LessWrong (Curated & Popular)

Six years ago, as covid-19 was rapidly spreading through the US, mysister was working as a medical resident. One day she was handed anN95 and told to "guard it with her life", because there weren'tany more coming.

N95s are made from meltblown polypropylene, produced from plasticpellets manufactured in a small number of chemical plants. Buildingmore would take too long: we needed these plants producing allthe pellets they could.

Braskem America operated plants in Marcus Hook PA and Neal WV. Ifthere were infections on-site, the whole operation would need to shutdown, and the factories that turned their pellets into mask fabricwould stall.

Companies everywhere were figuring out how to deal with this risk.The standard approach was staggering shifts, social distancing,temperature checks, and lots of handwashing. This reduced risk, butit was still significant: each shift change was an opportunity forsomeone to bring an infection from the community into the factory.

I don't know who had the idea, but someone said: what if wenever left? About eighty people, across both plants, volunteeredto move in. The plan was four weeks, twelve-hour [...]

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First published:
February 27th, 2026

Source:
https://www.lesswrong.com/posts/HQTueNS4mLaGy3BBL/here-s-to-the-polypropylene-makers

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I believe deeply in the existential importance of using AI to defend the United States and other democracies, and to defeat our autocratic adversaries.

Anthropic has therefore worked proactively to deploy our models to the Department of War and the intelligence community. We were the first frontier AI company to deploy our models in the US government's classified networks, the first to deploy them at the National Laboratories, and the first to provide custom models for national security customers. Claude is extensively deployed across the Department of War and other national security agencies for mission-critical applications, such as intelligence analysis, modeling and simulation, operational planning, cyber operations, and more.

Anthropic has also acted to defend America's lead in AI, even when it is against the company's short-term interest. We chose to forgo several hundred million dollars in revenue to cut off the use of Claude by firms linked to the Chinese Communist Party (some of whom have been designated by the Department of War as Chinese Military Companies), shut down CCP-sponsored cyberattacks that attempted to abuse Claude, and have advocated for strong export controls on chips to ensure a democratic advantage.

Anthropic understands that the Department of War, not [...]

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First published:
February 26th, 2026

Source:
https://www.lesswrong.com/posts/d5Lqf8nSxm6RpmmnA/anthropic-statement-from-dario-amodei-on-our-discussions

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This post is partly a belated response to Joshua Achiam, currently OpenAI's Head of Mission Alignment:

If we adopt safety best practices that are common in other professional engineering fields, we'll get there … I consider myself one of the x-risk people, though I agree that most of them would reject my view on how to prevent it. I think the wholesale rejection of safety best practices from other fields is one of the dumbest mistakes that a group of otherwise very smart people has ever made. —Joshua Achiam on Twitter, 2021

“We just have to sit down and actually write a damn specification, even if it's like pulling teeth. It's the most important thing we could possibly do," said almost no one in the field of AGI alignment, sadly. … I'm picturing hundreds of pages of documentation describing, for various application areas, specific behaviors and acceptable error tolerances … —Joshua Achiam on Twitter (partly talking to me), 2022

As a proud member of the group of “otherwise very smart people” making “one of the dumbest mistakes”, I will explain why I don’t think it's a mistake. (Indeed, since 2022, some “x-risk people” have started working towards these kinds [...]

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Outline:

(01:46) 1. My qualifications (such as they are)

(02:57) 2. High-reliability engineering in brief

(06:02) 3. Is any of this applicable to AGI safety?

(06:08) 3.1. In one sense, no, obviously not

(09:49) 3.2. In a different sense, yes, at least I sure as heck hope so eventually

(12:24) 4. Optional bonus section: Possible objections & responses

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First published:
February 2nd, 2026

Source:
https://www.lesswrong.com/posts/hiiguxJ2EtfSzAevj/are-there-lessons-from-high-reliability-engineering-for-agi

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Example of OpenErrata nitting the Sequences I just published OpenErrata on GitHub, a browser extension that investigates the posts you read using your OpenAI API key and underlines any factual claims that are sourceably incorrect. Once finished, it caches the results for anybody else reading the same articles so that they get them on immediate visit. If you don't have an OpenAI key, you can still view the corrections on posts other people have viewed, but it doesn't start new investigations.

I've noticed lately that while people do this sort of thing by pasting everything you read into ChatGPT, A. They don't have the time to do that, B. It duplicates work, and C. It takes around ~5 minutes to get a really good sourced response for most mid-length posts. I figure most of LessWrong is reading the same stuff, so if a good portion of the community begins using this or an extension like it, we can avoid these problems.

Here is OpenErrata at work with some recent LessWrong & Substack articles, published within the last week. I consider myself a cynical person, but I'm a little surprised at what a high percentage of the articles I read make [...]

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First published:
February 24th, 2026

Source:
https://www.lesswrong.com/posts/iMw7qhtZGNFxMRD4H/open-sourcing-a-browser-extension-that-tells-you-when-people

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TL;DR

We describe the persona selection model (PSM): the idea that LLMs learn to simulate diverse characters during pre-training, and post-training elicits and refines a particular such Assistant persona. Interactions with an AI assistant are then well-understood as being interactions with the Assistant—something roughly like a character in an LLM-generated story. We survey empirical behavioral, generalization, and interpretability-based evidence for PSM. PSM has consequences for AI development, such as recommending anthropomorphic reasoning about AI psychology and introduction of positive AI archetypes into training data. An important open question is how exhaustive PSM is, especially whether there might be sources of agency external to the Assistant persona, and how this might change in the future.

Introduction

What sort of thing is a modern AI assistant? One perspective holds that they are shallow, rigid systems that narrowly pattern-match user inputs to training data. Another perspective regards AI systems as alien creatures with learned goals, behaviors, and patterns of thought that are fundamentally inscrutable to us. A third option is to anthropomorphize AIs and regard them as something like a digital human. Developing good mental models for AI systems is important for predicting and controlling their behaviors. If our goal is to [...]

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Outline:

(00:10) TL;DR

(01:02) Introduction

(06:18) The persona selection model

(07:09) Predictive models and personas

(09:54) From predictive models to AI assistants

(12:43) Statement of the persona selection model

(16:25) Empirical evidence for PSM

(16:58) Evidence from generalization

(22:48) Behavioral evidence

(28:42) Evidence from interpretability

(35:42) Complicating evidence

(42:21) Consequences for AI development

(42:45) AI assistants are human-like

(43:23) Anthropomorphic reasoning about AI assistants is productive

(49:17) AI welfare

(51:35) The importance of good AI role models

(53:49) Interpretability-based alignment auditing will be tractable

(56:43) How exhaustive is PSM?

(59:46) Shoggoths, actors, operating systems, and authors

(01:00:46) Degrees of non-persona LLM agency en-US-AvaMultilingualNeural__ Green leaf or plant with yellow smiley face character attached.

(01:06:52) Other sources of persona-like agency

(01:11:17) Why might we expect PSM to be exhaustive?

(01:12:21) Post-training as elicitation

(01:14:54) Personas provide a simple way to fit the post-training data

(01:17:55) How might these considerations change?

(01:20:01) Empirical observations

(01:27:07) Conclusion

(01:30:30) Acknowledgements

(01:31:15) Appendix A: Breaking character

(01:32:52) Appendix B: An example of non-persona deception

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

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First published:
February 23rd, 2026

Source:
https://www.lesswrong.com/posts/dfoty34sT7CSKeJNn/the-persona-selection-model

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