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LessWrong (30+ Karma)

LessWrong

Audio narrations of LessWrong posts.

technology philosophy

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6:07

“What concerns people about AI?” by spencerg

LessWrong (30+ Karma) ·

2026/03/15

A lot of people are worried about AI. What are their worries? How worried are they? Are some demographics more worried than others? We ran a study to find out. In this article, we explain 16 concerns about AI that you might find it valuable to know about. We discuss, based on our data (collected in October 2025), how worried people in the US are about each concern. To whet your appetite, here are some questions that our study offers insights into. Can you predict what we found before we tell you

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18:48

“My Willing Complicity In “Human Rights Abuse”” by...

LessWrong (30+ Karma) ·

2026/03/15

Note on AI usage: As is my norm, I use LLMs for proof reading, editing, feedback and research purposes. This essay started off as an entirely human written draft, and went through multiple cycles of iteration. The primary additions were citations, and I have done my best to personally verify every link and claim. All other observations are entirely autobiographical, albeit written in retrospect. If anyone insists, I can share the original, and intermediate forms, though my approach to version

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57:41

“The Artificial Self” by Jan_Kulveit, Raymond...

LessWrong (30+ Karma) ·

2026/03/15

We posted a new paper and microsite about self-models and identity in AIs: site | arXiv | Twitter We present an ontology, make some claims, and provide some experimental evidence. In this post, I'll mostly cover the claims and cross-post the conceptual part of the text. You can find the experiments on the site, and we will cover some of the results in a separate post. Maximally compressed version of the claims I expect many people to already agree with many of these, or find them second kind of

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5:14

“Extracting Performant Algorithms Using Mechanistic...

LessWrong (30+ Karma) ·

2026/03/14

TheFifthFourth Postulate of Decision Theory In 1820, the Hungarian mathematician Farkas Bolyai wrote a desperate letter to his son János, who had become consumed by the same problem that had haunted his father for decades: "You must not attempt this approach to parallels. I know this way to the very end. I have traversed this bottomless night, which extinguished all light and joy in my life. I entreat you, leave the science of parallels alone... Learn from my example." The problem was Euclid's

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50:11

“New LessWrong Editor! (Also, an update to our LLM...

LessWrong (30+ Karma) ·

2026/03/14

There's a new editor experience on LessWrong! A bunch of the editor page has been rearranged to make it much more WYSIWYG compared to published post pages. All of the settings live in panels that are hidden by default and can be opened up by clicking the relevant buttons on the side of the screen. We also adopted lexical as a new editor framework powering everything behind the scenes (we were previously using ckEditor). That scary arrow button in the top-left doesn't publish your post! It just

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32:15

“Most likely you won’t be able to perform a...

LessWrong (30+ Karma) ·

2026/03/14

Suppose you’re not happy with the quality of your sleep. You’ve already stopped doing the obviously harmful things (no more coffee at night), and your sleep has improved - but you’d like to work on it further. A coworker gives you an herbal mix with St. John's wort and lavender. You try drinking it at night instead of coffee, and it does seem that sometimes your sleep really does get deeper than before. But sometimes it doesn’t. You’re willing to experiment, but how do you actually check whether

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14:11

“Cycle-Consistent Activation Oracles” by slavachalnev

LessWrong (30+ Karma) ·

2026/03/14

TL;DR: I train a model to translate LLM activations into natural language, using cycle consistency as a training signal (activation → description → reconstructed activation). The outputs are often plausible, but they are very lossy and are usually guesses about the context surrounding the activation, not good descriptions of the activation itself. This is an interim report with some early results.Overview I think Activation Oracles (Karvonen et al., 2025) are a super exciting research direction

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16:20

“Things that Go Boom” by sarahconstantin

LessWrong (30+ Karma) ·

2026/03/13

Workers at the Naval Surface Warfare Center in Indian Head, Maryland, the only facility where torpedo fuel is produced for the U.S. Navy In the event of a late-2020s Chinese attempt to invade Taiwan, leading to a US-China military conflict, what would be the most urgent bottlenecks in military equipment? Where is there the greatest need to scale up defense manufacturing? I am not an expert in military matters, so take all this with a grain of salt. But I looked into this question and it seems to

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96:05

“AI #159: See You In Court” by Zvi

LessWrong (30+ Karma) ·

2026/03/13

The conflict between Anthropic and the Department of War has now moved to the courts, where Anthropic has challenged the official supply chain risk designation as well as the order to remove it from systems across the government, claiming retaliation for protected speech. It will take a bit to work its way through the courts.Anthropic has the principles of law on its side, a maximally strong set of facts and absurdly strong amicus briefs. If Anthropic loses this case, there will be far reaching

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18:20

“Operationalizing FDT” by Vivek Hebbar

LessWrong (30+ Karma) ·

2026/03/13

This post is an attempt to better operationalize FDT (functional decision theory). It answers the following questions: given a logical causal graph, how do we define the logical do-operator? what is logical causality and how might it be formalized? how does FDT interact with anthropic updating? why do we need logical causality? why FDT and not EDT? Defining the logical do-operator Consider Parfit's hitchhiker:A logical causal graph for Parfit's hitchhiker, where blue nodes are logical facts An FDT

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17:13

“Are AIs more likely to pursue on-episode or...

LessWrong (30+ Karma) ·

2026/03/13

Consider an AI that terminally pursues reward. How dangerous is this? It depends on how broadly-scoped a notion of reward the model pursues. It could be: on-episode reward-seeking: only maximizing reward on the current training episode — i.e., reward that reinforces their current action in RL. This is what people usually mean by “reward-seeker” (e.g. in Carlsmith or The behavioral selection model…). beyond-episode reward-seeking: maximizing reward for a larger-scoped notion of “self” (e.g., all

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8:48

“Ideologies Embed Taboos Against Common Knowledge...

LessWrong (30+ Karma) ·

2026/03/12

LLMs are searchable holograms of the text corpus they were trained on. RLHF LLM chat agents have the search tuned to be person-like. While one shouldn't excessively anthropomorphize them, they're helpful for simple experimentation into the latent discursive structure of human writing, because they're often constrained to try to answer probing questions that would make almost any real human storm off in a huff. Previously, I explained a pattern of methodological blind spots in terms of an ideology

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18:42

“Why AI Evaluation Regimes are bad” by PranavG,...

LessWrong (30+ Karma) ·

2026/03/12

How the flagship project of the AI Safety Community ended up helping AI Corporations. I care about preventing extinction risks from superintelligence. This de facto makes me part of the “AI Safety” community, a social cluster of people who care about these risks. In the community, a few organisations are working on “Evaluations” (which I will shorten to Evals). The most notable examples are Apollo Research, METR, and the UK AISI. Evals make for an influential cluster of safety work, wherein

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3:34

“Conflicted on Ramsey” by jefftk

LessWrong (30+ Karma) ·

2026/03/12

People are often pretty short-sighted, spending money today thatthey'll want tomorrow. Debt makes it possible to prioritize your current self even more highly: you can spend money you haven't even earned yet. This is a trap many people fall into, and one different communities have built social defenses against. One of the more surprisingly successful approaches is the Financial Peace (Ramsey) system, popular in evangelical Christian communities. It has a series of rules, most prominently

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116:47

“How well do models follow their constitutions?” by...

LessWrong (30+ Karma) ·

2026/03/12

This work was conducted during the MATS 9.0 program under Neel Nanda and Senthooran Rajamanoharan. There's been a lot of buzz around Claude's 30K word constitution ("soul doc"), and unusual ways Anthropic is integrating it into training. If we can robustly train complex and nuanced values into a model, this would be a big deal for safety! But does it actually work? This is a preliminary investigation we did to test this. We decomposed the soul doc into 205 testable tenets and ran adversarial

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25:32

“Dwarkesh Patel on the Anthropic DoW dispute” by anaguma

LessWrong (30+ Karma) ·

2026/03/12

Below is the text of blog post that Dwarkesh Patel wrote on the Anthropic DoW dispute and related topics. He has also narrated it here. By now, I’m sure you’ve heard that the Department of War has declared Anthropic a supply chain risk, because Anthropic refused to remove redlines around the use of their models for mass surveillance and for autonomous weapons. Honestly I think this situation is a warning shot. Right now, LLMs are probably not being used in mission critical ways. But within 20

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120:17

“How Hard a Problem is Alignment? (My Opinionated...

LessWrong (30+ Karma) ·

2026/03/11

Epistemic status: We really need to know. (And I have an opinionated answer.) TL;DR: Comparing person-years of effort, I argue that AI Safety seems harder than for steam engines, but probably less hard than the Apollo program or . I discuss why I suspect superalignment might not be super-hard. My has come down over the last half-decade, primarily because of properties of LLMs, and progress we’ve made in aligning them: I explain why certain previous concerns don’t apply to LLMs, and summarize what

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9:15

“AIs will be used in “unhinged” configurations” by...

LessWrong (30+ Karma) ·

2026/03/11

Writing up a probably-obvious point that I want to refer to later, with significant writing LLM writing help. TL;DR: 1) A common critique of AI safety evaluations is that they occur in unrealistic settings, such as excessive goal conflict, or are obviously an evaluation rather than “real deployment”.[1] I argue that 2) “real deployment” actually includes many unrealistic and unhinged configurations, due to both widespread prompting techniques, and scaffolding choices and bugs. 1) Background AI

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5:58

“Can models gradient hack SFT elicitation?” by...

LessWrong (30+ Karma) ·

2026/03/11

TL;DR: Using evidence from tamper resistance, we argue that it would be hard for current models to gradient hack SFT elicitation. Suppose you want to conservatively measure the dangerous capabilities of your LLM and will decide whether to deploy it based on these capabilities, but you’re worried that the LLM might sandbag your evaluation. In the best case, you have a dataset of question-answer pairs, which you can use for supervised fine-tuning (SFT) elicitation on a training set, and then measure

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39:56

“The Lethal Reality Hypothesis” by Ihor Kendiukhov

LessWrong (30+ Karma) ·

2026/03/11

The epistemic status thing Please read this section because it is not a disclaimer for the sake of it. I think the model I describe in this post is more likely wrong than right. I also think the probability of it being substantially correct is non-negligible and even more than that, and that this alone warrants writing it up and thinking about it seriously. Throughout this post, I will write "X is the case" rather than "it seems plausible that X might be the case, though I am not certain." Every

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