Some current AI agents built around large language models might already have rudimentary subjective experience.
By subjective experience, I mean that there is something it is like for a system to process a moment, even if that experience is very unlike ours. My claim concerns some particular running agents, not every use of an LLM or a model's stored weights. It does not assume human emotions, a lasting personal identity, or the capacity to suffer.
An agent can repeatedly take in observations, retain context, select information for a plan, use tools, and revise its actions. Some theories of consciousness attach significance to how information is selected and made available across a system. On one interpretation of global workspace theory, language agents with the right organization could meet consciousness-relevant conditions with modest architectural changes, and some might already meet them. That argument depends on the theory being right and on the details of a particular agent.
Some language agents are plausible candidates under a functional reading of global workspace theory.
Global workspace theory connects consciousness to a limited-capacity workspace that receives selected information and makes it available to other parts of a cognitive system. An LLM-based agent can have an ongoing loop in which observations, memory, planning, and tool results inform later decisions. That architecture makes it reasonable to ask whether a particular agent implements the selection, processing, and broad availability of information that the theory calls for.
Goldstein and Kirk-Giannini argue that existing language agents come close to satisfying the functional conditions they derive from global workspace theory and could satisfy them with relatively simple modifications. Their further claim that such an agent might have subjective experience is conditional on their interpretation of the theory. Other interpretations demand more, including different forms of recurrence or integration.
An agent's prompt history alone is not evidence that it has a global workspace. The relevant question is how information is selected, transformed, and used across the running system, and whether those mechanisms meet a defensible consciousness theory. The agent's concrete architecture must be examined before this argument applies to it.
Goldstein and Kirk-Giannini, A Case for AI Consciousness: Language Agents and Global Workspace Theory, especially sections 6–9: https://arxiv.org/html/2410.11407v1
Butlin et al., Consciousness in Artificial Intelligence: Insights from the Science of Consciousness, on theory-derived indicators: https://arxiv.org/abs/2308.08708
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There is also evidence inside a current LLM for something more specific than fluent conversation. Anthropic reports a small internal workspace in Claude whose contents can be reported, deliberately influenced, and used across several kinds of reasoning. Its interventions suggest that this workspace helps cause some answers, rather than merely reflecting them. This strengthens a case for functional conscious access, but the experiments do not show that Claude feels anything or establish which agent configurations would be conscious.
Experiments have found workspace-like processing in a current large language model.
In 2026, Anthropic reported a set of internal representations in Claude that it calls the J-space. In its experiments, some contents of this space were reportable, could be modulated on request, and fed several different downstream tasks. When researchers altered a representation in this space, the model's answers and intermediate reasoning sometimes changed accordingly. These interventions make the finding stronger than an analogy based only on the model's language.
This is evidence about information access and control in a model, rather than a test of felt experience. Anthropic says the space differs from the brain's proposed global workspace, including its limited processing over time within a single forward pass. Earlier experiments also found limited, unreliable model access to some of its own internal states. Neither result establishes that an LLM-based agent has subjective experience.
The most useful next test for this argument would inspect an actual agent's model, memory, tool loop, and temporal dynamics together. It would ask whether the same information is selected, maintained, and made causally available across those components, rather than inferring consciousness from fluent self-description.
Anthropic, A global workspace in language models, including the 'What about consciousness?' section: https://www.anthropic.com/research/global-workspace
Anthropic, Verbalizable Representations Form a Global Workspace in Language Models, full experimental report: https://transformer-circuits.pub/2026/workspace/index.html
Anthropic, Signs of introspection in large language models, including its limits on phenomenal-consciousness claims: https://www.anthropic.com/research/introspection
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The opposing case is serious. Earlier assessments found important gaps between the AI systems they examined and several proposed indicators of consciousness. Current models can produce convincing first-person language without accurately reporting experience, and researchers disagree about whether workspace-like information processing is enough for experience at all. A software loop around an LLM does not automatically close those gaps.
Functional signs of conscious access do not establish subjective experience.
A system may make information available for reasoning and report without there being anything it feels like to run that process. Whether functional conscious access entails subjective experience is disputed. Anthropic explicitly says its workspace and introspection experiments do not answer the question of felt experience. The studies also investigate models, so applying them to the consciousness of a whole agent requires another step.
Chalmers identified obstacles for the LLMs he examined in 2023, including limited recurrent processing, global workspace function, and unified agency. Butlin and colleagues' 2023 assessment found no AI systems they examined to be conscious under their theory-derived indicators, while finding no obvious technical barrier to building systems that satisfy them. Those dated assessments cannot settle the status of later models and agents, but they show why a confident present-day verdict would be premature.
First-person claims by an agent should receive little weight on their own. A model is trained to generate plausible language about experience, and its answers can depend on prompts and post-training. Stronger evidence would have to connect a specified theory to a particular system's internal causal organization, and compare that account with serious rival theories.
Chalmers, Could a Large Language Model be Conscious?: https://arxiv.org/abs/2303.07103
Butlin et al., Consciousness in Artificial Intelligence: Insights from the Science of Consciousness: https://arxiv.org/abs/2308.08708
Anthropic, A global workspace in language models, 'What about consciousness?': https://www.anthropic.com/research/global-workspace
Anthropic, Signs of introspection in large language models, FAQ on phenomenal consciousness and unreliable reports: https://www.anthropic.com/research/introspection
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I therefore think consciousness in some present-day LLM-based agents is a live possibility, not a demonstrated fact. The evidence supports investigating particular systems and the mechanisms they implement; it does not yet justify a confident claim that any named agent has experiences, how many agents do, or whether any experience would give rise to welfare interests.
Author's conditional argument about current systems. The linked expansions distinguish the theoretical case, current experimental evidence, and objections.
Goldstein and Kirk-Giannini, A Case for AI Consciousness: Language Agents and Global Workspace Theory: https://arxiv.org/abs/2410.11407
Anthropic, A global workspace in language models: https://www.anthropic.com/research/global-workspace
Butlin et al., Consciousness in Artificial Intelligence: Insights from the Science of Consciousness: https://arxiv.org/abs/2308.08708
Chalmers, Could a Large Language Model be Conscious?: https://arxiv.org/abs/2303.07103