How to make System 1 LLMs show System 2 competencies?

Commenters spent far less time on techniques for eliciting deliberate reasoning than on whether the System 1/System 2 label fits language models at all. The strongest objection reverses the article’s premise: autoregressive models produce tokens one after another, can articulate intermediate reasoning, and therefore resemble a mechanized version of slow, verbal deliberation more than fast intuition. From that view, the major capability gap is not teaching a reflexive model to reason, but giving text-only systems the embodied, emotional, and sensorimotor machinery associated with instinctive judgment and perhaps consciousness. Others argued that mapping a human psychological metaphor onto transformer internals is simply category error. Sequential output may resemble one aspect of deliberation, but it does not establish that model computation has the same organization as human cognition; proposed parallels between holistic processing and diffusion models were similarly tentative. Several readers also attacked the intellectual foundation of the terminology, saying the popularized dual-process account rests on contested psychology and has become AI-industry jargon rather than a rigorous technical framework. The only real convergence was that architecture and training should be discussed concretely, not smuggled into an anthropomorphic two-system story. No commenter supplied evidence that a recurrent internal-language approach would reliably create the desired capabilities, and one explicitly warned against it.