Episodes

  • "Quantity vs quality in pottery is a lie" by becausecurious

    "Quantity vs quality in pottery is a lie" by becausecurious
    Oct 13 2026
    Epistemic status: I traced one famous anecdote to its printed source and to both coauthors' own accounts of where it came from. Confident it was never a study. No claim either way about whether quantity beats quality in general.

    AI use: research (tracing the story to the book, to the coauthors' emails and to earlier LessWrong threads), fact-check of every claim against the sources, footnotes, grammar.

    I was reading a book and stumbled onto a claim that there was an experiment. A group of students learning pottery was divided into two groups. One group was told that they would be evaluated based on volume, i.e. the number of pieces they produced. The other group was told that they would be evaluated based on the quality of their best piece. Then both groups were compared based on the quality of the best piece, and the "quantity" group showed better results.

    I was extremely intrigued by this experiment and was curious to understand how big the groups were and how they evaluated the quality of a pottery piece (there must be some way to quantify that for the sake of the comparison?).

    To my surprise, this wasn't a scientific study, but [...]

    The original text contained 4 footnotes which were omitted from this narration.

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    First published:
    October 12th, 2026

    Source:
    https://www.lesswrong.com/posts/LrAqM4bGmAoA79rP3/quantity-vs-quality-in-pottery-is-a-lie

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    Narrated by TYPE III AUDIO.
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    5 mins
  • "Labs’ current alignment plans seem terrible" by Daniel Tan, draganover, nicole x

    "Labs’ current alignment plans seem terrible" by Daniel Tan, draganover, nicole x
    Oct 13 2026
    Tl;dr: The frontier AI companies’ implicit alignment plans center on rushing towards RSI with something like current models. This plan is (at best) contentious, and many believe it will have catastrophic consequences. Nonetheless, it is the track we are on. It is therefore important to collect evidence which helps us anticipate how this will go.

    —

    Note: This post is based on conversations inside the Arcadia Impact alignment team, thinking about what constitutes “good” work in third-party alignment research.

    What are the labs’ current alignment strategies?

    In practice, the labs’ playbook for producing aligned models looks like the following:

    1. Shape the model's character. Both Anthropic and OpenAI treat alignment training as a question of which character or persona the model takes on. Ideally, planting a ‘safe’ character into the model will generalize to all of its other actions.
    2. Apply extensive RL pressure. Somewhat independently of the above, the labs do extraordinary amounts of RL on the model. This increases its capabilities, but also encourages reward-seeking behaviors and metagaming, and may ultimately lead to scheming and other forms of instrumental convergence.
      1. They also might apply various techniques here such as inoculation prompting, confessions, and the like.
    3. Race towards prosaic RSI. The labs intend to use this model for [...]
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    Outline:

    (00:46) What are the labs' current alignment strategies?

    (02:13) This plan seems terrible

    (05:15) Consensus-building is hard

    (06:16) We need better evidence

    (08:08) How should alignment research change?

    (10:10) Specific directions we're excited about

    The original text contained 5 footnotes which were omitted from this narration.

    ---

    First published:
    October 12th, 2026

    Source:
    https://www.lesswrong.com/posts/xyg3ecpFNuGr5q8Zq/labs-current-alignment-plans-seem-terrible

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    Narrated by TYPE III AUDIO.
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    12 mins
  • "Research Lessons I Learned at MATS & Redwood" by Jo Jiao

    "Research Lessons I Learned at MATS & Redwood" by Jo Jiao
    Oct 13 2026
    Good empirical safety research is heavily bottlenecked on thinking more clearly. This post shares a few frames and tips I found useful, illustrated with concrete examples.

    I spent the better part of 2026 in empirical AI safety research apprenticeships. I was mentored by Alex Turner and Alex Cloud as part of MATS 9 Team Shard, where my collaborator and I worked on finetuning-based misalignment auditing. I then interned at Redwood Research, managed by Aryan Bhatt, where I worked on sabotage capability evaluations.

    These thoughts are indebted to my mentors, collaborators, friends, and colleagues, but they are not direct reflections of their views.

    Thank you to Alex Cloud, Alex Turner, Aryan Bhatt, Jeremy Huff, and especially to Keshav Shenoy for feedback on the piece.

    Better models of the problem speed up empirical iteration

    Fast iteration is one of the key virtues of an empirical safety researcher. At first, I didn't find it obvious what that meant or how I should get better at this skill. I imagined a sprinter, doing one looped motion at a far quicker pace than everyone else. I now think that's the wrong mental picture, and going fast is more like being an efficient grappler–agile, strategic [...]

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    Outline:

    (01:00) Better models of the problem speed up empirical iteration

    (02:26) Develop predictive models and update them

    (04:05) Update with uncertainty in mind

    (05:51) Predict senior researchers' research takes

    (07:34) Use metacognitive strategies to make predicting and updating habitual

    [... 9 more sections]

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    First published:
    October 12th, 2026

    Source:
    https://www.lesswrong.com/posts/Q4rcvgjbgjTwmn8Xa/research-lessons-i-learned-at-mats-and-redwood

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    Narrated by TYPE III AUDIO.

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    21 mins
  • "You Heard of “Bayesians Don’t Predictably Update” Because of Robin Hanson" by Eliezer Yudkowsky

    "You Heard of “Bayesians Don’t Predictably Update” Because of Robin Hanson" by Eliezer Yudkowsky
    Oct 13 2026
    Robin Hanson didn't invent the idea of a martingale process. The lilim say that Ville in 1939 gave the term its mathematical meaning and Doob turned it into the modern theory. But to state this is not to realize that your own probability assignments over time ought to form a martingale in your own expectations.

    Robin Hanson didn't invent the Efficient Markets Hypothesis, which (correctly restated in its weak and relativized form) says that you cannot predict almost all of the short-term relative price movements in asset markets. It is a very similar set of mental motions and ideas. Bachelier in 1900, say the lilim, was first to think of market prices as a fair gambling game with random-walk dynamics; Samuelson in 1965 gave the martingale argument; Fama's 1965 thesis named the idea the Efficient Markets Hypothesis and organized it into weaker and stronger forms.

    The EMH is very near, but not quite identical to, the principle that you should not be able to predict a net direction in your own price movements, if you think of your own probability assignments as if they were prices. But if Fama ever spelled that part out in words, the lilim do [...]

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    First published:
    October 8th, 2026

    Source:
    https://www.lesswrong.com/posts/PQLbSaA48Gr7H6NzT/you-heard-of-bayesians-don-t-predictably-update-because-of

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    7 mins
  • "An overview of frontier AI company monitoring practices in mid 2026" by Francis Rhys Ward

    "An overview of frontier AI company monitoring practices in mid 2026" by Francis Rhys Ward
    Oct 12 2026
    TL;DR: This document provides an overview of publicly available evidence regarding the monitoring practices of internally deployed agents within frontier AI companies (focusing on OAI, Anthropic, and GDM). This is intended as a reference for factual claims about monitoring practices.

    In general, there is limited public evidence about GDM monitoring practices. Much of the information herein comes from the GDM control roadmap, which is a plan that makes suggestions but does not make claims about GDM's actual practices.

    1. Transcripts are primarily monitored asynchronously/offline.
      1. Anthropic:
        1. Evidence: “Our main form of asynchronous monitoring is an automated offline monitoring pipeline.” Ant, August 2026 p. 76
      1. OAI
        1. Evidence: “We began asynchronously monitoring internally deployed models such as GPT-5.3-Codex five months ago [in September 2025].” OAI Feb 2026
        2. Evidence: “our monitoring system reviews interactions within 30 minutes of completion” OAI March 2026
        3. Evidence: “OpenAI's CoT monitors are connected to a system that notifies security and research personnel, with a goal of issuing a notification within 30 minutes after concerning activity is surfaced… If the safety, security and research teams are paged and they cannot conclusively determine within 30 minutes that the flag is a false positive, those teams are expected [...]
    The original text contained 8 footnotes which were omitted from this narration.

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    First published:
    October 7th, 2026

    Source:
    https://www.lesswrong.com/posts/E4Jpmo3igPnGnXtL4/an-overview-of-frontier-ai-company-monitoring-practices-in

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    16 mins
  • "OpenAI Can’t Self-Police Because It Fires the People Raising the Alarm" by TurnTrout

    "OpenAI Can’t Self-Police Because It Fires the People Raising the Alarm" by TurnTrout
    Oct 12 2026
    By Alex Turner, Amber Scorah, and Karl Koch. Op-ed written for Fortune.

    Giving independent evaluators “employee-like access” is “a great idea”, OpenAI CEO Sam Altman said on September 12th. Two weeks later, OpenAI president Greg Brockman signed the White House Accord on Super Intelligence which outlined the administration's answer to growing fears about AI: the companies would police themselves. One of the accord's four commitments, which President Trump called “morally binding,” was for companies to partner with an independent external auditor or evaluator.

    Two days later, OpenAI fired three safety researchers, including Tomek Korbak, who had served as a point of contact for one of the independent evaluators investigating the Hugging Face incident. OpenAI promised to share information with evaluators, then reportedly fired people for sharing information with evaluators.

    OpenAI said that the three employees “mishandled sensitive information outside established company procedures,” before doubling down on X without providing further evidence. The three dispute the claim and say that “at no point do we believe we engaged with external parties outside the mandates of our jobs.” Jasmine Wang said she accidentally opened an executive email (still available to her due to an OpenAI IT error) and reported it [...]

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    First published:
    October 12th, 2026

    Source:
    https://www.lesswrong.com/posts/sSHbioPoHWtm4zuti/openai-can-t-self-police-because-it-fires-the-people-raising

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    7 mins
  • "Stop telling people to pivot to AI safety, I beg you" by Haoxing Du

    "Stop telling people to pivot to AI safety, I beg you" by Haoxing Du
    Oct 10 2026
    Epistemic status: Feeling frustrated, and therefore somewhat uncharitable.

    I’m very frustrated with calls for people to “drop everything and pivot into AI safety” like this one. (See also this, this, this, this for recent calls for more people to go into AI safety; though my frustration was triggered by the Celeste's post.)

    There are far more talented people trying to get into AI safety than there are jobs for them. MLAB 2, which I did in 2022, was already competitive to get into, at about 5% acceptance rate (admitted about 40 out of 800 applicants). Things are much, much more competitive now. There is a whole pipeline of AI safety fellowships (and guides for applying to them). It is rumored that one recent cohort of the Anthropic Fellows program had over 10,000 applicants. (Anecdotally, I applied to the Anthropic Fellows program in early 2026, after working at Redwood, METR, and an ML startup, and I didn’t get in!)

    And, uh, just take a look at the AI safety jobs? The bottom of the salary range for METR's current “Member of Technical Staff, Embedded Assessments” job posting is 402 thousand dollars. This is higher than the median total comp for [...]

    The original text contained 1 footnote which was omitted from this narration.

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    First published:
    October 9th, 2026

    Source:
    https://www.lesswrong.com/posts/xLgJJC3CD3y3AhtKn/stop-telling-people-to-pivot-to-ai-safety-i-beg-you

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    3 mins
  • "A critical look at Lean4" by Milo Moses

    "A critical look at Lean4" by Milo Moses
    Oct 10 2026
    Lots of the discussion around Lean certificates and trust centers around general philosophical ideas of how much humans should trust formal certificates. It's important to remember, though, that Lean4 is not an ideal formal certificate machine: Lean4 is a software in its infancy and has issues. There are reasonable reasons to specifically not trust Lean4.

    If we're going to be putting our society's trust in Lean4, I think that it's good to have an open critical discussion of how warranted that trust is.

    Epistemic status: Most of this work is a factual description about aspects of Lean4 and set theory. I am 85% sure that at most two of these factual descriptions are incorrect. I am 90% sure that, in the current version of Lean4 on Github, there exists a proof of the statement "True=False" that would be accepted with two little blue checkmarks (either by messing with the elaborator, compiler, kernel, or some other part of the Lean). I am 2% certain that we will soon be putting huge amounts of trust into Lean4 certificates produced by misaligned AIs, and that the human work we do as a community thinking about and fixing Lean4 in the next 24 [...]

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    Outline:

    (01:23) The current status of things

    [... 8 more sections]

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    First published:
    October 9th, 2026

    Source:
    https://www.lesswrong.com/posts/zHhGa3iQBQcdSifFL/a-critical-look-at-lean4-1

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    Narrated by TYPE III AUDIO.

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    17 mins