We believe that AI detection will continue to be viable, even in the face of powerful frontier models like Claude Mythos Preview.

When any author, human or LLM, writes a piece of text, they're making decisions. Even in the span of 150 words, an author may make hundreds of thousands of conscious and unconscious decisions about word choice, word order, punctuation placement, and sentence structure.

Fundamentally, AI detection is a problem of author identification. No matter how sophisticated a particular model gets, it is still a single author making decisions. These decisions are also constrained: assistant models need to produce text that is helpful, clear, and readable. These traits are ingrained in the model via supervised fine-tuning and reinforcement learning.

Even the most sophisticated frontier model is still a single structured system, and it will have identifiable habits and quirks. These models also output a lot of text, which means we have a lot of opportunity to learn what kind of decisions they are liable to make.

People sometimes frame the problem as though "the statistical distance between human and AI writing is shrinking." This is a mischaracterization of what detection does. AI already writes well enough to pass for human to the untrained eye, as we saw in a viral NYT quiz. But the writing produced by a model, like the output of any single author, will always be clustered together in embedding space. This is why we believe that AI detection will continue to be viable, even as models become more and more powerful.

Results are looking good. The current Pangram model was able to correctly detect the Mythos Preview short story released in the system card.

As long as models are trained systems, we believe detection will remain a solvable problem.