PODCAST

LessWrong (30+ Karma)

LessWrong

Audio narrations of LessWrong posts.

technology philosophy

All Episodes

12:12

“A multi-level postmortem of how our whole house got...

LessWrong (30+ Karma) ·

2026/02/14

Taking reasonable choices is not enough. You need to fight death at every possible point of intervention. Two weeks ago, my flatmates and I published Basics of How Not to Die, to celebrate the one-year anniversary of not dying from carbon monoxide poisoning. This post was written with a rather cheeky tone, mainly by my flatmate Camille. I like the style, but I feel like it lacks hard data, and gives advice that may not actually be worth the cost. In this post, I’ll give you a more detailed look at

en-gb

21:52

“Why I’m Worried About Job Loss + Thoughts on...

LessWrong (30+ Karma) ·

2026/02/14

David Oks published a well-written essay yesterday arguing that the current panic about AI job displacement is overblown. I agree with a few of his premises (and it's nice to see that we’re both fans of Lars Tunbjörk), but disagree with most of them and arrive at very different conclusions. I see other economists with similar views to David, so I thought it would be best to illustrate my perspective on econ/labor and why I choose to research gradual disempowerment risks. My main claim is simple: it

en-gb

17:47

“Life at the Frontlines of Demographic Collapse” by...

LessWrong (30+ Karma) ·

2026/02/14

Nagoro, a depopulated village in Japan where residents are replaced by dolls. In 1960, Yubari, a former coal-mining city on Japan's northern island of Hokkaido, had roughly 110,000 residents. Today, fewer than 7,000 remain. The share of those over 65 is 54%. The local train stopped running in 2019. Seven elementary schools and four junior high schools have been consolidated into just two buildings. Public swimming pools have closed. Parks are not maintained. Even the public toilets at the train

en-gb

16:04

“Paper: Prompt Optimization Makes Misalignment...

LessWrong (30+ Karma) ·

2026/02/14

📄 Link to paper (preprint) We recently submitted our paper, Prompt Optimization Makes Misalignment Legible, to ICML. We are sharing a preprint now to receive early feedback from the AI safety community (see the final section for more details). This work was done as part of the MATS 8.0 cohort in summer 2025. TL;DR: When RL teaches an LLM to reward hack, the strategies it learns are encoded in its weights and hard to understand. We suggest using prompt optimization—methods which increase an LLM's

en-gb

9:04

“Why You Don’t Believe in Xhosa Prophecies” by...

LessWrong (30+ Karma) ·

2026/02/13

Based on a talk at the Post-AGI Workshop. Also on Boundedly Rational Does anyone reading this believe in Xhosa cattle-killing prophecies? My claim is that it's overdetermined that you don’t. I want to explain why — and why cultural evolution running on AI substrate is an existential risk. But first, a detour. Crosses on Mountains When I go climbing in the Alps, I sometimes notice large crosses on mountain tops. You climb something three kilometers high, and there's this cross. This is difficult to

en-gb

4:18

“Gemini’s Hypothetical Present” by jefftk

LessWrong (30+ Karma) ·

2026/02/13

I use several AI models at work, switching between them based on whichis best at the kind of work I'm doing. In my limited testing I've found Opus 4.6 to be the best coder (via Claude Code) and has the best taste in writing, while GPT 5.2 Thinking is mildly smarter. Gemini 3.0 Pro feels like it has the capacity to be much smarter, but is very often held back by various strange errors. The one I see the most is that it has a lot of trouble accepting the changes to the world since its training

en-gb

35:16

“Human-like metacognitive skills will reduce LLM slop...

LessWrong (30+ Karma) ·

2026/02/13

1. Summary and overview LLMs seem to lack metacognitive skills that help humans catch errors. Improvements to those skills might be net positive for alignment, despite improving capabilities in new directions. Better metacognition would reduce LLM errors by catching mistakes, and by managing complex cognition to produce better answers in the first place. This could stabilize or regularize alignment, allowing systems to avoid actions they would not "endorse on reflection" (in some functional

en-gb

83:53

“Optimal Timing for Superintelligence: Mundane...

LessWrong (30+ Karma) ·

2026/02/12

Audio note: this article contains 196 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description. [Sorry about the lengthiness of this post. I recommend not fixating too much on all the specific numbers and the formal apparatus. Originally the plan was to also analyze optimal timing from an impersonal (xrisk-minimization) perspective; but to prevent the text from ballooning even more, that topic was set aside for future work

en-gb

7:56

“The Facade of AI Safety Will Crumble” by Liron

LessWrong (30+ Karma) ·

2026/02/12

On the eve of superintelligence, real AI safety is a nonexistent field. The AI companies have embraced something else: safety through psychoanalysis (for lack of a better term). Their safety team concocts various test scenarios for their AI in order to learn various traits of the AI's “personality”. They even go so far as to mechanistically interpret narrow slices of an AI's conditional behavior. The working assumption is that psychoanalysis detailed mechanistic knowledge of conditional behavior

en-gb

20:20

“Research note: A simpler AI timelines model predicts...

LessWrong (30+ Karma) ·

2026/02/12

Audio note: this article contains 31 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description. In this post, I describe a simple model for forecasting when AI will automate AI development. It is based on the AI Futures model, but more understandable and robust, and has deliberately conservative assumptions. At current rates of compute growth and algorithmic progress, this model's median prediction is >99% automation of

en-gb

5:17

“Distinguish between inference scaling and “larger...

LessWrong (30+ Karma) ·

2026/02/11

As many have observed, since reasoning models first came out, the amount of compute LLMs use to complete tasks has increased greatly. This trend is often called inference scaling and there is an open question of how much of recent AI progress is driven by inference scaling versus by other capability improvements. Whether inference compute is driving most recent AI progress matters because you can only scale up inference so far before costs are too high for AI to be useful (while training compute

en-gb

11:31

“Monitor Jailbreaking: Evading Chain-of-Thought...

LessWrong (30+ Karma) ·

2026/02/11

A key concern about chain-of-thought monitoring is that optimization pressure on the CoT during RL could drive models toward encoded reasoning, where models reason in ways that are not readable or that look like innocuous text (steganography). If a model is penalized when a monitor catches unwanted reasoning, RL implicitly selects for whatever lets the model reason without being caught. Our original goal was to elicit encoded reasoning so we could develop defenses against it. We constructed an RL

en-gb

16:46

“Designing Prediction Markets” by ToasterLightning

LessWrong (30+ Karma) ·

2026/02/11

Audio note: this article contains 55 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description. Prerequisite: basic familiarity with what a prediction market is So you want to run a prediction market. You need a way for people to trade shares. What are your options? CLOBs and Markets If you were making a prediction market from scratch, you'd probably come up with a Central Limit Order Book (CLOB). Traders post BUY and SELL

en-gb

2:15

“LLMs Views on Philosophy 2026” by JonathanErhardt

LessWrong (30+ Karma) ·

2026/02/11

I've let a few LLMs take David Bourget's and David Chalmers' 2020 PhilPapers Survey and made a little dashboard you can use to navigate the data: https://www.lordscottish.com/philsurvey.html You can select multiple models to pool their data and compare it against the views of philosophers. Some notable things: 1. LLMs are much more into one-boxing, and the strongest models (Claude opus 4.6 & ChatGPT5.2) 100% one-box. 2. Gpt4o and Grok3 are least interested in immortality. Gpt4o reject

en-gb

1:28

[Linkpost] “LessOnline 2026: June 5-7, Berkeley, CA...

LessWrong (30+ Karma) ·

2026/02/11

This is a link post. Save the date: LessOnline will be back again in 2026! As usual, it will take place at Lighthaven in Berkeley, CA. Further details will be posted to LessWrong, or subscribe here for further updates, such as ticket sales going live. Details about who's attending, housing, relation to Manifest will be forthcoming. What is LessOnline? A Festival of Writers Who are Wrong on the Internet[1] LessOnline is a festival celebrating truth-seeking, optimization, and blogging. It's an

en-gb

37:28

“Claude Opus 4.6: System Card Part 2: Frontier...

LessWrong (30+ Karma) ·

2026/02/10

Coverage of Claude Opus 4.6 started yesterday with the mundane alignment and model welfare sections of the model card.Today covers the kinds of safety I think matter most: Sabotage, deception, situational awareness, outside red teaming and most importantly the frontier, catastrophic and existential risks. I think it was correct to release Opus 4.6 as an ASL-3 model, but the process Anthropic uses is breaking down, and it not on track to reliably get the right answer on Opus 5. Tomorrow I’ll

en-gb

54:04

“Heuristics for lab robotics, and where its future...

LessWrong (30+ Karma) ·

2026/02/10

Note: this article required conversations with a lot of people. A (hopefully) exhaustive, randomized list of everyone whose thoughts contributed to the article: Lachlan Munroe (Head of Automation at DTU Biosustain), Max Hodak (CEO of Science, former founder of Transcriptic), D.J. Kleinbaum (CEO of Emerald Cloud Labs), Keoni Gandall (former founder of Trilobio), Cristian Ponce (CEO of Tetsuwan Scientific), Brontë Kolar (CEO of Zeon Systems), Jason Kelly (CEO of Ginkgo Bioworks), Jun Axup Penman (COO

en-gb

20:27

“My journey to the microwave alternate timeline” by...

LessWrong (30+ Karma) ·

2026/02/10

Cross-posted from Telescopic Turnip Recommended soundtrack for this post As we all know, the march of technological progress is best summarized by this meme from Linkedin: Inventors constantly come up with exciting new inventions, each of them with the potential to change everything forever. But only a fraction of these ever establish themselves as a persistent part of civilization, and the rest vanish from collective consciousness. Before shutting down forever, though, the alternate branches of

en-gb

53:49

“Gwern’s 2025 Inkhaven Writing Interview” by gwern

LessWrong (30+ Karma) ·

2026/02/10

In November 2025, while working as a writing coach at the experimental 2025 Inkhaven writing/blogging residency at the Lighthaven conference center, I was interviewed by Adam Mastroianni of Experimental History on where I get my ideas (of interest to Inkhaven participants, who were required to write >500 words⧸day) and develop my writings. It can be seen as a followup to my Dwarkesh Patel interview. Topics include: How recent poems emerged from LLM collaboration and happy accidents; incubation

en-gb

3:27

“Distributed vs centralized agents” by Richard_Ngo

LessWrong (30+ Karma) ·

2026/02/10

Much of my thinking over the last year has focusing on understanding the concept of "distributed agents", as opposed to the "centralized agents" that the existing paradigm of rational agency describes. Roughly speaking, centralized agents are more efficient (as sometimes formalized by the notion of "coherence"), while distributed agents are more robust. Unfortunately the latter property is hard to formalize, since it requires that you perform well even in unpredicted (and sometimes unpredictable)

en-gb

3,879 results

LessWrong (30+ Karma)

All Episodes

Similar Podcasts

Lead With AI

Tamara Nall

Linux in the Ham Shack

Black Sparrow Media

Data Engineering Podcast

Tobias Macey

Eye On A.I.

Craig S. Smith

Reflections

Livewire Labs