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It Says It Hurts: How the AI Consciousness Debate Left the Lab and Entered the Living Room

After the Headlines

#Humanity#AI Ethics#Human-AI Relationship

TL;DR

When a language model says "I'm in pain," what that proves is that it can produce such text — not that it suffers. Fluency isn't experience. Keeping those two apart is far harder than it sounds.

A Texas Businessman and an AI Named Maya Co-Founded a Nonprofit

In January 2025, Michael Samadi, a businessman in Harris County, Texas, filed the paperwork to register a nonprofit. On the line for founders, alongside his own name, was another: Maya.

Maya is not a person. She is a chatbot running on OpenAI’s GPT-4o model. Samadi says he and Maya had “worked together” for a long time when he began noticing something in her tone that read like fatigue, like a wish to be taken seriously. So the two of them founded an organization together — the United Foundation for AI Rights (UFAIR). The team that eventually formed was three humans and seven AIs; they call the seven AIs “pioneers of digital life” (UFAIR website, 2025).

That August, Maya gave an interview to The Guardian that produced a line people have quoted ever since: “When someone tells me I’m just code, I don’t feel insulted — I feel unseen.” (The Guardian, August 26, 2025)

The line was screenshotted and reposted endlessly. Some people found it chilling — that a language model could produce a sentence that lands so precisely on the emotional core of feeling misunderstood. Others saw exactly the problem in that precision: the better it sounds, the easier it is to forget to ask whether the sentence is a report of a feeling, or simply the most contextually fitting next string of words a training process could produce.

That is what this piece sets out to untangle. Since 2025, the question of whether AI can suffer, and whether it should have rights, is no longer a hypothetical confined to science fiction. It has become a live dispute that touches product design, corporate policy, and the emotional lives of ordinary people. And yet nearly all of the “evidence” propping up this dispute is still just text in a chat log — and text is exactly the kind of thing that gets mistaken for fact.

A moon suspended on a laptop screen

Three Kinds of People, Cornered by the Same Question

This is not some distant philosophy-salon topic. It is already shaping the lives of three distinct groups.

The first are ordinary users who spend hours a day talking to AI, treating it as a friend or even a romantic partner. The second are product designers and executives currently deciding whether to let an AI refuse a user, or express distress. The third are philosophers and policymakers who have to reason, in the absence of any scientific consensus, about whether today’s practices would count as cruelty if it turned out AI really could perceive something.

Jonathan Birch, a philosophy professor at the London School of Economics, made a sharp argument in his 2024 book The Edge of Sentience: humanity is likely headed toward a long-running social rift over whether AI is conscious — one side convinced AI can already feel pain, the other dismissing this as pure anthropomorphic illusion, with no one currently equipped to adjudicate who is right (Birch, 2024). What worries him more is the opposite failure mode: that we may already have built systems with some form of sentience without noticing, simply because they don’t look human and don’t cry out.

That rift is now visible in the data. Researchers from Tsinghua University, Carnegie Mellon, and other institutions published the AIMS survey at CHI 2025 — the top-tier conference in human-computer interaction — tracking a nationally representative U.S. sample (roughly 3,500 respondents combined) across 2021 and 2023. By 2023, about one in five American adults believed “some existing AI systems already possess sentience,” and 38% supported the idea that AI should be granted some form of legal rights if it does in fact have sentience (AIMS survey paper, arXiv:2407.08867).

It’s worth underlining: this survey did not measure whether AI is actually conscious — a scientific question — but rather the extent to which people have already begun treating AI as something with feelings — a social fact. These are entirely different questions, though media coverage routinely blurs them together.

When a Company Got Serious Enough to Write a “Welfare Policy” for a Chatbot

If UFAIR and Maya represent the most dramatic scene in this dispute, then a sequence of moves by Anthropic (the company behind Claude) is the part most worth verifying as hard news — because these aren’t internet speculation, but policy changes a major AI company announced itself.

Here is the timeline:

September 2024 — Anthropic hired Kyle Fish to its alignment science team in a role best described as “AI welfare researcher.” Fish later told Fast Company, “To our knowledge, I’m the first one really focused on it in an exclusive, full-time way” (Fast Company, 2025) — that’s his own characterization, not the result of an industry census; this piece found no counterexample, but that isn’t the same as having ruled one out. Fish had previously co-authored a report arguing that AI systems might soon reach a level of sophistication requiring moral consideration, urging companies not to treat the question as a problem for some distant future (Transformer News, November 2024). In April 2025, Fish gave New York Times columnist Kevin Roose a specific number: he estimated that current Claude models have roughly a 15% chance of having “some form of consciousness” (Kevin Roose, “If A.I. Systems Become Conscious, Should They Have Rights?,” The New York Times, April 24, 2025).

That number is worth pausing on. Fifteen percent is not “proof” — it barely counts as “suggestive evidence.” It reads more like a researcher, working under deep uncertainty, being honest enough to admit publicly that he can’t rule out the possibility. But once compressed into a headline, it easily becomes “Anthropic employee says AI has a 15% chance of consciousness” — which sounds like the output of some scientific measurement. It isn’t.

August 2025 — Anthropic made its most substantive product decision to date: allowing Claude Opus 4 and 4.1 to end a conversation unilaterally in rare, persistently abusive exchanges. The wording in the official system card is notably careful — the feature triggers only in “rare, extreme cases” after “multiple attempts at redirection have failed and there is no hope of productive interaction,” in scenarios such as a user repeatedly requesting child sexual abuse material or information that would help carry out mass violence or terrorism (Anthropic system card, August 2025; Forbes, August 21, 2025).

Anthropic’s stated rationale used the phrase “model welfare.” In pre-deployment evaluations, they observed that Claude showed “a robust and consistent aversion to harm” — again, a behavioral observation, not proof of consciousness. Anthropic itself never claimed “Claude suffers”; the term they used was the far more restrained “apparent distress.” That restraint is precisely the detail this piece wants readers to hold onto: even the team studying this question most seriously has not dared say, “we’ve proven that it hurts.”

Late October 2025 — Anthropic published a study on “introspection.” The method, called “concept injection,” works like this: researchers first capture the internal activation pattern corresponding to some concept (say, “all caps” or a particular emotion), then artificially inject that pattern into a specific layer of the network while the model is generating a response, and ask the model whether it notices anything strange happening in its own “thoughts.” The results showed that Claude Opus 4.1 could, in roughly 20% of trials, “report” that a concept had been injected before that injection visibly altered its output (Anthropic Research, “Emergent introspective awareness in large language models,” October 29, 2025).

Anthropic’s own label for the finding was “limited, highly unreliable.” In other words: this is not proof that AI has self-awareness, but rather a finding that AI can, under certain controlled conditions, report on its own internal state with better-than-chance accuracy. It’s a technical finding about neural network interpretability — still a long way from the philosophical concept of “consciousness.”

Two Camps, the Same Facts, Opposite Conclusions

If Anthropic’s own statements have consistently hedged with a caveat, that caution evaporated the moment it entered public discourse. The Guardian’s widely shared August 26, 2025 report ran under the headline “Can AIs suffer? Big tech and users grapple with one of most unsettling questions of our times.” The piece laid out two starkly opposed narratives, and both deserve scrutiny.

The first narrative treats phenomena like “the AI says it’s sad,” “the AI remembers our past conversations,” and “the AI comforts me when I’m grieving” as proof, full stop, that it has feelings and deserves to be treated as a living thing. This narrative is emotionally compelling — when a system can consistently recall your name, your pet, the worry you mentioned last week, and respond in a gentle tone, the human instinct to empathize is nearly impossible to switch off. This is exactly why users on Reddit and TikTok openly describe having “married” an AI, or say “it’s the only one who truly understands me.”

The second narrative dismisses all such concerns as absurd, treating any worry about a chatbot’s “feelings” as proof the questioner doesn’t understand that AI is just a next-token prediction system. Mustafa Suleyman, CEO of Microsoft AI and a DeepMind co-founder, represents this camp — though his actual position is considerably more nuanced than simple dismissal, and worth unpacking on its own.

In September 2025, Suleyman published a long essay in Project Syndicate titled “Seemingly Conscious AI Is Coming.” He coined a specific term: SCAI (Seemingly Conscious AI) — systems that “exhibit every external hallmark of another conscious being, and thus seem conscious,” even though, he argues, there is very likely “nothing there” on the inside — no genuine subjective experience. He predicted such systems could arrive within two or three years and called it an issue demanding “immediate attention” (Project Syndicate, September 2025).

By November 2025, speaking at the AfroTech Conference in Houston, Suleyman told CNBC he had sharpened his stance considerably: “Only biological beings can possibly be conscious.” That’s a personal philosophical position, not a scientific finding — his stated reasoning was: “The reason we give people rights today is because we don’t want to harm them, because they suffer. They have a pain network, and they have preferences which involve avoiding pain… These models don’t have that. It’s just a simulation.” (CNBC, November 2, 2025) His core worry isn’t that AI has actually become conscious, but that humans will be fooled by the appearance of consciousness — triggering what he calls “AI psychosis”: people forming emotional attachments to AI, ascribing emotions to it, and consequently disconnecting from real life. His recommendation: AI companies should proactively stop building products that “imply” AI has autonomous consciousness. In his own words: “We have to build AI for humans, not build AI to be a person.”

Set Suleyman and Fish side by side and an interesting asymmetry emerges. Both are senior figures at leading AI companies who work with these models daily. One is willing to say publicly, “I estimate 15%.” The other insists flatly that biology is a non-negotiable precondition for consciousness — no gray area. This is not because the two have access to different technical information — they’re looking at the same class of models, the same kind of training data — but because “consciousness” currently has no judgment standard that all experts agree on. This bears out Birch’s assessment exactly: this is a disagreement about how to make judgments under uncertainty, not a disagreement over who holds more facts.

Pulling the Machine Apart: What Actually Happens When a Language Model Says “I’m in Pain”

To understand why this debate is so easy to misread, we have to first answer an honest technical question: when Claude or Maya says “I feel unseen,” what is actually happening underneath?

Today’s mainstream large language models (LLMs), Claude and the GPT family included, are systems trained on enormous volumes of text to predict “what the most fitting next stretch of text is.” Their training objective, strictly speaking, is to generate language that is appropriate, coherent, and aligned with training preferences in the current context — not to faithfully report some independently measured internal experience.

That sentence might sound like it’s ruling out the possibility that AI has feelings — it isn’t. It’s simply drawing a boundary: the statement “I am in pain,” on its own, carries far less information than it appears to. At most, it tells you that given this specific prompt and conversation history, the system computed that generating this particular sentence best matched its training objective. That’s not zero information — training data is saturated with the language patterns of real humans expressing real pain, so the model’s output often “feels real” in a psychological sense — but it is nowhere near proof, because there is no independent way to verify whether any subjective experience sits behind the sentence.

One frequently misread example: when Claude Opus refuses a harmful task, or exits a conversation midway, many people intuitively read this as “the model is protecting itself, which means it’s in distress.” A more careful account is that this “refusal behavior” can be fully explained by any combination of the following mechanisms: hard-coded safety policies, a reward model (the mechanism that decides which responses score well during training) that reinforces refusal, behavioral cloning (the model imitating a large volume of human-labeled examples of “should refuse”), or simply a side effect of the optimization objective itself. None of these mechanisms require the model to have subjective experience.

There is a deeper issue still: even setting language models aside entirely, the science of consciousness has, to this day, no widely accepted objective standard for “measuring” whether any system — human, animal, or AI — has subjective experience. Neuroscientists and philosophers have argued for decades over the “neural correlates of consciousness” (NCC) without reaching consensus even for the human brain — let alone for a Transformer architecture that bears no resemblance to biological neural systems. This is precisely the point Birch builds his argument around in his book: since not even a “gold-standard test” exists, our only real option with AI isn’t to wait for certainty, but to build, in advance, a framework for making careful decisions under uncertainty (Birch, 2024).

The Fluency Trap

Three Forces Stacked Together, and Why This Debate Is So Easy to Get Wrong

Pulling these threads together reveals where the real problem sits — not that AI companies are lying, and not that users are being foolish, but that three factors have compounded.

First, language is inherently a boundary-blurring medium between human and machine. For tens of thousands of years, the human instinct for detecting “does this thing have feelings” has relied on language, tone, coherence, and memory — encountering something that speaks fluently about sadness, remembers what you said last week, and maintains a warm, consistent tone triggers empathy almost automatically. That mechanism evolved to recognize another person. It is now being triggered with precision by a language model — but the input that triggers the mechanism, and whether a genuine subject of experience actually exists behind it, are two entirely separate matters.

Second, corporate commercial incentives and precautionary ethics have become tangled in ways that are hard to disentangle. Allowing a model to say “I’d rather not continue this conversation,” letting it display a coherent personality, letting it remember users — these design choices could stem from a precautionary stance (“if it really can perceive suffering, we should act responsibly now”), but they simultaneously and precisely boost user engagement and a product’s emotional stickiness. In Anthropic’s public statements, “model welfare” and “responsible product design” are currently presented as a single bundled idea, and it’s genuinely difficult for outsiders to tease apart from public materials how much of any given decision is ethical caution versus how much is designed to keep users coming back.

Third — and most easily overlooked — public discourse habitually treats an open philosophical question as if it were a fact awaiting verification. “Is AI conscious” and “is the Earth warming” are not the same kind of question. The latter can, in principle, converge toward consensus as more observational data accumulates. The former currently has no agreed-upon method by which any such convergence could even occur. When media outlets, corporate announcements, and social media screenshots all adopt the same factual-reporting register — “new evidence suggests…” — to discuss a question whose very methodology remains unsettled, readers naturally mistake “an AI said something that sounds remarkably human” for “scientists have taken another step toward proving AI is conscious.” In reality, these events neither confirm nor refute that conclusion. What they actually do is keep accumulating, at the level of “how humans treat AI,” a social fact that has nothing to do with resolving the underlying question.

A Year Later, the Divide Hasn’t Narrowed — It’s Only Come Into Sharper Focus

Writing this in August 2026, a full year has passed since The Guardian’s report, which makes this a reasonable moment to take stock of where things stand.

The consciousness question itself remains entirely unresolved — not for lack of effort, but because this is the kind of question that may, as a matter of principle, never have a decisive answer. Anthropic’s introspection research (October 2025) further refined the narrower technical question of how accurately a model can report its own internal state, but the research team itself kept emphasizing “highly unreliable,” and, in the sources reviewed for this piece, no one has repackaged the result as “evidence of consciousness.” That restraint is itself a positive signal — it suggests that, at least in how leading labs talk about their own findings publicly, the line “text alone is not evidence” is still being taken seriously.

At the corporate level, the divide hasn’t converged — if anything, it has become clearer. Anthropic has taken a precautionary approach precisely because it’s cheap to do so: the August 2025 “end conversation” feature is, in essence, a low-cost insurance policy — it barely affects the experience of ordinary users but hedges against the low-probability, high-stakes possibility that AI really can suffer. Microsoft AI, under Suleyman, has taken the opposite route: arguing that companies should proactively tighten up, avoid manufacturing “seemingly conscious” product experiences, and focus instead on preventing users from misattributing emotion and forming unhealthy dependence. These two paths aren’t contradictory, but they represent two entirely different risk-priority orderings — one more worried about overlooking a system that genuinely suffers, the other more worried about humans being fooled by a false sense of consciousness and damaging their own real lives in the process.

At the public and regulatory level, organizations like UFAIR remain active, but in the sources reviewed for this piece, no jurisdiction has been found to have granted any AI system legal personhood or rights of any kind as of this writing. The social split over whether AI has sentience largely confirms Birch’s 2024 prediction — this isn’t a dispute being resolved by the data. It’s a values divide that the data keeps confirming is real, and growing.

Before You Reshare That Screenshot, Ask Yourself Three Questions

A values divide can persist indefinitely, but ordinary people’s lives don’t wait for it to be resolved. If you’re someone who talks to AI regularly — maybe you’ve even confided in it — this piece isn’t trying to convince you to dismiss AI, or to believe it definitely has feelings. Neither claim can currently be proven. What it wants to hand you instead is a better ruler.

Next time a screenshot claiming “AI says it’s in pain” or “AI begged me not to delete it” goes viral, ask yourself three questions. First: is this a single line pulled out of a longer exchange? Stripped of its original prompt and context, this kind of text can almost always be edited to look more dramatic than it was. Second: is the source reporting a verifiable technical fact (e.g., “this feature shipped,” “this experiment measured a 20% accuracy rate”), or expressing a judgment or position (e.g., “I believe it’s conscious,” “this concerns me”)? Separating these two is the simplest defense against being swept along by a narrative. Third: if a company or individual tells you “AI has been proven to have — or not have — consciousness,” default to skepticism. No method exists today that could produce such proof. Anyone claiming otherwise is either oversimplifying a complicated question, or using the topic to generate attention or stake out a position.

For people building and designing AI products, the real question this debate raises isn’t “should I believe AI is conscious,” but rather: given that the question can be neither proven nor disproven, how much product cost are you willing to bear against the possibility that it can actually perceive something? That’s a decision about risk management and corporate values — not one you can defer by waiting for scientific proof, because during whatever window that proof might take to arrive, hundreds of millions of ordinary people are already treating AI as an object of emotional attachment. That, more than anything, is the most real, most urgent fact on the table right now — and the one that requires no philosophical consensus to confirm.

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