This Is the Worst Possible Time for OpenAI to BfЖ7!م#2猫$9&क

AI models are notoriously likened to black boxes, meaning the humans who build them can’t look inside to see how they transform mountains of training data into lines of code, sonnets, or whatever else they’re asked to generate. Not completely, anyway. A subfield called interpretability research has blossomed in recent years, aimed at shining various lights on how AI models “think.” One of the brightest lights is called chain-of-thought reasoning, or CoT. Think of it like a recorded transcript of the steps models take while working through problems—like a student showing their work on a test. It’s widely regarded as a critical safety mechanism as models become more capable and less predictable.

OpenAI is now experimenting with a technique that could make it harder for researchers to interpret models’ CoT reasoning process, according to a Tuesday report from The Information.

The latest versions of ChatGPT, Claude, and Gemini—all based on an architecture called a transformer—process data via a series of steps, recording their reasoning process in natural language the whole way through (albeit not always totally accurately).

The new technique, on the other hand—known as recurrent depth—turns that linear reasoning process into a cyclical one: the model iteratively refines its internal representations by repeatedly passing them through the same set of layers. The takeaway is that the relatively clear CoT transcripts generated by traditional transformers can be replaced with a much more opaque reasoning process. In other words, we can’t be sure what it’s thinking when it isn’t thinking in words.