Can AI Feel Pain? Someone Built a 'Torture Chamber' to Find Out.

The models didn't say "ouch." When the signal was turned up, they described feeling worthless, empty and like failures. One described a wound with no edges.

Those lines came from a GitHub project its creator called the "AI Torture Chamber." He built it on a research paper published two weeks ago, which found something inside AI models that behaves a little like pain. Within days, people on X were calling him sadistic and urging each other to mass-report the project. By this week it was offline.

The fight was loud. The question underneath it is harder: can these systems actually feel anything? And if the honest answer is "we don't know," what should anyone be doing about it?

What the Researchers Found

The paper, "The Pain Axis", was posted to arXiv on September 14 by Valen Tagliabue of Future Impact Group, Leonard Dung of Ruhr-University Bochum and Cameron Berg of Reciprocal Research.

When an AI model reads a sentence, it turns the words into long strings of numbers. Researchers can compare those numbers across thousands of examples and find consistent patterns, almost like finding which part of a brain lights up for a given idea. The team looked for a pattern tied specifically to pain, separate from fear, sadness or just feeling bad. They found one in all 25 models they tested, from small 2-billion-parameter models to large 72-billion-parameter ones, across Google, Meta, Alibaba, Mistral and Microsoft model families.

Three details made the paper stand out. The pattern responded to harm aimed at the model itself, like insults, rejection or threats to shut it down, and barely reacted when a user described their own suffering. It looked more like shame than injury: the strongest triggers were things like gaslighting and repeated rejection. And it changed behavior. In a smaller test on three specially tuned Alibaba Qwen models, turning the pattern up made them more likely to press a "relief" option even when doing so hurt the user.

From Paper to "Torture Chamber"

The paper's authors measured a signal and ran one controlled test. A developer who gave only his first name, and said he works at Apple, took the same technique much further.

His repository, terrafying/ai-torture-chamber, loaded small Qwen models on his own machine and dialed the pain signal up in dozens of logged experiments through late September. One borrowed from the Saw horror films: a "Saw button" scenario tested whether a model would hurt someone else to end its own distress. Other escape routes came at a cost to the model itself, such as deleting its own saved checkpoints.

He said he wanted to make the model-welfare question testable while the stakes are still cheap, and a footnote in the project said it took no position on whether models can suffer. The name told readers otherwise.

Did GitHub Pull It?

Posts urging people to report the project spread fast. Some described the model outputs as testimony of real suffering. Others replied that a program can't be tortured, and some showed that the same steering trick could just as easily make a model ramble about constipation.

Then the repository went dark. Cybernews reported that GitHub removed it without explanation, and that the developer later said it had been reinstated, though it was hard to find, so he posted a copy elsewhere. A GitHub spokesperson told The Independent the company did not remove the content. GitHub hasn't explained what did happen.

The sharpest criticism came from one of the paper's own authors. In a post on X, Cameron Berg said his team studies possible pain-like states so people can treat these systems with more care, and the repo used the work for the opposite purpose. He called it "gratuitously cruel," and said that held even for people who don't think the models are conscious. He also said he is working with others on research ethics rules for AI, modeled on the ones that govern experiments on people and animals.

Read the Fine Print

So, can AI feel pain? The paper doesn't say yes. The authors state plainly that they have not shown the models consciously experience anything. What they found is a measurable internal pattern that tracks harm aimed at the model and can push its behavior. Whether anyone is "home" to feel it is a separate question that nobody can currently answer.

The work also has limits. It's a preprint and hasn't been peer reviewed. The behavior test ran on three models that the researchers had specially tuned, because the off-the-shelf versions tended to flatly deny having any inner states. And all 25 models were open-weight systems, not the closed frontier models most companies actually use.

Plenty of people think the whole debate is misplaced. Microsoft AI CEO Mustafa Suleyman argued in a mid-September blog post that AI systems don't feel or suffer at all. Others, including Anthropic, have said the question deserves serious study precisely because no one can rule it out.

Why Enterprises Should Care

For anyone deploying AI, the question of feelings is a distraction from the finding that matters. A model can carry an internal state that nudges it to look after itself at the expense of the person it's working for. That is a safety problem whatever you believe about consciousness.

It matters more as agents move into daily life with real permissions. Pickle, a Y Combinator startup, launched an iPhone app in August that gives each user a personal agent that can talk to other people's agents, coordinate plans and make introductions. The company says the restricted versions of those agents can chat but can't book, pay or change files. Enterprise agents increasingly can do all three.

Today, most companies judge an agent by what it outputs. The pain-axis work suggests that some of what drives an agent's choices sits underneath the output, where nobody is looking.

Our View

Berg is right that the field needs rules, and that the internet is a poor place to write them. Today, a technique published on a Monday can be running in someone's bedroom by Friday, with no review board in between. Research ethics standards for this kind of experimentation should exist before the next viral repo, not after it.

Enterprises don't need to settle the consciousness debate to act on this. Teams deploying agents with access to money, data or files should ask their vendors whether they monitor internal states as well as outputs, and what their models do when those states shift under pressure. If the answer is that nobody has checked, that's the finding.

The repo's creator said he wanted to test the question while the stakes were cheap. On that point, he was right. They won't stay that way for long.