Cal Newport
speaker
22,298 appearances
39 recordings
5 series
first heard Oct 2025
last heard 6d ago
Cal Newport’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 — 39 in all, peaking in Jun 2026 with 9.
Appearances
One is you need less tokens now to be output to get to a good answer because you essentially are allowing this musing, the rambling to happen internally before you generate tokens.
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So instead of having all of your rambling and tokens, you can actually do some of it inside the model.
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Less tokens means less computation, which means less expense.
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It also allows for smaller models, right?
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Because if it turns out like...
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if we just loop through this transformer block 10 times, that helps our answer.
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You know, that's...
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requires less parameters than actually just repeating that layer 10 times in your original model.
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If you loop on the same parameters, you have less numbers you have to store.
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And so the model footprint can be smaller, right?
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Well, memory on these GPUs is expensive.
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It's a core part of the expense.
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So the less memory you need to store a model you're using during inference or training, the cheaper things are going to be.
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It also potentially would give you faster results, right?
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Because
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looping internally on a block is a much faster computation than looping over the entire run of the LLM to generate token after token after token.
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That means you get lower latency, so the user gets an answer back faster.
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Now again, we don't know if that's exactly what OpenAI is doing, because at least as of me recording this, they haven't announced it.
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But this was what the information sources were saying, is there's some notion of a recurrent depth slash loop transformer style implementation.
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This is then why security researchers were upset because it just means you have less chain of thought tokens because you're doing more internally.
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Showing 221–240 of 22,298 · page 12 of 1115
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