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