Now you've successfully trained the next model not to put anything suspicious in its chain of thought. And unfortunately that's exactly what OpenAI and Anthropic seem to be doing (sometimes accidentally)
What OpenAI have reportedly done with Astra is switch from a traditional Transformer to a "looped" one, where inputs are looped through some layers more than once, but with some limit, so now perhaps you get 200 steps of compute (layers) per token generated, rather than just 100.
But, 200 steps is still not enough to answer any question, so you still need COT, but perhaps not such a long COT.
If you allowed the Transformer to loop "as long as it wanted" before generating each token, like a person thinking before talking, then you wouldn't need an external COT ("thinking out loud"), because it would all be internal.
Those steps could either be all internal (looped), therefore hidden, or with partial external visibility due to emitting a token every N steps. The complaint about Astra, and moving in the direction of hidden COT, is that it makes these models far harder to monitor and debug.
I agree that most of what these companies doing - synthetic data, etc - amounts to trying to squeeze all the juice out of the pre-training data, although the recent trend of RL post-training via agents running in custom task simulation environments does change that a bit - these environments, and the rewards they provide, are a new source of data.