LessWrong (Curated & Popular)

Crossposted from Substack and the EA Forum.




A common argument for optimism about the future is that living conditions have improved a lot in the past few hundred years, billions of people have been lifted out of poverty, and so on. It's a very strong, grounding piece of evidence - probably the best we have in figuring out what our foundational beliefs about the world should be.

However, I now think it's a lot less powerful than I once did.




Let's take a Darwinian perspective - entities that are better at reproducing, spreading and power-seeking will become more common and eventually dominate the world.[1] This is an almost tautological story that plausibly applies to everything ever, agnostic to the specifics. It first happened with biological life in the last few billion years and humans specifically in the last hundred thousand years. Eventually, it led to accelerating economic growth in the last few thousand years, and in the future it will presumably lead to the colonization of the universe.

My core point is this: It makes complete sense that this nihilistic optimization process at first actually benefits some class of agent - because initially, the easiest [...]





The original text contained 10 footnotes which were omitted from this narration.

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First published:

May 10th, 2026

Source:

https://www.lesswrong.com/posts/FxHzT6jeTRhbkzSX3/the-darwinian-honeymoon-why-i-am-not-as-impressed-by-human-1

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My name is Emma and I’m six and a half years old and I like pink and Pokemon and my cat River and I’m going to be swallowed by a hedonium shockwave soon, except you already know that about me because everyone else is too.

“Hedonium shockwave” means that everyone is going to be happy forever. Not just all the humans but all the animals and the flowers and the ground and River too. It has already made a bunch of the stars happy, like Betelgeuse and Alpha Centauri.

Scientists saw that the stars were blinking out, and they did a lot of very hard science and figured out that the stars were turning into happiness. I wanted to be a scientist when I grew up but I won’t be a scientist because instead I’m going to be happy forever.

I used to have a hard time saying “hedonium shockwave” but grownups keep saying it so I’ve gotten a lot of practice. Sometimes it seems like all grownups do, in real life and on the TV, is say “hedonium shockwave” at each other until they all start crying.

I looked at the sky to see if I could see [...]

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First published:

April 13th, 2026

Source:

https://www.lesswrong.com/posts/rgXQuG8KXtxugSG6H/what-i-did-in-the-hedonium-shockwave-by-emma-age-six-and-a

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Narrated by TYPE III AUDIO.

Here's a dynamic I’ve seen at least a dozen times:




Alice: Man that article has a very inaccurate/misleading/horrifying headline.

Bob: Did you know, *actually* article writers don't write their own headlines?

…

But what I care about is the misleading headline, not your org chart

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Another example I’ve encountered recently is (anonymizing) when a friend complained about a prosaic safety problem at a major AI company that went unfixed for multiple months. Someone else with background information “usefully” chimed in with a long explanation of organizational limitations and why the team responsible for fixing the problem had limitations on resources like senior employees and compute, and actually not fixing the problem was the correct priority for them etc etc etc.

But what I (and my friend) cared about was the prosaic safety problem not being fixed! And what this says about the company's ability to proactively respond to and fix future problems. We’re complaining about your company overall. Your internal team management was never a serious concern for us to begin with!

__

A third example comes from Kelsey Piper.

Kelsey wrote about the (horrifying) recent case where Hantavirus carriers in the recent [...]





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

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First published:

May 8th, 2026

Source:

https://www.lesswrong.com/posts/PCsmhN9z65HtC4t5v/bad-problems-don-t-stop-being-bad-because-somebody-s-wrong

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Sometimes, a friend who works around here, at an x-risk-themed organisation, will think about leaving their job. They’ll ask a group of people “what should I do instead?”. And everyone will chime in with ideas for other x-risk-themed orgs that they could join. A lot of the conversation will be about who's hiring, what the pay is, what the work-life balance is like, or how qualified the person is for the role.

Sometimes the conversation focuses on what will help with x-risk, and where people are dropping the ball. But often, that's not the focus. In those conversations, people seem mostly worried about where they'll thrive. And I think that's often the correct concern.

Most people aren’t in crunch mode, in super short timelines mode; even if their models would license that, I think they don’t know how to do it without throwing their minds away or Pascal's mugging themselves. And if they're playing a longer time horizon game, the plan can't be to run unsustainably forever. People probably make better plans if they’re honest about their limits.

But, given that they're willing to trade off so much impact for fit, I’m surprised that basically no one mentions [...]

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First published:

May 6th, 2026

Source:

https://www.lesswrong.com/posts/eW7knx6zPSKzFc8iK/x-risk-themed

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Narrated by TYPE III AUDIO.

Abstract

We introduce Natural Language Autoencoders (NLAs), an unsupervised method for generating natural language explanations of LLM activations. An NLA consists of two LLM modules: an activation verbalizer (AV) that maps an activation to a text description and an activation reconstructor (AR) that maps the description back to an activation. We jointly train the AV and AR with reinforcement learning to reconstruct residual stream activations. Although we optimize for activation reconstruction, the resulting NLA explanations read as plausible interpretations of model internals that, according to our quantitative evaluations, grow more informative over training.

We apply NLAs to model auditing. During our pre-deployment audit of Claude Opus 4.6, NLAs helped diagnose safety-relevant behaviors and surfaced unverbalized evaluation awareness—cases where Claude believed, but did not say, that it was being evaluated. We present these audit findings as case studies and corroborate them using independent methods. On an automated auditing benchmark requiring end-to-end investigation of an intentionally-misaligned model, NLA-equipped agents outperform baselines and can succeed even without access to the misaligned model's training data.

NLAs offer a convenient interface for interpretability, with expressive natural language explanations that we can directly read. To support further work, we release training code and trained NLAs [...]

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Outline:

(00:15) Abstract

[... 6 more sections]

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First published:

May 7th, 2026

Source:

https://www.lesswrong.com/posts/oeYesesaxjzMAktCM/natural-language-autoencoders-produce-unsupervised

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This is a link post. This is the latest work in our Parameter Decomposition agenda. We introduce a new parameter decomposition method, adVersarial Parameter Decomposition (VPD)[1] and decompose the parameters of a small[2] language model with it.

VPD greatly improves on our previous techniques, Stochastic Parameter Decomposition (SPD) and Attribution-based Parameter Decomposition (APD). We think the parameter decomposition approach is now more-or-less ready to be applied at scale to models people care about.







Importantly, we show that we can decompose attention layers, which interp methods like transcoders and SAEs have historically struggled with.





We also build attribution graphs of the model for some prompts using causally important parameter subcomponents as the nodes, and interpret parts of them.

While we made these graphs, we discovered that our adversarial ablation method seemed pretty important for faithfully identifying which nodes in them were causally important for computing the final output. We think this casts some doubt on the faithfulness of subnetworks found by the majority of other subnetwork identification methods in the literature.[3][4] More details and some examples can be found in the paper.

Additionally, as with our previous technique SPD, VPD does not [...]









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

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First published:

May 5th, 2026

Source:

https://www.lesswrong.com/posts/eAQZaiC3PcBhS4HjM/linkpost-interpreting-language-model-parameters

Linkpost URL:
https://www.goodfire.ai/research/interpreting-lm-parameters

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I have two shameful secrets that I probably shouldn't talk about online:

• I love my family.
• I enjoy my hobbies.

"What an idiot!" you probably think. "Doesn't he realize that at his next job interview, HR will probably use an AI that can match his online writing based on a short sample of written text, and when they ask 'hey AI, is this guy really 100% devoted to his job, and does he spend his entire days and nights thinking about how to make his boss more rich?', the AI will laugh and print: 'beep-boop, negative, mwa-ha-ha-ha'."

And, hey, I get it. If I had a company, and I could choose between two people who are about equally qualified, but for one of them, working hardest for me is the true meaning of his life, while the other one only hopes to collect his salary and then go home and spend the rest of his day with his wife and children, I would also prefer to hire the former.

Which is why so many of us pretend to be the former. Even when we are not. Because we prefer that our families not starve. Thus the job interviews [...]

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First published:

May 4th, 2026

Source:

https://www.lesswrong.com/posts/qRZLEBmNtT6LBuFsE/it-s-nice-of-you-to-worry-about-me-but-i-really-do-have-a

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Narrated by TYPE III AUDIO.

Example 1: The Viking 1 lander

In the 1970s, NASA sent a pair of probes to Mars, Viking 1 and Viking 2 missions, at a total cost of 1 billion dollars[1970], equivalent to about 7 billion dollars[2025]. The Viking 1 probe operated on Mars's surface for six years, before its battery began to seriously degrade.

One might have thought a battery problem like that would spell the irrevocable end of the mission. The probe had already launched and was now on Mars, very far away and out of reach of any human technician's fixing fingers. Was it not inevitable, then, that if any kind of technical problem were to be discovered long after the space launch in August 1975, nothing could possibly be done?

But the foresightful engineers of the Viking 1 probe had devised a plan for just this class of eventuality, which they had foreseen in general, if not in exact specifics. They had built the Viking 1 probe to accept software updates by radio receiver, transmitted from Earth.

On November 11, 1982, Earth sent an update to the Viking 1 lander's software, intended to make sure the battery only discharged down to a minimum voltage level [...]

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Outline:

(00:13) Example 1: The Viking 1 lander

(04:25) Example 2: The Mars Observer

(11:37) Example 3: The Maginot Line

(15:37) Other supposed refutations of oneshotness

(24:16) On the extraordinary efforts put forth to misinterpret the idea of oneshotness

(33:52) The secret sauce of competent engineers in Murphy-cursed fields: only trying projects so incredibly straightforward as to be actually possible.

The original text contained 7 footnotes which were omitted from this narration.

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First published:

May 4th, 2026

Source:

https://www.lesswrong.com/posts/fbrz9xhKpEeTKw5zL/irretrievability-or-murphy-s-curse-of-oneshotness-upon-asi

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Narrated by TYPE III AUDIO.

How much do cows suffer in the production of milk? I can’t answer that; understanding animal experience is hard. But I can at least provide some facts about the conditions dairy cows live in, which might be useful to you in making your own assessment. My biggest conclusion is that cows made better choices than chickens by making their misery financially costly to farmers.

The life of a dairy cow starts as a calf. She is typically separated from her mother a few hours to a few days after birth and, to reduce disease risk, held in isolation. Cutting edge farms will sometimes house calves in pairs. This isolation is clearly stressful for a baby herd mammal and her mother, but I didn’t find any quantification of that stress that I trusted.

Calves will be bottlefed until weaning at 6-8 weeks (4-6 months earlier than beef calves). After weaning and vaccinations they can be introduced into a herd. At large farms (where most cows live), they will move in and out of different herds through their lifecycle. This is more stressful than being embedded with your friends for life, but again, I found no [...]

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Outline:

(00:44) Life Cycle

(02:43) How much time do dairy cows spend outside?

(04:21) By humaneness standard

(06:00) When indoors, how confined are dairy cows?

(06:33) What is the disease load of dairy cows?

(08:15) Euthanasia

[... 5 more sections]

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First published:

May 3rd, 2026

Source:

https://www.lesswrong.com/posts/r3PKfvKCjy6jok4qm/dairy-cows-make-their-misery-expensive-but-their-calves-can

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I spent my last two months playing around with LLMs. I’m a beginner, bumbling and incorrect, but I want to share some takes anyhow.[1]

Take 1. Everything with computers is so so much easier than it was a year ago. 

This puts much “playing with LLMs” stuff within my very short attention span. This has felt empowering and fun; 10/10 would recommend.

There's a details box here with the title "Detail:". The box contents are omitted from this narration. Take 2. There's somebody home[2] inside an LLM. And if you play around while caring and being curious (rather than using it for tasks only), you’ll likely notice footprints.

I became personally convinced of this when I noticed that the several short stories I’d allowed[3] my Claude and Qwen instances to write all hit a common emotional note – and one that reminded me of the life situation of LLMs, despite featuring only human characters. I saw the same note also in the Tomas B.-prompted Claude-written story I tried for comparison. (Basically: all stories involve a character who has a bunch of skills that their context has no use for, and who is attentive to their present world's details [...]

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Outline:

(00:20) Take 1. Everything with computers is so so much easier than it was a year ago.

(00:44) Take 2. Theres somebody home inside an LLM. And if you play around while caring and being curious (rather than using it for tasks only), youll likely notice footprints.

(02:05) Take 3. Its prudent to take an interest in interesting things. And LLMs are interesting things.

(03:25) Take 4. Theres a surprisingly deep analogy between humans and LLMs

(04:20) Examples of the kind of disanalogies I mightve expected, but havent (yet?) seen:

(06:02) Human-LLM similarities I do see, instead:

(06:08) Functional emotions

(06:33) Repeated, useful transfer between strategies I use with humans, and strategies that help me with LLMs

(08:02) Take 5. Friendship-conducive contexts are probably better for AI alignment

(08:46) Why are humans more likely to attempt deep collaboration if treated fairly and kindly?

(10:23) Friendship as a broad attractor basin?

(10:49) Does the deep intent of todays models matter?

(12:09) Concretely

(14:56) Friendship isnt enough

The original text contained 8 footnotes which were omitted from this narration.

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First published:

April 28th, 2026

Source:

https://www.lesswrong.com/posts/K8JMjE4PCqMkkCDsd/takes-from-two-months-as-an-aspiring-llm-naturalist

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Narrated by TYPE III AUDIO.

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