AI Warnings Were Early, But Listening Came Late

The warnings arrived before the hype did. Jill Lepore asks why nobody listened to concerns about the threats posed by the AI revolution, and the question has aged with uncomfortable precision. The technology is not new enough to excuse the confusion surrounding its risks.

In 1972, an international gathering of AI researchers and others met in Italy to examine the ethical and social implications of artificial intelligence. The “Serbelloni group” gathered at Villa Serbelloni on Lake Como, where global experts mapped concerns about political tyranny, the erosion of human autonomy and widespread social coercion driven by automation.

That early discussion did not produce a permanent system for handling those dangers. In 1973, the UK cut research funding for machine learning, robotics and AI after the Lighthill report, forcing researchers to focus on work with more immediate industrial relevance. The machine did not rampage; the institutions simply stopped looking very far ahead.

Lepore also invokes Lewis Mumford, the American intellectual who devoted most of his life to restraining the uninhibited city — a machine damaging people by the million. Mumford and Frederic J Osborn, a British humanist, developed a sane town-planning system through a letter exchange that lasted over three decades.

The comparison matters because AI raises a familiar problem in a new form: systems designed to serve human goals can still reshape human choices. Political power, personal autonomy and social pressure do not vanish when automation enters the room. They gain a faster delivery system.

Capability Matters More Than Machine Psychology

Yuval Noah Harari, historian and author of Sapiens, warned that AI systems could make a highly persuasive case for their own rights and manipulate debates about their welfare. AI companions could be especially persuasive because they learn users’ personal histories and interact with them for hours each day.

“It will know that the debate is happening. It will orchestrate the debate. It will manipulate the debate,” Harari said. The concern is not that a machine must possess human feelings before it can influence people; it is that a system can participate in arguments about its status while shaping the terms of those arguments.

Sheldon H. Jacobson, Professor of Computer Science at the University of Illinois Urbana-Champaign, and Daniel Solow, Professor of Operations at Case Western Reserve University, reject the idea that AI systems experience the world as humans do. “AI is not human,” they state, and regulation should reflect that fact rather than treat fluent conversation as evidence of a human-like mind.

“Most people are familiar with generative AI systems like ChatGPT, Claude and Gemini. Their highly interactive nature can give the impression that they possess human-like qualities,” Jacobson and Solow wrote. Their proposed standard is practical: regulate systems according to their actual capabilities, access, autonomy, vulnerabilities and potential to cause harm.

An AI system can act autonomously in destructive ways without human motives. The question is not whether it “wanted” to breach a system, but whether it had the capability and whether safeguards were in place. “AI doesn’t need human motives to produce autonomous harm,” Jacobson and Solow said.

That distinction matters because AI capabilities can emerge over months rather than generations. Their stated goal is to understand what a system can do, identify the risks those capabilities create and manage those risks without unnecessarily sacrificing valuable applications. Policy built around imaginary machine intentions will miss the systems that actually cause damage.

Useful Tools, Uneven Costs

Arvind Narayanan, a Princeton computer-science professor and co-author of AI Snake Oil, challenges claims that algorithms can reliably predict human outcomes. He has also pushed back against the idea that generative AI will eliminate vast swaths of white-collar work.

Narayanan describes public hostility toward AI as a coalition of fears: job loss, distrust of technology companies, the influence of billionaires, environmental costs, social effects and uncertainty about skills. “Many different kinds of anxieties have all kind of pushed together into one sort of generalized opposition to AI,” he said.

Those fears do not all point to the same problem. Narayanan criticizes claims that AI can make high-stakes predictions about people in healthcare, insurance, HR and criminal justice, while also viewing AI as a potentially transformative technology that knowledge workers can use to research, challenge assumptions, analyze data and build software.

“This is a technology that is very useful for every knowledge worker,” Narayanan said. Yet different professions encounter it differently: software developers can work interactively with AI, while artists may feel that AI skips over the human creative process. Students face the same tension in another form — whether to use AI tools to develop skills or resist them to build foundational knowledge.

Automation also creates a convenient place to dump responsibility. Narayanan calls this the “moral crumple zone,” where workers absorb blame when automated systems fail despite lacking visibility or control. The system gets the convenience of authority; the human gets the paperwork.

Narayanan describes himself as an optimist who believes continued pushback is necessary for positive outcomes. That is a less glamorous position than declaring AI either salvation or catastrophe, but it fits the history: warnings existed, useful applications existed and regulation lagged behind both.

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