LessWrong (30+ Karma)

LessWrong

Audio narrations of LessWrong posts.

  1. fa 1 h

    “WSJ Article Claiming China Has Matched Anthropic Is Obvious Nonsense” by Zvi

    The Wall Street Journal printed an outright false headline and heavily misleading story claiming this, which of course was uncritically amplified by the usual suspects. I post this now on its own so that we have a place to link to, to explain the situation. Headline News WSJ Headline (Obvious Nonsense): ​China Has Matched Anthropic in Cybersecurity, Resetting AI Race. That. Did. Not. Happen. The post even claims, explicitly, that Claude Opus 4.8 similarly ‘matches’ Claude Mythos, a claim which is even more obviously false. Shame upon the Wall Street Journal. I fear Gell-Mann Amnesia. If they can get something as important as this so completely wrong, what about everything else? I am skipping over the parts that involve accurate reporting, or minor quibbles. It seems important to focus on clearly debunking the central false claims. Alas, the mistakes made here very much rhyme with mistakes being made throughout all this by the White House, and that get latched onto by certain bad actors, who have played a large part in leaving us unprepared for the Mythos Moment. For a full understanding of GLM-5.2, which is indeed an impressive [...] --- Outline: (00:27) Headline News (02:09) What Makes Mythos Special (03:16) Going Over The Detailed Claims (07:38) One Helpful Note (08:18) The Overall Impression Is Extremely Wrong (08:48) All Of This Has Happened Before And Will Happen Again --- First published: June 29th, 2026 Source: https://www.lesswrong.com/posts/bpBYm5jiS4tpyzuDS/wsj-article-claiming-china-has-matched-anthropic-is-obvious --- 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.

    10 min

  2. fa 1 h

    “P(doom) is a Dumb Meme” by Max Harms

    Look, I'm as much of a Rationalist with a special interest in AI x-risk as anyone. But oh my god do I hate talking about "P(doom)". When it first started showing up in the wake of ChatGPT, I assumed that it was floating around variously adjacent circles of faux-intellectuals, but surely everyone in my circles could see how braindead it was... right? (This post was partially inspired by a recent conversation with Liron about Doom Debates.[1]) I guess it's time for me to focus on a place where I'm shocked that everyone else is dropping the ball.[2] P(doom) is Hopelessly Vague Let's start with the ambiguity. Does "doom" mean... extinction? A lot of people think so! I have personally encountered people who think catastrophic harms from AI are likely, but the risks of all humans dying are low. They're like "Sure, 99.999% of humans might die from AI, but the AI will obviously want to keep thousands of humans alive for science and potential trade with aliens and stuff, so my P(doom) is approximately 0%." That might sound crazy. Surely you, dear reader, know exactly what "doom" means. You know, for example, which of these count as doom and [...] --- Outline: (00:45) P(doom) is Hopelessly Vague (04:09) Inside Views, Outside Views, and Likelihood Ratios (08:31) P(doom) is Fatalistic (13:03) Counterarguments (16:25) A Sense That More Is (Memetically) Possible The original text contained 8 footnotes which were omitted from this narration. --- First published: June 29th, 2026 Source: https://www.lesswrong.com/posts/6h7aAd4aw8YgCAbF6/p-doom-is-a-dumb-meme --- 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.

    18 min

  3. fa 4 h

    “GPT-5.6: The System Card” by Zvi

    While we wait for a general release, the system card is the best hint as to what is going on with the new candidate for America's Next Top Model, GPT-5.6. This is only an OpenAI model card, so by my standards it's a light read. There's a lot of things that you get in an Anthropic card, that are missing in an OpenAI card. Overall, the card gives a clear and consistent impression that GPT-5.6-Sol is a substantial improvement over GPT-5.5, but still short of Mythos. OpenAI calls it a ‘step function better’ than GPT-5.5. That seems accurate. OpenAI: Sol is our new flagship and a step function better than GPT-5.5. Terra delivers performance competitive to GPT-5.5 at 2x lower cost. Luna is our most cost-efficient model, delivering strong capability at our lowest cost. Together, the GPT-5.6 family gives people and developers more choice in how they balance intelligence, speed, and cost. Once available, pricing for GPT-5.6-Sol will be $5/$30, the same as GPT-5.5. Terra is $2.5/$15, Luna is $1/$6. They claim it will be on Cerebras at 750 TPS, which is insanely fast. Capacity will be limited, at least at first. [...] --- Outline: (03:49) What's In A Name? (04:26) Fix This Code (07:08) Crossover Event Requested (07:43) Disallowed Content (3) (09:03) Avoiding Accidental Data-Destructive Actions (3.3) (09:29) Are You Sure? (3.4) (09:58) Jailbreaks (4.1) (10:14) Prompt Injection (4.2) (10:40) HealthBench (5.1) (11:00) Dynamic Mental Health Adversarial User Simulations (5.2) (12:21) Hallucinations (6) (12:50) Isolated Misaligned Actions (7.1) (13:10) Going Overboard (7.2) (18:11) Chain of Thought Evaluations (7.3) (19:18) Bias (8) (19:27) Preparedness (9) (20:15) Biological Risks (9.1.1) (22:15) Cybersecurity (9.1.2) (28:40) External Cyber Evaluation FrontierCyber from Irregular (9.1.2.5) (30:32) Cyber Conclusions (31:07) Recursive Self-Improvement (9.1.3) (32:22) METR Warns Us (9.1.3.6) (35:04) Everything Is Under Control (37:44) Metagaming (7.4) (40:17) Apollo Research and Sandbagging (43:09) Safeguards (9.3) (50:01) Better Not Call Sol Yet The original text contained 2 footnotes which were omitted from this narration. --- First published: June 28th, 2026 Source: https://www.lesswrong.com/posts/JFjNmPTbH8kL6xtp6/gpt-5-6-the-system-card --- 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.

    54 min

  4. fa 4 h

    “A reading list for generalists” by Dylan Bowman

    I, along with many others in AI safety, believe there is a shortage of generalists in the community and that there exist many projects and efforts that by default will not happen unless they are owned by a strong generalist[1][2][3]. As someone who is a reasonably good generalist, I decided to assemble a reading list of the essays and blog posts that have personally helped me the most. I would love others to comment with pieces they think should be on this list. The crux of this reading list is the idea that if you’re working hard as a generalist on a project you care a lot about, then by rigorously applying the lessons from these documents you will improve more quickly than you otherwise would. By the numbers: I’ve attached 18 documents to start this reading list.The authors cited more than once are Paul Graham (5), Ben Kuhn (4), Ethan Perez (2), and Greg Brockman (2). Sam Altman and Eliezer Yudkowsky also have their fingerprints over a lot of the content.The items are 15 blog posts, 1 blog comment, 1 interview transcript in blog post form, and 1 book. Dispositional What characteristics should you [...] --- Outline: (01:15) Dispositional (01:41) Strategy (03:09) Project leadership (04:10) Interpersonal/organizational The original text contained 3 footnotes which were omitted from this narration. --- First published: June 28th, 2026 Source: https://www.lesswrong.com/posts/sH4cFDDjRdGrn3p2o/a-reading-list-for-generalists --- Narrated by TYPE III AUDIO.

    5 min

  5. fa 15 h

    “What comes with cheap math?” by abramdemski

    Thanks to conversations with Anson Berns, Gurkenglass, Roman Malov, Sahil, Sam Eisenstat, and others. Over the past two months, I've been doing a lot of "vibe research" (like vibe coding, but for research). Anson Berns started coming to my office hours, and we've been collaborating on a project modeling trust between logical inductors. In addition to talking once a week, we've been exchanging raw AI chats as well as AI-generated summaries of what has been done (the raw chats are nice because they allow me to generate my own AI summaries focusing on what I'm most curious about). I've been asking Claude to use Lean to verify everything, so there's a somewhat good chance there's real results of interest here, but I haven't (yet) been reading the Lean proofs (or even the theorem statements) -- instead I've just been chatting with AI about how the Lean proofs went and whether they really formalized what was claimed in english+latex, and focused on understanding the proofs myself in the same way I'd normally read a math paper. There have already been several times when this methodology has caught big gaps between what was claimed and what was verified in Lean, so [...] --- First published: June 28th, 2026 Source: https://www.lesswrong.com/posts/gS5skwXeeQdStwsPu/what-comes-with-cheap-math --- Narrated by TYPE III AUDIO.

    7 min

  6. fa 1 dia

    “Do LLMs Have Desires?” by Christopher Ackerman

    Work conducted with Yujun Zhou (yzhou25@nd.edu) and supported by SPAR TL;DR: In paired-choice paradigms, LLMs report consistent preferences over outcomes (e.g., types and number of lives saved, types of policies enacted)Some have suggested that this indicates that LLMs have human-like value systemsWe design an experimental framework where LLMs are able to modulate their output quality based on prompt contextWe find that LLMs modulate their output quality in response to effort exhortations, role-play instructions, and harmfulness cues, but NOT to opportunities to achieve the outcomes they report preferring in the paired-choice experimentsWe suggest that paired-choice paradigms do not provide evidence that LLMs have human-like (i.e., behavior-motivating) value systems, and that our paradigm offers a way to measure the degree to which LLMs have desires Paper describing the work in detail here LLMs report that they prefer some things to others. In paired-choice experiments, where they are repeatedly presented with two options and asked to select the one that they prefer, coherent utility structures emerge: LLMs consistently report preferring certain types of things, and their choices reveal the ability to make quantitative tradeoffs between things and exhibit transitivity (e.g., if they choose A over B and [...] --- First published: June 28th, 2026 Source: https://www.lesswrong.com/posts/8GvYyqDuQDJnEAky3/do-llms-have-desires --- 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.

    14 min

  7. fa 1 dia

    “Agents as Webs of Beliefs” by Richard_Ngo

    In this post I’ll sketch out an informal model of intelligent agents as webs of beliefs (or belief webs for short). The belief webs framework pulls together ideas from active inference, agent foundations and machine learning. In doing so it aims to unify beliefs, goals and actions as three facets of a single phenomenon. Few of these ideas are original to me, but I haven't seen anyone tie them together in a single place before. I've flagged the frameworks I'm drawing from throughout the post. Beliefs are held together by local consistency constraints The core premise of belief webs is that an agent's beliefs are typically locally consistent with nearby beliefs but not necessarily globally consistent with all its other beliefs (except, perhaps, in the limit of ideal rationality). This poses a problem for frameworks which describe agents in terms of a single probability distribution (as causal graphs, Solomonoff induction, and active inference do). Two frameworks which are capable of handling global inconsistency are Richardson's probabilistic dependency graphs (PDGs) and Garrabrant induction. (They focus on empirical inconsistency and logical inconsistency respectively, but I’ll abstract away from that difference for now.) We can roughly analogize the nodes in PDGs to [...] --- Outline: (00:40) Beliefs are held together by local consistency constraints (03:11) Actions are beliefs (07:27) Goals are beliefs (14:06) Open problems for belief webs The original text contained 6 footnotes which were omitted from this narration. --- First published: June 27th, 2026 Source: https://www.lesswrong.com/posts/M39Z2CvyfaxZdaxR4/agents-as-webs-of-beliefs --- Narrated by TYPE III AUDIO.

    17 min

  8. fa 1 dia

    “Austin & Oli on funding and incubating projects” by Austin Chen, habryka

    @habryka and I recently spoke about his plans to improve the AI safety funding ecosystem with a better S-Process platform, and my new incubator for EA/AIS software projects, Surplus (since launched; apply now!) We also cover: hot takes on different funders; what kinds of founders might succeed in the age of vibecoding; whether to do direct work or go meta; and what we respect and criticize in each other. Watch along here: I've transcribed the full conversation at https://peruse.sh/ep/austin-chen-and-oliver-habryka-on-funding-incubating-project. (Beware: the AI makes notable edits for readability, sometimes distorting what the speaker meant. If specific phrasing is cruxy, listen to the audio.) Selected quotes The cursed game of philanthropy Oli: "Philanthropy is one of the most cursed games in existence... The default outcome of what happens when rich people try to do philanthropy is that they think about starting a foundation, they imagine hiring someone on the market and ask themselves: who am I going to show up and feel comfortable trusting most of my net worth to? That doesn't make any sense. And so what they often end up doing is making a family office. The only way to solve this principal-agent problem is to choose [...] --- Outline: (00:55) Selected quotes (06:54) Chapters (08:32) Referenced links (09:02) Full transcript (09:07) Critiques of SFF's grant process \[0:00\] (11:26) The SFF application process \[2:26\] (12:50) The speculation grant freeze for advocacy orgs \[3:40\] (14:29) A lower-trust, more transparent funding process \[5:04\] (16:26) How the S-process works \[6:53\] (20:18) Naming and communicating the value to funders \[10:54\] (25:42) EA philanthropy and the principal-agent problem: Open Philanthropy, Longview \[15:51\] (31:11) How much funding is coming \[21:28\] (32:32) Surplus: the incubator \[22:33\] (34:46) Why for-profits over nonprofits \[24:37\] (37:33) The ideal founder profile \[27:11\] (40:52) Whether writers can found startups in the vibe-coding era \[30:26\] (42:19) Monetizing public communications projects \[31:45\] (53:00) Oliver's case for the incubator \[42:09\] (54:34) On professional grantmakers \[44:04\] (57:53) Whether infrastructure work is more direct than safety research \[47:36\] (01:01:41) The case for a better AI safety journal \[51:08\] (01:04:03) Mutual feedback \[53:17\] (01:10:04) How to help: LessWrong, Surplus, and the S-process \[1:01:01\] --- First published: June 27th, 2026 Source: https://www.lesswrong.com/posts/Jh2xsoySxacQDJMwz/austin-and-oli-on-funding-and-incubating-projects --- Narrated by TYPE III AUDIO.

    1 h 14 min
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Audio narrations of LessWrong posts.

Fitxa tècnica

  • Creació
    LessWrong
  • Anys en actiu
    2023 - 2026
  • Episodis
    250
  • Qualificació
    Explícit
  • Copyright
    © 2026 All rights reserved
  • Lloc web del programa
    LessWrong (30+ Karma)

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