In late 2025, artificial intelligence companies like OpenAI popularized the idea of automating the process of selecting which model is best for a task. This allowed users to simply send their prompt or request and let the system determine whether to respond using a “fast” or a “slow” reasoning model.
In a 2022 paper, I suggested that this kind of strategic deployment of slow, more reflective reasoning could be crucial to good judgment and decision-making. Since then, I have been synthesizing the arguments and evidence for this view in a paper titled, “Strategic Reflectivism in Intelligent Systems”.
Now that the new paper’s accepted in Lecture Notes In Computer Science, I recorded a podcast of me reading the final proofs of the paper, which argues that one key to intelligence in humans and machines is pragmatic switching between intuitive and reflective thinking based on the goals of the system. The paper has a wide range of implications for applied science, computer science, decision science, and epistemology.
Continue reading Upon Reflection, Ep. 16: Strategic ReflectivismPodcast: Play in new window | Download (Duration: 29:00 — 39.8MB)
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