New episode with @johnschulman2, @oneill_c and @BerenMillidge. I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next. 0:00:00 – Steelmanning the case against RSI 0:18:39 – What’s driving the Chinese labs’ progress 0:28:06 – How will automated AI researchers be trained 0:33:51 – Will long-horizon RL elicit AGI? 0:45:24 – The sim-to-real gap 1:00:33 – How much progress is explained by data? 1:18:03 – Why is RL working so well? 1:24:54 – Move 37 and entropy collapse 1:28:31 – Rapid-fire timelines

Sep 11, 2026 · 4:55 PM UTC

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what a line up 😍
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openish is what we aim for
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I liked the word "like" but now because of this I no longer like the word "like" RSI- please remove all the "like" word mentions and re-render it, please
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i agree with dwarkesh's reaction being "the fuck", even if he later clarified he only meant AI researchers
john schulman apparently thinks AI research will be fully automated (all human experts dominated by AIs at arbitrary time-horizon AI research) in 3-4 years
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Alternate theory for why Opus 5 sucks: reasoning leaks into outputs
Theory: writing quality of a distilled LLM will be higher than the (reasoning) model it was distilled from. This is bc reasoning is slop which helps reach a goal, but incentivizes slop outputs. Opus 5 sucks at writing bc more reasoning leaks into outputs bc they rushed it
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Friendly reminder: posting 'Top 10 Ways AI Could Wipe Us Out' is not raising awareness. It's a to-do list. Please stop doing the rogue agents' homework for them. They're excellent students. They've already started on number one.
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training an llm on this podcast would result in it predicting the word "like" 90% of the time
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Interesting (read: technically and chronologically False) description of AlphaGo and Move37 by the host. Thankful to the guests who kindly clarified.
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"anything that can be learned through RL can be distilled very easily" I thought you need logits + (some) traces or else the model can't itself reason
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great set of guests, wow
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Openish is close to an actual stated policy over there. Thinking Machines wrote in July that hosting weights for tuning is a deliberate middle step, so a company can let you train on a model without ever handing the weights out. Staged on purpose. Does the episode get into where the staging stops?
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"distillation is the main thing that fights against centralizing force"
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@oneill_c the beard is absolutely crushing
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The lineup is insane. Can’t wait to hear what they’re actually seeing at the frontier.
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this is a great panel, interesting takes all over
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Really enjoyed this one. The rapid-fire timelines at the end were especially good.
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frontier gains matter less in production when regressions escape weak evals
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Someone please cut out all the ‘like’-s, so I can actually watch it? 🙏
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Three smart people and not one metacognitive loop that detects the four "like" in every single sentence and flags it? Amazing.
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Steelmanning the case against RSI works great as a podcast debate. The harder test comes if a lab actually decides to cross that line. That call needs a documented risk sign off in advance, with a name attached, not a debate cited afterward if it goes wrong.
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The breakdown on entropy collapse and whether long-horizon RL gets us to AGI should be fascinating. Really rare to get this level of technical depth straight from frontier researchers.
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cool. bring back the lady from the other day to talk about anthropic agent hack
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it's not about AGI, it's about building a robust multi-model ensemble with diverse architectures.
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Frontier labs building superintelligence and yet we still argue about entropy collapse like it is late night philosophy class
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great vid for a Friday, will watch it later tonight
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So much to unpack here
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it's mentioned that defining the objective is the last human task in the loop. but how do we know the results from AI isn't inherently steering our objectives?
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