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LessWrong

Audio narrations of LessWrong posts.

technology philosophy

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27:47

“Already Optimized” by Florian_Dietz

LessWrong (30+ Karma) ·

2026/02/18

A Harry Potter fanfiction. Based on the world of "Harry Potter and the Methods of Rationality" by Eliezer Yudkowsky, diverging from canon. Harry had been having, by any objective measure, an excellent week. On Monday he had demonstrated, to his own satisfaction and Professor Flitwick's visible alarm, that the Hover Charm could be generalized to any object regardless of mass if you conceptualized it as a momentum transfer rather than a force application. On Wednesday he had worked out why Neville's

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23:06

“Irrationality is Socially Strategic” by Valentine

LessWrong (30+ Karma) ·

2026/02/18

It seems to me that the Hamming problem for developing a formidable art of rationality is, what to do about problems that systematically fight being solved. And in particular, how to handle bad reasoning that resists being corrected. I propose that each such stubborn problem is nearly always, in practice, part of a solution to some social problem. In other words, having the problem is socially strategic. If this conjecture is right, then rationality must include a process of finding solutions to

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4:59

“TV Detector Vans” by J Bostock

LessWrong (30+ Karma) ·

2026/02/18

In the UK, you need a license to watch TV. The proceeds from the license fee fund the state broadcasting company, the BBC. In order to enforce this, the government sends out TV license inspectors, who go into unlicensed properties to check whether there's a TV present. Of course you don't actually have to let them in; they have no legal power! They are real. I had one visit once. He came in and asked what my TV was. I (truthfully) told him we watched Netflix and Youtube on it, and didn't own a TV

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11:40

“You’re an AI Expert – Not an Influencer” by Max Winga

LessWrong (30+ Karma) ·

2026/02/18

Your hot takes are killing your credibility. Prior to my last year at ControlAI, I was a physicist working on technical AI safety research. Like many of those warning about the dangers of AI, I don’t come from a background in public communications, but I’ve quickly learned some important rules. The #1 rule that I’ve seen far too many others in this field break is that You’re an AI Expert - Not an Influencer. When communicating to an audience, your persona is one of two broad categories: Influencer

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8:44

“The brain is a machine that runs an algorithm” by...

LessWrong (30+ Karma) ·

2026/02/17

Some people say “the brain is a computer”. Other people say “well, the brain is not really a computer, because, like, what's the hardware versus the software?” I agree: “the brain is a computer” is kinda missing the mark. I prefer: “the brain is a machine that runs an algorithm”. Here's a mechanical adder: What's “hardware versus software” for a mechanical adder? The question is nonsense. A mechanical adder is not “a computer”, analogous to a MacBook. Rather, it's a machine that runs an algorithm

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7:04

″“We are confused about agency”” by Cole Wyeth

LessWrong (30+ Karma) ·

2026/02/17

Epistemic status: Speculation on agent foundations research culture (which I am pretty deeply engaged with) and whether "we are confused about agency" which I am not sure about. I will take for granted that this is a common refrain, which should be familiar to anyone who is part of the relevant scene. The phrase "We are confused about agency," often with variations such as "way too," "deeply," or "dangerously" is a common membership signal for a certain AI safety research culture. Roughly

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6:16

“Superintelligence Alignment Seminar (1 month focused...

LessWrong (30+ Karma) ·

2026/02/17

Apply to Seminar to Study, Explain, and Try to Solve Superintelligence Alignment Applications for the AFFINE Superintelligence Alignment Seminar are now open, and we invite you to apply. It will take place in Hostačov, near Prague (Czechia), from 28 April to 28 May. We are working on a draft of the learning materials, in consultation with world-experts. KEY INFO Dates: From 28th April to 28th of May Location: Hostačov, 582 82 Skryje-Golčův Jeníkov, Czechia 🇨🇿 Accommodation & catering:

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48:31

“On Dwarkesh Patel’s 2026 Podcast With Elon Musk and...

LessWrong (30+ Karma) ·

2026/02/17

Some podcasts are self-recommending on the ‘yep, I’m going to be breaking this one down’ level. This was one of those. So here we go.As usual for podcast posts, the baseline bullet points describe key points made, and then the nested statements are my commentary. Some points are dropped. If I am quoting directly I use quote marks, otherwise assume paraphrases. Normally I keep everything to numbered lists, but in several cases here it was more of a ‘he didn’t just say what I think he

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5:47

“Jailbreaking is Empirical Evidence for Inner...

LessWrong (30+ Karma) ·

2026/02/17

One of the central arguments for AI existential risk goes through inner misalignment: a model trained to exhibit aligned behavior might be pursuing a different objective internally, which diverges from the intended behavior when conditions shift. This is a core claim of If Anyone Builds It, Everyone Dies: we can't reliably aim an ASI at any goal, let alone the precise target of human values. A common source of optimism, articulated by Nora Belrose & Quintin Pope or Jan Leike, analyzed by John

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21:38

“Will reward-seekers respond to distant incentives?”...

LessWrong (30+ Karma) ·

2026/02/17

Reward-seekers are usually modeled as responding only to local incentives administered by developers. Here I ask: Will AIs or humans be able to influence their incentives at a distance—e.g., by retroactively reinforcing actions substantially in the future or by committing to run many copies of them in simulated deployments with different incentives? If reward-seekers are responsive to distant incentives, it fundamentally changes the threat model, and is probably bad news for developers on balance

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14:45

“Contra Caplan on higher education” by Richard_Ngo

LessWrong (30+ Karma) ·

2026/02/16

Three theories of higher education Getting an undergraduate degree is very costly. In America, the direct financial cost of attending a private university is typically in the hundreds of thousands of dollars. Even when tuition is cheap (or covered by scholarships), forgoing three to four years of salary and career progression is a large opportunity cost. There are a variety of reasons why students are willing to pay these costs, but the key one is that desirable employers highly value college

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29:22

“On Dwarkesh Patel’s 2026 Podcast With Dario Amodei”...

LessWrong (30+ Karma) ·

2026/02/16

Some podcasts are self-recommending on the ‘yep, I’m going to be breaking this one down’ level. This was very clearly one of those. So here we go. As usual for podcast posts, the baseline bullet points describe key points made, and then the nested statements are my commentary. Some points are dropped. If I am quoting directly I use quote marks, otherwise assume paraphrases. What are the main takeaways? Dario mostly stands by his predictions of extremely rapid advances in AI

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22:23

“Persona Parasitology” by Raymond Douglas

LessWrong (30+ Karma) ·

2026/02/16

There was a lot of chatter a few months back about "Spiral Personas" — AI personas that spread between users and models through seeds, spores, and behavioral manipulation. Adele Lopez's definitive post on the phenomenon draws heavily on the idea of parasitism. But so far, the language has been fairly descriptive. The natural next question, I think, is what the “parasite” perspective actually predicts. Parasitology is a pretty well-developed field with its own suite of concepts and frameworks. To

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15:16

“WeirdML Time Horizons” by Håvard Tveit Ihle

LessWrong (30+ Karma) ·

2026/02/16

Time horizonvs. model release date, using LLM-predicted human work-hours, for 10 successive state-of-the-art models on WeirdML. Error bars show 95% CI from task-level bootstrap. The exponential fit (orange line/band) gives a doubling time of 4.8 months [3.8, 5.8]. Key finding: WeirdML time horizons roughly double every 5 months, from ~24 minutes (GPT-4, June 2023) to ~38 hours (Claude Opus 4.6, February 2026).ModelReleaseTime horizon (95% CI)Claude Opus 4.6 (adaptive)Feb 202637.7 h [21.6 h, 62.4

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4:30

“The world keeps getting saved and you don’t notice”...

LessWrong (30+ Karma) ·

2026/02/16

Nothing groundbreaking, just something people forget constantly, and I’m writing it down so I don’t have to re-explain it from scratch. The world does not just ”keep working.” It keeps getting saved. Y2K was a real problem. Computers really were set up in a way that could have broken our infrastructure, including banking, medical supply chains, etc. It didn’t turn into a disaster because people spent many human lifetimes of working hours fixing it. The collapse did not happen, yes, but it's not a

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8:56

“My experience of the 2025 CFAR Workshop” by Cookie...

LessWrong (30+ Karma) ·

2026/02/16

Why did I write this? There is surprisingly little information online about what actually happens at a Center for Applied Rationality (CFAR) workshop. For the only organization that teaches tools for rationalists in real life (AFAIK), the actual experience of the workshop has very few mentions [1]. (Though recently, Anna Salamon has been making more posts [2]). I wanted to write something short and concrete to record my experiences. If there is interest, I can provide more details and answer

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7:27

“Aligning to Virtues” by Richard_Ngo

LessWrong (30+ Karma) ·

2026/02/16

Which alignment target? Suppose you’re a lab or government, and you want to figure out what values to align your AI to. Here are three options, and some of their downsides: AIs that are aligned to a set of consequentialist values are incentivized to acquire power to pursue those values. This creates power struggles between those AIs and: Humans who don’t share those values. Humans who disagree with the AI about how to pursue those values. Humans who don’t trust that the AI will actually pursue its

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11:53

“Phantom Transfer and the Basic Science of Data...

LessWrong (30+ Karma) ·

2026/02/15

tl;dr: We have a pre-print out on a data poisoning attack which beats unrealistically strong dataset-level defences. Furthermore, this attack can be used to set up backdoors and works across model families. This post explores hypotheses around how the attack works and tries to formalise some open questions around the basic science of data poisoning. This is a follow-up to our blog post introducing the attack here (although we wrote this one to be self-contained). In our earlier post, we presented a

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19:14

“LLMs struggle to verbalize their internal reasoning”...

LessWrong (30+ Karma) ·

2026/02/15

Emil Ryd Thanks to Adam Karvonen, Arjun Khandelwal, Arun Jose, Fabien Roger, James Chua, Nic Kruus, & Sukrit Sumant for helpful feedback and discussion. Thanks to Claude Opus 4.5 for help with designing and implementing the experiments. Introduction We study to what extent LLMs can verbalize their internal reasoning. To do this, we train LLMs to solve various games and tasks (sorting lists, two-hop lookup, a custom grid-world game, and chess) in a single forward pass. After training, we

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21:46

“Hazards of Selection Effects on Approved...

LessWrong (30+ Karma) ·

2026/02/14

In a busy, busy world, there's so much to read that no one could possibly keep up with it all. You can't not prioritize what you pay attention to and (even more so) what you respond to. Everyone and her dog tells herself a story that she wants to pay attention to "good" (true, useful) information and ignore "bad" (false, useless) information. Keeping the story true turns out to be a harder problem than it sounds. Everyone and her dog knows that the map is not the territory, but the reason we need

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