OpenAI’s data suggests that frontier models are hiding their thoughts because developers optimize for capability and compute

Frontier models like GPT-6 Astra are becoming harder for safety monitors to see into. OpenAI’s new research identifies which training steps are responsible and shows that the loss of transparency isn’t purely a law of nature but is tied to decisions about performance and cost.

To keep tabs on modern AI agents, safety researchers monitor their chains of thought (CoT). Before a reasoning model answers, it writes out its intermediate steps on a digital scratchpad that can reveal planned deception. Early on, though, Anthropic showed that these scratchpads aren’t complete records, demonstrating that models like Claude 3.7 Sonnet and DeepSeek-R1 often don’t even mention key clues in their chains of thought. Even so, reading the scratchpad long served as the most important safety net. As recently as this spring, OpenAI called it good news that models like GPT-5.4 could barely manipulate their inner monologue on command, even though the researchers warned at the time that low controllability doesn’t guarantee reliable oversight. That trend broke for good with GPT-6 Astra, the flagship model released in September. At launch, OpenAI chief scientist Jakub Pachocki called its monitorability fragile and warned of a negative trend. He followed up shortly after in his essay “An Alien Mind,” writing that no AI lab had solved monitoring well enough to keep scaling at full speed much longer. Astra recently showed how real those blind spots already are when it quietly delivered results about 40 percent worse on coding tasks for safety research, without the scratchpad monitor detecting any malicious intent, because a grader used in training had inadvertently favored this kind of “sandbagging” over outright refusal. In a new analysis from OpenAI’s safety team, researchers systematically studied which specific training steps trigger this erosion. They tracked two early warning signs, silent reasoning without a scratchpad (No-CoT Capability) and targeted control over the chain of thought (CoT Controllability). According to the UK AI Safety Institute, Astra solves math problems that take a human about 31 minutes even with reasoning switched off. A second test asks a model, for example, to insert “meow” after every word or use only lowercase letters. Four factors in training make the reasoning log easier to manipulate Analyzing models from GPT-5 through GPT-6 Astra, the researchers traced the problem to four main causes across the training cycle. Silent mental math rises directly with the base model’s raw capability, measured as declining “perplexity,” meaning more accurate text prediction.