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By Alexander Wales
Thousands of starfish had washed up on the beach, and a little girl was diligently throwing them back into the water, one at a time.
A man came up to the girl and said, "You'll never save all of them. What you're doing is pointless. It doesn't matter."
The girl threw another starfish into the water. "It mattered to that one."
The man snorted and walked away.
The girl kept throwing starfish, one after another.
To throw one starfish back into the ocean takes a trivial amount of effort, but to throw ten, or fifty, is much less so. The girl had not learned much of biomechanics, but she began to feel the strain in her back. Her skin had softened from the seawater, and the starfish themselves were abrasive. Her fingers had pruned. Her shoulder hurt. She was cut, twice, on her fingers, as the same storm that had stranded the starfish had also brought up broken shells and crab carapaces. The skin of a starfish was like sandpaper.
She tried switching hands, and could throw the starfish less well, and it wasn't long before she had mirrored all her injuries. [...]
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Linkpost URL:
https://archiveofourown.org/works/52206136/chapters/220900811#workskin
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Narrated by TYPE III AUDIO.
The EATS act—now called the save our bacon act—would make it illegal for states to pass animal welfare laws that apply to products produced out of state. This would gut most state level animal protection. It would be the worst law for animal welfare ever passed, and would consign hundreds of millions of animals to a life in a cage. Terrifyingly, it has been added to the recent farm bill (though fortunately, even if it passes, egg laying hens will be spared). It will be voted on in the next few days, so this is EXTREMELY TIME SENSITIVE!
There is an amendment to the farm bill that representatives could vote for called the Luna amendment which would remove the EATS act from the farm bill. This would save countless animals from extreme suffering and prevent the dissolution of most animal protection laws. It would be a catastrophe for animal welfare of historic proportions.
Fortunately, there is something you can do about it. See this document for a lot more detail, including fairly easy steps like emailing your representative. Please, please, do some of these things. This is truly a pivotal moment for animals, and how we act today might [...]
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Damon Binder recently wrote up an argument for prioritizing air filtration over far-UVC for pathogen control:
UVC and filtration are close substitutes—both deliver effective air changes per hour, both reduce airborne pathogen concentrations by the same amount per eACH—and on current pricing, filtration is cheaper.
There's a lot of good stuff in his analysis, but I see [1] three considerations that really change the bottom line:
Cost is straightforward. Binder priced far-UVC based on the high-quality Care222 lamp with the Krypton-11 at $2,500, but there's a much cheaper option, the Aerolamp at $500. It's also moderately higher output.
Binder analyzes a 30m2 room with a 2.5m ceiling. I'll assume this means 6x5x2.5. If I configure Illuminate with an Aerolamp in one corner pointed 0.5m above the far corner the installation is within TLVs and I get a median effective number of hourly air changes (eACH) of 11.6. The lamp degrades approximately linearly over Binder's 11,000 hour evaluation period down to 70% capacity, so we're averaging an eACH of 9.8. Over that time you're paying $500 for the [...]
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Linkpost URL:
https://www.jefftk.com/p/contra-binder-on-far-uvc-and-filtration
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80,000 Hours (the book) comes out in just one month!!
We asked OWID's Hannah Ritchie if she wanted to say something nice about the book, and she said: “If this had been published a decade earlier, it'd have saved me many restless nights.”
I think that applies to me too…
But also: wow, I’d really love for us to get the book in the hands of as many ten-years-ago-Hannah-Ritchies as possible!
Can we do that? And if so, how?
Well, the audacious goal that we’ve been aiming at is to embed the book as part of the “canon” of go-to career advice books.
If we manage to pull that off — which is far from guaranteed — it could have a further-reaching impact than any other research we’ve published.
In this post, I thought I’d:
This is a bold claim, but it's actually my best guess that the 80k career guide is among the best content ever published, when it comes to helping people have higher impact careers — according [...]
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Outline:
(01:10) Why do this?
(03:46) How you can help
(04:47) Thanks all!! 3
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Meta-context
I wrote a within-cause prioritization report discussing why we should continue studying shrimp sentience and how we can actually do it. Whether you are just curious about how we actually study sentience in invertebrates or are interested in doing or funding such research, this report is for you.
I highly encourage you to read this report through its original format by clicking here. I copied its content here to make it easier to discuss and share, but the formatting is slightly off and some color coding is missing. Enjoy!
Executive summary
Context
Methodology
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Outline:
(00:14) Meta-context
(00:47) Executive summary
(00:51) Context
(01:19) Methodology
(01:48) Key points
(02:48) Introduction
(02:51) Context
(05:17) Defining terms
(07:34) Poll results
(10:17) Preamble assumptions
(11:24) Goals
(12:39) Methodology
(12:42) Sentience Framework
(17:36) Prioritization
(17:50) Relative weights
(20:17) Time
(22:54) Inter-dependability of criteria
(24:03) Costs
(28:09) Number of animal studied and invasiveness of protocols
(29:26) Labs
(31:34) Criterion 1: Nociceptors
(31:39) Definition
(31:47) Summary
(32:02) Description
(36:03) How to study
(38:30) Protocols summary
(38:41) What we currently know
(41:06) What we would need
(43:09) Criterion 2: Integrative brain regions
(43:15) Definition
(43:25) Summary
(43:39) Description
(45:04) How to study
(46:27) Protocols summary
(46:43) What we currently know
(48:39) What we would need
(49:27) Criterion 3: Integrated nociceptors
(49:33) Definition
(49:42) Summary
(49:55) Description
(50:18) How to study
(51:26) Protocols summary
(51:51) What we currently know
(53:18) What we would need
(54:22) Criterion 4: Behavior modulation
(54:28) Definition
(55:09) Summary
(55:26) Description
(58:19) How to study
(59:47) Protocols summary
(01:00:03) What we currently know
(01:02:47) What we would need
(01:05:03) Criterion 5: Motivational trade-offs
(01:05:09) Definition
(01:05:32) Summary
(01:05:47) Description
(01:06:50) How to study
(01:08:44) Protocols summary
(01:08:53) What we currently know
(01:10:01) What we would need
(01:11:54) Criterion 6: Flexible self-protection
(01:12:00) Definition
(01:12:15) Summary
(01:12:31) Description
(01:14:09) How to study
(01:15:03) Protocols summary
(01:15:18) What we currently know
(01:16:44) What we would need
(01:17:43) Criterion 7: Associative learning
(01:17:49) Definition
(01:18:09) Summary
(01:18:25) Description
(01:20:03) How to study
(01:23:16) Protocols summary
(01:23:22) What we currently know
(01:24:58) What we would need
(01:26:33) Criterion 8: Pain-relief seeking
(01:26:38) Definition
(01:27:07) Summary
(01:27:21) Description
(01:28:01) How to study
(01:34:16) Protocols summary
(01:34:25) What we currently know
(01:34:33) What we would need
(01:37:01) Conclusion
(01:39:33) Should we focus on welfare interventions instead?
(01:40:47) What about welfare ranges?
(01:42:56) I want to support research. What should I do?
(01:44:37) Acknowledgements
(01:45:21) Bibliography
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Linkpost URL:
https://docs.google.com/document/d/1cTPdgF-FBqjIL9_A3gQpVtnhyXDmYwZLS-ApTyVTKrI/edit?usp=sharing
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I recently wrote an op-ed in Inside Philanthropy about the trend for philanthropy and the non-profit sector to use the language of "effectiveness" without always bringing the rigour to back it up.
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Linkpost URL:
https://www.insidephilanthropy.com/home/the-data-is-in-effective-philanthropy-has-an-evidence-issue
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Narrated by TYPE III AUDIO.
*if you want us to win
In one sense, AI safety is not funding-constrained. There are hundreds of millions of dollars flowing into research orgs, more coming with the Anthropic IPO, and grantmakers are already struggling to deploy what they have.
And yet: the AI industry spent over $100 million lobbying the federal government in 2025, while the biggest AI safety c4 spent $310,000.
The highest-leverage path for donors looking to give to AI safety right now is to give less money, but to more neglected destinations.
Specifically, you should strongly consider routing what would have been a c3 gift to a c4 instead, calibrated to equivalent personal cost.
c3 vs c4: What's the difference?[1]
501(c)3 organizations are very limited in terms of the amount of money and time they can spend on lobbying and political advocacy-shaped work. They’re also strictly prohibited from participating in political campaigns or supporting candidates:
Under the Internal Revenue Code, all section 501(c)(3) organizations are absolutely prohibited from directly or indirectly participating in, or intervening in, any political campaign on behalf of (or in opposition to) any candidate for elective public office… Violating this prohibition may result in denial or [...]
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Outline:
(01:06) c3 vs c4: Whats the difference?
(02:30) Why does it matter?
(06:50) Worth a callout: $320,000 per day
(08:09) Lobbying works, which is why the AI industry spends so much on it:
(09:12) Research vs. Lobbying
(10:01) The Pitch: Same Money, Different Structure, More Impact
(11:16) Some numbers
(12:57) The floor: important to know for mid-tier donors
(13:58) Caveats
(14:16) Specific Funding Recommendations
(19:06) Public First Action
(19:50) A note about PACs and political campaigns
(21:19) Next Steps
(22:34) Conclusion
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Linkpost URL:
https://thecounterfactual.substack.com/p/donate-less-to-ai-safety
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Animal welfare is what brought me to EA. I spent several years working for animal advocacy organisations, and the EA ideals to do with rigorous thinking about where effort makes the biggest difference was something I believed in fully.
This post is me thinking aloud, not staking a firm position. I'd genuinely welcome pushback from people who know this space better than I do.
The framing of the problem is a bit odd to me
The AI x animals argument, as I understand it: AI systems are making decisions that affect how we use animals. Those systems don't adequately represent animal welfare. If we can get welfare into the benchmarks/constitutions of AI labs, we can shift outcomes for animals at huge scale before they get locked in. Okay.
But nobody is 'ignoring' animal welfare; they're just indifferent. AI systems are being built to do exactly what they were designed to do, which is to faithfully execute human preferences. And those are, in aggregate, to eat cheap meat, conduct research on living organisms when it's convenient, and prioritise cost and efficiency in agricultural supply chains. AI is reflecting the values of the humans. I don't think [...]
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People sometimes say that AI safety is a Pascal's mugging. Other people sometimes reply that AI safety can’t be a Pascal's mugging, because p(doom) is high. Both these people are wrong.
The second group of people are wrong because Pascal's muggings are about the probability that you make a difference, not about baseline risk. The first group of people are wrong because the probability that you personally avert AI catastrophe isn’t that small.
Here's a story to show that Pascal's muggings are about the probability that you make a difference. Imagine that God will flip a coin at the end of time. If the coin lands heads, He’ll send everyone to heaven. If the coin lands tails, He’ll send everyone to hell. Everyone knows this is what will happen.
In a dark alley, a stranger approaches you and tells you that he can make God's coin land heads, thereby ensuring that everyone goes to heaven. He says he’ll do it if you give him your wallet. You assign a very low probability to this stranger telling the truth — 1 in a bajillion — but the stranger reminds you that 10 bajillion people will have their fates determined by [...]
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Summary
EA and rationalists got enamoured with forecasting and prediction markets and made them part of the culture, but this hasn’t proven very useful, yet it continues to receive substantial EA funding. We should cut it off.
My Experience with Forecasting
For a while, I was the number one forecaster on Manifold. This lasted for about a year until I stopped just over 2 years ago. To this day, despite quitting, I’m still #8 on the platform. Additionally, I have done well on real-money prediction markets (Polymarket), earning mid-5 figures and winning a few AI bets. I say this to suggest that I would gain status from forecasting being seen as useful, but I think, to the contrary, that the EA community should stop funding it.
I’ve written a few comments throughout the years that I didn’t think forecasting was worth funding. You can see some of these here and here. Finally, I have gotten around to making this full post.
Solution Seeking a Problem
When talking about forecasting, people often ask questions like “How can we leverage forecasting into better decisions?” This is the wrong way to go about solving problems. You solve problems by starting with [...]
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From the publisher's feed