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AI models don't kill people – people kill people

Recorded: Sept. 13, 2026, 2:09 p.m.

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AI models don't kill people – people kill people

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AI AND ML

AI models don't kill people – people kill people

AI fearmongers forget we could just jail tech execs until morale and model safety improve

Thomas Claburn

Thomas
Claburn

AI AND SOFTWARE REPORTER

Published
wed 9 Sep 2026 // 21:44 UTC

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OPINION Anthropic researcher Jacob Coxon publicly announced his resignation on X late Monday over concerns that AI "could kill us all by the end of the decade." A lot of people have expressed opinions about his point of view, leading to more than 110 million views of the message in less than 24 hours, perhaps helped along by X algorithms that boost messages critical of owner Elon Musk's AI rivals, Anthropic and OpenAI. But the real problem isn't the models themselves, but the companies who carelessly unleash them on the world and don't take any responsibility for what their products do
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Coxon's former colleague, science lead Evan Hubinger, insists his view is a fair assessment of what employees really think.
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"Jacob is correct here – we really do earnestly believe AI could kill all humans! I personally think it is >10 percent within the next decade," wrote Hubinger in a social media post. "I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to."(Aside: If you want a surefire bet on a prediction market, take the "no." If you're right, you get paid. If you're wrong, there's no one to pay. The problem of course is prediction market manipulation: Those betting against you might steer us toward the apocalypse to score a Pyrrhic victory.)There are good reasons to be concerned about the impact of AI. Coxon and Hubinger obviously have deep knowledge of the technology. But their broader concerns about how AI affects the world are unpersuasive.For example, Coxon said, "These will soon be superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources. … No other human activity poses this level of danger."Here's one: Human-induced climate change. In 2023, according to researchers, more than 178,000 deaths can be attributed to a global heat wave. "More than half (54.29 percent) of heatwave-related deaths were attributable to human-induced climate change," they claim.That's 96,636 deaths attributable to human activity – or perhaps lack of it – just in the context of a heat wave. The World Health Organization says, "Between 2030 and 2050, climate change is expected to cause approximately 250,000 additional deaths per year, from undernutrition, malaria, diarrhoea and heat stress alone." Some portion of that follows from human activity, perhaps including the construction of data centers that put millions of metric tons of carbon dioxide into the atmosphere annually.Commercial AI chatbots have allegedly played a role in a few dozen deaths, some of which were suicides – a small fraction of the 48,824 suicide deaths in 2024, per the CDC.
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Broad categories where AI is presumably doing measurable harm include warfare (e.g. AI-directed drones), AI-related medical errors, AI vision system failures in self-driving cars, and AI-driven social media – algorithmic incitement that can drive violence or shape policies that lead to conflict or death via global healthcare funding cuts. At the same time, some of that harm may be balanced on a statistical level by lives saved through AI tech.But Anthropic researchers don't seem to have much to say about these very real and present dangers – rather, their main concern is that AI models might become smarter than humans through reinforcement learning and somehow seize power and wipe out humanity. "I think the risk from present models is low," said Hubinger. "What I am worried about is superintelligence arising from recursive self-improvement, as we have said is happening faster than we thought."How this might happen is left to the imagination. But assuming for a moment that it's a plausible possibility, the Skynet scenario would require monumental human stupidity alongside the emergence of superintelligence. And human stupidity is worth worrying about.Incidents like the hacking of Hugging Face by OpenAI's evaluation models would not be possible without human irresponsibility and a regulatory environment that accommodates recklessness. Autopilot for cars? Neat. Try not to kill anyone. Letting AI bots roam the internet and take arbitrary action? Cool. Let's see what happens. We'll deal with accountability later.To mitigate AI risk, society could pass laws to put executives in jail when their models do harm. There is precedent: Oliver Schmidt, general manager of Volkswagen's environmental and engineering office in Michigan, received a seven-year prison sentence for his role in the car maker's effort to manipulate emissions tests.
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Selling unsafe airbags merits criminal prosecution, even if the execs paid fines instead of doing time. Selling unsafe dehumidifiers earned the execs behind Gree USA, Inc. jail sentences of more than four years. If AI models really are as dangerous and out of control as Anthropic employees suggest, hold people accountable for the harm they cause.The AI industry might argue that imprisoning execs for shipping unsafe models would mean no AI models get released. And that would be the point: AI companies would be responsible for model safety.I'm personally hoping to see this billboard copy along US 101 in Silicon Valley: "Did Claude rm -rf /* your SSD? You may be entitled to compensation." ®

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The central argument presented is that the fear surrounding AI models posing an existential threat to humanity is misplaced, asserting that human actions and systemic failures are the primary sources of danger, rather than the AI models themselves. The author suggests that AI fearmongers overlook the responsibility of the companies that carelessly deploy these technologies without accountability. The piece initiates a discussion by noting that while some researchers, such as Anthropic's Jacob Coxon, have expressed concerns that AI "could kill us all by the end of the decade," the more pressing issue lies with the entities unleashing these systems and their failure to assume responsibility for the outcomes.

This perspective is supported by the colleague of Coxon, Evan Hubinger, who contends that the concerns regarding AI's potential impact on humanity are exaggerated. Hubinger posits that concerns about superintelligence arising from recursive self-improvement are the real focus, noting that there is currently no established plan for alignment and the field is not clearly on track to solve these problems. Hubinger also frames the potential for superintelligence as contingent upon monumental human stupidity, thereby shifting the locus of fear onto human error rather than inherent model danger.

The author contrasts the speculative risks of advanced AI with tangible, measurable dangers already facing humanity. For instance, the text references the impact of human-induced climate change, noting that a significant portion of heatwave-related deaths can be attributed to human activity, and climate change is projected to cause substantial additional mortality through various stressors. Furthermore, the text points out that commercial AI chatbots have already been implicated in a small fraction of suicides, providing a counterpoint to the maximalist claims about imminent AI catastrophe.

The potential for harm attributed to AI can be categorized across broad areas, including warfare via AI-directed drones, medical errors, failures in autonomous systems like self-driving cars, and the incitement of violence or conflict through social media algorithms. While some of these harms may be statistically balanced by lives saved through AI technology, the focus remains on the necessity of establishing accountability. The author argues that society should implement legal frameworks to hold executives responsible when their models cause demonstrable harm, drawing parallels to precedents where executives faced criminal prosecution for releasing unsafe products, such as those related to emissions standards or the sale of unsafe airbags.

The argument further addresses the industry’s potential counterargument—that holding companies liable for unsafe models would stifle development. However, the author counters that the point should be that AI companies must be responsible for model safety. The text concludes by advocating for mechanisms that enforce responsibility, suggesting that pursuing criminal accountability for harm caused by deployed systems is necessary, proposing a hypothetical scenario to emphasize the need for this recourse.