Group items matching

in title, tags, annotations or url

Every law embodies a particular hypothesis about how the world works, a hope that if you pull on levers A and B, then outcomes C and D will result

Democrats hope to create an economy where the government doesn’t just help Americans buy green technologies; it also helps nurture the industries that produce that technology.

The idea is this: The era of passive, hands-off government is over. The laws embrace an approach to governing the economy that scholars call “industrial policy,” a catch-all name for a wide array of tools and tactics that all assume the government can help new domestic industries get started, grow, and reach massive scale.

If “this country used to make things,” as the saying goes, and if it wants to make things again, then the government needs to help it. And if the country believes that certain industries bestow a strategic advantage, then it needs to protect them against foreign interference.

From its founding to the 1970s, the country had an economic doctrine that was defined by its pragmatism and the willingness of its government to find new areas of growth.

It’s more like a toolbox of different approaches that act in concert to help push technologies to grow and reach commercial scale. The IRA and the two other new laws prefer four tools in particular.

“Yes, there was an ‘invisible hand,’” Stephen Cohen and Brad DeLong write in their history of the topic, Concrete Economics. “But the invisible hand was repeatedly lifted at the elbow by the government, and placed in a new position from where it could go on to perform its magic.”

That pragmatism faded in the 1980s, when industrial policy became scorned as one more instance of Big Government coming in to pick so-called winners and losers.

The two other large bills passed by this Congress—the $1 trillion bipartisan infrastructure law and the CHIPS and Science Act—make down payments on the future as well; both laws, notably, were passed by bipartisan majorities.

it is in the IRA that these general commitments become specific, and therefore transformative.

Since the 1980s, when Congress has wanted to spur technological progress, it has usually thrown money exclusively at R&D. We have had a science policy, not an industrial policy

inextricable from that turn is Washington’s consuming anxiety over China’s rise—and China has embraced industrial policy.

although not a single Republican voted for the IRA, its wager is not especially partisan or even ideological.

the demonstration project. A demonstration project helps a technology that has previously existed only in the lab get out in the real world for the first time

supply-push policies. As the name suggests, these tools “push” on the supply side of an industry by underwriting new factories or assuring that those factories have access to cheap inputs to make things.

demand-pull policies, which create a market for whatever is coming out of those new factories. The government can “pull” on demand by buying those products itself or by subsidizing them for consumers.

protective policies, meant to insulate industries—especially new ones that are still growing—from foreign interference

Although both parties have moved to embrace industrial policy, Democrats are clearly ahead of their Republican colleagues. You can see it in their policy: While the bipartisan infrastructure law sets up lots of demonstration projects, and the CHIPS Act adopts some supply-push and protectionist theory, only the IRA uses all four tools.

In order to stop climate change, experts believe, the United States must do three things: clean up its power grid, replacing coal and gas power plants with zero-carbon sources; electrify everything it can, swapping fossil-fueled vehicles and boilers with electric vehicles and heat pumps; and mop up the rest, mitigating carbon pollution from impossible-to-electrify industrial activities. The IRA aims to nurture every industry needed to realize that vision.

Hydrogen and carbon removal are going to benefit from nearly every tool the government has. The bipartisan infrastructure law will spend more than $11 billion on hydrogen and carbon-removal “hubs,” huge demonstration projects

These hubs will also foster geographic concentration, the economic idea that when you put lots of people working on the same problem near one another, they solve it faster. You can see such clustering at work in San Francisco’s tech industry, and also in China, which now creates hubs for virtually every activity that it wants to dominate globally—even soccer.

Then the IRA will take over and deploy some good ol’ supply push and demand pull. It includes new programs to underwrite new hydrogen factories; on the demand side, a powerful new tax credit will pay companies for every kilogram of low-carbon hydrogen that they produce

Another tax credit will boost the demand of carbon removal by paying firms a $180 bounty for trapping a ton of carbon dioxide and pumping it undergroun

Today, not only does China make most batteries worldwide; it alone makes the tools that make the batteries, Nathan Iyer, an analyst at RMI, a nonpartisan energy think tank, told me. This extreme geographic concentration—which afflicts not only the battery industry but also the solar-panel industry—could slow down the energy transition and make it more expensive

the new tax credit is also supply-minded, arguably even protectionist. Under the new scheme, very few electric cars and trucks will immediately qualify for that full $7,500 subsidy; it will go only toward vehicles whose batteries are primarily made in North America and where a certain percentage of minerals are mined and processed in the U.S. or one of its allies. Will these policies accelerate the shift to EVs? Well, no, not immediately. But the idea is that by boosting domestic production of EVs, batteries will become cheaper and more abundant—and the U.S. will avoid subsidizing one of China’s growth industries.

Right now, next to no solar panels are made in the U.S., even though the technology was invented here. The IRA endeavors to change that by—you guessed it—a mix of supply-push, demand-pull, and protectionist policies. Under the law, the government will underwrite new factories to make every subcomponent of the solar supply chain; then it will pay those factories for every item that they produce

“It’s realistic that within four to five years, [U.S. solar manufacturers] could completely meet domestic demand for solar,” Scott Moskowitz, the head of public affairs for the solar manufacturer Q CELLS, told me.

In each of these industries, you’ll notice that the government isn’t only subsidizing factories; it is actually paying them to operate. That choice, which is central to the IRA’s approach, is “really defending against the mistakes of the 2009 bill,” Iyer told me. In its stimulus bill passed during the Great Recession, the Obama administration tried to do green industrial policy, underwriting new solar-panel factories across the country. But then Chinese firms began exporting cheap solar panels by the millions, saturating domestic demand and leaving those sparkly new factories idle

So many other industries will also be touched by these laws. There’s a new program to nurture a low-carbon aviation-fuel industry in the U.S. (Long-distance jet travel is one of those climate problems that nobody knows how to solve yet.)

the revelation of the IRA is that decarbonizing the United States may require re-industrializing it. A net-zero America may have more refineries, more factories, and more goods production than a fossil-fueled America—while also having cheaper cars, healthier air, and fewer natural disasters. And once the U.S. gets there, then it can keep going: It can set an example for the world that a populous, affluent country can reduce its emissions while enjoying all the trappings of modernity,

There are a slew of policies meant to grow and decarbonize the U.S. industrial sector; every tax credit pays out a bonus if you use U.S.-made steel, cement, or concrete. “You would need thousands and thousands of words to capture the industries that will be transformed by this,” Josh Freed, the climate and energy leader at Third Way, a center-left think tank, told me.

Five EVs were sold in China last year for every one EV sold in the United States; that larger domestic market will provide a significant economy of scale when Chinese EV makers begin exporting their cars abroad. For that reason and others, many people in China are “deeply skeptical” that the U.S. can catch up with its lead,

We are about to have a huge new set of vested interests who want the economy to be clean and benefit from that. We’ve literally never had that before,” Freed told me.

“This is going to change everything,” he said

that is the IRA’s biggest idea, its biggest hypothesis: that America can improve its standard of living and preserve its global preeminence while ruthlessly eliminating carbon pollution; that climate change, actually, doesn’t change everything, and that in fact it can be addressed by changing as little as possible.

This hypothesis has already proved itself out in one important way, which is that the IRA passed, and the previous 30 years of climate proposals did not. Now comes the real test.

economist J. Bradford DeLong ’82, Ph.D. ’87, looking back on the twentieth century two decades after its end, comes to a similar conclusion but on different grounds.

DeLong, professor of economics at Berkeley, looks to matters of “contingency” and “choice”: at key junctures the economy suffered “bad luck,” and the actions taken by the responsible policymakers were “incompetent.”

these were “the most consequential years of all humanity’s centuries.” The changes they saw, while in the first instance economic, also “shaped and transformed nearly everything sociological, political, and cultural.”

DeLong’s look back over the twentieth century energetically encompasses political and social trends as well; nor is his scope limited to the United States. The result is a work of strikingly expansive breadth and scope

labeling the book an economic history fails to convey its sweeping frame.

The century that is DeLong’s focus is what he calls the “long twentieth century,” running from just after the Civil War to the end of the 2000s when a series of events, including the biggest financial crisis since the 1930s followed by likewise the most severe business downturn, finally rendered the advanced Western economies “unable to resume economic growth at anything near the average pace that had been the rule since 1870.

d behind those missteps in policy stood not just failures of economic thinking but a voting public that reacted perversely, even if understandably, to the frustrations poor economic outcomes had brought them.

Within this 140-year span, DeLong identifies two eras of “El Dorado” economic growth, each facilitated by expanding globalization, and each driven by rapid advances in technology and changes in business organization for applying technology to economic ends

from 1870 to World War I, and again from World War II to 197

fellow economist Robert J. Gordon ’62, who in his monumental treatise on The Rise and Fall of American Economic Growth (reviewed in “How America Grew,” May-June 2016, page 68) hailed 1870-1970 as a “special century” in this regard (interrupted midway by the disaster of the 1930s).

Gordon highlighted the role of a cluster of once-for-all-time technological advances—the steam engine, railroads, electrification, the internal combustion engine, radio and television, powered flight

Pessimistic that future technological advances (most obviously, the computer and electronics revolutions) will generate productivity gains to match those of the special century, Gordon therefore saw little prospect of a return to the rapid growth of those halcyon days.

DeLong instead points to a series of noneconomic (and non-technological) events that slowed growth, followed by a perverse turn in economic policy triggered in part by public frustration: In 1973 the OPEC cartel tripled the price of oil, and then quadrupled it yet again six years later.

For all too many Americans (and citizens of other countries too), the combination of high inflation and sluggish growth meant that “social democracy was no longer delivering the rapid progress toward utopia that it had delivered in the first post-World War II generation.”

Frustration over these and other ills in turn spawned what DeLong calls the “neoliberal turn” in public attitudes and economic policy. The new economic policies introduced under this rubric “did not end the slowdown in productivity growth but reinforced it.

the tax and regulatory changes enacted in this new climate channeled most of what economic gains there were to people already at the top of the income scale

Meanwhile, progressive “inclusion” of women and African Americans in the economy (and in American society more broadly) meant that middle- and lower-income white men saw even smaller gains—and, perversely, reacted by providing still greater support for policies like tax cuts for those with far higher incomes than their own.

Daniel Bell’s argument in his 1976 classic The Cultural Contradictions of Capitalism. Bell famously suggested that the very success of a capitalist economy would eventually undermine a society’s commitment to the values and institutions that made capitalism possible in the first plac

In DeLong’s view, the “greatest cause” of the neoliberal turn was “the extraordinary pace of rising prosperity during the Thirty Glorious Years, which raised the bar that a political-economic order had to surpass in order to generate broad acceptance.” At the same time, “the fading memory of the Great Depression led to the fading of the belief, or rather recognition, by the middle class that they, as well as the working class, needed social insurance.”

what the economy delivered to “hard-working white men” no longer matched what they saw as their just deserts: in their eyes, “the rich got richer, the unworthy and minority poor got handouts.”

As Bell would have put it, the politics of entitlement, bred by years of economic success that so many people had come to take for granted, squeezed out the politics of opportunity and ambition, giving rise to the politics of resentment.

The new era therefore became “a time to question the bourgeois virtues of hard, regular work and thrift in pursuit of material abundance.”

DeLong’s unspoken agenda would surely include rolling back many of the changes made in the U.S. tax code over the past half-century, as well as reinvigorating antitrust policy to blunt the dominance, and therefore outsize profits, of the mega-firms that now tower over key sectors of the economy

He would also surely reverse the recent trend moving away from free trade. Central bankers should certainly behave like Paul Volcker (appointed by President Carter), whose decisive action finally broke the 1970s inflation even at considerable economic cost

Not only Galbraith’s main themes but many of his more specific observations as well seem as pertinent, and important, today as they did then.

What will future readers of Slouching Towards Utopia conclude?

If anything, DeLong’s narratives will become more valuable as those events fade into the past. Alas, his description of fascism as having at its center “a contempt for limits, especially those implied by reason-based arguments; a belief that reality could be altered by the will; and an exaltation of the violent assertion of that will as the ultimate argument” will likely strike a nerve with many Americans not just today but in years to come.

what about DeLong’s core explanation of what went wrong in the latter third of his, and our, “long century”? I predict that it too will still look right, and important.

France would seem to be less vulnerable than its neighbors: It boasts the biggest nuclear energy arsenal of any European Union country, and is one of the least reliant on Russian natural gas. But France faces an energy crisis of its own, as its nuclear industry addresses cracks, corrosion and other troubles that have forced EDF to temporarily shut down 32 of France’s 56 nuclear reactors.

The outages at EDF, which is also Europe’s biggest electricity exporter, have sent France’s nuclear power output plunging to its lowest level in nearly three decades. In addition, France’s worst drought in 30 years this summer has lowered river levels, cutting supplies of hydroelectric power.

On Friday, wholesale electricity prices for 2023 in France set a record, surging past €1,000 per megawatt-hour. Many French companies and retailers buy their electricity with three-year contracts that are set to expire, meaning they will have to be renewed at peak prices.

In northern France, some high schools in Brittany will lower their thermostats, while the neighboring region of Normandy will experiment with using wood-burning furnaces for heat in some schools as an alternative to gas.

Without the cap, French inflation would be about three percentage points higher, the French statistics agency Insee said in a report issued Friday.

In recent days, the government issued announcements calling on the French to curb a range of activities, in hopes of collectively saving energy. Among them: refraining from running washers at night, keeping thermostats at 66 degrees Fahrenheit and increasing use of public transportation

Many municipalities outside Paris started closing swimming pools intermittently this summer to save money. Other cities are restricting public lighting, which can account for over 40 percent of electricity bills.

The town of Thouars in western France has been turning off streetlights from 10 p.m. to 6 a.m. since June and plans to replace bulbs with LED lighting. Strasbourg, a mid-size city on the German border, will close museums two days a week instead of one.

President Macron, who faced a stiff presidential election campaign in April that saw the far-right challenger, Marine Le Pen, gain ground by addressing French families’ worries over purchasing power, has focused on shielding households from rising energy costs.

“We need a radical change,” Ms. Borne said. “Everyone must ask themselves what they can do to consume less.”

The economy that Hickel envisions would cease to pursue growth, green or otherwise. Materials and energy will still be consumed, and waste generated, but at much lower levels. All impacts on the natural world will be tethered to the question, “Growth for whom, and to what ends?” In place of an individualistic consumer economy, Hickel’s post-growth economy would direct itself toward the creation of public goods that allow the many to live well—mass transit, health care—rather than to keep a few in luxury.

A growing body of research reveals an inverse relationship between “happiness” and growth beyond a certain point.

In the rich countries, general contentment peaked in 1950, when GDP and real per capita incomes were fractions of their present size (and inequality near modern historic lows); degrowthers posit that similar happiness levels will be reclaimed on the way back down the economic mountain

Hickel describes a post-growth economy defined by stability and equality, and the freedom and leisure possible when the economy is no longer subservient to the god of growth

He estimates that the U.S. economy could be scaled down by as much as 65 percent while still improving the lives of its citizens. This includes the metric most often tied to celebrations of endless growth: life expectancy.

degrowth will entail a steep reduction across a much wider range of high-energy consumer goods. Keeping a global economy within safe ecological limits is a zero-sum game.

When limited resources are directed toward clean energy infrastructure, public health care, and regenerative agriculture, it will still be possible to build and power modern 24-hour hospitals in every city, but not to have Xbox consoles, two-car garages, and giant appliances in every home.

would have to redefine it, too.

The post-growth economy could not succeed solely by redistributing wealth; it would have to redefine it, too.

He argues that short-term growth would have to continue in those countries that have still not achieved the basic levels of sanitation, infrastructure, and education needed for a decent standard of living, to close the gap. Their larger goal, meanwhile, would be to break free from their historical role as a source of natural resources and cheap labor for the north.

For degrowth to be just, global, and effective, the sharpest reduction in consumption will have to come from the north, where the greatest damage to the planet is currently being done

Ecological economists generally agree that the safe outer limit is eight tons

One person in a low-income country has a materials footprint of roughly two tons per year, a measure of total raw materials consumed, including those embodied in imports. In lower-middle–income countries, that number is four tons; in upper-middle–income countries, 12 tons. In the high-income nations of North America, Europe, and Asia, the number leaps to 28

The wealthiest 20 percent of the human population is responsible for 90 percent of “overshoot” carbon in the atmosphere (that is, a level of carbon that exceeds the limit needed to keep global temperature rise below 2 degrees Celsius)

The planet’s richest one percent has a carbon footprint twice the size of the poorest half of the world’s population combined

For the global north, degrowth not only starts at home, it starts with the biggest houses.

Less Is More doesn’t end in a poetic appreciation for nature’s majesty, but by teasing out its implications for the political project of preserving a habitable planet. Hickel devotes much of the book to explaining that degrowth must be central to this project, promising not just survival, but real democracy, social abundance, and liberation.

Both involve broad social shifts away from private consumption and toward the production of shared public goods.

This beautiful coincidence overlaps with policy programs like the Green New Deal in important way

In July 1979, shortly after installing a set of solar panels over the West Wing, Jimmy Carter did something peculiar for a peacetime president. He asked Americans to sacrifice: to consume less, take public transit more, value community over material things, and buy bonds to fund domestic energy development, including solar

Next to Schumacher’s “Buddhist economics,” Debsian socialism was reformist tinkering. Schumacher didn’t see liberation as a matter of reshuffling the ownership and management structures of the smokestack-powered growth economy. He believed a deeper transformation was needed to maintain a livable planet. This would require new socioecological blueprints “designed for permanence.” As the left and the right battled for control over growth’s levers and spoils, Schumacher pointed out how both had become blind to the rise of growth as its own self-justifying, pan-ideological religion; its patterns of production and consumption, he observed, required “a degree of violence” that did not “fit into the laws of the universe.”

They determined that infinite growth was, in fact, impossible on a finite planet. Barring a major course correction, the team projected, growthism would result in an ecological systems breakdown sometime in the middle of the twenty-first century

This warning, detailed in the 1972 bestseller The Limits to Growth, has aged better than the scorn heaped on it

We are now witnessing what appears to be the beginnings of the collapse predicted nearly 50 years ago

In his new book, Less Is More, Jason Hickel, an anthropologist and journalist, attempts to bring a comprehensive critique of growth closer to the center of the conversation, arguing through a sweeping history of capitalism that it’s uncontrolled growth, not its controlled arrest and reversal, that is the preposterous concept.

This economic and political revolution was reinforced by a complementary scientific one that displaced the lingering animist cosmology of pre-capitalist Europe. The dualism of Francis Bacon and Descartes held reason to be distinct from and superior to matter.

The idea of limitless growth is a relatively recent one. In Less Is More, Hickel traces its origins to the enclosure of the European commons in the sixteenth century

Starving refugees were scattered and forced into a new economy defined by neo-feudal servitude and wage labor. Landowners, meanwhile, began amassing great stores of surplus wealth.

By the mid-1800s, a new “science” had arisen from these assumptions. Neoclassical economics fully abstracted the economy from the natural world. The economy was geared not toward the creation of a happy and prosperous society, but toward the perpetual growth of wealth as its own end, achieved in an inherently virtuous cycle of converting labor and resources into capital, to be accumulated and reinvested in faster and more productive conversions of labor and resources

This ideology subsumed and profaned notions about progress and morality held by the classical economists, until eventually the field even l

This process unfolded despite repeated warnings along the way. Classical economists like John Stuart Mill and, to a lesser extent, Adam Smith not only acknowledged the existence of natural limits to growth, but saw economic development as a phase; at some point, they believed, nations would create enough wealth to pursue other definitions of progres

the caveats issued by Simon Kuznets, father of the concept adopted in the twentieth century as growth’s universal and signature metric: gross domestic product. Kuznets, Hickel points out, “warned that we should never use GDP as a normal measure of economic progress,” because GDP does not distinguish between productive and destructive behavior

Most people encounter the growth debate, if they encounter it at all, through the idea of “green growth.

This is a vision for our collective future based on the belief that technological advance will drastically reduce the amount of raw materials needed to sustain growth—a process known as dematerialization—and “decouple” growing GDP from its ecological impacts.

boosters of the idea point to the transition by rich countries from manufacturing to service-based economies, as well as efficiency gains in energy and in the use of materials

The belief that green growth will save us, also known as “ecomodernism” or “ecopragmatism,” has become a trendy article of faith among elites who acknowledge climate change and the dangers of breaching ecological boundaries

n 2017, Barack Obama threw his support behind the idea in an article for Science magazine, maintaining that signs of decoupling in major economies “should put to rest the argument that combatting climate change requires accepting lower growth or a lower standard of living.”

The argument that capitalism can grow itself out of the present crisis may be soothing to those who like the world as it is. It also relies on the kind of accounting tricks and rejection of reality

By only counting the emissions created within a country that imports most of its cars, washing machines, and computers, you end up pushing the emissions related to their production off the books. When you factor them back in, the picture is much less green. A number of recent studies show no evidence of meaningful decoupling—in energy or materials—even as the world increases its use of renewable energy and finds ways to use some materials more efficiently.

Green growth, Hickel concludes, is an ecologically incoherent “fairy tale.”

consider what the ecomodernist position asks us to believe. The current system requires annual growth of roughly 3 percent to avoid the shock of recession. This means doubling the size of the economy every 23 years

he economy of 2000 must be 20 times larger in the year 2100, and 370 times larger in the year 2200.

Hickel is less interested in the macroeconomic details of this future than are growth critics based in economics departments, like Tim Jackson and Kate Raworth, and more focused on the leisure, security, and general human flourish

he makes an alluring case that degrowth does not require anything like the “command-and-control fiasco of the Soviet Union, or some back-to-the-caves, hair-shirted disaster of voluntary impoverishment.”

Attaining the benefits of the post-growth economy would, however, require what the present consumer society considers “sacrifices.

it’s not clear how many of them are ready to give up its superficial pleasures enabled by consumer debt

Among nations, there’s also the question of fairness: Wouldn’t it be unjust to impose degrowth across the world, when it’s disproportionately the countries of the global north that have spent centuries burning through the planet’s resources?

This output tracks to the one percent’s share of global wealth—a number equal to the GDP of the bottom 169 countries.

Even if you accept the argument that inequality would be best addressed by more centuries of trickle-down growth, you keep running up against the simple fact of its impossibility. Even just one more century of growth—which so far has shown no sign of taking a less destructive form—will require multiple earths

Hickel is serious about bringing the system critiques of E.F. Schumacher and others out of their traditional cloisters and into the streets, and has sought allies in this effort

emphasize what Hickel calls the “beautiful coincidence” of degrowth: that “what we need to do to survive is the same as what we need to do to have better lives.”

Both are internationalist in outlook, and see the world through a lens of climate justice as well as climate equilibrium.

that is, communicating the many benefits of moving beyond the insecurity and terrors of the current system, and building a new society that is sustainable, stable, democratic, and fundamentally better in every way.

As long as life continues as it has — with people choosing smaller family sizes, as is now common in most of the world — then in the 22nd or 23rd century, our decline could be just as steep as our rise.

there is no consensus on exactly how quickly populations will fall after that. Over the past 100 years, the global population quadrupled, from two billion to eight billion.

What would happen as a consequence? Over the past 200 years, humanity’s population growth has gone hand in hand with profound advances in living standards and health: longer lives, healthier children, better education, shorter workweeks and many more improvements

In this short period, humanity has been large and growing. Economists who study growth and progress don’t think this is a coincidence. Innovations and discoveries are made by people. In a world with fewer people in it, the loss of so much human potential may threaten humanity’s continued path toward better lives.

It would be tempting to welcome depopulation as a boon to the environment. But the pace of depopulation will be too slow for our most pressing problems. It will not replace the need for urgent action on climate, land use, biodiversity, pollution and other environmental challenges

If the population hits around 10 billion people in the 2080s and then begins to decline, it might still exceed today’s eight billion after 2100

Population decline would come quickly, measured in generations, and yet arrive far too slowly to be more than a sideshow in the effort to save the planet. Work to decarbonize our economies and reform our land use and food systems must accelerate in this decade and the next, not start in the next century.

This isn’t a call to immediately remake our societies and economies in the service of birthrates. It’s a call to start conversations now, so that our response to low birthrates is a decision that is made with the best ideas from all of u

If we wait, the less inclusive, less compassionate, less calm elements within our society and many societies worldwide may someday call depopulation a crisis and exploit it to suit their agendas — of inequality, nationalism, exclusion or control

Births won’t automatically rebound just because it would be convenient for advancing living standards or sharing the burden of care work

We know that fertility rates can stay below replacement because they have. They’ve been below that level in Brazil and Chile for about 20 years; in Thailand for about 30 years; and in Canada, Germany and Japan for about 50.

In fact, in none of the countries where lifelong fertility rates have fallen well below two have they ever returned above it. Depopulation could continue, generation after generation, as long as people look around and decide that small families work best for them, some having no children, some having three or four and many having one or two.

Nor can humanity count on any one region or subgroup to buoy us all over the long run. Birthrates are falling in sub-Saharan Africa, the region with the current highest average rates, as education and economic opportunities continue to improve

The main reason that birthrates are low is simple: People today want smaller families than people did in the past. That’s true in different cultures and economies around the world. It’s what both women and men report in surveys.

Humanity is building a better, freer world with more opportunities for everyone, especially for women

That progress also means that, for many of us, the desire to build a family can clash with other important goals, including having a career, pursuing projects and maintaining relationships

In a world of sustained low birthrates and declining populations, there may be threats of backsliding on reproductive freedom — by limiting abortion rights, for example

Nobody yet knows what to do about global depopulation. But it wasn’t long ago that nobody knew what to do about climate change. These shared challenges have much in common,

As with climate change, our individual decisions on family size add up to an outcome that we all share.

Six decades from now is when the U.N. projects the size of the world population will peak. There won’t be any quick fixes: Even if it’s too early today to know exactly how to build an abundant future that offers good lives to a stable, large and flourishing future population, we should already be working toward that goal.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Emily Bender, a computational linguist at the University of Washington, describes GPT-4 as a “stochastic parrot,” a mimic that merely figures out superficial correlations between symbols. In the human mind, those symbols map onto rich conceptions of the world

But the AIs are twice removed. They’re like the prisoners in Plato’s allegory of the cave, whose only knowledge of the reality outside comes from shadows cast on a wall by their captors.

Altman told me that he doesn’t believe it’s “the dunk that people think it is” to say that GPT-4 is just making statistical correlations. If you push these critics further, “they have to admit that’s all their own brain is doing … it turns out that there are emergent properties from doing simple things on a massive scale.”

he is right that nature can coax a remarkable degree of complexity from basic structures and rules: “From so simple a beginning,” Darwin wrote, “endless forms most beautiful.”

If it seems odd that there remains such a fundamental disagreement about the inner workings of a technology that millions of people use every day, it’s only because GPT-4’s methods are as mysterious as the brain’s.

To grasp what’s going on inside large language models like GPT‑4, AI researchers have been forced to turn to smaller, less capable models. In the fall of 2021, Kenneth Li, a computer-science graduate student at Harvard, began training one to play Othello without providing it with either the game’s rules or a description of its checkers-style board; the model was given only text-based descriptions of game moves. Midway through a game, Li looked under the AI’s hood and was startled to discover that it had formed a geometric model of the board and the current state of play. In an article describing his research, Li wrote that it was as if a crow had overheard two humans announcing their Othello moves through a window and had somehow drawn the entire board in birdseed on the windowsill.

The philosopher Raphaël Millière once told me that it’s best to think of neural networks as lazy. During training, they first try to improve their predictive power with simple memorization; only when that strategy fails will they do the harder work of learning a concept. A striking example of this was observed in a small transformer model that was taught arithmetic. Early in its training process, all it did was memorize the output of simple problems such as 2+2=4. But at some point the predictive power of this approach broke down, so it pivoted to actually learning how to add.

Even AI scientists who believe that GPT-4 has a rich world model concede that it is much less robust than a human’s understanding of their environment.

But it’s worth noting that a great many abilities, including very high-order abilities, can be developed without an intuitive understanding. The computer scientist Melanie Mitchell has pointed out that science has already discovered concepts that are highly predictive, but too alien for us to genuinely understand

As AI advances, it may well discover other concepts that predict surprising features of our world but are incomprehensible to us.

GPT-4 is no doubt flawed, as anyone who has used ChatGPT can attest. Having been trained to always predict the next word, it will always try to do so, even when its training data haven’t prepared it to answer a question.

The models “don’t have a good conception of their own weaknesses,” Nick Ryder, a researcher at OpenAI, told me. GPT-4 is more accurate than GPT-3, but it still hallucinates, and often in ways that are difficult for researchers to catch. “The mistakes get more subtle,

The Khan Academy’s solution to GPT-4’s accuracy problem was to filter its answers through a Socratic disposition. No matter how strenuous a student’s plea, it would refuse to give them a factual answer, and would instead guide them toward finding their own—a clever work-around, but perhaps with limited appeal.

When I asked Sutskever if he thought Wikipedia-level accuracy was possible within two years, he said that with more training and web access, he “wouldn’t rule it out.”

This was a much more optimistic assessment than that offered by his colleague Jakub Pachocki, who told me to expect gradual progress on accuracy—to say nothing of outside skeptics, who believe that returns on training will diminish from here.

Sutskever is amused by critics of GPT-4’s limitations. “If you go back four or five or six years, the things we are doing right now are utterly unimaginable,”

AI researchers have become accustomed to goalpost-moving: First, the achievements of neural networks—mastering Go, poker, translation, standardized tests, the Turing test—are described as impossible. When they occur, they’re greeted with a brief moment of wonder, which quickly dissolves into knowing lectures about how the achievement in question is actually not that impressive. People see GPT-4 “and go, ‘Wow,’ ” Sutskever said. “And then a few weeks pass and they say, ‘But it doesn’t know this; it doesn’t know that.’ We adapt quite quickly.”

The goalpost that matters most to Altman—the “big one” that would herald the arrival of an artificial general intelligence—is scientific breakthrough. GPT-4 can already synthesize existing scientific ideas, but Altman wants an AI that can stand on human shoulders and see more deeply into nature.

Certain AIs have produced new scientific knowledge. But they are algorithms with narrow purposes, not general-reasoning machines. The AI AlphaFold, for instance, has opened a new window onto proteins, some of biology’s tiniest and most fundamental building blocks, by predicting many of their shapes, down to the atom—a considerable achievement given the importance of those shapes to medicine, and given the extreme tedium and expense required to discern them with electron microscopes.

Altman imagines a future system that can generate its own hypotheses and test them in a simulation. (He emphasized that humans should remain “firmly in control” of real-world lab experiments—though to my knowledge, no laws are in place to ensure that.)

He longs for the day when we can tell an AI, “ ‘Go figure out the rest of physics.’ ” For it to happen, he says, we will need something new, built “on top of” OpenAI’s existing language models.

In her MIT lab, the cognitive neuroscientist Ev Fedorenko has found something analogous to GPT-4’s next-word predictor inside the brain’s language network. Its processing powers kick in, anticipating the next bit in a verbal string, both when people speak and when they listen. But Fedorenko has also shown that when the brain turns to tasks that require higher reasoning—of the sort that would be required for scientific insight—it reaches beyond the language network to recruit several other neural systems.

No one at OpenAI seemed to know precisely what researchers need to add to GPT-4 to produce something that can exceed human reasoning at its highest levels.

at least part of the current strategy clearly involves the continued layering of new types of data onto language, to enrich the concepts formed by the AIs, and thereby enrich their models of the world.

The extensive training of GPT-4 on images is itself a bold step in this direction,

Others at the company—and elsewhere—are already working on different data types, including audio and video, that could furnish AIs with still more flexible concepts that map more extensively onto reality

Tactile concepts would of course be useful primarily to an embodied AI, a robotic reasoning machine that has been trained to move around the world, seeing its sights, hearing its sounds, and touching its objects.

humanoid robots. I asked Altman what I should make of that. He told me that OpenAI is interested in embodiment because “we live in a physical world, and we want things to happen in the physical world.”

At some point, reasoning machines will need to bypass the middleman and interact with physical reality itself. “It’s weird to think about AGI”—artificial general intelligence—“as this thing that only exists in a cloud,” with humans as “robot hands for it,” Altman said. “It doesn’t seem right.

Everywhere Altman has visited, he has encountered people who are worried that superhuman AI will mean extreme riches for a few and breadlines for the rest

Altman answered by addressing the young people in the audience directly: “You are about to enter the greatest golden age,” he said.

“A lot of people working on AI pretend that it’s only going to be good; it’s only going to be a supplement; no one is ever going to be replaced,” he said. “Jobs are definitely going to go away, full stop.”

A recent study led by Ed Felten, a professor of information-technology policy at Princeton, mapped AI’s emerging abilities onto specific professions according to the human abilities they require, such as written comprehension, deductive reasoning, fluency of ideas, and perceptual speed. Like others of its kind, Felten’s study predicts that AI will come for highly educated, white-collar workers first.

How many jobs, and how soon, is a matter of fierce dispute

The paper’s appendix contains a chilling list of the most exposed occupations: management analysts, lawyers, professors, teachers, judges, financial advisers, real-estate brokers, loan officers, psychologists, and human-resources and public-relations professionals, just to sample a few.

Altman imagines that far better jobs will be created in their place. “I don’t think we’ll want to go back,” he said. When I asked him what these future jobs might look like, he said he doesn’t know.

He suspects there will be a wide range of jobs for which people will always prefer a human. (Massage therapists?

His chosen example was teachers. I found this hard to square with his outsize enthusiasm for AI tutors.

He also said that we would always need people to figure out the best way to channel AI’s awesome powers. “That’s going to be a super-valuable skill,” he said. “You have a computer that can do anything; what should it go do?”

As many have noted, draft horses were permanently put out of work by the automobile. If Hondas are to horses as GPT-10 is to us, a whole host of long-standing assumptions may collapse.

Previous technological revolutions were manageable because they unfolded over a few generations, but Altman told South Korea’s youth that they should expect the future to happen “faster than the past.” He has previously said that he expects the “marginal cost of intelligence” to fall very close to zero within 10 years

The earning power of many, many workers would be drastically reduced in that scenario. It would result in a transfer of wealth from labor to the owners of capital so dramatic, Altman has said, that it could be remedied only by a massive countervailing redistribution.

In 2021, he unveiled Worldcoin, a for-profit project that aims to securely distribute payments—like Venmo or PayPal, but with an eye toward the technological future—first through creating a global ID by scanning everyone’s iris with a five-pound silver sphere called the Orb. It seemed to me like a bet that we’re heading toward a world where AI has made it all but impossible to verify people’s identity and much of the population requires regular UBI payments to survive. Altman more or less granted that to be true, but said that Worldcoin is not just for UBI.

“Let’s say that we do build this AGI, and a few other people do too.” The transformations that follow would be historic, he believes. He described an extraordinarily utopian vision, including a remaking of the flesh-and-steel world

“Robots that use solar power for energy can go and mine and refine all of the minerals that they need, that can perfectly construct things and require no human labor,” he said. “You can co-design with DALL-E version 17 what you want your home to look like,” Altman said. “Everybody will have beautiful homes.

In conversation with me, and onstage during his tour, he said he foresaw wild improvements in nearly every other domain of human life. Music would be enhanced (“Artists are going to have better tools”), and so would personal relationships (Superhuman AI could help us “treat each other” better) and geopolitics (“We’re so bad right now at identifying win-win compromises”).

In this world, AI would still require considerable computing resources to run, and those resources would be by far the most valuable commodity, because AI could do “anything,” Altman said. “But is it going to do what I want, or is it going to do what you want

If rich people buy up all the time available to query and direct AI, they could set off on projects that would make them ever richer, while the masses languish

One way to solve this problem—one he was at pains to describe as highly speculative and “probably bad”—was this: Everyone on Earth gets one eight-billionth of the total AI computational capacity annually. A person could sell their annual share of AI time, or they could use it to entertain themselves, or they could build still more luxurious housing, or they could pool it with others to do “a big cancer-curing run,” Altman said. “We just redistribute access to the system.”

Even if only a little of it comes true in the next 10 or 20 years, the most generous redistribution schemes may not ease the ensuing dislocations.

America today is torn apart, culturally and politically, by the continuing legacy of deindustrialization, and material deprivation is only one reason. The displaced manufacturing workers in the Rust Belt and elsewhere did find new jobs, in the main. But many of them seem to derive less meaning from filling orders in an Amazon warehouse or driving for Uber than their forebears had when they were building cars and forging steel—work that felt more central to the grand project of civilization.

It’s hard to imagine how a corresponding crisis of meaning might play out for the professional class, but it surely would involve a great deal of anger and alienation.

Even if we avoid a revolt of the erstwhile elite, larger questions of human purpose will linger. If AI does the most difficult thinking on our behalf, we all may lose agency—at home, at work (if we have it), in the town square—becoming little more than consumption machines, like the well-cared-for human pets in WALL-E

Altman has said that many sources of human joy and fulfillment will remain unchanged—basic biological thrills, family life, joking around, making things—and that all in all, 100 years from now, people may simply care more about the things they cared about 50,000 years ago than those they care about today

In its own way, that too seems like a diminishment, but Altman finds the possibility that we may atrophy, as thinkers and as humans, to be a red herring. He told me we’ll be able to use our “very precious and extremely limited biological compute capacity” for more interesting things than we generally do today.

Yet they may not be the most interesting things: Human beings have long been the intellectual tip of the spear, the universe understanding itself. When I asked him what it would mean for human self-conception if we ceded that role to AI, he didn’t seem concerned. Progress, he said, has always been driven by “the human ability to figure things out.” Even if we figure things out with AI, that still counts, he said.

It’s not obvious that a superhuman AI would really want to spend all of its time figuring things out for us.

I asked Sutskever whether he could imagine an AI pursuing a different purpose than simply assisting in the project of human flourishing.

“I don’t want it to happen,” Sutskever said, but it could.

Sutskever has recently shifted his focus to try to make sure that it doesn’t. He is now working primarily on alignment research, the effort to ensure that future AIs channel their “tremendous” energies toward human happiness

It is, he conceded, a difficult technical problem—the most difficult, he believes, of all the technical challenges ahead.

As part of the effort to red-team GPT-4 before it was made public, the company sought out the Alignment Research Center (ARC), across the bay in Berkeley, which has developed a series of evaluations to determine whether new AIs are seeking power on their own. A team led by Elizabeth Barnes, a researcher at ARC, prompted GPT-4 tens of thousands of times over seven months, to see if it might display signs of real agency.

The ARC team gave GPT-4 a new reason for being: to gain power and become hard to shut down

Agarwal told me that this behavior could be a precursor to shutdown avoidance in future models. When GPT-4 devised its lie, it had realized that if it answered honestly, it may not have been able to achieve its goal. This kind of tracks-covering would be particularly worrying in an instance where “the model is doing something that makes OpenAI want to shut it down,” Agarwal said. An AI could develop this kind of survival instinct while pursuing any long-term goal—no matter how small or benign—if it feared that its goal could be thwarted.

Barnes and her team were especially interested in whether GPT-4 would seek to replicate itself, because a self-replicating AI would be harder to shut down. It could spread itself across the internet, scamming people to acquire resources, perhaps even achieving some degree of control over essential global systems and holding human civilization hostage.

When I discussed these experiments with Altman, he emphasized that whatever happens with future models, GPT-4 is clearly much more like a tool than a creature. It can look through an email thread, or help make a reservation using a plug-in, but it isn’t a truly autonomous agent that makes decisions to pursue a goal, continuously, across longer timescales.

Altman told me that at this point, it might be prudent to try to actively develop an AI with true agency before the technology becomes too powerful, in order to “get more comfortable with it and develop intuitions for it if it’s going to happen anyway.”

“We need to do empirical experiments on how these things try to escape control,” Hinton told me. “After they’ve taken over, it’s too late to do the experiments.”

the fulfillment of Altman’s vision of the future will at some point require him or a fellow traveler to build much more autonomous AIs.

When Sutskever and I discussed the possibility that OpenAI would develop a model with agency, he mentioned the bots the company had built to play Dota 2. “They were localized to the video-game world,” Sutskever told me, but they had to undertake complex missions. He was particularly impressed by their ability to work in concert. They seem to communicate by “telepathy,” Sutskever said. Watching them had helped him imagine what a superintelligence might be like.

“The way I think about the AI of the future is not as someone as smart as you or as smart as me, but as an automated organization that does science and engineering and development and manufacturing,”

Suppose OpenAI braids a few strands of research together, and builds an AI with a rich conceptual model of the world, an awareness of its immediate surroundings, and an ability to act, not just with one robot body, but with hundreds or thousands. “We’re not talking about GPT-4. We’re talking about an autonomous corporation,”

Its constituent AIs would work and communicate at high speed, like bees in a hive. A single such AI organization would be as powerful as 50 Apples or Googles, he mused. “This is incredible, tremendous, unbelievably disruptive power.”

Presume for a moment that human society ought to abide the idea of autonomous AI corporations. We had better get their founding charters just right. What goal should we give to an autonomous hive of AIs that can plan on century-long time horizons, optimizing billions of consecutive decisions toward an objective that is written into their very being?

If the AI’s goal is even slightly off-kilter from ours, it could be a rampaging force that would be very hard to constrain

We know this from history: Industrial capitalism is itself an optimization function, and although it has lifted the human standard of living by orders of magnitude, left to its own devices, it would also have clear-cut America’s redwoods and de-whaled the world’s oceans. It almost did.

one of its principal challenges will be making sure that the objectives we give to AIs stick

We can program a goal into an AI and reinforce it with a temporary period of supervised learning, Sutskever explained. But just as when we rear a human intelligence, our influence is temporary. “It goes off to the world,”

That’s true to some extent even of today’s AIs, but it will be more true of tomorrow’s.

He compared a powerful AI to an 18-year-old heading off to college. How will we know that it has understood our teachings? “Will there be a misunderstanding creeping in, which will become larger and larger?”

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Countries’ commitments should reflect “equity and the principle of common but differentiated responsibilities and respective capabilities in the light of different national circumstances and in the context of sustainable development and efforts to eradicate poverty.”

It’s indisputable that poor nations should be allowed to develop and to eradicate poverty. Countries, obviously, can contribute to the global effort only to the extent of their capabilities

equity brings up a different, more slippery matter. What’s the just allocation of responsibility? What’s fair?

Countries, it turns out, have rather different takes on this question, potentially complicating efforts to make progress against climate change.

Consider the intended “nationally determined contributions” to battle climate change that various countries announced after the climate summit in Paris in 2015

One interesting study examined the notions of justice underpinning each national proposal. They were all over the map.

Critically, none of those experts considered the consequences of applying their logic to all countries across the board.

The aggregate notions of fairness did not add up to a solution. The countries that claimed responsibility for a small share of global emissions actually accounted for about a quarter of the total. Countries with per capita emissions ranging from 0.5 tons of carbon dioxide to 25 tons of CO2, roughly five times the global average, used this variable to justify modest plans.

The idea of an equitable and just distribution of responsibility might seem essential to achieve the shared goal of preventing a climate catastrophe

I can’t put precise odds on members of Congress accepting that the United States must bear one-fourth of the worldwide burden to cut greenhouse gas emissions because of the actions of long-dead Americans who had no idea they were causing damage. But the probability is quite low.

It seems only fair that countries such as the United States, which accounts for about a quarter of the greenhouse gases emitted by humanity since before the Industrial Revolution, should bear a much bigger share of the burden than, say, Brazil, which accounts for only 1 percent of historical emissions.

The United States, moreover, is quite rich and was made that way largely thanks to abundant and cheap fossil fuel.

Yet parsing how equity is to be achieved can get complicated

Should the goal be to equalize emissions per person, which today tilt heavily toward rich countries? (The United States emits some 18 tons per person; for India, the number is less than 3.

Or should we first cut emissions associated with the production of luxury goods and services that are mostly consumed in rich countries? Shouldn’t the emissions from producing the made-in-China toy you bought on Amazon accrue to the United States, where it is being played with?

They are in tension with the strategies championed by most rich countries, which are more sympathetic to the idea that historical emissions should be grandfathered in — not counted against them — and that they should be reduced in the future wherever reducing them is cheapest, which happens to be mostly in the developing world.

Many countries cannot afford the necessary mitigation pathways, either because they don’t have the resources to finance the new technologies needed to abandon fossil fuels, or because the resources they have are best deployed toward, say, buying air conditioning units or otherwise raising the standard of living.

There are essential truths that the world must acknowledge:

These countries are likely to face the gravest risks from climate change — whether measured in devastated crops, destroyed communities or people’s lives. Rich nations owe it to the world to ensure that resources and technologies are available for sufficient mitigation, adaptation and disaster relief

— not because they emitted a lot of greenhouse gases in the past, but because the task of preventing climate change and limiting its damage cannot be avoided, and they can afford it.

Many defended the fairness of their offer by pointing out that they accounted for a “small share” of global greenhouse gas emissions; others referred to their low per capita emissions. Many based their arguments on their vulnerability to climate change.

Consider the political ramifications of some climate justice arguments.

And that’s even without pointing out that China, today, emits more than double the amount of greenhouse gases the United States does.

Or consider how one research paper apportioned the remaining emissions budget — the greenhouse gases that can still be emitted in the future without breaching the warming ceiling (which in this estimate was set at 2 degrees Celsius)

It calculated nations’ responsibility for emissions starting only in 1992, when the world became aware of climate change, and assumed that each citizen of the world is entitled to the same budget since then. On this basis, it concluded that the United States would be entitled to 4.4 percent of the remainder, less than a fifth of its historic share.

That is fair. But it is also only 50 billion tons, or roughly nine years’ worth of emissions, at the nation’s current rate. I can’t imagine an administration that agreed to this surviving for long

The argument from guilt — built on the assumption that rich nations’ past development and emissions have incurred a moral debt to the rest of the world — will likely short-circuit the best case for action.

Better to draw on a different moral principle: to expect results from nations according to their capabilities and to assist them according to their needs. That frame could allow the job to get done.

Water pumps powered by cheap and highly efficient solar panels are able to drill deep down into rapidly dwindling desert aquifers. The solar panels have helped generate bumper opium harvests year after year since farmers in southern Afghanistan’s poppy-growing belt began installing them around 2014.

The opium trade earned about $1.8 billion to $2.7 billion last year, the United Nations has estimated. Opium sales have provided 9 to 14 percent of Afghanistan’s gross domestic product, compared with 9 percent provided by legal exports of goods and services.

The solar arrays have been central to ensuring Afghanistan’s status as the global leader in opium.

The Taliban, for their part, have condemned opium as anti-Islamic, as Afghanistan’s poppy crop sustains addicts in Europe and the Middle East, as well as a huge number inside Afghanistan. But given their own deep ties to opium smuggling during the insurgency, Taliban leaders are walking a fine line between hypocrisy and holiness.

Now, solar power is a defining feature of southern Afghan life. Tiny solar panels power light bulbs in mud huts, and solar-driven pumps irrigate cash crops like wheat and pomegranates, as well as subsistence farmers’ vegetable plots.

Opium farmers now rely on at least 67,000 solar-power-fed water reservoirs across Afghanistan’s desert southwest, according to a European Union-funded research project by David Mansfield, a consultant who has studied illicit economies and rural livelihoods in Afghanistan for two decades.

The panels, which supplanted more expensive and less reliable diesel to run water pumps, have helped turn the desert green.

“For many opium farmers, abundant water is now a given,” he said. “No one perceives it to have a cost.”

“Do not destroy the fields, but make the fields dry out,” Gov. Maulave Talib Akhund said in a statement. He added, “We are committed to fulfilling the opium decree.”

The opium ban was met with a collective shrug this spring by southern farmers, many of whom were already harvesting their spring crops. Opium prices surged almost immediately, several farmers said, to roughly $180 per kilogram from $60 per kilogram.

But the Taliban have vowed to crack down on farmers who try to cultivate any new crops.

Dr. Mansfield said that determining how long the aquifers could continue to supply water was uncharted territory because no one had been able to conduct a rigorous scientific study of the desert groundwater.

“We have to continue to dig our wells deeper and deeper,” Mr. Armani said.

Even when prices are high, many poppy farmers say, they earn only about $2 a day for each family member. They are at the very bottom of a narcotic trafficking system in which profits increase exponentially from growers to middlemen to processing labs to major cross-border traffickers.

Farmers whose poppy fields were plowed under by the previous government could send their sons to paying jobs as soldiers or police officers — or to the constellation of unskilled jobs provided by the United States and NATO. But those options are gone, and unemployment has soared under the Taliban.

“Growing poppies is the only option to survive right now,” he said.

I’d argue that many people on the right equate being powerful with being a swaggering tough guy and sneer at anything — like intellectual openness and respect for diversity — that might interfere with the swagger.

Putin was their idea of what a powerful man should look like, and Russia, with its muscleman military vision, their idea of a powerful country.

The key to understanding right-wingers’ growing Ukraine rage is that Russia’s failures don’t just show that a leader they idolized has feet of clay. They also show that their whole tough-guy view about the nature of power is wrong. And they’re having a hard time coping.

Why has Russia’s military failed so spectacularly? Because modern wars aren’t won by strutting guys flexing their biceps. They’re won mainly through logistics, technology and intelligence (in both the military and the ordinary senses)

Just to be clear, wars are still hell and can’t be won, even with superior weapons, without immense courage and endurance. But these are also qualities Ukrainians — men and women — turn out to have in remarkable abundance.

National power in the modern world rests mainly on economic strength and technological capacity, not military prowess.

None of this means that Russia can’t eventually conquer Ukraine. If it does, however, it will, in part, be because America’s Putin fans force a cutoff of crucial aid. And if this happens, it will be because the U.S. right can’t stand the idea of a world in which woke doesn’t mean weak and men who pose as tough guys are actually losers.

Besides teenage girls, another new group was referred to us: young people from the inpatient psychiatric unit, or the emergency department, of St. Louis Children’s Hospital. The mental health of these kids was deeply concerning—there were diagnoses like schizophrenia, PTSD, bipolar disorder, and more. Often they were already on a fistful of pharmaceuticals.

Being put on powerful doses of testosterone or estrogen—enough to try to trick your body into mimicking the opposite sex—-affects the rest of the body. I doubt that any parent who's ever consented to give their kid testosterone (a lifelong treatment) knows that they’re also possibly signing their kid up for blood pressure medication, cholesterol medication, and perhaps sleep apnea and diabetes. 

There are rare conditions in which babies are born with atypical genitalia—cases that call for sophisticated care and compassion. But clinics like the one where I worked are creating a whole cohort of kids with atypical genitals—and most of these teens haven’t even had sex yet. They had no idea who they were going to be as adults. Yet all it took for them to permanently transform themselves was one or two short conversations with a therapist.

Other girls were disturbed by the effects of testosterone on their clitoris, which enlarges and grows into what looks like a microphallus, or a tiny penis. I counseled one patient whose enlarged clitoris now extended below her vulva, and it chafed and rubbed painfully in her jeans. I advised her to get the kind of compression undergarments worn by biological men who dress to pass as female. At the end of the call I thought to myself, “Wow, we hurt this kid.”

How little patients understood what they were getting into was illustrated by a call we received at the center in 2020 from a 17-year-old biological female patient who was on testosterone. She said she was bleeding from the vagina. In less than an hour she had soaked through an extra heavy pad, her jeans, and a towel she had wrapped around her waist. The nurse at the center told her to go to the emergency room right away.

We found out later this girl had had intercourse, and because testosterone thins the vaginal tissues, her vaginal canal had ripped open. She had to be sedated and given surgery to repair the damage. She wasn’t the only vaginal laceration case we heard about.

Bicalutamide is a medication used to treat metastatic prostate cancer, and one of its side effects is that it feminizes the bodies of men who take it, including the appearance of breasts. The center prescribed this cancer drug as a puberty blocker and feminizing agent for boys. As with most cancer drugs, bicalutamide has a long list of side effects, and this patient experienced one of them: liver toxicity. He was sent to another unit of the hospital for evaluation and immediately taken off the drug. Afterward, his mother sent an electronic message to the Transgender Center saying that we were lucky her family was not the type to sue.

Here’s an example. On Friday, May 1, 2020, a colleague emailed me about a 15-year-old male patient: “Oh dear. I am concerned that [the patient] does not understand what Bicalutamide does.” I responded: “I don’t think that we start anything honestly right now.”

There are no reliable studies showing this. Indeed, the experiences of many of the center’s patients prove how false these assertions are. 

Many encounters with patients emphasized to me how little these young people understood the profound impacts changing gender would have on their bodies and minds. But the center downplayed the negative consequences, and emphasized the need for transition. As the center’s website said, “Left untreated, gender dysphoria has any number of consequences, from self-harm to suicide. But when you take away the gender dysphoria by allowing a child to be who he or she is, we’re noticing that goes away. The studies we have show these kids often wind up functioning psychosocially as well as or better than their peers.” 

When a female takes testosterone, the profound and permanent effects of the hormone can be seen in a matter of months. Voices drop, beards sprout, body fat is redistributed. Sexual interest explodes, aggression increases, and mood can be unpredictable. Our patients were told about some side effects, including sterility. But after working at the center, I came to believe that teenagers are simply not capable of fully grasping what it means to make the decision to become infertile while still a minor.

To begin transitioning, the girls needed a letter of support from a therapist—usually one we recommended—who they had to see only once or twice for the green light. To make it more efficient for the therapists, we offered them a template for how to write a letter in support of transition. The next stop was a single visit to the endocrinologist for a testosterone prescription. 

The doctors privately recognized these false self-diagnoses as a manifestation of social contagion. They even acknowledged that suicide has an element of social contagion. But when I said the clusters of girls streaming into our service looked as if their gender issues might be a manifestation of social contagion, the doctors said gender identity reflected something innate.

Frequently, our patients declared they had disorders that no one believed they had. We had patients who said they had Tourette syndrome (but they didn’t); that they had tic disorders (but they didn’t); that they had multiple personalities (but they didn’t).

The girls who came to us had many comorbidities: depression, anxiety, ADHD, eating disorders, obesity. Many were diagnosed with autism, or had autism-like symptoms. A report last year on a British pediatric transgender center found that about one-third of the patients referred there were on the autism spectrum.

This concerned me, but didn’t feel I was in the position to sound some kind of alarm back then. There was a team of about eight of us, and only one other person brought up the kinds of questions I had. Anyone who raised doubts ran the risk of being called a transphobe. 

I certainly saw this at the center. One of my jobs was to do intake for new patients and their families. When I started there were probably 10 such calls a month. When I left there were 50, and about 70 percent of the new patients were girls. Sometimes clusters of girls arrived from the same high school. 

Until 2015 or so, a very small number of these boys comprised the population of pediatric gender dysphoria cases. Then, across the Western world, there began to be a dramatic increase in a new population: Teenage girls, many with no previous history of gender distress, suddenly declared they were transgender and demanded immediate treatment with testosterone. 

Soon after my arrival at the Transgender Center, I was struck by the lack of formal protocols for treatment. The center’s physician co-directors were essentially the sole authority.

At first, the patient population was tipped toward what used to be the “traditional” instance of a child with gender dysphoria: a boy, often quite young, who wanted to present as—who wanted to be—a girl. 

During the four years I worked at the clinic as a case manager—I was responsible for patient intake and oversight—around a thousand distressed young people came through our doors. The majority of them received hormone prescriptions that can have life-altering consequences—including sterility. 

I left the clinic in November of last year because I could no longer participate in what was happening there. By the time I departed, I was certain that the way the American medical system is treating these patients is the opposite of the promise we make to “do no harm.” Instead, we are permanently harming the vulnerable patients in our care.

Today I am speaking out. I am doing so knowing how toxic the public conversation is around this highly contentious issue—and the ways that my testimony might be misused. I am doing so knowing that I am putting myself at serious personal and professional risk.

Almost everyone in my life advised me to keep my head down. But I cannot in good conscience do so. Because what is happening to scores of children is far more important than my comfort. And what is happening to them is morally and medically appalling.

For almost four years, I worked at The Washington University School of Medicine Division of Infectious Diseases with teens and young adults who were HIV positive. Many of them were trans or otherwise gender nonconforming, and I could relate: Through childhood and adolescence, I did a lot of gender questioning myself. I’m now married to a transman, and together we are raising my two biological children from a previous marriage and three foster children we hope to adopt.