Nathaniel Whittemore
speaker
51,694 appearances
202 recordings
3 series
first heard Oct 2025
last heard yesterday
Nathaniel Whittemore’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 202 in all, peaking in Jun 2026 with 31.
Appearances
Whereas existing LLMs optimize for human preference, i.e., write-ups and chat responses that human raiders prefer, the new System 1 models, the first of which is JEV, optimize for calibrated decisions, or answers with epistemically honest probabilities.
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Yeah.
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So what does that actually mean?
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Well, let's look at how Mike Taylor from Every describes it.
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He writes, think of it as a smart if-then statement that determines what happens next when you're automating a workflow.
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Say you're building software that prioritizes customer service requests, and you write code that asks the model, does this customer sound angry?
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Jev might answer 0.9, which means there's an estimated 90% probability that the answer is yes based on.
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What the model learned in training.
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You could also provide categories you define like annoyed, irritated, offended, furious, and enraged, and learn that the customer was 60% likely to be classified as furious, with only a 10% probability of being enraged.
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Going on to explain why this matters and how it differs in LLMs, Mike continues, with an answer of 0.9, very likely to be angry, the software might automatically proceed to escalate the
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the customer concern to a manager.
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Or if it answers 0.1, not likely to be angry, that request might be deprioritized.
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However, chatbots are trained to respond with flowery text, like, you're absolutely right, this customer does sound very angry.
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Would you like me to compose a draft email response in a friendly, supportive tone?
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This text response would cause the program you're building to crash because it was expecting a number between 0 and 1, not an essay.
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Teal Fellow Michael Lee says, this allows a class of decision-making that was neither suited to dumb, unintelligent code, nor to slow, expensive LLMs.
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As Chubby sums up, JEV is an AI model built for decisions rather than text generation.
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And it is important to note that the trade-off here is that this model does not generate text.
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It is not, in other words, a replacement for LLMs in general.
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It's a replacement for a certain category of work that LLMs do, where they've been very square peg mashed into a round hole to do it.
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