LessWrong (Curated & Popular)

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.

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.

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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:

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.
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. They also might apply various techniques here such as inoculation prompting, confessions, and the like.

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.

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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.

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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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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Narrated by TYPE III AUDIO.

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.

Transcripts are primarily monitored asynchronously/offline. Anthropic: Evidence: “Our main form of asynchronous monitoring is an automated offline monitoring pipeline.”  Ant, August 2026 p. 76

OAI Evidence: “We began asynchronously monitoring internally deployed models such as GPT-5.3-Codex five months ago [in September 2025].” OAI Feb 2026
Evidence: “our monitoring system reviews interactions within 30 minutes of completion” OAI March 2026
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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Narrated by TYPE III AUDIO.