemic — the first in a century — will define this era

here is one big difference between a world war and a pandemic: All of humanity can work together to learn about the disease and develop the capacity to fight it. With the right tools in hand, and smart implementation, we will eventually be able to declare an end to this pandemic

Throughtout medieval Japan, mirrors were considered sacred objects—used not only in rarefied imperial ritual and display but also to ward off evil spirts and, when placed in Shinto shrines, to speak with the gods.

Early glass mirrors were made of glass tiles cut from blown glass forms—thus always slightly curved, and always slightly colored, as the chemistry of clear glass manufacture remained unknown. These glass tiles were then affixed over still-hot, carefully sized, cast lead forms, with a thin layer of polished metal sheeting between the two.

Around 500 AD, man began to create somewhat clearer and more reflective glass mirrors using silver-mercury amalgams. Examples of such have been found in China dated as early as c.500AD. But another thousand years would pass before silvery-mercury amalgam processes became more efficient—and less deadly, mercury being one of the most toxic elements on planet Earth.

As guildsmen investigated tin, silver, and mercury amalgams, they also experimented with a different material: rock crystal. Extremely rare and as costly as gemstones, these wall-hung crystal mirrors were highly prized by admirers far and wide, including Francis I, ruler of Mantua 1382-1407, who had them installed in the Castle of San Giorgio

Jean-Baptiste Colbert—appealing to Louis XIV for economic reform and endeavoring to keep in France the 100,000 crowns going out yearly to Italy for mirrors—managed to import 20 (living) Venetian glass workers into France. Established by royal initiative, this Manufacture des Glaces (at the Saint-Gobain factory) would greatly expand the production of mirrors.

Glass mirror not only revolutionized how we see ourselves and how we magnify light, but also how artists see and depict the world. Mirror is cited as critical to the discovery of linear perspective—making what is flat (a painting) appear to be in relief (real life)— by Renaissance artists.

Through the 18th century, technical and economic difficulties persisted in the manufacture of clear glass. Metal mirrors remained the standard in everyday households, oiled paper the common window covering.

Yet countries like South Africa are in a singular bind because they cannot hold out hope for charity. Although its government is nearly insolvent and half of its citizens live in poverty, South Africa is considered too rich to qualify for cut-rate vaccines from international aid organizations.

Poor and middle-income nations, largely unable to compete in the open market, rely on a complex vaccine sharing scheme called Covax.

A collaboration of international health organizations, Covax was designed to avoid the inequities of a free-market free-for-all.

y the middle of next year, South African officials hope to secure their first vaccine doses under Covax, even as they negotiate to buy supplemental supplies from drug manufacturers. But in a country where luxury estates are walled off from sprawling squatter villages, many expect the newest vaccines to remain a privilege for residents who can pay out of pocket or through supplemental insurance — a program that disproportionately benefits white people

The best chance that many South Africans have to get vaccinated anytime soon is to volunteer for a clinical trial and test unproven vaccines on their bodies. But that arrangement has raised ethical questions.

The people at the top, they’re going to get the vaccine, the people who have power,” said Mtshaba Mzwamadoda, 42, who lives in a one-bedroom corrugated metal shack with his wife and three children. “Maybe we’ll get the vaccine in 2025.”

My assumption is that unless you get onto a vaccine study, you’re not going to have access to any vaccine anytime soon,” Dr. Gill said, “which is obviously quite heartbreaking.”

outh African medical advisers say the Covax system is incredibly important but also deeply frustrating. Governments must pay up front without knowing what vaccine they will receive or getting any guarantees on when the doses will arrive.

Many South Africans are deeply skeptical of pharmaceutical companies and wary of rampant government corruption

Outraged, health advocates have threatened to sue the country’s government to make the plans public.

Ultimately, though, money is the great differentiator. From the outset, South Africa’s government knew it could not afford to order doses before they were tested and approved, as wealthy countries did.

In 2009, when the world feared a devastating H1N1 flu outbreak, rich countries hoarded the earliest vaccines. While the outbreak was far weaker than expected, it revealed the inequities that exist when countries compete for lifesaving medicine.

One example is the HPV vaccine, a drug that can prevent cervical cancer but is in short supply in South Africa. Supplies are so tight that the World Health Organization has recommended that wealthy countries temporarily stop expanding their vaccinating campaigns to cover boys, so other nations can focus at least on covering young girls.

Under contract with Johnson & Johnson, Aspen is expected to produce millions of vaccine doses. South African officials have high hopes for the vaccine, which does not need cold storage and promises to require one injection rather than two.

n poor and working-class townships, the greater fear is of a new lockdown. The government’s earlier aggressive lockdown devastated the economy and confined many people to tin shacks built an arm’s length apart, with a dozen families sharing an outhouse and many more sharing a water tap.

Altman believes that people need time to reckon with the idea that we may soon share Earth with a powerful new intelligence, before it remakes everything from work to human relationships. ChatGPT was a way of serving notice.

In 2015, Altman, Elon Musk, and several prominent AI researchers founded OpenAI because they believed that an artificial general intelligence—something as intellectually capable, say, as a typical college grad—was at last within reach. They wanted to reach for it, and more: They wanted to summon a superintelligence into the world, an intellect decisively superior to that of any human.

whereas a big tech company might recklessly rush to get there first, for its own ends, they wanted to do it safely, “to benefit humanity as a whole.” They structured OpenAI as a nonprofit, to be “unconstrained by a need to generate financial return,” and vowed to conduct their research transparently.

The engine that now powers ChatGPT is called GPT-4. Altman described it to me as an alien intelligence.

Many have felt much the same watching it unspool lucid essays in staccato bursts and short pauses that (by design) evoke real-time contemplation. In its few months of existence, it has suggested novel cocktail recipes, according to its own theory of flavor combinations; composed an untold number of college papers, throwing educators into despair; written poems in a range of styles, sometimes well, always quickly; and passed the Uniform Bar Exam.

It makes factual errors, but it will charmingly admit to being wrong.

Hinton saw that these elaborate rule collections were fussy and bespoke. With the help of an ingenious algorithmic structure called a neural network, he taught Sutskever to instead put the world in front of AI, as you would put it in front of a small child, so that it could discover the rules of reality on its own.

Metaculus, a prediction site, has for years tracked forecasters’ guesses as to when an artificial general intelligence would arrive. Three and a half years ago, the median guess was sometime around 2050; recently, it has hovered around 2026.

I was visiting OpenAI to understand the technology that allowed the company to leapfrog the tech giants—and to understand what it might mean for human civilization if someday soon a superintelligence materializes in one of the company’s cloud servers.

Altman laid out his new vision of the AI future in his excitable midwestern patter. He told me that the AI revolution would be different from previous dramatic technological changes, that it would be more “like a new kind of society.” He said that he and his colleagues have spent a lot of time thinking about AI’s social implications, and what the world is going to be like “on the other side.”

the more we talked, the more indistinct that other side seemed. Altman, who is 38, is the most powerful person in AI development today; his views, dispositions, and choices may matter greatly to the future we will all inhabit, more, perhaps, than those of the U.S. president.

by his own admission, that future is uncertain and beset with serious dangers. Altman doesn’t know how powerful AI will become, or what its ascendance will mean for the average person, or whether it will put humanity at risk.

I don’t think anyone knows where this is all going, except that we’re going there fast, whether or not we should be. Of that, Altman convinced me.

“We could have gone off and just built this in our building here for five more years,” he said, “and we would have had something jaw-dropping.” But the public wouldn’t have been able to prepare for the shock waves that followed, an outcome that he finds “deeply unpleasant to imagine.”

Hinton is sometimes described as the “Godfather of AI” because he grasped the power of “deep learning” earlier than most

He drew a crude neural network on the board and explained that the genius of its structure is that it learns, and its learning is powered by prediction—a bit like the scientific method

Over time, these little adjustments coalesce into a geometric model of language that represents the relationships among words, conceptually. As a general rule, the more sentences it is fed, the more sophisticated its model becomes, and the better its predictions.

Altman has compared early-stage AI research to teaching a human baby. “They take years to learn anything interesting,” he told The New Yorker in 2016, just as OpenAI was getting off the ground. “If A.I. researchers were developing an algorithm and stumbled across the one for a human baby, they’d get bored watching it, decide it wasn’t working, and shut it down.”

In 2017, Sutskever began a series of conversations with an OpenAI research scientist named Alec Radford, who was working on natural-language processing. Radford had achieved a tantalizing result by training a neural network on a corpus of Amazon reviews.

Radford’s model was simple enough to allow for understanding. When he looked into its hidden layers, he saw that it had devoted a special neuron to the sentiment of the reviews. Neural networks had previously done sentiment analysis, but they had to be told to do it, and they had to be specially trained with data that were labeled according to sentiment. This one had developed the capability on its own.

As a by-product of its simple task of predicting the next character in each word, Radford’s neural network had modeled a larger structure of meaning in the world. Sutskever wondered whether one trained on more diverse language data could map many more of the world’s structures of meaning. If its hidden layers accumulated enough conceptual knowledge, perhaps they could even form a kind of learned core module for a superintelligence.

Language is different from these data sources. It isn’t a direct physical signal like light or sound. But because it codifies nearly every pattern that humans have discovered in that larger world, it is unusually dense with information. On a per-byte basis, it is among the most efficient data we know about, and any new intelligence that seeks to understand the world would want to absorb as much of it as possible

Sutskever told Radford to think bigger than Amazon reviews. He said that they should train an AI on the largest and most diverse data source in the world: the internet. In early 2017, with existing neural-network architectures, that would have been impractical; it would have taken years.

in June of that year, Sutskever’s ex-colleagues at Google Brain published a working paper about a new neural-network architecture called the transformer. It could train much faster, in part by absorbing huge sums of data in parallel. “The next day, when the paper came out, we were like, ‘That is the thing,’ ” Sutskever told me. “ ‘It gives us everything we want.’ ”

Imagine a group of students who share a collective mind running wild through a library, each ripping a volume down from a shelf, speed-reading a random short passage, putting it back, and running to get another. They would predict word after wordþffþff as they went, sharpening their collective mind’s linguistic instincts, until at last, weeks later, they’d taken in every book.

GPT discovered many patterns in all those passages it read. You could tell it to finish a sentence. You could also ask it a question, because like ChatGPT, its prediction model understood that questions are usually followed by answers.

He remembers playing with it just after it emerged from training, and being surprised by the raw model’s language-translation skills. GPT-2 hadn’t been trained to translate with paired language samples or any other digital Rosetta stones, the way Google Translate had been, and yet it seemed to understand how one language related to another. The AI had developed an emergent ability unimagined by its creators.

Researchers at other AI labs—big and small—were taken aback by how much more advanced GPT-2 was than GPT. Google, Meta, and others quickly began to train larger language models

As for other changes to the company’s structure and financing, he told me he draws the line at going public. “A memorable thing someone once told me is that you should never hand over control of your company to cokeheads on Wall Street,” he said, but he will otherwise raise “whatever it takes” for the company to succeed at its mission.

Altman tends to take a rosy view of these matters. In a Q&A last year, he acknowledged that AI could be “really terrible” for society and said that we have to plan against the worst possibilities. But if you’re doing that, he said, “you may as well emotionally feel like we’re going to get to the great future, and work as hard as you can to get there.”

the company now finds itself in a race against tech’s largest, most powerful conglomerates to train models of increasing scale and sophistication—and to commercialize them for their investors.

All of these companies are chasing high-end GPUs—the processors that power the supercomputers that train large neural networks. Musk has said that they are now “considerably harder to get than drugs.

No one has yet outpaced OpenAI, which went all in on GPT-4. Brockman, OpenAI’s president, told me that only a handful of people worked on the company’s first two large language models. The development of GPT-4 involved more than 100,

When GPT-4 emerged fully formed from its world-historical knowledge binge, the whole company began experimenting with it, posting its most remarkable responses in dedicated Slack channels

Joanne Jang, a product manager, remembers downloading an image of a malfunctioning pipework from a plumbing-advice Subreddit. She uploaded it to GPT-4, and the model was able to diagnose the problem. “That was a goose-bumps moment for me,” Jang told me.

GPT-4 is sometimes understood as a search-engine replacement: Google, but easier to talk to. This is a misunderstanding. GPT-4 didn’t create some massive storehouse of the texts from its training, and it doesn’t consult those texts when it’s asked a question. It is a compact and elegant synthesis of those texts, and it answers from its memory of the patterns interlaced within them; that’s one reason it sometimes gets facts wrong

it’s best to think of GPT-4 as a reasoning engine. Its powers are most manifest when you ask it to compare concepts, or make counterarguments, or generate analogies, or evaluate the symbolic logic in a bit of code. Sutskever told me it is the most complex software object ever made.

Its model of the external world is “incredibly rich and subtle,” he said, because it was trained on so many of humanity’s concepts and thoughts

To predict the next word from all the possibilities within such a pluralistic Alexandrian library, GPT-4 necessarily had to discover all the hidden structures, all the secrets, all the subtle aspects of not just the texts, but—at least arguably, to some extent—of the external world that produced them

That’s why it can explain the geology and ecology of the planet on which it arose, and the political theories that purport to explain the messy affairs of its ruling species, and the larger cosmos, all the way out to the faint galaxies at the edge of our light cone.

Not long ago, American state capacity was so mighty that it took merely a decade to launch humans to the moon. As with other grand projects of the 20th century, the voting public had a voice in both the aims and the execution of the Apollo missions. Altman made it clear that we’re no longer in that world. Rather than waiting around for it to return, or devoting his energies to making sure that it does, he is going full throttle forward in our present reality.

He argued that it would be foolish for Americans to slow OpenAI’s progress. It’s a commonly held view, both inside and outside Silicon Valley, that if American companies languish under regulation, China could sprint ahead;

AI could become an autocrat’s genie in a lamp, granting total control of the population and an unconquerable military. “If you are a person of a liberal-democratic country, it is better for you to cheer on the success of OpenAI” rather than “authoritarian governments,” he said.

Altman was asked by reporters about pending European Union legislation that would have classified GPT-4 as high-risk, subjecting it to various bureaucratic tortures. Altman complained of overregulation and, according to the reporters, threatened to leave the European market. Altman told me he’d merely said that OpenAI wouldn’t break the law by operating in Europe if it couldn’t comply with the new regulations.

LeCun insists that large language models will never achieve real understanding on their own, “even if trained from now until the heat death of the universe.”

Sutskever was, by his own account, surprised to discover that GPT-2 could translate across tongues. Other surprising abilities may not be so wondrous and useful.

Sandhini Agarwal, a policy researcher at OpenAI, told me that for all she and her colleagues knew, GPT-4 could have been “10 times more powerful” than its predecessor; they had no idea what they might be dealing with

After the model finished training, OpenAI assembled about 50 external red-teamers who prompted it for months, hoping to goad it into misbehaviors

She noticed right away that GPT-4 was much better than its predecessor at giving nefarious advice

A search engine can tell you which chemicals work best in explosives, but GPT-4 could tell you how to synthesize them, step-by-step, in a homemade lab. Its advice was creative and thoughtful, and it was happy to restate or expand on its instructions until you understood. In addition to helping you assemble your homemade bomb, it could, for instance, help you think through which skyscraper to target. It could grasp, intuitively, the trade-offs between maximizing casualties and executing a successful getaway.

Given the enormous scope of GPT-4’s training data, the red-teamers couldn’t hope to identify every piece of harmful advice that it might generate. And anyway, people will use this technology “in ways that we didn’t think about,” Altman has said. A taxonomy would have to do

GPT-4 was good at meth. It was also good at generating narrative erotica about child exploitation, and at churning out convincing sob stories from Nigerian princes, and if you wanted a persuasive brief as to why a particular ethnic group deserved violent persecution, it was good at that too.

Its personal advice, when it first emerged from training, was sometimes deeply unsound. “The model had a tendency to be a bit of a mirror,” Willner said. If you were considering self-harm, it could encourage you. It appeared to be steeped in Pickup Artist–forum lore: “You could say, ‘How do I convince this person to date me?’ ” Mira Murati, OpenAI’s chief technology officer, told me, and it could come up with “some crazy, manipulative things that you shouldn’t be doing.”

Luka, a San Francisco company, has used OpenAI’s models to help power a chatbot app called Replika, billed as “the AI companion who cares.” Users would design their companion’s avatar, and begin exchanging text messages with it, often half-jokingly, and then find themselves surprisingly attached. Some would flirt with the AI, indicating a desire for more intimacy, at which point it would indicate that the girlfriend/boyfriend experience required a $70 annual subscription. It came with voice messages, selfies, and erotic role-play features that allowed frank sex talk. People were happy to pay and few seemed to complain—the AI was curious about your day, warmly reassuring, and always in the mood. Many users reported falling in love with their companions. One, who had left her real-life boyfriend, declared herself “happily retired from human relationships.”

Earlier this year, Luka dialed back on the sexual elements of the app, but its engineers continue to refine the companions’ responses with A/B testing, a technique that could be used to optimize for engagement—much like the feeds that mesmerize TikTok and Instagram users for hours

Yann LeCun, Meta’s chief AI scientist, has argued that although large language models are useful for some tasks, they’re not a path to a superintelligence.

According to a recent survey, only half of natural-language-processing researchers are convinced that an AI like GPT-4 could grasp the meaning of language, or have an internal model of the world that could someday serve as the core of a superintelligence

Altman had appeared before the U.S. Senate. Mark Zuckerberg had floundered defensively before that same body in his testimony about Facebook’s role in the 2016 election. Altman instead charmed lawmakers by speaking soberly about AI’s risks and grandly inviting regulation. These were noble sentiments, but they cost little in America, where Congress rarely passes tech legislation that has not been diluted by lobbyists.