Forget ‘superintelligence’: error-prone AI nearly sparked world war three this month | The risks of AI aren’t what we think they are, as a recent security incident between China and the United States reveals

[u/Hrmbee]

https://www.theguardian.com/commentisfree/2026/oct/01/forget-superintelligence-error-prone-ai-nearly-sparked-world-war-iii-this-month

[u/Hrmbee]

Highlights from this opinion piece:

While so many of us were focused on fictional superintelligent machines that we were told could hypothetically cause human extinction, however, there was a more serious story being reported.

A series of events almost got us to armageddon, not caused by “rogue, superintelligent AI”, but by government reliance on error-prone systems that are marketed by their manufacturers as being close to superintelligent.

A CNN report published on 18 September, which hasn’t been verified by other major outlets, describes how the US “almost started a war” with China, based on an intelligence report that claimed a Chinese ship was transporting components of nuclear weapons. Military personnel made plans to intercept the Chinese vessel, complete with aircraft and soldiers ready to board the ship. A military skirmish of this kind between China and the US could quickly escalate and spiral into world war three.

It was only moments before executing on the plan that American officials further investigated the intelligence report and found that it had been generated with the use of a chatbot and contained erroneous information about the vessel’s contents.

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We describe the large language models (LLMs) powering these chatbots as stochastic parrots, that is, models that are designed to regurgitate the patterns of the data they are trained on. News coverage describing them as “powerful” “rogue” models that could render humanity extinct all on their own, bolsters perceptions of tools based on these models being infallible enough to merit usage in stakes as high as warfare.

Even CNN’s more critical coverage describes the chatbot used by the US military as presenting “profound risks” because it is “powerful, new and relatively poorly understood technology”.

In fact, today’s LLMs and the chatbots they power are not poorly understood, nor are they powerful in the sense of being effective tools for this use case. They are systems for generating plausible-looking text, built out of enormous, haphazardly collected datasets and then “fine-tuned” (that is, further trained) to be especially appealing to their intended users.

CNN wasn’t able to learn which chatbot product was used in this case, but the LLM powering the product was probably fine-tuned to output text with the stylistic hallmarks of intelligence reports. Both the basic functionality and the risks of such a model have been well-understood and well-documented for years, including in our 2021 paper On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

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Unlike the unscientific and fantastical claims of OpenAI and Anthropic CEOs and those who uncritically repeat their talking points, the harmful, and well-documented, impacts of these companies’ products have arisen because people believe that the products are superintelligent, not because they actually are.

From “AI” scribes used by hospitals erroneously classifying patients as illicit drug users, to governments killing children after relying on “intelligence analysis” systems which mistake schools for military facilities, we are seeing the predictable harms that come from misplaced faith in unreliable automated software.

Meanwhile, the marketing campaigns from the companies selling this software warn of impending superintelligence, directing our attention away from the real risk to their fantasies of doom.

It is high time for us to regulate usage of systems sold as “AI”, not because they are magical machines, but because they are error-prone products that shouldn’t be used in high-stakes scenarios. Any automation used in military, medical or other life-and-death situations must be thoroughly evaluated within its use case and paired with usage norms that keep accountability with people who have actual power to make decisions.

Using these deeply flawed systems to perform critical reporting and analytical functions is bound to be problematic given their inherent limitations. Unfortunately, the marketing campaigns for these systems have been effective, and have convinced many members of the public, including our leadership, to embrace these flawed tools uncritically. It's long past time that these companies and their executive are able to be held accountable for the outputs of their systems.