Group items tagged

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.

But no serious scientist claims “total certainty” about the future of climate, just a range of increasingly alarming probabilities; no one is demanding “abrupt and expensive” changes in public policy, just an intensification of efforts long underway with increasingly reliable and affordable new technologies; and, yes, treating your opponents as evil morons is rarely a good political strategy

The same blather can be found in this week’s column by Jonah Goldberg, lamenting Max Boot’s sudden volte-face on the issue. Jonah has a point about Boot’s somewhat too instant makeover into a resistance icon (I’ve made it myself), but on the substance of climate change, what defense of the American right does Goldberg have? Zippo. He argues that “there are a lot of different views on climate change on the right.” I find that about as convincing as the argument that there are a lot of different views on race among Harvard’s faculty.

More to the point, the hypothesis of carbon-created climate change doesn’t just have “some legitimate science” on its side, as Goldberg puts it, but a completely overwhelming majority of the science

You should, of course, retain some skepticism always. It’s possible, for example, that natural selection may be replaced as the core scientific consensus about how life on Earth evolved. Possible. But do we have to express skepticism every time new science based on that hypothesis emerges

I honestly can’t see how the science of this can be right or left. It’s either our best working hypothesis or not.

Inaction because of uncertainty only makes sense if the threat is distant and not too calamitous. But when there’s a chance of it being truly catastrophic, and the evidence in its favor keeps strengthening, a sane person adjusts

A conservative person — someone attuned to risk — will take out insurance, in case the worst happens.

Why is every other government on Earth committed to tackling this (rhetorically at least) and every other center-right party on Earth taking this very seriously? (Check out this page about environmental policy in the British Conservative party — aimed getting to zero carbon emissions by 2050 — and see if you even recognize the debate on the right in the U.S.)

The kicker, of course, is that the current GOP is not just skeptical of climate science and dragging its feet on doing anything about climate change. It is actively pursuing policies aimed at intensifying environmental devastation. Trump’s EPA is attempting to gut the regulation of carbon; it has tried to sabotage the only most prominent global agreement on the matter; it celebrates carbon-based energy and rhapsodizes about coal; it has slapped a 30 percent tariff on solar panels; its tax reform hurt solar and wind investment

For allegedly intelligent conservatives like Stephens and Goldberg to devote energy toward climate skepticism while turning a blind eye to vigorous Republican climate vandalism is, quite simply contemptible. I am not reading their minds here. I’m reading their columns. On this question — as on fiscal policy — they’re not skeptics or conservatives; they are dogmatists, sophists, and enablers of environmental vandalism. They reveal Republicanism’s calculated assault on the next generations — piling them with unimaginable debt and environmental chaos. This isn’t the cultural conservatism of Burke; it’s the selfish nihilism of Rand.

a quote. It was the first time a major global leader spoke to the U.N. on the question: “It is life itself — human life, the innumerable species of our planet — that we wantonly destroy. It is life itself that we must battle to preserve … The danger of global warming is as yet unseen but real enough for us to make changes and sacrifices so we may not live at the expense of future generations. That prospect is a new factor in human affairs. It is comparable in its implications to the discovery of how to split the atom, indeed its results could be even more far-reaching … We should always remember that free markets are a means to an end. They would defeat their object if by their output they did more damage to the quality of life through pollution than the well-being they achieve by the production of goods and services.”

That leader also made a core moral argument: “No generation has a freehold on this Earth; all we have is a life tenancy with a full repairing lease.

Those words were Margaret Thatcher’s in 1989. She devoted her entire U.N. speech to conservation and climate change. If the subject was real enough in 1989 to make sacrifices and changes, how much more so almost 30 years later?

The difference between Thatcher and today’s Republicans is quite a simple one. She believed in science (indeed was trained as a scientist). She grasped the moral dimensions of the stewardship of the Earth from one generation to another. She did not engage in the cowardice of sophists. And unlike these tools and fools on today’s American right, she was a conservative.

The real question, it seems to me, is therefore an almost philosophical one: Do these exceptions prove or disprove a general rule?

I’d argue that, by and large, they prove it

The number of people with a mismatch between chromosomes and hormones, or with ambiguous genitalia, is surpassingly small. Well under one percent is a useful estimate.

Similarly with a transgender identity: It absolutely exists but is also very rare — some estimates put it at around 0.7 percent of the population

Gay men and lesbians who have unambiguous male and female sex organs and identity but an attraction to their own sex are also pretty rare (whatever we’d like to think). Maybe 2 to 5 percent, with some outliers

Does this mean that general assumptions about most people being either male or female and heterosexual and cisgendered are misplaced or even offensive? Hardly. I’m gay but usually assume that everyone I meet is straight until I know otherwise

And I don’t mind the hetero assumption applying to me either. It’s a reasonable statistical inference, not bigotry. And I can always set them, er, straight.

My preferred adjective for sex and gender is bimodal, rather than binary. What bimodal means is that there are two distinct and primary modes with some variations between them

Think of it as two big mountains representing, in sex matters, well over 95 percent of humans, with a long, low valley between them, representing the remaining percent.

Everyone is equally human. But clearly the human experience of sex is one thing for almost everyone and a different thing for a few.

Do we infer from this that we need to junk the categories of male and female altogether, as many critical gender theorists argue? That seems insane to me

These two modes actually define the entire landscape of sex (the exceptions are incomprehensible without them), and the bimodal distribution is quite obviously a function of reproductive strategy (if we were all gay, or intersex, we’d cease to exist as a species before too long)

Ditto the transgender experience: Does the fact that less than one percent of humans feel psychologically at odds with their biological sex mean that biological sex really doesn’t exist and needs to be defined away entirely? Or does it underline just how deep the connection between sex and gender almost always is?

We are not a threat to straights; we’re a complement. Transgender people do not threaten the categories of male and female; they pay, in some ways, homage to them.

On the left, there’s too much defensiveness about being in a minority

But being in a minority — even a tiny one — need not be demoralizing, if we have self-confidence. I’d argue it can lead, through struggle and challenge, to a more deeply examined self — and to a resilience that can only be earned and is no one else’s to give.

You will, in fact, end up with … an individual human being!

It’s stupid to pretend they are entirely normal, because it gives the concept of normality too much power over us. Their abnormality is a neutral thing, like left-handedness: a fact, not a judgment. And why on earth should we feel defensive about that?

But what surprised me was the positive response to a single, minor point I made about intersectionality.

In some ways, I argued, the intersectional move on the hard left is a good thing — because it complicates things. It’s no longer enough just to consider race, for example, as a signifier of oppression without also considering gender or orientation or gender identity, national origin, immigrant status, etc. When society is made up entirely of various intersecting oppressions, as the social-justice left believes, it’s vital not to leave any potential grievance out.

By the same token, of course, an oppressor can also be identified in multiple, intersectional ways

It can get very complicated very fast.

Let’s push this to its logical conclusion. Let’s pile on identity after identity for any individual person; place her in multiple, overlapping oppression dynamics, victim and victimizer, oppressor and oppressed; map her class, race, region, religion, marital status, politics, nationality, language, disability, attractiveness, body weight, and any other form of identity you can

After a while, with any individual’s multifaceted past, present, and future, you will end up in this multicultural world with countless unique combinations of endless identities in a near-infinite loop of victim and victimizer.

And the fact that this society is run overwhelmingly on heterosexual lines makes sense to me, given their overwhelming majority. As long as the government does not actively persecute or enable the persecution of a minority, who cares

In the end, all totalizing ideologies disappear up their own assholes. With intersectionality, we have now entered the lower colon

It’s good to see this kind of realism. Remember, back in 2016 Donald Trump promised that he would restore coal to its former greatness, reopening shuttered mines — and voters in coal country believed him

The union’s document is in effect an endorsement, at least in principle, of the Biden administration’s plans to make action against climate change a centerpiece of its boost to infrastructure spending

It’s also a small but encouraging vindication of the thinking behind Build Back Better, the belief that climate action is most likely to be politically feasible if it eschews economic purism and relies more on carrots than on sticks.

The council, whose creation was announced in 2017, calls for carbon fees whose proceeds would be redistributed to families. This plan is part of a “bipartisan road map” for action.

This is, however, not the path the Biden administration is taking. Why?

First, the economic case for relying almost exclusively on a carbon tax misses the crucial role of technological development.

The reason large reductions in emissions look much easier to achieve now than they did a dozen years ago is that we’ve seen spectacular progress in renewable energy: a 70 percent fall since 2009 in the cost of wind power, an 89 percent fall in the cost of solar power.

And this technological progress didn’t just happen. It was at least partly a result of investments made by the Obama administration.

In retrospect, however, it is clear that government spending provided a crucial technological lift. And this suggests that public investment, as well as or even instead of a carbon tax, can be a way forward in fighting climate change.

Second, the idea that a carbon tax can achieve bipartisan support is hopelessly naïve. Only 14 percent of Republicans even accept the notion that climate change is an important issue

What might win over at least some of these voters, however, is the kind of program the United Mine Workers is calling for: targeted spending designed to help retrain former miners and support development in coal country communities.

while there’s a compelling case against relying exclusively on a carbon tax to fight climate change, public investment alone also probably isn’t enough. Eventually we will almost surely have to put a price on greenhouse gas emissions, politically difficult though that will be.

Promoting job creation in West Virginia or eastern Kentucky won’t be easy, and may be impossible.

we can and should make a good-faith effort to help workers and regions that will lose as we try to avoid environmental catastrophe, and in general to make climate policy as politically palatable as possible, even at some cost in efficiency. Climate action is too important a task to insist that it be done perfectly.

City leaders are scrambling to address the problem with law enforcement strategies including increased police presence and working with the FBI. The Leaders Become Legends program, by contrast, is funded through the city's economic development arm, far upstream from law enforcement.

Tax incentives and legal requirements for green energy are driving a healthy demand for this trade. For the most part, says Jackson, bosses in this industry are supportive and colleagues friendly. But not always. Part of the training, says Jackson, is managing expectations.

"Our total mission is to not let them go back to prison, or be dead," Jackson says. "As well as dealing with our trauma from just being so called Black in America."

Despite their frequency, the police stops never get easier. Between these encounters and the violence in his community, Churn acknowledges that he lives with a lot of fear and deep sense of injustice.

we need to do more, for the simple reason that they may not pay off fast enough. Climate change is a timed test, one of the first that our civilization has faced, and with each scientific report the window narrows.

Political change usually involves slow compromise, and that’s in a working system, not a dysfunctional gridlock such as the one we now have in Washington.

I suspect that the key to disrupting the flow of carbon into the atmosphere may lie in disrupting the flow of money to coal and oil and gas.

And, if the world were to switch decisively to solar and wind power, Chase would lend to renewable-energy companies, too. Indeed, it already does, though on a much smaller scale.

The same is true of the asset-management and insurance industries: without them, the fossil-fuel companies would almost literally run out of gas, but BlackRock and Chubb could survive without their business.

The terminal will spit out the current league tables, which rank loan volume: showing, for example, which banks are lending the most money to railroad builders or to copper miners—or to fossil-fuel companies.

And the trend is remarkable: in the three years since the signing of the Paris climate accord, which was designed to help the world shift away from fossil fuels, the banks’ lending to the industry has increased every year, and much of the money goes toward the most extreme forms of energy development.

The biggest oil companies might still be able to self-finance their continuing operations, but “the pure-play frackers will find finance impossible,” Buckley said. “Coal-dependent rail carriers and port owners and coal-mine contracting firms will all be hit.”

“the impacts of that social signal would be significant immediately, while the economic impacts from transitioning off of fossil fuels would happen over time.”

But four-fifths of the world’s population lives in nations that currently pay to import fossil fuels, and their economies would benefit, as ample financing would allow them to transition relatively quickly to low-cost solar and wind power.

n some ways, the insurance industry resembles the banks and the asset managers: it controls a huge pool of money and routinely invests enormous sums in the fossil-fuel industry.

Insurance companies are the part of our economy that we ask to understand risk, the ones with the data to really see what is happening as the climate changes, and for decades they’ve been churning out high-quality research establishing just how bad the crisis really is.

The second thing that makes insurance companies unique is that they don’t just provide money; they provide insurance. If you want to build a tar-sands pipeline or a coal-fired power plant or a liquefied-natural-gas export terminal, you need to get an insurance company to underwrite the plan.

But it’s both simple and powerful to switch your bank account: local credit unions and small-town banks are unlikely to be invested in fossil fuels,

Financial institutions can help with that work, but their main usefulness lies in helping to break the power of the fossil-fuel companies.

But between climate change and remote work, the state is facing questions that uniquely cut to the core of its economic identity.

f workers untethered from their offices flee the state, or companies start basing more high-paid workers elsewhere, it will have huge ramifications for California’s outlook

The median price for single-family homes and condos in the state is closing in on $600,000, according to the real estate site Zillow, more than twice the national level. The figure reflects a longstanding shortage that has also caused rising rents, crowded households and two-hour commutes used to offset the cost of living. Much more than taxes, the reason that companies move jobs out of the state is lower-priced housing and the lower labor costs that go with it.

Economists and planners have long counseled that the best way to relieve this pressure is to build more housing near the coastal job centers, but California has continued to sprawl, a pattern that has undermined the state’s own emission-reduction goals by encouraging longer commutes, while placing more homes in fire zones. In 2010, the last year with available data, nearly a third of California housing was in the so-called wildland-urban interface, where wildfire risk is greatest, according to the U.S. Forest Service.

This would seem like an easy enough mandate. After all, California has invested heavily in renewable energy, was the first state to mandate solar power in new homes, and is run by a governor who has spoken of a “a climate damn emergency” and recently signed an executive order banning sales of new gas-powered cars in 15 years.

Having a prosperous and growing economy ultimately means finding new ways to add jobs and homes. So California’s looming battles over climate change promise to be another round in a debate that predates statehood, which is how many people it really wants, and how much water will be required to sustain them.

A world that is warmer, drier and more crowded may not be the world they asked for, but they’ll still be looking for jobs, while coping with the world as it is.

Scientists, meanwhile, have helped Texas become a leader in renewable energy. Texas leads the nation in wind-generated power, and produced more energy from wind and solar combined than from coal, according to 2020 data from the Electric Reliability Council of Texas.

What Texans, and people everywhere, need from our leaders now is a good example. Put science first. Put life-saving measures first. Take the long view, rather than worrying about short-term political repercussions among a base of supporters. That's how we'll get through this together.

Heiting says burning that methane is a way of reducing the greenhouse gas emissions that are currently contributing to climate change. Methane released from dairy farms, for example, has far more global warming potential than the carbon dioxide released when that methane is burned.

So the company would then mix that lower-carbon gas with hydrogen gas, which has no carbon emissions when it's burned.

"This is not going to happen without policy support," she says. "We need production tax credits for renewable natural gas and hydrogen just like we put in place for wind and solar."

"Hydrogen is pretty well suited to solve a lot of problems at once and really be this unifier between renewable energy and our society's energy needs," Ramsey says. "This is a big opportunity for oil and gas companies, but also for electric utilities and renewable developers."

This is what connects figures as disparate as Jordan Peterson and J.D. Vance and Peter Thiel and Donald Trump. These are the ideas that unite both the mainstream and the weirder figures of the so-called postliberal right, from Patrick Deneen to the writer Bronze Age Pervert.

I think the Republican Party’s collapse into incoherence reflects the fact that much of the modern right is reactionary, not conservative

As Paul Valéry, the French poet, once said, “Ancient Greece is the most beautiful invention of the modern age.” To treat Andreessen’s essay as an argument misses the point. It’s a vibe. And the vibe is reactionary.

It’s a coalition obsessed with where we went wrong: the weakness, the political correctness, the liberalism, the trigger warnings, the smug elites. It’s a coalition that believes we were once hard and have become soft; worse, we have come to lionize softness and punish hardness.

The story of the reactionary follows a template across time and place. It “begins with a happy, well-ordered state where people who know their place live in harmony and submit to tradition and their God,” Mark Lilla writes in his 2016 book, “The Shipwrecked Mind: On Political Reaction.”

He continues:Then alien ideas promoted by intellectuals — writers, journalists, professors — challenge this harmony, and the will to maintain order weakens at the top. (The betrayal of elites is the linchpin of every reactionary story.) A false consciousness soon descends on the society as a whole as it willingly, even joyfully, heads for destruction. Only those who have preserved memories of the old ways see what is happening. Whether the society reverses direction or rushes to its doom depends entirely on their resistance.

The Silicon Valley cohort Andreessen belongs to has added a bit to this formula. In their story, the old way that is being lost is the appetite for risk and inequality and dominance that drives technology forward and betters human life. What the muscled ancients knew and what today’s flabby whingers have forgotten is that man must cultivate the strength and will to master nature, and other men, for the technological frontier to give way

Now Andreessen has distilled the whole ideology to a procession of stark bullet points in his latest missive, the buzzy, bizarre “Techno-Optimist Manifesto.”

it’s the pairing of the reactionary’s sodden take on modern society with the futurist’s starry imagining of the bright tomorrow. So call it what it is: reactionary futurism

Andreessen’s argument is simple: Technology is good. Very good. Those who stand in its way are bad.

“The Enemy.” The list is long, ranging from “anti-greatness” to “statism” to “corruption” to “the ivory tower” to “cartels” to “bureaucracy” to “socialism” to “abstract theories” to anyone “disconnected from the real world … playing God with everyone else’s lives”

So who is it, exactly, who extinguishes the dancing star within the human soul?

Our present society has been subjected to a mass demoralization campaign for six decades — against technology and against life — under varying names like “existential risk,” “sustainability,” “E.S.G.,” “sustainable development goals,” “social responsibility,” “stakeholder capitalism,” “precautionary principle,” “trust and safety,” “tech ethics,” “risk management,” “degrowth,” “the limits of growth.”

The enemy, in other words, is anything or anyone who might seek to yoke technology to social goals or structures

For years, I’ve been arguing for politics to take technology more seriously, to see new inventions as no less necessary than social insurance and tax policy in bringing about a worthier world. Too often, we debate only how to divvy up what we already have. We have lost the habit of imagining what we could have; we are too timid in deploying the coordinated genius and muscle of society

I’ve been digging into the history of where and when we lost faith in technology and, more broadly, growth. At the core of that story is an inability to manage, admit or even see when technologies or policies go awry

The turn toward a less-is-more politics came in the 1970s, when the consequences of reckless growth became unignorable

Did we, in some cases, overcorrect? Absolutely. But the only reason we can even debate whether we overcorrected is because we corrected: The Clean Air Act and the Clean Water Act and a slew of other bills and regulations did exactly what they promised.

It is telling that Andreessen groups sustainability and degrowth into the same bucket of antagonists

Degrowth is largely, though not wholly, skeptical of technological solutions to our problems

But the politics of sustainability — as evidenced in legislation like the Inflation Reduction Act — have settled into another place entirely: a commitment to solving our hardest environmental problems by driving technology forward, by investing and deploying clean energy infrastructure at a scale unlike anything the government has done since the 1950s.

Andreessen focuses at some length on the nuclear future he believes we’ve been denied —

but curiously ignores the stunning advances in solar and wind and battery power that public policy has delivered.

He yearns for a kind of person, not just a kind of technology. “We believe in ambition, aggression, persistence, relentlessness — strength,” he writes, italics included. “We believe in merit and achievement. We believe in bravery, in courage.”

There are ways in which these virtues have become undervalued, in which the left, in particular, has a dysfunctional relationship with individual achievement and entrepreneurial élan.

Andreessen’s ideas trace an odd, meme-based philosophy that has flourished in some corners of the internet known as effective accelerationism

“Effective accelerationism aims to follow the ‘will of the universe’: leaning into the thermodynamic bias towards futures with greater and smarter civilizations that are more effective at finding/extracting free energy from the universe,”

“E/acc has no particular allegiance to the biological substrate for intelligence and life, in contrast to transhumanism.” OK!

Take Andreessen’s naming of trust and safety teams as among his enemies.

That, in a way, is my core disagreement with Andreessen. Reactionary futurism is accelerationist in affect but deccelerationist in practice

How has that worked out? A new analysis by Similarweb found that traffic to twitter.com fell in the United States by 19 percent from September 2022 to September 2023 and traffic on mobile devices fell by almost 18 percent. Indications are that advertising revenue on the platform is collapsing.

Andreessen spends much of his manifesto venerating the version of markets that you hear in the first few weeks of Econ 101, before the professor begins complicating the picture with all those annoying market failures

Throughout his essay, Andreessen is at pains to attack those who might slow the development of artificial intelligence in the name of safety, but nothing would do more to freeze progress in A.I. than a disaster caused by its reckless deployment

It is hard to read Andreessen’s manifesto, with its chopped-up paragraphs and its blunt jabs of thought delivered for maximum engagement and polarization, and not feel that Andreessen now reflects the medium in which he has made his home: X. He doesn’t just write in the way the medium rewards. He increasingly seems to think in its house style, too.

One reason I left Twitter long ago is that I noticed that it was a kind of machine for destroying trust. It binds you to the like-minded but cuts you from those with whom you have even modest disagreements

There is a reason that Twitter’s rise was conducive to politics of revolution and reaction rather than of liberalism and conservatism. If you are there too often, seeing the side of humanity it serves up, it is easy to come to think that everything must be burned down.

Musk purchased Twitter (in an acquisition that Andreessen Horowitz helped finance) and gutted its trust and safety teams. The result has been a profusion of chaos, disinformation and division on his platform

Treating so much of society with such withering contempt will not speed up a better future. It will turn people against the politics and policies of growth, just as it did before. Trust is the most essential technology of all.

Home construction boomed in these places, fed by a bubble that put people in freshly built houses they really couldn't afford.

One plot was valued at $1.5 million before the zoning changed to allow cultivation, he said; now it's in escrow for $4 million.

Adelanto, which once flirted with bankruptcy, is now seeing a land rush and people willing to spend millions of dollars to secure property and permits to grow marijuana legally, considering a future where millions of Californians will legally be permitted to enjoy weed recreationally.

Elizabeth Brown, who's with Lee & Associates, said land that was going for 50 cents to 90 cents per square foot is now going for $12 to $14.

For a couple of years, solar energy plants were going to save everybody from the recession. Bolstered by money potentially available from the federal economic stimulus, dozens of solar projects were proposed for the desert last decade.

In this case, given the lack of competition, the city has to figure out how much it wants to milk from growers for its own coffers. The reporter notes that a tax plan similar to the one in nearby Desert Hot Springs (the other town to permit cultivation) could net Adelanto $6 million per year if all goes well, equal to nearly half of the city's entire annual budget.

The big unknown is whether other cities might change their minds and decide to allow pot growers in, something that is bound to happen if Adelanto succeeds and sloughs off its reputation as the poorest small town in the High Desert.

And as an interesting touch, it could end up driving out the local representation from General Atomics Aeronautical Systems, the manufacturers of the Predator drone, because their landlords can get more money from the marijuana growers.

Grid operators say it simply doesn't make sense to pinpoint any one generation source for criticism.

Wind turbines did, in fact, freeze. But so did natural gas wells. And pipelines. And critical pipes at coal and nuclear power plants. And equipment panels.

Blaming wind and solar is a political move, Bird says. What's really needed — in Texas and elsewhere — is better preparation.

“Based on our research, I can’t see evidence that we can reach 1.9C,” he said. “But whether we are now on course for 1.9C or 2.7C, the main point is that we should focus on concrete action.”

China has released five documents detailing plans to achieve its dual goals of peaking carbon emissions in 2030 and reaching net zero by 2060. “If you read those reports you can find all of our actions, but nobody reads everything,”

As an example, he said the working guidance document on carbon peaking and neutrality outlined a strict control on the increase of coal consumption during the 14th five-year period and then a gradual reduction during the following five years. “That means China will peak coal consumption around 2025, though that is not a line you will see in the document. You need to interpret it and nobody [outside China] can do that.”

Similarly, he said the government 1+N policy system provided a roadmap of 37 tasks that the country needed to take until 2060 on areas ranging from legislation and policy to technology and finance

There will be another 30 documents published in the coming year that break down actions needed in key sectors, such as building and transport, as well as major industries including steel and chemicals. “No country has issued so many documents to support its targets,” he said. “It’s a holistic solution, but nobody knows.”

China’s two different targets pose very different challenges, he said. “The peaking issue is easy. More difficult is how to achieve neutrality … We are in transition. Our concern in the future is not that China is too slow, but that it is too fast.”

“Our coal-fired plants have a life of 10 to 12 years. If we shut them down, who will pay for the stranded assets? Who will employ the laid-off workers?”

By the end of this decade, the government plans to reach 1200GW of wind and solar power, which would exceed the entire installed electricity capacity of the US, he said.

As at previous Cops, China will also push wealthy nations to make greater financial contributions to developing countries, which have done least to cause the climate crisis but suffer most from its consequences.

“China would like more effort on supporting developing countries,” he said. “If we are going to aim for 1.5C instead of 2C, then there has to be an increase in the funds available to make that happen.”

“1.5C is possible, but it would carry a cost, social and economic. If we cannot solve these problems equally, especially for developing countries, then it is not a real target.”

“We are all in the same boat, but different cabins,” he said. “Some live in a big space and eat too much. We need balance.”

But these measures have costs. Schooling time is cut short, so students learn less. People do not travel to their jobs, so work is less productive. The heat kept some farmers inside and stunted harvests, so crop yields fell and global food prices increased. Social life is disrupted.

The geography of poor countries — many are close to the Equator — is not the only reason climate change is such a burden for them. Their poverty is another factor, leaving them with fewer resources to adapt.

“Climate change is one of the most profound inequities of the modern era,”

There is a paradox to the climate crisis. Because India never fully industrialized, it has not released as many greenhouse gases as the U.S., European nations and other rich countries. But because it has not industrialized, it also has fewer resources to adapt than the richer, polluting nations.

Fewer than 10 percent of Indians have air-conditioning at home.

The rush for clean energy technologies, like solar and wind power, is an effort to break that tension — to give countries a way to industrialize without the planet-warming pollution. With climate disasters already hitting much of the world, that effort is in a race against time to prevent more crises like India’s.

Moody’s Investors Service, a major credit ratings agency, has already said that it wants to take climate into account when weighing the financial health of companies and municipalities.

t says that “water- and vector-borne diseases such as malaria and dengue fever will likely be the largest direct effect of changes in human health and the associated productivity loss.”

The hardest-hit economies will be some of the fastest-growing ones — Brazil, Russia, India, China and South Africa, the report says.

report also forecasts lower oil and natural gas demand, dealing a blow to oil-exporting countries, especially in the Middle East. It forecasts that Saudi GDP will drop more than 10 percent by 2048; the kingdom would be the country harmed the most by climate change, hurting government revenue

Of the 12 largest economies, India will be the worst hit, the report says, with GDP growing 2.5 percentage points more slowly than it would without the effects of climate change

The country’s service industry will be hit by heat stress, agricultural productivity will fall, and health-care costs will climb

the scenarios only go through 2048. The Moody’s report says “the distress compounds over time and is far more severe in the second half of the century.”

He added: “Most of the models go out 30 years, but, really, the damage to the economy is in the next half-century, and we haven’t developed the tools to look out that far.”

That’s why it is so hard to get people focused on this issue and get a comprehensive policy response,” Zandi said. “Business is focused on the next year, or five years out.”

Chubb, one of the biggest insurance firms in the United States, on Monday said it would no longer sell insurance to new coal-fired power plants or sell new policies to companies that derive more than 30 percent of their revenue from the mining of coal used in power plants.

Hammond said that the company still needs to stop insuring new coal mines and the oil sands, or tar sands, in northern Alberta.

“new coal projects cannot be built without insurance, and Chubb just dealt a blow to the dozens of companies that are still betting on the expansion of coal globally.”

the chief economist of Equinor, the Norwegian oil company previously known as Statoil, has written a report that looks at three scenarios for climate change and its impact on global economies, especially on energy. Only one of those, the report said, would lead to a sustainable path, but that path comes with enormous challenges. To reach that set of targets by 2050, “almost all use of coal must be eradicated

oil demand would need to be halved, and natural gas demand trimmed by more than 10 percent.

more than half of new cars would have to be electric vehicles by 2030

Electricity demand will double, yet wind and solar would equal the entire current electricity output, a leap from current levels

Waerness also said that the company currently assumes a carbon price of $55 a ton when considering whether to finance new energy projects

Meanwhile, increasing the scrutiny on Microsoft’s plan are its dealings with fossil fuel companies, which have been highlighted by some as evidence of hypocrisy as it makes climate pledges. In 2019 alone, the technology company had entered into long-term partnerships with three major oil companies, including ExxonMobil, that will be using Microsoft’s technology to expand oil production by as much as 50,000 barrels a day over the coming years. The staggering amount of carbon this would release into the atmosphere would not be included on Microsoft’s expanded carbon ledger.

To begin, Microsoft will focus on protecting forests and planting trees to capture carbon. This strategy has long been used to offset emissions, but Microsoft is hoping to improve their outcomes by using remote-sensing technology to accurately estimate the carbon storage potential of forests to ensure no major deforestation is occurring in their allotments. To achieve these goals, Microsoft will be partnering with Pachama, a Silicon Valley startup that will survey 60,000 hectares of rainforest in the Amazon, plus an additional 20,000 hectares across north-eastern states of the US for the company. According to Kesley

The carbon produced when burning the biomass is captured before it is released into the atmosphere and then injected at a very high pressure into rock formations deep underground. Not only does this remove carbon from the natural cycle, the biomass absorbs CO2 as it grows.

The second concern is that the transition from coal to biofuel would require setting aside vast tracts of arable land – some estimates say one to two times the size of India.

Perhaps the most futuristic of the technologies outlined in Microsoft’s carbon negative plan is direct air capture (DAC). This involves machines that essentially function like highly efficient artificial trees, drawing existing carbon out of the air and transforming it into non-harmful carbon-based solids or gasses.

Microsoft’s plan for intensive investment in this industry is exciting for those working in the field. Klaus Lackner, a theoretical physicist working on DAC, has been arguing since the 1990s that carbon removal is the only feasible way to stop significant temperature rises. “We’ve shown that this method is technologically feasible, but nobody has wanted them,” he said. “Microsoft have said ‘we get it’. It will cost them money, but it will allow the technologie

While the technologies that Microsoft are betting on are still in their nascent stages, in the past few years there has been some encouraging progress in the negative emissions industry. Lackner and Arizona State University recently signed a deal with Silicon Kingdom, an Irish-based company, to manufacture his carbon-suck machines. The plan is to install them on wind and solar farms, and then sell the captured carbon to beverage companies to make carbonated drinks. In the UK, Drax power plant, which was once among Europe’s most polluting, transitioned from coal to biofuel this year.

Given the not insignificant risk of failure, some propose that relying on nascent or future technology as a solution to the climate crisis represents a moral hazard – the promise of carbon removal functions as an incentive for governments and major polluters to not change their behavior now.

When asked about this concern by the Guardian, Microsoft’s Joppa responded that in the short term, the energy demands of a growing global population will probably still need a mix of renewable and traditional energy sources. By remaining in discourse with these industries, he said, Microsoft hopes to help them change and transition to a better model in the future. “It’s extremely hard to lead if there’s no one there to follow,” he added.

“Carter was our only president who had a visceral environmental and ecological attachment. That was part of his being,” Speth says. “We had an opportunity in 1980 – but we’ve lost 40 years in the pursuit of a climate-safe path. We can no longer avoid serious and destructive changes, period. That didn’t have to happen.”

it’s important to recognize the example Carter set for looking ahead, in a culture that prizes soundbites and short-term gains. “Carter was a trained engineer who believed in science,” Speth points out. “He understood things on a global scale, and believed in forecasting. Preparing for the long run is rare in politics.”

“If there is a gene for duty, responsibility and the will to tackle messy problems with little or no potential for political gain,” he writes, “Jimmy Carter was born with it.”