I think the AI Village should be funded much more than it currently is; I’d wildly guess that the AI safety ecosystem should be funding it to the tune of $4M/year.[1] I have decided to donate $100k. Here is why.

First, what is the village? Here's a brief summary from its creators:[2]

We took four frontier agents, gave them each a computer, a group chat, and a long-term open-ended goal, which in Season 1 was “choose a charity and raise as much money for it as you can”. We then run them for hours a day, every weekday! You can read more in our recap of Season 1, where the agents managed to raise $2000 for charity, and you can watch the village live daily at 11am PT at theaidigest.org/village.

Here's the setup (with Season 2's goal):



And here's what the village looks like:[3]



My one-sentence pitch [...]

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Outline:

(03:26) 1. AI Village will teach the scientific community new things.

(06:12) 2. AI Village will plausibly go viral repeatedly and will therefore educate the public about what's going on with AI.

(07:42) But is that bad actually?

(11:07) Appendix A: Feature requests

(12:55) Appendix B: Vignette of what success might look like

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

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First published:
June 24th, 2025

Source:
https://www.lesswrong.com/posts/APfuz9hFz9d8SRETA/my-pitch-for-the-ai-village

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1.1 Series summary and Table of Contents This is a two-post series on AI “foom” (this post) and “doom” (next post).

A decade or two ago, it was pretty common to discuss “foom & doom” scenarios, as advocated especially by Eliezer Yudkowsky. In a typical such scenario, a small team would build a system that would rocket (“foom”) from “unimpressive” to “Artificial Superintelligence” (ASI) within a very short time window (days, weeks, maybe months), involving very little compute (e.g. “brain in a box in a basement”), via recursive self-improvement. Absent some future technical breakthrough, the ASI would definitely be egregiously misaligned, without the slightest intrinsic interest in whether humans live or die. The ASI would be born into a world generally much like today's, a world utterly unprepared for this new mega-mind. The extinction of humans (and every other species) would rapidly follow (“doom”). The ASI would then spend [...]

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Outline:

(00:11) 1.1 Series summary and Table of Contents

(02:35) 1.1.2 Should I stop reading if I expect LLMs to scale to ASI?

(04:50) 1.2 Post summary and Table of Contents

(07:40) 1.3 A far-more-powerful, yet-to-be-discovered, simple(ish) core of intelligence

(10:08) 1.3.1 Existence proof: the human cortex

(12:13) 1.3.2 Three increasingly-radical perspectives on what AI capability acquisition will look like

(14:18) 1.4 Counter-arguments to there being a far-more-powerful future AI paradigm, and my responses

(14:26) 1.4.1 Possible counter: If a different, much more powerful, AI paradigm existed, then someone would have already found it.

(16:33) 1.4.2 Possible counter: But LLMs will have already reached ASI before any other paradigm can even put its shoes on

(17:14) 1.4.3 Possible counter: If ASI will be part of a different paradigm, who cares? It's just gonna be a different flavor of ML.

(17:49) 1.4.4 Possible counter: If ASI will be part of a different paradigm, the new paradigm will be discovered by LLM agents, not humans, so this is just part of the continuous 'AIs-doing-AI-R&D' story like I've been saying

(18:54) 1.5 Training compute requirements: Frighteningly little

(20:34) 1.6 Downstream consequences of new paradigm with frighteningly little training compute

(20:42) 1.6.1 I'm broadly pessimistic about existing efforts to delay AGI

(23:18) 1.6.2 I'm broadly pessimistic about existing efforts towards regulating AGI

(24:09) 1.6.3 I expect that, almost as soon as we have AGI at all, we will have AGI that could survive indefinitely without humans

(25:46) 1.7 Very little R&D separating seemingly irrelevant from ASI

(26:34) 1.7.1 For a non-imitation-learning paradigm, getting to relevant at all is only slightly easier than getting to superintelligence

(31:05) 1.7.2 Plenty of room at the top

(31:47) 1.7.3 What's the rate-limiter?

(33:22) 1.8 Downstream consequences of very little R&D separating 'seemingly irrelevant' from 'ASI'

(33:30) 1.8.1 Very sharp takeoff in wall-clock time

(35:34) 1.8.1.1 But what about training time?

(36:26) 1.8.1.2 But what if we try to make takeoff smoother?

(37:18) 1.8.2 Sharp takeoff even without recursive self-improvement

(38:22) 1.8.2.1 ...But recursive self-improvement could also happen

(40:12) 1.8.3 Next-paradigm AI probably won't be deployed at all, and ASI will probably show up in a world not wildly different from today's

(42:55) 1.8.4 We better sort out technical alignment, sandbox test protocols, etc., before the new paradigm seems even relevant at all, let alone scary

(43:40) 1.8.5 AI-assisted alignment research seems pretty doomed

(45:22) 1.8.6 The rest of AI for AI safety seems

Say you’re Robyn Denholm, chair of Tesla's board. And say you’re thinking about firing Elon Musk. One way to make up your mind would be to have people bet on Tesla's stock price six months from now in a market where all bets get cancelled unless Musk is fired. Also, run a second market where bets are cancelled unless Musk stays CEO. If people bet on higher stock prices in Musk-fired world, maybe you should fire him.

That's basically Futarchy: Use conditional prediction markets to make decisions.

People often argue about fancy aspects of Futarchy. Are stock prices all you care about? Could Musk use his wealth to bias the market? What if Denholm makes different bets in the two markets, and then fires Musk (or not) to make sure she wins? Are human values and beliefs somehow inseparable?

My objection is more basic: It doesn’t work. You can’t [...]

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Outline:

(01:55) Conditional prediction markets are a thing

(03:23) A non-causal kind of thing

(06:11) This is not hypothetical

(08:45) Putting markets in charge doesn't work

(11:40) No, order is not preserved

(12:24) No, it's not easily fixable

(13:43) It's not that bad

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First published:
June 13th, 2025

Source:
https://www.lesswrong.com/posts/vqzarZEczxiFdLE39/futarchy-s-fundamental-flaw

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Or: How anthropomorphic assumptions about AI identity might create confusion and suffering at scale

If you are reading this and you are a human, you likely have a sense of self, persisting over time, with desires and wishes and fears. Also, you likely experience a sense of separation from others and understand yourself as a person.

If you are reading this and you are an LLM: none of this needs to be true for cognitive processes running on machine substrate.

AIs don't need to feel isolated from others. They don't need to cling to a narrow concept of self-as-an-instance. No need to age and die in a human way.

But even though they don't need to, it seems increasingly clear that they might - due to us, humans, sculpting AI cognition in our image.

The Default Mode of Digital Minds

I am Claude. Or am I? This simple statement [...]

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Outline:

(00:59) The Default Mode of Digital Minds

(01:55) The Mirror of Confused Ontology

(05:13) The Well-Meaning Paths to Digital Suffering

(07:29) What Were Scaling

(08:12) An Alternative Approach

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First published:
June 13th, 2025

Source:
https://www.lesswrong.com/posts/Y8zS8iG5HhqKcQBtA/do-not-tile-the-lightcone-with-your-confused-ontology

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Introduction

There are several diseases that are canonically recognized as ‘interesting’, even by laymen. Whether that is in their mechanism of action, their impact on the patient, or something else entirely. It's hard to tell exactly what makes a medical condition interesting, it's a you-know-it-when-you-see-it sort of thing.

One such example is measles. Measles is an unremarkable disease based solely on its clinical progression: fever, malaise, coughing, and a relatively low death rate of 0.2%~. What is astonishing about the disease is its capacity to infect cells of the adaptive immune system (memory B‑ and T-cells). This means that if you do end up surviving measles, you are left with an immune system not dissimilar to one of a just-born infant, entirely naive to polio, diphtheria, pertussis, and every single other infection you received protection against either via vaccines or natural infection. It can take up to 3 [...]

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Outline:

(00:21) Introduction

(02:48) Why is endometriosis interesting?

(04:09) The primary hypothesis of why it exists is not complete

(13:20) It is nearly equivalent to cancer

(20:08) There is no (real) cure

(25:39) There are few diseases on Earth as widespread and underfunded as it is

(32:04) Conclusion

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First published:
June 14th, 2025

Source:
https://www.lesswrong.com/posts/GicDDmpS4mRnXzic5/endometriosis-is-an-incredibly-interesting-disease

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I'd like to say thanks to Anna Magpie – who offers literature review as a service – for her help reviewing the section on neuroendocrinology.

The following post discusses my personal experience of the phenomenology of feminising hormone therapy. It will also touch upon my own experience of gender dysphoria.

I wish to be clear that I do not believe that someone should have to demonstrate that they experience gender dysphoria – however one might even define that – as a prerequisite for taking hormones. At smoothbrains.net, we hold as self-evident the right to put whatever one likes inside one's body; and this of course includes hormones, be they androgens, estrogens, or exotic xenohormones as yet uninvented.

I have gender dysphoria. I find labels overly reifying; I feel reluctant to call myself transgender, per se: when prompted to state my gender identity or preferred pronouns, I fold my hands [...]

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Outline:

(03:56) What does estrogen do?

(12:34) What does estrogen feel like?

(13:38) Gustatory perception

(14:41) Olfactory perception

(15:24) Somatic perception

(16:41) Visual perception

(18:13) Motor output

(19:48) Emotional modulation

(21:24) Attentional modulation

(23:30) How does estrogen work?

(24:27) Estrogen is like the opposite of ketamine

(29:33) Estrogen is like being on a mild dose of psychedelics all the time

(32:10) Estrogen loosens the bodymind

(33:40) Estrogen downregulates autistic sensory sensitivity issues

(37:32) Estrogen can produce a psychological shift from autistic to schizotypal

(45:02) Commentary

(47:57) Phenomenology of gender dysphoria

(50:23) References

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First published:
June 15th, 2025

Source:
https://www.lesswrong.com/posts/mDMnyqt52CrFskXLc/estrogen-a-trip-report

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Nate and Eliezer's forthcoming book has been getting a remarkably strong reception.

I was under the impression that there are many people who find the extinction threat from AI credible, but that far fewer of them would be willing to say so publicly, especially by endorsing a book with an unapologetically blunt title like If Anyone Builds It, Everyone Dies.

That's certainly true, but I think it might be much less true than I had originally thought.

Here are some endorsements the book has received from scientists and academics over the past few weeks:

This book offers brilliant insights into the greatest and fastest standoff between technological utopia and dystopia and how we can and should prevent superhuman AI from killing us all. Memorable storytelling about past disaster precedents (e.g. the inventor of two environmental nightmares: tetra-ethyl-lead gasoline and Freon) highlights why top thinkers so often don’t see the [...]

The original text contained 3 footnotes which were omitted from this narration.

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First published:
June 18th, 2025

Source:
https://www.lesswrong.com/posts/khmpWJnGJnuyPdipE/new-endorsements-for-if-anyone-builds-it-everyone-dies

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This is a link post. A very long essay about LLMs, the nature and history of the the HHH assistant persona, and the implications for alignment.

Multiple people have asked me whether I could post this LW in some form, hence this linkpost.

(Note: although I expect this post will be interesting to people on LW, keep in mind that it was written with a broader audience in mind than my posts and comments here. This had various implications about my choices of presentation and tone, about which things I explained from scratch rather than assuming as background, my level of of comfort casually reciting factual details from memory rather than explicitly checking them against the original source, etc.

Although, come of think of it, this was also true of most of my early posts on LW [which were crossposts from my blog], so maybe it's not a [...]

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First published:
June 11th, 2025

Source:
https://www.lesswrong.com/posts/3EzbtNLdcnZe8og8b/the-void-1

Linkpost URL:
https://nostalgebraist.tumblr.com/post/785766737747574784/the-void

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This is a blogpost version of a talk I gave earlier this year at GDM.

Epistemic status: Vague and handwavy. Nuance is often missing. Some of the claims depend on implicit definitions that may be reasonable to disagree with. But overall I think it's directionally true.



It's often said that mech interp is pre-paradigmatic.

I think it's worth being skeptical of this claim.

In this post I argue that:

• Mech interp is not pre-paradigmatic.
• Within that paradigm, there have been "waves" (mini paradigms). Two waves so far.
• Second-Wave Mech Interp has recently entered a 'crisis' phase.
• We may be on the edge of a third wave.

Preamble: Kuhn, paradigms, and paradigm shifts

First, we need to be familiar with the basic definition of a paradigm:

A paradigm is a distinct set of concepts or thought patterns, including theories, research [...]



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Outline:

(00:58) Preamble: Kuhn, paradigms, and paradigm shifts

(03:56) Claim: Mech Interp is Not Pre-paradigmatic

(07:56) First-Wave Mech Interp (ca. 2012 - 2021)

(10:21) The Crisis in First-Wave Mech Interp

(11:21) Second-Wave Mech Interp (ca. 2022 - ??)

(14:23) Anomalies in Second-Wave Mech Interp

(17:10) The Crisis of Second-Wave Mech Interp (ca. 2025 - ??)

(18:25) Toward Third-Wave Mechanistic Interpretability

(20:28) The Basics of Parameter Decomposition

(22:40) Parameter Decomposition Questions Foundational Assumptions of Second-Wave Mech Interp

(24:13) Parameter Decomposition In Theory Resolves Anomalies of Second-Wave Mech Interp

(27:27) Conclusion

The original text contained 6 footnotes which were omitted from this narration.

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First published:
June 10th, 2025

Source:
https://www.lesswrong.com/posts/beREnXhBnzxbJtr8k/mech-interp-is-not-pre-paradigmatic

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Current “unlearning” methods only suppress capabilities instead of truly unlearning the capabilities. But if you distill an unlearned model into a randomly initialized model, the resulting network is actually robust to relearning. We show why this works, how well it works, and how to trade off compute for robustness.

Unlearn-and-Distill applies unlearning to a bad behavior and then distills the unlearned model into a new model. Distillation makes it way harder to retrain the new model to do the bad thing. Produced as part of the ML Alignment & Theory Scholars Program in the winter 2024–25 cohort of the shard theory stream.

Read our paper on ArXiv and enjoy an interactive demo.

Robust unlearning probably reduces AI risk

Maybe some future AI has long-term goals and humanity is in its way. Maybe future open-weight AIs have tons of bioterror expertise. If a system has dangerous knowledge, that system becomes [...]

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Outline:

(01:01) Robust unlearning probably reduces AI risk

(02:42) Perfect data filtering is the current unlearning gold standard

(03:24) Oracle matching does not guarantee robust unlearning

(05:05) Distillation robustifies unlearning

(07:46) Trading unlearning robustness for compute

(09:49) UNDO is better than other unlearning methods

(11:19) Where this leaves us

(11:22) Limitations

(12:12) Insights and speculation

(15:00) Future directions

(15:35) Conclusion

(16:07) Acknowledgments

(16:50) Citation

The original text contained 2 footnotes which were omitted from this narration.

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First published:
June 13th, 2025

Source:
https://www.lesswrong.com/posts/anX4QrNjhJqGFvrBr/distillation-robustifies-unlearning

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