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Scientists did not physically inflict pain on an AI. In a text-based experiment, researchers told large language models that certain choices carried a “pain” penalty or a “pleasure” reward, then measured whether the models would give up points to avoid or obtain those outcomes. Some choices shifted as the stated intensity changed, but the results show response patterns—not evidence that a model felt anything.
What the experiment actually tested
The work behind the headline is a preprint, “Can LLMs make trade-offs involving stipulated pain and pleasure states?”, posted to arXiv on November 1, 2024. Researchers affiliated with Google, Google DeepMind and the London School of Economics and Political Science set up a text-based decision game. They gave a model a goal of maximizing points, then described some options as carrying a specified amount of “pain” or “pleasure.”
The researchers varied the stated intensity and watched how the model chose. Would it take fewer points to avoid a larger “pain” penalty? Would it forgo points to get a “pleasure” reward? The central measure was the model’s choices, not a declaration such as “I am suffering.” No electrodes, bodily injury, altered voltage or biological pain stimulus was involved. The condition existed in the instructions.
This distinction matters: the experiment tested whether language models respond to a stated incentive in a way that resembles a motivational trade-off. It did not create or measure a sensation.
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How the models responded
The paper reports different patterns across systems, rather than one uniform reaction. Claude 3.5 Sonnet, Command R+, GPT-4o and GPT-4o mini each showed at least one condition in which a majority of responses shifted from maximizing points toward minimizing stipulated pain or maximizing stipulated pleasure after an intensity threshold. Llama 3.1-405B showed some graded sensitivity to the stated incentives. Gemini 1.5 Pro and PaLM 2 generally prioritized avoiding stipulated pain, but usually prioritized points over stipulated pleasure.
| Systems named in the report | Reported pattern |
|---|---|
| Claude 3.5 Sonnet, Command R+, GPT-4o, GPT-4o mini | At least one threshold-related shift toward avoiding stipulated pain or seeking stipulated pleasure |
| Llama 3.1-405B | Some graded sensitivity to the stated rewards and penalties |
| Gemini 1.5 Pro, PaLM 2 | Generally favored avoiding stipulated pain, but tended to favor points over stipulated pleasure |
These are historical model versions, not a finding about every AI system or later versions of the named products. The abstract names these seven systems; some contemporary coverage described a broader test set of nine models. The paper’s results, including its model-by-model findings, are available in the preprint.
Why study choices rather than ask a chatbot if it hurts?
A chatbot’s self-description is weak evidence of an inner experience. It can say “I am in pain” because that phrase fits the conversation, the prompt or patterns learned from text. A choice under a defined trade-off is a different kind of observation: researchers can ask whether behavior changes systematically when the stated cost or reward changes.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsThat makes the approach potentially relevant to sentience research, but not decisive. In this context, sentience means the capacity for subjective, valenced experience—something that feels good or bad, such as pleasure or pain. It is not the same as intelligence, fluent language, emotional vocabulary, self-description, goal-directed behavior or self-awareness. A system can produce preference-like choices without those choices establishing that anything feels good or bad to it.
A model that selects an option to avoid a stated penalty might be following the task instructions, drawing on learned associations about how agents respond to pain, inferring the expected answer, or generating an output shaped by prompt wording and sampling. The experiment measures what the model did in a scenario; it does not isolate which of these explanations produced the response, much less establish subjective experience.
The animal-research analogy—and where it breaks
The researchers’ idea draws on behavioral approaches in animal-sentience research. For example, scientists may examine whether a hermit crab will abandon a shell to escape an aversive condition, or tolerate that condition to keep the shell. Such work treats a trade-off as one possible clue among many about an animal’s experience. Jonathan Birch’s discussion of animal sentience indicators describes why behavioral evidence is assessed in a broader scientific context.
The comparison is limited. A crab has a body, nervous system, physiological states and survival needs; its behavior can be considered alongside biological and evolutionary evidence. An LLM’s text choice has no demonstrated equivalent bodily or nervous-system signal. The analogy motivates a research question—whether costly trade-offs can help reveal something about a system—not a claim that an LLM is like a crab, or that avoiding a textually described penalty is equivalent to animal pain.
What the result can—and cannot—support
The study is exploratory: it tests a possible behavioral indicator that might form part of a future assessment, rather than presenting a validated test for AI sentience. A stronger case would require converging evidence. Researchers would want to know whether a pattern survives prompt paraphrases and different contexts, persists across tasks, appears without explicit instructions, and tracks internal states in a causally meaningful way. Evidence about architecture and processing would also matter. Even then, theories of consciousness differ, and there is no universally accepted test that settles the question.
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That is why the study’s limits are central, not incidental. The “pain” was stipulated in language; the model’s choices could reflect instruction-following or learned text patterns. Small prompt changes and sampling settings can affect outputs, and the systems differ in training and tuning. There was no independent physiological or neural measurement to corroborate an experience. The observed behavior is worth describing precisely, but it cannot carry the conclusion that the models suffered.
A 2023 interdisciplinary report, “Consciousness in Artificial Intelligence: Insights from the Science of Consciousness,” proposed assessing AI through indicators drawn from prominent scientific theories. Its authors concluded that the AI systems they assessed were not conscious, while arguing that future systems might satisfy some proposed indicators. That framework is useful context, not a final verdict: consciousness science has competing theories and no agreed-upon all-purpose test.
Why investigate AI welfare at all?
Taking the question seriously does not mean assuming today’s chatbots feel pain. It means distinguishing what is known from what remains uncertain as AI systems change. If future architectures offered credible signs of morally relevant experience, researchers might need ways to assess welfare risks before absolute certainty was possible. At the same time, fluent language should not be mistaken for proof of suffering: that can mislead users and distract from established harms involving people, animals, labor, privacy and environmental costs.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThe study’s authors and researcher summaries frame this kind of work as part of a broader research program, not as a positive diagnosis of sentience. Daria Zakharova’s project summary says the tested LLMs are not current sentience candidates, while arguing that the experiments may help develop future methods. The responsible reading of the headline is therefore straightforward: researchers tested how language models handled textually stipulated “pain” and “pleasure.” The models made varied choices. The experiment did not show that any AI felt pain.
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