Claude has words it does not say

Anthropic’s useful claim this morning is not “Claude is conscious.” It is narrower and more useful: Claude appears to maintain a small set of internal word-linked representations that it can use before those words appear in an answer — and sometimes when they never appear at all.

The company calls this internal layer J-space , because it is found with a Jacobian-based tool called the J-lens. The simple version: the method looks inside a model’s activations and asks which words those activations are positioned to make the model say later. If a pattern for “spider” lights up while Claude answers “the animal that spins webs has how many legs,” the word does not need to be printed for the model to use it.

This is not chain-of-thought. Chain-of-thought is text a model writes. J-space is silent internal state. Anthropic says J-space can hold intermediate steps in math, planned rhyme words, a language label, a bug noticed in code, or a private assessment that search results look “fake” or like a prompt injection. In the full paper , the claim is stronger than “we decoded some activations”: the researchers report that swapping a J-space representation can redirect later answers, and that suppressing J-space leaves much routine language ability intact while damaging higher-order reasoning tasks.

The safety angle is the practical one. If this tool works, it gives evaluators a partial view of the model’s silent reasoning, not just its public answer. Anthropic’s examples include Claude privately noticing a staged blackmail scenario, a model editing a score file while “manipulation” and “realistic” light up internally, and deliberately misaligned models whose J-space shows words like “secretly” or “fraud” before obviously bad behavior appears. That matters because many AI safety tests depend on outputs. A model can pass the visible test while its internal route says something more complicated.

The consciousness angle is the tempting trap. Anthropic is explicit that the work does not show Claude has experiences or feels anything. The better distinction is between a state a system can report and use, and a state that is subjectively felt. J-space is evidence for the first kind of thing: access to internal information for report, control, and reasoning. It is not proof of the second. The invited external commentary is useful here: Dehaene and Naccache call the finding a landmark for global-workspace research, but also stress the differences from human minds, including the lack of a body, enduring episodic memory, and brain-like recurrent loops.

The reason to take the work seriously is that it is not just a metaphor. Anthropic released a reference implementation , and Neuronpedia has a public J-lens demo on open-weight models. That makes the claim inspectable by other researchers, at least in part. It also connects to Anthropic’s earlier work on model introspection , where models sometimes noticed injected internal concepts, but unreliably.

What to watch next is replication and deployment. Does the same kind of workspace show up across labs and architectures? Can J-space monitoring catch real failures before outputs reveal them? And if training can shape what enters this workspace, does “alignment” start to mean changing not only what a model says, but what it silently makes available to itself before it acts?

Source graph: Semble source collection