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Arvind Narayanan has spent years puncturing Silicon Valley\u2019s grandest claims about artificial intelligence. The Princeton computer-science professor co-wrote AI Snake Oil<\/em>, a book that challenges the notion that algorithms can reliably predict who will be a good employee, which patients will get sick, or who might commit crimes. He has also pushed back on the idea that generative AI is about to eliminate vast swaths of white-collar work, calling work something like a \u201csandwich\u201d whose bun is growing even as the meat shrinks.<\/p>\n But Narayanan does not dismiss the public\u2019s mounting hostility toward AI. He thinks the backlash is real, understandable\u2014and far more complicated than any one thing. It\u2019s a coalition of different fears, he said: \u201cmany different kinds of anxieties have all kind of pushed together into one sort of generalized opposition to AI.\u201d<\/p>\n What looks like AI phobia, he argued, is really a collection of anxieties about fear of job loss; distrust of powerful technology companies; anger over the influence of billionaires; concern about environmental costs; unease over the technology\u2019s social effects; and, for younger people, uncertainty over what skills they need to retain in a labor market increasingly built around AI.<\/p>\n Narayanan\u2019s critique is not that generative AI is useless, or that workers should refuse to use it\u2014and he stressed that his \u201csnake oil\u201d criticism largely does not extend to generative AI. He said he views AI as a potentially transformative technology that knowledge workers can already use to research, challenge assumptions, analyze data, and build software. His warning targets a different class of AI claims: systems marketed as capable of making high-stakes predictions about people.<\/p>\n Hospitals, insurers, human-resources departments, and criminal-justice systems have all adopted or considered machine-learning systems meant to forecast future behavior or outcomes. Narayanan is skeptical of those applications because the future is inherently hard to predict\u2014and because dubious forecasts can drive consequential decisions about hiring, coverage, bail, or policing.<\/p>\n \u201cGenerative AI, we do criticize for some of the hype that attaches to it,\u201d he said. \u201cBut we\u2019re also very clear that this is a technology that is very useful for every knowledge worker.\u201d<\/p>\n Narayanan has argued with his sandwich metaphor that the near-term workplace consequence may be more complicated than AI just eliminating jobs: AI can expand the layers of checking, supervision, and verification required to use it responsibly. In adversarial fields such as law, for instance, one side\u2019s AI-enabled productivity can compel the other side to match it, so the total volume of work keeps expanding rather than shrinking.<\/p>\n The bigger risk, he said, may be a workplace in which people still have jobs but are relegated to what he called \u201cjanitorial work\u201d \u2014 his phrase, but he acknowledged that others had circled the same idea with different words. Monitoring systems perform much of the intellectual labor while workers absorb the blame when something goes wrong. <\/p>\n He also invoked a second term for that arrangement: the \u201cmoral crumple zone,\u201d a phrase more widely used in AI-ethics circles. Just as crumple zones in cars absorb impact to protect the vehicle, the person nominally in charge becomes the one punished for the failure of an automated system they lack the visibility or authority to truly control.<\/p>\n \u201cIt\u2019s not inevitable,\u201d Narayanan said of that outcome. \u201cThere are many design choices throughout the AI pipeline.\u201d<\/p>\n That prospect helps explain why opposition to AI cannot be reduced to generic anxiety about change, Narayanan said. The same technology can empower one profession and alienate another.<\/p>\n Software developers can work with AI interactively\u2014asking it to find bugs, testing its output, and incorporating suggestions throughout a project. The human stays in the loop. Narayanan calls this a \u201cgrowth cycle,\u201d as opposed to a \u201cdependence spiral,\u201d in which users delegate mundane tasks but retain the expertise to evaluate the system and do the essential thinking themselves.<\/p>\n For artists, the experience is often starkly different. A prompt produces a finished image, creating the impression that the system has skipped over the human creative process rather than supporting it. \u201cThere are genuine reasons, based on the way that AI has been designed, that different professions understandably have very different reactions,\u201d Narayanan said.<\/p>\n AI also poses a distinct problem for the students Narayanan teaches, both undergraduate and graduate. They are expected to become fluent in tools they\u2019ll encounter in the workplace, particularly in computing and other knowledge-intensive fields. But leaning too heavily on AI can deprive them of the foundational skills needed to judge whether a system\u2019s output is actually right.<\/p>\n \u201cThey\u2019re in a bind,\u201d Narayanan said. \u201cTo what extent should you be using AI versus resisting it to build up your own skills?\u201d<\/p>\n That conflict is especially acute because faculty haven\u2019t settled on an answer \u2014 and Narayanan said faculty are often \u201cclueless\u201d and lack \u201cbravery\u201d on the subject. Universities are still working out what a healthy integration of AI into instruction looks like, he added, and that uncertainty itself breeds anxiety among students who feel they have little choice but to adapt.<\/p>\n Narayanan\u2019s diagnosis isn\u2019t a case for resignation. He described himself as an optimist \u2014 not the kind of techno-optimist who believes innovation naturally produces good outcomes if critics and regulators simply get out of the way, but one whose optimism is contingent on people continuing to push back.<\/p>\n \u201cI think tech has generally in the past led to good outcomes,\u201d he said, \u201cbut only because there were a lot of people worrying about what could go wrong and because we were able to regulate things in time.\u201d<\/p>\n He has a preferred analogy for where this ends up: AI is going to do for cognitive work what cranes did for physical work. We still build skyscrapers; we just don\u2019t carry the steel ourselves. We could have built autonomous cranes if we wanted to, he said, but we decided that was too dangerous. The person operating the crane still decides where the beam goes.<\/p>\n \u201cIt\u2019s never too late,\u201d he said when asked whether AI phobia has hardened into something irreversible. \u201cI think some negative impacts have already materialized, but even those can be reversed.\u201d<\/p>\n A less flattering theory, however, is buried in Narayanan\u2019s own case study. He is a tenured computer scientist who has spent decades training himself to break problems into parts, evaluate evidence, and think rigorously before ever touching a keyboard. That is precisely the muscle that he says AI rewards rather than replaces: he uses it \u201cto go deeper,\u201d not \u201cto go faster,\u201d because he knows how to do everything himself. But many of his students\u2014and many professionals in the workforce\u2014can\u2019t go deeper with these tools yet.<\/p>\n When his own children, ages 4 and 7, wanted to learn a new topic, he didn\u2019t need a course or a consultant. He built them a custom app in 15 minutes\u2014one of roughly 50 he has made for them, including a phonics tool that helped his son start reading at age three\u2014because he already understood what good pedagogy looked like and simply used AI to execute it faster. He argued for a world where every parent can design AI-powered tools that help their children learn new things, suited to each parent and each child\u2019s particular style. \u201cThat\u2019s a whole big part of my life now,\u201d he said, \u201cand I myself use AI heavily for learning, and every day I come into work feeling like I have superpowers that even the projects that, you know, many of the projects that I\u2019m doing today would have been hard to even conceive of five years ago.\u201d<\/p>\n This is a rare skill set, Narayanan acknowledged. Most people, including plenty of comfortable, credentialed professionals, do not have it. And that may be the least discussed driver of AI phobia: not fear of the technology itself, but a quiet suspicion that the technology will entrench intellectual inequality \u2014\u00a0because many people do not know how to think the right way to use the tool. Narayanan effectively agreed with this when pressed. It\u2019s not that people fear thinking machines. It\u2019s that AI exposes, in real time, who already knows how to think.<\/p>\n Still, Narayanan insisted that real progress is being dismissed. \u201cI think it\u2019s tragic to me that that story of how it\u2019s giving us superpowers is being missed in all the narratives that are going around.\u201d<\/p>\n For this story,\u00a0<\/em>Fortune\u00a0journalists used generative AI as a research tool. An editor verified the accuracy of the information before publishing.<\/em><\/p>\n<\/div>\n #Princetons #Snake #Oil #author #real #fear #isnt #thinking #machines<\/p>\n","protected":false},"excerpt":{"rendered":" Arvind Narayanan has spent yea… <\/p>\n","protected":false},"author":1,"featured_media":2623,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[4993,535,4994,546,4995,717,4991,62,4992,1970],"class_list":["post-2622","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-finance-news","tag-author","tag-disruption","tag-fear","tag-isnt","tag-machines","tag-oil","tag-princetons","tag-real","tag-snake","tag-thinking"],"yoast_head":"\n\nSnake oil, redefined<\/h2>\n
The \u2018moral crumple zone\u2019<\/h2>\n
Why programmers and artists see it differently<\/h2>\n
Students caught in a bind<\/h2>\n
An optimist, not a booster<\/h2>\n
The skill he isn\u2019t naming<\/h2>\n