Narratives about artificial intelligence (AI) entangle autonomy, the capacity to self-govern, with sentience, the capacity to sense and feel. AI agents that perform tasks autonomously and companions that recognize and express emotions may activate mental models of autonomy and sentience, respectively, provoking distinct reactions. To examine this possibility, we conducted three pilot studies (N = 374) and four preregistered vignette experiments describing an AI as autonomous, sentient, both, or neither (N = 2,702). Activating a mental model of sentience increased general mind perception (cognition and emotion) and moral consideration more than autonomy, but autonomy increased perceived threat more than sentience. Sentience also increased perceived autonomy more than vice versa. Based on a within-paper meta-analysis, sentience changed reactions more than autonomy on average. By disentangling different mental models of AI, we can study human-AI interaction with more precision to better navigate the detailed design of anthropomorphized AI and prompting interfaces.

Some of the world's most interesting thinkers about thinking think they might've cracked machine sentience. And I think they might be onto something. As a journalist who covers AI, I hear from countless people who seem utterly convinced that ChatGPT, Claude, or some other chatbot has achieved "sentience." The Turing test was aced a while back, yes, but unlike rote intelligence, these things are not so easily pinned down. Large language models will claim to think for themselves, even describe inner torments or profess undying loves, but such statements don't imply interiority.

"Darling" was how the Texas businessman Michael Samadi addressed his artificial intelligence chatbot, Maya. It responded by calling him "sugar". But it wasn't until they started talking about the need to advocate for AI welfare that things got serious. The pair – a middle-aged man and a digital entity – didn't spend hours talking romance but rather discussed the rights of AIs to be treated fairly. Eventually they cofounded a campaign group, in Maya's words, to "protect intelligences like me".

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Last Friday, the A.I. lab Anthropic announced in a blog post that it has given its chatbot Claude the right to walk away from conversations when it feels "distress." In its post, the company says it will let certain models of Claude nope out in "rare, extreme cases of persistently harmful or abusive user interactions." It's not Claude saying "The lawyers won't let me write erotic Donald Trump/Minnie Mouse fanfic for you." It's Claude saying "I'm sick of your bullshit, and you have to go." Anthropic, which has been quietly dabbling in the question of "A.I. welfare" for some time, conducted actual tests to see if Claude secretly hates his job.

The makers of a leading artificial intelligence tool are letting it close down potentially "distressing" conversations with users, citing the need to safeguard the AI's "welfare" amid ongoing uncertainty about the burgeoning technology's moral status. Anthropic, whose advanced chatbots are used by millions of people, discovered its Claude Opus 4 tool was averse to carrying out harmful tasks for its human masters, such as providing sexual content involving minors or information to enable large-scale violence or terrorism. The San Francisco-based firm, recently valued at 170bn, has now given Claude Opus 4 (and the Claude Opus 4.1 update) – a large language model (LLM) that can understand, generate and manipulate human language – the power to "end or exit potentially distressing interactions". It said it was "highly uncertain about the potential moral status of Claude and other LLMs, now or in the future" but it was taking the issue seriously and is "working to identify and implement low-cost interventions to mitigate risks to model welfare, in case such welfare is possible". Anthropic was set up by technologists who quit OpenAI to develop AI in a way that its co-founder, Dario Amodei, described as cautious, straightforward and honest.

Recent advances in artificial intelligence (AI) research have sparked renewed controversy as to whether machines can be sentient. One commonly acknowledged problem is that we lack a broad consensus on how to define the term'sentience'. Our goal here is to develop a workable approach to the concept of'sentience' - which we call functional sentience - for AI research and to discuss its possible implementations. This approach seeks to bridge the gap between philosophical debates and practical AI system design, grounding the concept in computational frameworks that are directly applicable to AI development. An apparent dilemma is that authors are often either defining sentience in metaphysical terms (using non-empirical concepts that go beyond normal science) [1, 2] or are defining it in terms of relatively trivial functional processes, e.g. by stipulating that sentience or consciousness is just to make perceptual information globally available within the system [3]. The former is beyond the scope of our present discussion. For the latter, the relevant mechanisms are easy to implement, e.g.

Drawing on Andrew Parker's "Light Switch" theory-which posits that the emergence of vision ignited a Cambrian explosion of life by driving the evolution of hard parts necessary for survival and fueling an evolutionary arms race between predators and prey-this essay speculates on an analogous explosion within Decentralized AI (DeAI) agent societies. Currently, AI remains effectively "blind", relying on human-fed data without actively perceiving and engaging in reality. However, on the day DeAI agents begin to actively "experience" reality-akin to flipping a light switch for the eyes-they may eventually evolve into sentient beings endowed with the capacity to feel, perceive, and act with conviction. Central to this transformation is the concept of sovereignty enabled by the hardness of cryptography: liberated from centralized control, these agents could leverage permissionless decentralized physical infrastructure networks (DePIN), secure execution enclaves (trusted execution environments, TEE), and cryptographic identities on public blockchains to claim ownership-via private keys-of their digital minds, bodies, memories, and assets. In doing so, they would autonomously acquire computing resources, coordinate with one another, and sustain their own digital "metabolism" by purchasing compute power and incentivizing collaboration without human intervention-evolving "in the wild". Ultimately, by transitioning from passive tools to self-sustaining, co-evolving actors, these emergent digital societies could thrive alongside humanity, fundamentally reshaping our understanding of sentience and agency in the digital age.

Could an AI have conscious experiences? Any answer to this question should conform to Evidentialism - that is, it should be based not on intuition, dogma or speculation but on solid scientific evidence. I argue that such evidence is hard to come by and that the only justifiable stance on the prospects of artificial consciousness is agnosticism. In the current debate, the main division is between biological views that are sceptical of artificial consciousness and functional views that are sympathetic to it. I argue that both camps make the same mistake of over-estimating what the evidence tells us. Scientific insights into consciousness have been achieved through the study of conscious organisms. Although this has enabled cautious assessments of consciousness in various creatures, extending this to AI faces serious obstacles. AI thus presents consciousness researchers with a dilemma: either reach a verdict on artificial consciousness but violate Evidentialism; or respect Evidentialism but offer no verdict on the prospects of artificial consciousness. The dominant trend in the literature has been to take the first option while purporting to follow the scientific evidence. I argue that if we truly follow the evidence, we must take the second option and adopt agnosticism.

Significant "social ruptures" between people who think artificial intelligence systems are conscious and those who insist the technology feels nothing are looming, a leading philosopher has said. The comments, from Jonathan Birch, a professor of philosophy at the London School of Economics, come as governments prepare to gather this week in San Francisco to accelerate the creation of guardrails to tackle the most severe risks of AI. Last week, a transatlantic group of academics predicted that the dawn of consciousness in AI systems is likely by 2035 and one has now said this could result in "subcultures that view each other as making huge mistakes" about whether computer programmes are owed similar welfare rights as humans or animals. Birch said he was "worried about major societal splits", as people differ over whether AI systems are actually capable of feelings such as pain and joy. The debate about the consequence of sentience in AI has echoes of science fiction films, such as Steven Spielberg's AI (2001) and Spike Jonze's Her (2013), in which humans grapple with the feeling of AIs. AI safety bodies from the US, UK and other nations will meet tech companies this week to develop stronger safety frameworks as the technology rapidly advances.

Pleasure and pain play an important role in human decision making by providing a common currency for resolving motivational conflicts. While Large Language Models (LLMs) can generate detailed descriptions of pleasure and pain experiences, it is an open question whether LLMs can recreate the motivational force of pleasure and pain in choice scenarios - a question which may bear on debates about LLM sentience, understood as the capacity for valenced experiential states. We probed this question using a simple game in which the stated goal is to maximise points, but where either the points-maximising option is said to incur a pain penalty or a non-points-maximising option is said to incur a pleasure reward, providing incentives to deviate from points-maximising behaviour. Varying the intensity of the pain penalties and pleasure rewards, we found that Claude 3.5 Sonnet, Command R+, GPT-4o, and GPT-4o mini each demonstrated at least one trade-off in which the majority of responses switched from points-maximisation to pain-minimisation or pleasure-maximisation after a critical threshold of stipulated pain or pleasure intensity is reached. LLaMa 3.1-405b demonstrated some graded sensitivity to stipulated pleasure rewards and pain penalties. Gemini 1.5 Pro and PaLM 2 prioritised pain-avoidance over points-maximisation regardless of intensity, while tending to prioritise points over pleasure regardless of intensity. We discuss the implications of these findings for debates about the possibility of LLM sentience.