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He calls these expansions of meaning “concept creep.”

critics may hold concept creep responsible for damaging cultural trends, he writes, “such as supposed cultures of fear, therapy, and victimhood, the shifts I present have some positive implications.”

How did a working-class mom get arrested, lose her fast food job, and temporarily lose custody of her 9-year-old for letting the child play alone at a nearby park?

The concept of abuse expanded too far.

Classically, psychological investigations recognized two forms of child abuse, physical and sexual, Haslam writes. In more recent decades, however, the concept of abuse has witnessed “horizontal creep” as new forms of abuse were recognized or studied. For example, “emotional abuse” was added as a new subtype of abuse. Neglect, traditionally a separate category, came to be seen as a type of abuse, too.

Meanwhile, the concept of abuse underwent “vertical creep.” That is, the behavior seen as qualifying for a given kind of abuse became steadily less extreme. Some now regard any spanking as physical abuse. Within psychology, “the boundary of neglect is indistinct,” Haslam writes. “As a consequence, the concept of neglect can become over-inclusive, identifying behavior as negligent that is substantially milder or more subtle than other forms of abuse. This is not to deny that some forms of neglect are profoundly damaging, merely to argue that the concept’s boundaries are sufficiently vague and elastic to encompass forms that are not severe.”

Concept creep is inevitable and vital if society is to make good use of new information. But why has the direction of concept creep, across so many different concepts, trended toward greater sensitivity to harm as opposed to lesser sensitivity?

Haslam endorses two theories

Before 9/11, the notion of torturing prisoners was verboten. After the Bush Administration’s torture was made public, popular debate focused on mythical “ticking time bomb” scenarios, in which a whole city would be obliterated but for torture. Now Donald Trump suggests that torture should be used more generally against terrorists. Torture is, as well, an instance in which people within the field of psychology pushed concept creep in the direction of less sensitivity to harm,

The other theory posits an ideological explanation. “Psychology has played a role in the liberal agenda of sensitivity to harm and responsiveness to the harmed,” he writes “and its increased focus on negative phenomena—harms such as abuse, addiction, bullying, mental disorder, prejudice, and trauma—has been symptomatic of the success of that social agenda.”

Jonathan Haidt, who believes it has gone too far, offers a fourth theory. “If an increasingly left-leaning academy is staffed by people who are increasingly hostile to conservatives, then we can expect that their concepts will shift, via motivated scholarship, in ways that will help them and their allies (e.g., university administrators) to prosecute and condemn conservatives,

there are many reasons to be concerned about excessive sensitivity to harm:

While Haslam and Haidt appear to have meaningfully different beliefs about why concept creep arose within academic psychology and spread throughout society, they were in sufficient agreement about its dangers to co-author a Guardian op-ed on the subject.

It focuses on how greater sensitivity to harm has affected college campuses.

“Of course young people need to be protected from some kinds of harm, but overprotection is harmful, too, for it causes fragility and hinders the development of resilience,” they wrote. “As Nasim Taleb pointed out in his book Antifragile, muscles need resistance to develop, bones need stress and shock to strengthen and the growing immune system needs to be exposed to pathogens in order to function. Similarly, he noted, children are by nature anti-fragile – they get stronger when they learn to recover from setbacks, failures and challenges to their cherished ideas.”

police officers fearing harm from dogs kill them by the hundreds or perhaps thousands every year in what the DOJ calls an epidemic.

After the terrorist attacks of September 11, 2001, the Bush Administration and many Americans grew increasingly sensitive to harms, real and imagined, from terrorism

Dick Cheney declared, “If there's a 1% chance that Pakistani scientists are helping al-Qaeda build or develop a nuclear weapon, we have to treat it as a certainty in terms of our response. It's not about our analysis ... It's about our response.” The invasion of Iraq was predicated, in part, on the idea that 9/11 “changed everything,”

One concerns the field of psychology and its incentives. “It could be argued that just as successful species increase their territory, invading and adapting to new habitats, successful concepts and disciplines also expand their range into new semantic niches,” he theorizes. “Concepts that successfully attract the attention of researchers and practitioners are more likely to be applied in new ways and new contexts than those that do not.”

Concept creep can be necessary or needless. It can align concepts more or less closely with underlying realities. It can change society for better or worse. Yet many who push for more sensitivy to harm seem unaware of how oversensitivty can do harm.

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.

NIMROD NOVIK: The group that worked on it, called Commanders for Israel’s Security, it’s over 500 Israeli retired generals, as well as their equivalents from the Mossad, Shin Bet Security, National Security Council, the entire Israeli security establishment. And we formed a team. We felt that Israeli policy was far too reactive and far too conservative for the good of the country, national security, short and long-term.

We had not anticipated the trauma of Oct. 7, but we certainly anticipated things getting from bad to worse, unless Israel changes course.

we came up with a plan that suggested even though a two-state solution, as you said, is not on this side of the horizon, but given that eventually, it’s the only solution that we believe serves Israel’s security and well-being long-term, as a strong Jewish democracy, we mapped out what can and should be done in the coming two, three years to reverse the slide towards the disaster of a one-state solution.

NIMROD NOVIK: There were primarily two governing concepts, if you will, of the Israeli policy. Again, calling it policy is giving it more credit than deserved. Israeli government have been reluctant to determine the end game of our relationship with the Palestinians. Where do we want to see ourselves and them two years, five years, 50 years from now? No decision has been made since the Oslo era.

As a result, what we’ve seen was a policy based on insisting on separating the Gaza Strip, ruled by Hamas from the West Bank, ruled sort of by the Palestinian Authority. Separation was one principle

And the other one was dubbed status quo, even though it was an illusion, because nothing was static about it. As a matter of fact, creeping annexation has been accelerating under various governments.

The more territory was taken by settlements, the more extreme settlers were conducting violent raids into Palestinian civil populations. The more the Palestinian Authority, internally defective, becoming more and more authoritarian, more and more detached from its own constituents, less responsive, less capable of governance, losing control over large swaths of West Bank territory, forcing the I.D.F. to enter more and more

It was a slide into a state where the Palestinian Authority would cease to function as the promise of the nucleus of a Palestinian state.

If we look at it today, it’s already perhaps the municipal government of the city of Ramallah, rather than of the West Bank, and weakening the Palestinian Authority by choking it financially. By not allowing it to demonstrate to its people that it is the vehicle that will bring them one day to their aspiration of statehood, on the one hand, and making sure that Hamas controls Gaza, the two tracks spelled disaster.

So I must confess, we had not anticipated that the disaster will look the way it did on Oct. 7, but we certainly realized that the policy in Gaza of rounds of violence every year, every two years, every 18 months, and buying off relative tranquility by funding Hamas through the auspices of Qatar, allowing it to arm and rearm, the inherent contradictions in the policy were quite apparent

There’s a right-wing one-state solution. I think when you mentioned the finance minister, Bezalel Smotrich, I think if you read things he has written in the past, he is looking for a one-state solution. He wants to crush Palestinian dreams of statehood and repress Palestinians sufficiently that they stop believing they can ever have anything better and eventually content themselves to Israeli rule and live quietly within that in order to gain better lives.

NIMROD NOVIK: I’ll put it bluntly. I believe that a two-state solution is inevitable, not because we wish it and not because it’s nice, not because Palestinians deserve self-determination — which they do, but that’s not a historic imperative. I believe that the two-state solution is inevitable because these two people are not going to live happily ever after under one roof.

For that to happen, for the two people to stay in one state, one of two things have to happen. Either Israelis will agree to grant Palestinian equal rights in that one state and therefore become a minority, or at least, a slim majority in our own country, and that’s never going to happen. Israelis are not going to agree to be less than the overwhelming majority in our own country.

Or Palestinians will agree forever to forgo equal rights, which I suspect is as unreasonable expectation as the other. So we will separate.

NIMROD NOVIK: Civil separation with overall security control — continued security control until a two-state agreement ushers in alternative security arrangements, is a concept that basically suggests reversing the creeping annexation, which is no longer creeping. It’s now galloping.

So the idea is to start reversing the slide towards one-state reality in the opposite direction, of reducing the friction between the two populations, increasing the capacity of the P.A. to perform, while maintaining the overall security controlled by Israel until a deal is struck.

You often hear when you talk to people in Israel about different paths that could be taken. Well, we don’t have anybody to negotiate with. The Palestinian Authority doesn’t have credibility. Hamas wants our destruction

And a core premise of the report is that there are things Israel can do unilaterally, that it doesn’t need a partner to do things that will make the situation better from its perspective and create conditions maybe for deals in the future. So tell me what is in Israel’s power here. What would you actually recommend to do tangibly?

NIMROD NOVIK: It’s not a genetic deformation of the Palestinians that they cannot govern themselves. This is nonsense. We had a period after the second intifada, the years 2007, 2008, where the Palestinian Authority, there was a prime minister by the name of Salam Fayyad. First, he was finance minister, later on prime minister, who revitalized the Palestinian Authority in a dramatic way. The authority was on the rise. People were proud in it, its own population. They could have won elections at that point.

And then Netanyahu was elected in 2009. Now, obviously, we are the strongest party. We hold most of the cards by far. And when we decide that we are going to choke the Palestinian Authority, the Palestinian Authority will choke

Now the second trend that happened was that Mahmoud Abbas, President Abbas, known as Abu Mazen, the early Abu Mazen was a very different person than the late one with whom we are dealing today. He became increasingly nondemocratic, authoritarian, autocratic, paranoid, removing from his vicinity and from position of power all the best and brightest that were working during that era

. Things went from bad to worse, Israel doing its share in weakening the P.A. and the P.A. leadership became more claustrophobic. All these can change.

At the moment, the West Bank is a Swiss cheese. It’s 169 islands of Palestinian-controlled areas surrounded each by Israeli-controlled territory. So we wanted to reduce that by half so that contiguity will have a security, law and order, and economic well-being effect.

We suggested a host of economic measures that enable the Palestinian Authority to deliver for the people, which is the opposite of what’s happening now, when our minister of finance is choking the Palestinian Authority by withholding funds that are theirs by the agreement Israel collects taxes for the Palestinian Authority, VAT and others. And we are supposed to automatically transfer them to the Palestinian Authority. It’s the main chunk of their budget.

Just around the headland is Sevastopol, the protected port and naval base where Tolstoy once served on the ramparts. During the Second World War, it was besieged and leveled by German bombers despite a heroic stand by the Soviet Army and partisans

An hour’s car ride away is Yalta, where czars vacationed and Chekhov wrote “The Cherry Orchard.”

In 1783, when Catherine the Great wrested control from the Tatar khan and the Ottoman Turks, hundreds of thousands of Tatars fled the advancing Russian armies. A century and a half later, in 1944, those who remained behind were scooped up by Stalin and deported to Central Asia

Has Crimea also now become a Sudetenland? Or is it just a Grenada? Some Western commentators have already suggested the former, comparing President Vladimir V. Putin’s dispatch of Russian forces to Hitler’s 1938 annexation of German-populated parts of Czechoslovakia.

In a poll carried out in late February by the independent, Moscow-based Levada Center, 43 percent of Russians called the overthrow of Mr. Yanukovych a violent coup and 23 percent labeled the developing situation a civil war. A plurality of respondents saw the entire affair as an orchestrated attempt by the West to draw Ukraine into its geopolitical orbit.

This interpretive frame may be hard to understand, but some things are not wrong just because Russians happen to believe them. Russian news crews were covering a real story in Ukraine: the chaotic dismantling of a legally sanctioned government, the quick breakdown of an agreed framework for new elections, and the creeping transformation of political disputes into ethnic ones.

he Crimean affair is a grand experiment in Mr. Putin’s strategy of equivalence: countering every criticism of his government’s behavior with a page from the West’s own playbook. If his government has a guiding ideology, it is not the concept of restoring the old Soviet Union. It is rather his commitment to exposing what Russian politicians routinely call the “double standards” of American and European foreign policy and revealing the hidden workings of raison d’état — the hardnosed and pragmatic calculation of interests that average citizens from Moscow to Beijing to New Delhi actually believe drives the policies of all great powers.

The United States typically interprets its own actions through the lens of its principles. It reads the principles of other countries from their behavior. In most instances that leads to precisely the hypocrisies that Russia, China and other countries find so easy to condemn.

First, the European Union, the United States, and Russia must all agree that the principal goal is to prevent greater violence

European and American officials must be clear on the reasons why the international community should band together to condemn Russian actions. It is not because of the violation of national sovereignty — a concept imperfectly defended by Americans and Europeans in recent years — but because Mr. Putin’s reserving the right to protect the “Russian-speaking population” of Ukraine is an affront to the basis of international order

It is Mr. Putin who has made ethnic nationalism a defining element of foreign policy.

The future of Ukraine is now no longer about Kiev’s Independence Square, democracy in Ukraine or European integration. It is about how to preserve a vision of Europe — and, indeed, of the world — where countries give up the idea that people who speak a language we understand are the only ones worth protecting.

the characters in the core three modern shows—Discovery, Picard, and Strange New Worlds—are less concerned with professionalism and duty and more concerned with personal morality, authenticity, and teamwork.

Dylan Roth, writing for Fanbyte, suggested that as Star Trek has aged, it has “changed from a series about benign authority to one about stalwart heroes protecting an institution from moral decay.” This is true enough, but I also think there’s something else going on with the modern Trek shows. Namely, the atmosphere and philosophy of the shows is much less comfortable with the maxims of professionalism and duty that were foundational to pre-2017 Star Trek media.

Modern Star Trek, much like older Star Trek, often presents its main characters as moral paragons, but whereas older Trek would usually depict them embodying Starfleet’s ideals in the presence of challenging aliens, modern Trek is more likely to establish their uprightness by contrast with the faceless and untrustworthy institution of Starfleet itself. Both eras do both things, at least occasionally, but the ratio has notably shifted.

WHY THE CHANGE? Part of it probably has to do with the other material that Star Trek writers are drawing from. The ’60s and ’90s-era Trek writers either served in the military themselves or were drawing from science fiction written by people who had. (Gene Roddenberry, the creator of Star Trek, and many of the great science-fiction authors of the mid-twentieth century, including Robert Heinlein, Arthur Clarke, Isaac Asimov, Frank Herbert, and Walter M. Miller Jr., each served in some capacity in the World War II-era U.S. or British armed forces.

difference in leadership style is everywhere between the two eras. The Strange New Worlds version of Capt. Christopher Pike has been repeatedly praised for being more collaborative than commanding (I struggle to remember a time he has ever raised his voice), whereas Capt. Sisko shouts at everyone, as only the great Avery Brooks can shout. Picard’s version of its title character trades all his twentieth-century grouchy gravitas for a more grandfatherly role; his inspirational speeches now seek to buoy his friends’ confidence rather than inspire subordinates to high achievement.

Old-school Sisko reminds his crew of the expectations he has for them and unsubtly critiques their behavior as unbecoming of Starfleet officers. He acknowledges their difficulties (“I know it’s hot. . .”), but leaves no doubt that he expects them to perform their duties as professionals anyway. New-school Tilly motivates her command by making it clear that she sees and hears their concerns, and encourages them to work together by seeing the value in their unique life experiences.

modern Trek writers are far less likely to have served—but are far more likely to have worked in twenty-first-century corporate America, which has a rather different set of norms and concepts of professionalism.

more fundamentally, popular science fiction today—as written by authors like N.K. Jemisin, Martha Wells, and Tamsyn Muir—is more likely to be concerned with questions of identity and combating imperialism. It is also more likely to be written from marginalized perspectives, which have valid reasons to distrust institutions and authority. For many of these writers, concepts like “professionalism” have questionable implications

Besides, nobody likes any of America’s institutions anymore (and for all that Star Trek is ostensibly international, it is a fundamentally American franchise). Gallup’s polling about Americans’ faith in U.S. institutions shows it hovering at or near record-breaking lows, spawning a great deal of hand-wringing from people across the political spectrum. These apparently untrustworthy institutions range from purely political ones (the presidency, the Supreme Court, etc.) to “the church or organized religion” (whatever that means), “banks,” and “newspapers.”

What are professionalism and duty if not the suppression of individual quirks in service of some larger goal or institution? Duty overrides individual desires or assessments of right and wrong.

But older Trek nevertheless believed in duty, because it believed that Starfleet was a fundamentally good institution, even if it may be failed by individual bad or misguided actors. It elevated Starfleet’s regulations and codes of conduct almost to the status of holy wri

it is difficult to be seriously inspired by the notion of duties if one has a deep distrust of the institutions that assign such duties.

Of course the characters written by twenty-first-century authors, who are animated by the same deep distrust of American institutions as the rest of us, are less likely to justify themselves with the language of duty than they are by reference to personal morality and authenticity. And of course they’re going to be skeptical of rank and hierarchy because they don’t believe these things are necessarily signs of actual merit or accomplishment any more than the rest of us do.

Yet I worry. If Star Trek is supposed to start from the assumption that Starfleet and the Federation are quasi-utopian, I worry about what it says about our collective imaginations if we can’t even let the institutions of that fictional utopia be utopian. If we can’t even trust Starfleet, who can we trust?

In the past 500 years, Russia has been invaded several times from the west. The Poles came across the European Plain in 1605, followed by the Swedes under Charles XII in 1707, the French under Napoleon in 1812, and the Germans—twice, in both world wars, in 1914 and 1941.

In Poland, the plain is only 300 miles wide—from the Baltic Sea in the north to the Carpathian Mountains in the south—but after that point it stretches to a width of about 2,000 miles near the Russian border, and from there, it offers a flat route straight to Moscow. Thus Russia’s repeated attempts to occupy Poland throughout history; the country represents a relatively narrow corridor into which Russia could drive its armed forces to block an enemy advance toward its own border, which, being wider, is much harder to defend.

On the other hand, Russia’s vastness has also protected it; by the time an army approaches Moscow, it already has unsustainably long supply lines, which become increasingly difficult to protect as they extend across Russian territory. Napoleon made this mistake in 1812, and Hitler repeated it in 1941.

Just as strategically important—and just as significant to the calculations of Russia’s leaders throughout history—has been the country’s historical lack of its own warm-water port with direct access to the oceans.

Many of the country’s ports on the Arctic freeze for several months each year. Vladivostok, the largest Russian port on the Pacific Ocean, is enclosed by the Sea of Japan, which is dominated by the Japanese

it prevents the Russian fleet from operating as a global power, as it does not have year-round access to the world’s most important sea-lanes.

when protests in Ukraine brought down the pro-Russia government of Viktor Yanukovych and a new, more pro-Western government came to power, Putin had a choice. He could have respected the territorial integrity of Ukraine, or he could have done what Russian leaders have done for centuries with the bad geographic cards they we

Enter Ivan the Terrible, the first tsar. He put into practice the concept of attack as defense—consolidating one’s position at home and then moving outward

He extended his territory east to the Ural Mountains, south to the Caspian Sea, and north toward the Arctic Circle. Russia gained access to the Caspian, and later the Black Sea, thus taking advantage of the Caucasus Mountains as a partial barrier between itself and the Mongols.

Now the Russians had a partial buffer zone and a hinterland—somewhere to fall back to in the case of invasion. No one was going to attack them in force from the Arctic Sea, nor fight their way over the Urals to get to them

to invade it from the south or southeast you would have to have a huge army and a very long supply line, and you would have to fight your way past defensive positions.

In the 18th century, Russia, under Peter the Great—who founded the Russian Empire in 1721—and then Empress Catherine the Great, expanded the empire westward, occupying Ukraine and reaching the Carpathian Mountains.

Now there was a huge ring around Moscow; starting at the Arctic, it came down through the Baltic region, across Ukraine, to the Carpathians, the Black Sea, the Caucasus, and the Caspian, swinging back around to the Urals, which stretched up to the Arctic Circle.

Two of Russia’s chief preoccupations—its vulnerability on land and its lack of access to warm-water ports—came together in Ukraine in 2014

early Russia, known as the Grand Principality of Moscow, was indefensible. There were no mountains, no deserts, and few rivers.

He chose his own kind of attack as defense, annexing Crimea to ensure Russia’s access to its only proper warm-water port, and moving to prevent NATO from creeping even closer to Russia’s border.

From the Grand Principality of Moscow, through Peter the Great, Stalin, and now Putin, each Russian leader has been confronted by the same problems. It doesn’t matter if the ideology of those in control is czarist, communist, or crony capitalist—the ports still freeze, and the European Plain is still flat.