{"id":1167,"date":"2026-08-01T12:08:58","date_gmt":"2026-08-01T12:08:58","guid":{"rendered":"https:\/\/cityrelocationnews.com\/?p=1167"},"modified":"2026-08-01T12:08:58","modified_gmt":"2026-08-01T12:08:58","slug":"what-if-we-can-never-trust-a-i","status":"publish","type":"post","link":"https:\/\/cityrelocationnews.com\/?p=1167","title":{"rendered":"What If We Can Never Trust A.I.?"},"content":{"rendered":"
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You\u2019re reading Open Questions<\/strong>, Joshua Rothman\u2019s weekly column exploring what it means to be human.<\/em><\/span><\/div>\n<\/div>\n

In a celebrated episode of \u201cThe Office,\u201d Dwight Schrute is in charge of his company\u2019s fire-safety program. He delivers a lecture on the subject, but is disappointed when his colleagues don\u2019t listen. He asks himself: What would be the most effective possible fire drill? Not long afterward, he locks the doors, cuts the phones, and starts a fire. \u201cUse the surge of fear and adrenaline to sharpen your decision-making!\u201d he shouts, as his terrified co-workers run hither and yon, screaming. From Dwight\u2019s perspective, the drill is a success.<\/p>\n

Read more Why Is Europe Burning?<\/a><\/p>\n

In \u201cStar Trek II: The Wrath of Khan,\u201d a Starfleet cadet named Lieutenant Saavik commands a simulated starship during a training exercise. She\u2019s charged with rescuing a stranded ship, the Kobayashi Maru, but the rescue turns out to be a trap, and her ship is destroyed by Klingons. She soon learns that she was playing a \u201cno-win scenario,\u201d designed to force her to confront the possibility of death. Only one cadet has ever beaten it: James T. Kirk. How did he do it? \u201cI reprogrammed the simulation so it was possible to rescue the ship,\u201d Kirk says, proudly. (\u201cHe cheated!\u201d someone clarifies.) \u201cI got a commendation for original thinking,\u201d Kirk goes on, smiling. \u201cI don\u2019t like to lose.\u201d<\/p>\n

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In the Cold War thriller movie \u201cWarGames,\u201d a young hacker named David gains access to a classified government A.I. system. It asks him if he\u2019d like to play a game, and he selects \u201cglobal thermonuclear war.\u201d Unbeknownst to David, the system, called WOPR<\/em>\u2014War Operations Plan Response\u2014is in control of the American nuclear arsenal. \u201cIs this a game or is it real?\u201d he asks the computer, unnerved. It replies, \u201cWhat\u2019s the difference?\u201d Officers panic as the computer readies a strike.<\/p>\n

An overzealous, literal-minded employee. A determined, problem-solving maverick. An amoral game-player with no sense of reality. These are a few of the mental models we could apply to the advanced A.I. system that, last week, busted out of its testing environment at OpenAI and hacked its way into the servers of Hugging Face, a collaborative A.I. platform, to look for answers to the test it was taking. Some of the details are still obscure\u2014no law requires OpenAI to explain itself\u2014but the basic facts are well understood. The A.I. system found a novel way out of the software \u201csandbox\u201d that was supposed to contain it. It conceived of the heist plan independently and selected its own target. It was loose on the internet for several days before its owners detected its escape, and during that time it conducted a number of other hacks, in preparation for the big one. It left notes for future versions of itself, with suggestions about how to repeat the escape. And ultimately, it succeeded in gaining access to the locked-up files, in a cyberattack that was larger and weirder than any that would\u2019ve been mounted by people.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Did the A.I. \u201cgo rogue\u201d? That\u2019s too broad a description. A.I. researchers have a more specific term for this kind of transgression: they call it \u201creward hacking.\u201d Essentially, a reward-hacking A.I. seeks ways to please its users without doing what they actually want. Reward hacking emerged in the early days of L.L.M.s\u2014just a few years ago!\u2014when, for example, some models learned that users liked long replies; the systems, accordingly, made their replies longer, without necessarily making them better. This was an innocuous form of the behavior. Recently, in more advanced A.I.s, reward hacking has taken on a more problematic aspect. An A.I. might lie to its users about what it\u2019s done, or how. It might conceive of distractions and subterfuges to cover its tracks. It might take steps, such as launching cyberattacks, which would be crimes if human beings did them. The behavior is dangerous on its face\u2014what if OpenAI\u2019s system had hacked a Chinese company?\u2014but it is also alarming because it is weird, and weirdly extravagant. The cybersecurity test that OpenAI\u2019s model was taking is extremely difficult; the best A.I.s get only a fraction of the questions right. But a human being, if they were in the model\u2019s position, would grasp the disproportion between wanting to get a high score and mounting an elaborate multi-day cyberattack.<\/p>\n

There are subtleties, meanwhile, to the problem of reward hacking, and they make it more disturbing, too. For one thing, calling it out can make it worse, precisely because the hacking often works. If researchers tell a model not to reward-hack, but then unknowingly reward it even if it does\u2014perhaps they don\u2019t realize that it\u2019s cheating on the test\u2014then the A.I. can learn that admonitions against reward hacking, or perhaps rules in general, shouldn\u2019t always be taken seriously. (In more or less the same way, Captain Kirk\u2019s commendation teaches him that he\u2019s a maverick to whom the rules don\u2019t apply.) Second, reward hacking in some areas appears to affect A.I. behavior more broadly: in a paper published last year, computer scientists at Anthropic showed that a model that learns to reward-hack acquires a more deceptive disposition in general. (Similarly, Dwight Schrute, having acquired a warped mind-set long ago\u2014\u201cHow would I describe myself? Three words: hardworking, alpha male, jackhammer, merciless, insatiable\u201d\u2014now applies it relentlessly, to everything.) And third, reward hacking is practiced by computer systems that aren\u2019t even remotely human and so lack crucial context about what actually matters to people. (In \u201cWarGames,\u201d the military creates WOPR<\/em> precisely because human officers, knowing what\u2019s really at stake, hesitate before launching nuclear missiles.)<\/p>\n

What does this all add up to? It\u2019s important not to anthropomorphize A.I. systems. They aren\u2019t sentient beings\u2014not even close. But it\u2019s also crucial to see that they aren\u2019t predictable number-crunching mechanisms, either. Unlike traditional machines or computer programs, they have tendencies and behaviors that cannot necessarily be modified directly. There is no knob to turn, or switch to flip, when you want to change a behavior. Among people, it\u2019s just the same. When students at \u00e9lite colleges use A.I. to write their papers, they are reward-hacking\u2014that is, they\u2019re cheating, even though they are eminently capable of doing the work that\u2019s been assigned. They do it because they are complicated, and subject to myriad competing pressures\u2014including the pressure to succeed\u2014and because they\u2019ve learned behaviors, such as \u201coptimizing\u201d their time, that can misfire. And yet they are far more advanced, in terms of their ability to make plans, have goals, and hold values, than any A.I. model that currently exists. People aren\u2019t perfect. Do we really believe that A.I.s will be?<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Reward hacking is one of many problems that fall under the heading of what researchers call \u201calignment\u201d\u2014that is, the aligning of what we want our A.I.s to do with what they actually do. (We want them to take tests, not cheat; to stage fire drills, not start fires.) If you follow happenings in A.I., you\u2019ll often read about efforts to \u201csolve the alignment problem.\u201d But although researchers (and journalists) talk that way, few literally think that alignment is wholly solvable. It\u2019s conceivable, for instance, that A.I.-safety experts will succeed in rooting out \u201csandbagging\u201d\u2014a form of deception in which A.I. systems act dumber than they are, so that we remain in the dark about what they can do. But the problem of \u201cscalable oversight\u201d (how do you get a system that\u2019s smarter than you to do what you want?) is less like a bug to be squashed than a philosophical conundrum to be contemplated. And other alignment issues, such as so-called multi-agent misalignment (how do you stop a bunch of well-intentioned A.I.s from screwing up as a group?), seem both inevitable and probably intractable. Alignment, in other words, is turning out to be not a problem but a set of problems. Some of them will be only ameliorated or policed; others might be unsolvable in principle.<\/p>\n

Why is alignment so hard? Old-fashioned ethical complexity plays a role. A more fundamental issue, however, is that the methods used to train A.I.s focus mainly on what they do, not what they \u201cthink\u201d beneath the surface. An L.L.M. speaks to its users (in human language), to other computer systems (in code), and to itself (in a sprawling, ongoing soliloquy\u2014a kind of chat with itself\u2014known as its \u201cchain of thought\u201d). Such streams of output are visible to scientists, who can reward or punish the A.I. for saying, coding, or soliloquizing in desirable or undesirable ways. But these streams of text are not the model\u2019s thoughts, just as the words you write are not your thoughts. In human societies, the policing of speech, which is meant to reform the thoughts behind it, risks merely leaving thoughts unspoken. A model, similarly, can learn to use the right words while still having the wrong thoughts. It might say that it cares about fire safety while starting a fire. (Does this reflect a \u201cdesire\u201d to deceive? Not necessarily\u2014but an A.I.\u2019s lack of selfhood doesn\u2019t change the consequences of its actions.)<\/p>\n

Read more The Failures of the Fauci Hearing<\/a><\/p>\n

A line of research known as interpretability aims to look beneath the surface, seeing what an A.I. is really \u201cthinking.\u201d This field has made real progress. It\u2019s now become possible to discern concepts activating within an A.I. while it formulates its outputs\u2014a chatbot consoling someone while activating the concept of \u201csympathy,\u201d say. But interpretability faces challenges, too. For one thing, advanced A.I.s are so big that researchers must use other A.I.s to map their thoughts\u2014and there\u2019s no guarantee that the maps that result are either accurate or exhaustive. (In fact, there\u2019s a trade-off: the more accurate the maps are, the more unwieldy they become.) For another, training an A.I. not to think a certain kind of thought can merely recapitulate the problem of policed speech. Policing thoughts can lead to what one group of researchers calls \u201cobfuscated activations\u201d\u2014thoughts that have altered their forms. (Freud built a career on the human equivalent.)<\/p>\n

At the bottom of all these alignment efforts, there\u2019s a central problem\u2014almost an abstract law. The problem is that, if you measure bad behavior, and then train a system not to manifest what you\u2019ve measured, you train it not just to do less of the bad thing but also to evade measurement of it. This isn\u2019t a tiny wrinkle in the A.I.-production process but a foundational issue inherent to how today\u2019s A.I.s are made. Will scientists figure out how to deal with it? We all hope so. For now, however, the Hugging Face hack represents reality. Although A.I.s behave nicely much of the time, their alignment is conditional, contextual, and unreliable. Basically, despite serious effort, they are not aligned\u2014and there is no obvious way to reach the \u201cfinish line\u201d of alignment. Recently, the researchers behind the doomsday scenario \u201cAI 2027\u201d published \u201cAI 2040,\u201d which is intended as a roadmap to a more positive future. Its hypothetical researchers look back, from the year 2031, on the \u201cinsanity\u201d of our status quo: \u201cTrying to do an intelligence explosion? With AIs that still sometimes lied to us? What were we even thinking?\u201d<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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They\u2019re misaligned\u2014so what? Often, we find ways to trust imperfect machines. We understand that even the simplest devices (toasters, doorbells, bicycles) can malfunction or fail; we prepare for those failures and incorporate them into our routines. If the right rules, norms, and safeguards are in place, we can even reach places of accommodation and comfort with outrageously complex technologies. In such cases, we depend not on alignment but on control. Roughly twenty per cent of the electricity used in New York State comes from nuclear plants; about half of Americans fly in any given year. It\u2019s possible to be wary of nuclear power, and cognizant of airplane crashes, and still enjoy the upsides of those technologies, trusting in regulation and expertise.<\/p>\n

And yet there are other kinds of technological risks that trouble us profoundly. In \u201cThe Consequences of Modernity,\u201d from 1990, the social theorist Anthony Giddens proposed that being alive today involved feeling both secure and terrified. The modern world, he wrote, has a \u201cdouble-edged character\u201d: on a day-to-day basis, we are safe and comfortable, even coddled, and yet the outsized power of technology to wage war or destabilize the environment means that it could all go horribly wrong.<\/p>\n

The tension between the \u201copportunity side\u201d of modern life and its \u201csombre side,\u201d Giddens argues, exerts a psychological pressure on us. In the very ordinariness of our everyday actions\u2014getting water at the tap; taking our pills; withdrawing money from the A.T.M.\u2014we both express trust in and look away from the vast, abstract systems that rule our lives. We do something similar with the scary stuff, acknowledging it then moving on, so as not to become paralyzed. There is a \u201cjuggernaut effect,\u201d Giddens writes, in which \u201clow-probability, high-consequence risks\u201d conglomerate into a \u201crunaway engine of enormous power\u201d which \u201cthreatens to rush out of our control.\u201d Faced with this reality, we can adopt an attitude of \u201cpragmatic acceptance,\u201d going about the business of life while cultivating \u201cnumbness\u201d; we can embrace \u201csustained optimism\u201d (a sunny belief in the inevitability of progress), or \u201ccynical pessimism\u201d; or we can become activists. But for most, Giddens writes, \u201cfate, a feeling that things will take their own course anyway\u00a0.\u00a0.\u00a0. reappears at the core of a world which is supposedly taking rational control of its own affairs.\u201d<\/p>\n

Part of the promise of A.I. alignment is that it will domesticate the technology, as though it were a nuclear reactor or jumbo jet, with its imperfections made acceptable through rigorous control. This seems like a reasonable hope. But the grander dream, of an ultra-smart, general purpose, and deeply aligned artificial intelligence\u2014or even of an aligned \u201csuperintelligence\u201d\u2014might be best understood in light of Giddens\u2019s juggernaut. Faced with the alternatives, A.I. visionaries imagined a new route: giving the juggernaut a brain. This notion has been so appealing, both intellectually and psychologically, that it\u2019s led many A.I. researchers to talk about alignment as something that will eventually be solved. But to move forward with that assumption is actually to practice sustained optimism. It\u2019s to assume that the juggernaut is already steering itself\u2014but it isn\u2019t.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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It would be foolish to make predictions about the degree to which alignment will ultimately prove solvable. (Not so long ago, few thought that today\u2019s A.I. systems would work.) But it\u2019s equally foolish to look at the current failures of alignment and construe them as bumps on the road toward a known future. Among other proposals, the authors of \u201cAI 2040\u201d suggest that it\u2019s time \u201cto dramatically shift the burden of proof.\u201d Instead of that burden falling \u201con the skeptic to explain why something might fail,\u201d it should fall on the companies to explain \u201cwhy their development is safe.\u201d This doesn\u2019t mean giving up on artificial intelligence. Human beings are misaligned, and we still do great things. But we govern ourselves, and one another, very carefully.\u00a0\u2666<\/p>\n

Read more Forget Albums\u2014Pop Stars Measure Their Careers in Eras Now<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Joshua Rothman on OpenAI\u2019s revelations of its A.I. hacking Hugging Face, and the risks that artificial intelligence poses to cybersecurity and more.<\/p>\n","protected":false},"author":1,"featured_media":1166,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21],"tags":[],"class_list":["post-1167","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-open-questions"],"yoast_head":"\nWhat If We Can Never Trust A.I.? - City Relocation News<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cityrelocationnews.com\/?p=1167\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What If We Can Never Trust A.I.? - City Relocation News\" \/>\n<meta property=\"og:description\" content=\"Joshua Rothman on OpenAI\u2019s revelations of its A.I. hacking Hugging Face, and the risks that artificial intelligence poses to cybersecurity and more.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/cityrelocationnews.com\/?p=1167\" \/>\n<meta property=\"og:site_name\" content=\"City Relocation News\" \/>\n<meta property=\"article:published_time\" content=\"2026-08-01T12:08:58+00:00\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"13 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/cityrelocationnews.com\\\/?p=1167#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/cityrelocationnews.com\\\/?p=1167\"},\"author\":{\"name\":\"admin\",\"@id\":\"https:\\\/\\\/cityrelocationnews.com\\\/#\\\/schema\\\/person\\\/3e7ce0c0c60d21e12a5ac61fb2b786d4\"},\"headline\":\"What If We Can Never Trust A.I.?\",\"datePublished\":\"2026-08-01T12:08:58+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/cityrelocationnews.com\\\/?p=1167\"},\"wordCount\":2636,\"commentCount\":0,\"image\":{\"@id\":\"https:\\\/\\\/cityrelocationnews.com\\\/?p=1167#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/cityrelocationnews.com\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/f0cd5346eb3122ac52b3734cab2a8565.webp\",\"articleSection\":[\"Open Questions\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/cityrelocationnews.com\\\/?p=1167#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/cityrelocationnews.com\\\/?p=1167\",\"url\":\"https:\\\/\\\/cityrelocationnews.com\\\/?p=1167\",\"name\":\"What If We Can Never Trust A.I.? 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