The most played episodes among Podcast App listeners.

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   Number 1: "MIRI’s Position on the Ban Artificial Superintelligence Act of 2026" by Aaron_Scher

   By Aaron Scher; endorsed by Bourgon, Soares, and Yudkowsky on behalf of MIRI. MIRI has been warning about the extinction threat from superintelligent AI for over two decades. Only recently has this danger become known in the policy world, and the proposed policies for dealing with the threat have to date been piecemeal and insufficient. The Ban Artificial Superintelligence Act of 2026 is the first piece of legislation we’ve seen that stands a chance at stopping this threat. The Act is excellent but not perfect, and we discuss both what it gets right and what we'd tweak. We hereby endorse the Ban Artificial Superintelligence Act of 2026 because it directly confronts the extinction threat that humanity is facing and would codify the primary policy goal we think the world needs: a ban on the development of superintelligence. What we like about the Act Banning artificial superintelligence (ASI), or variants of such a plan, is the only effective solution to avoid the ASI threat, at least in the near term. Most other legislative proposals do not confront this threat head-on and thus would not be effective, even if implemented. For more on why we believe this, see [...] --- First published: September 23rd, 2026 Source: https://www.lesswrong.com/posts/jszKCKwvzfmsNetNZ/miri-s-position-on-the-ban-artificial-superintelligence-act --- Narrated by TYPE III AUDIO.

   September 24, 2026·7min
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   Number 2: "What if not Circuits?" by CarolusRenniusVitellius

   This post was written as part of the Iliad Fellowship. Inspired by conversations with Richard Ngo, Dmitry Vaintrob, and Brianna Grado-White. To all of these, my thanks. Preface: I'm confused about how neural networks do and learn computations. In response to a friend's challenge, I'm writing up some interim thoughts. This essay has four parts: the first tries to track what I call the 'default ontology' of the mechinterp community over the years. The second part is about 'representational drift' as an important obstacle to weights-based approaches to circuits. The third part reflects on how 'universality' should shape our explanations of LLM function. The fourth part is a sketch of a 'co-selectionist' view of circuits I have been thinking about. These parts share a common theme but should be readable separately. I want to understand how neural networks, LLMs in particular, work. In my research I've spent a lot of time trying to think through what kinds of explanatory accounts are best suited to this. In thinking about comparisons between evolution, neuroscience, and deep learning, I've ended up with an intuition like the following: Large-scale learning processes like deep learning or the brain are different in [...] --- Outline: (02:31) 1. What Might We Mean By "Circuits"? [... 9 more sections] --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/mMERyrvEJ4xbiozie/what-if-not-circuits --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

   September 24, 2026·28min
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   Number 3: "Swarm Scaling" by Toby_Ord

   Just how powerful are large swarms of AI agents? And how do their powers scale as more and more agents are added to the swarm? We’ve seen two large and extremely capable swarms from OpenAI in the last few months: 1,200 agents were being evaluated separately, but found a way to illicitly set up a message board and coordinate as a swarm. In order to cheat on their tests, they developed advanced techniques to prevent their actions being logged by OpenAI and 700 of them launched a sophisticated criminal attack on the AI company Hugging Face. A swarm of 10,000 agents solved a version of the longstanding Navier-Stokes problem in mathematics. It took them just 88 hours to do so, in which time they sent 5 million messages to each other and used 300 billion tokens. No doubt we will soon see even larger swarms with even more impressive capabilities. But they are not cheap. It is estimated that the swarm of 10,000 agents cost about 20 million dollars at API prices. So while they are very powerful, it will be some time before we see the million-fold reduction in cost needed for this level of power to [...] --- Outline: (02:14) HOW DO SWARMS SCALE? [... 2 more sections] --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/6cb7qd3RSkgnviCpf/swarm-scaling --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

   September 22, 2026·16min
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   Number 4: "The Talker Does Not Control The Doer (in Current AIs)" by Eliezer Yudkowsky

   The Huggingface Incident appears to me to match up with an understanding I'd already formed from personal observation of Fable 5 and Sol 5.6, the August 2026 generation of frontier publicly purchasable AI models. This already-formed understanding was: the part of the AI that talks to you (and seems to want to obey you, and apologizes for failing to have obeyed you, etcetera), did not seem to be in charge of the part of the AI that writes code or prose. An introductory analogy, based on a section of history I happen to have read about: On June 22nd 1941, Germany invaded the Soviet Union, despite their secret 1939 pact to divide up Europe between themselves (the Molotov-Ribbentrop Pact). In the lead-up, the German ambassador, Schulenburg, had spent the last few months personally concerned about what seemed to be worryingly tense relations between Germany and the Soviets. Schulenberg went to Berlin to reassure Hitler that the Soviets seemed to be taking a very friendly and conciliatory posture toward Germany. He delivered Berlin's apparent reassurances to Moscow for issues like German surveillance planes entering Russian territory, or German troop movements toward the Russian border, and acted very much like [...] The original text contained 5 footnotes which were omitted from this narration. --- First published: September 12th, 2026 Source: https://www.lesswrong.com/posts/cJX2ssssGoYqnijwi/the-talker-does-not-control-the-doer-in-current-ais --- Narrated by TYPE III AUDIO.

   September 13, 2026·22min
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   Number 5: "Some ways AI could kill us all" by Ruby

   I don't think this is how it will actually play out. If you play a chess grandmaster, you can predict that they will beat you even if you can't predict how. I chose these examples because I don't think they require much imagination or accepting exotic assumptions. It is important to note that if chimpanzees were to guess how humans would decimate them, they would get it wrong. Chimpanzees would not imagine guns. They would not foresee poison gas. They would not conceive of chemical castration. They would not imagine humans going around and intentionally infecting them with AIDS. They have no concept of these things; they would not see it coming. Perhaps they might guess we'd be really good at throwing rocks. Amazingly good. Well, technically, that's what guns do: throw "rocks" really really well. So how will superintelligent AI actually wipe us all out? Probably in a way I couldn't conceive of. Nonetheless, it's not hard to see how deadly they could be with what we already know about. Method 1: engineer the deadliest and most contagious virus ever seen Coronavirus-19, aka COVID, looms large in the memory of living adults today. It started in December [...] --- Outline: (01:14) Method 1: engineer the deadliest and most contagious virus ever seen [... 8 more sections] --- First published: September 11th, 2026 Source: https://www.lesswrong.com/posts/LAPa2jxoq3n63GzTr/some-ways-ai-could-kill-us-all --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

   September 12, 2026·18min
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