The learning aspect could be strategically crucial with respect to what the first transformatively-useful AIs should look like; also see e.g. discussion here and here. In the sense that this should add further reasons to think the first such AIs should probably (differentially) benefit from learning from data using intermediate outputs like CoT; or at least have a pretraining-like phase involving such intermediate outputs, even if this might be later distilled or modified some other way—e.g. replaced with [less transparent] recurrence.
One additional probably important distinction / nuance: there are also theoretical results for why CoT shouldn’t just help with one-forward-pass expressivity, but also with learning. E.g. the result in Auto-Regressive Next-Token Predictors are Universal Learners is about learning; similarly for Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks, Why Can Large Language Models Generate Correct Chain-of-Thoughts?, Why think step by step? Reasoning emerges from the locality of experience, Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks.
The learning aspect could be strategically crucial with respect to what the first transformatively-useful AIs should look like; also see e.g. discussion here and here. In the sense that this should add further reasons to think the first such AIs should probably (differentially) benefit from learning from data using intermediate outputs like CoT; or at least have a pretraining-like phase involving such intermediate outputs, even if this might be later distilled or modified some other way—e.g. replaced with [less transparent] recurrence.