This New Theory on How AI 'Thinks' Could Impact Whether It Kills Us All
By Truman Dickerson
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AI neurons and their connections are easier to understand than we thought, according to a researcher's new findings.
: Sergi Reboredo/VW Pics/Universal Images Group via Getty Images
October 16, 2026, 6:05 PM GMT+2
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How AI models think may be easier to decipher than we thought.
An independent researcher who formerly worked at OpenAI published a report on Thursday that some in the AI world are describing as a bombshell. It could have implications as safety researchers scramble to get the jump on AI models that may not have humanity's best interests in mind.
Previously, the prevailing wisdom was that AI "neurons," which, like those in a human brain, light up when an AI model encounters specific stimuli, were ultimately uninterpretable. Meaning, basically, that there was no way to guess why a specific neuron would fire when an AI model was exposed to, say, a picture of a cat.
Those building AI models at places like OpenAI and Anthropic don't write instructions for everything a model does. Instead, they train neurons to respond to information and influence one another. As the AI learns, it develops its own ways of processing that information, and humans have not yet cracked the code of how it works.
That means those building AI models know what goes into the AI and what comes out, but not necessarily what happens in between. So if an AI goes rogue, they don't always know why.
The author of the new report, however, says he may have discovered patterns in how the neurons react to stimuli that make sense. Nick Cammarata, who said he left OpenAI in 2023, said AI neural networks are knowable, so long as you look carefully.
In one example, he says that a single AI neuron might react to both a cat and the front of a car. Humans would see those as two very different things, and different neurons would fire as a result. The AI, however, sees something related, perhaps the triangle shape of the two, and a single neuron fires for both.
For years, AI researchers had assumed that AI models packed unrelated things into a single neuron to save space.
"Almost the entire network is interpretable if we look closely enough," Cammarata wrote.