
Sign up to save your podcasts
Or


The Animal Welfare Department (AWD) at Rethink Priorities supports high-impact strategies to help animals, especially where suffering is vast and largely neglected. Therefore, one of our focus areas is wild animal welfare (WAW), where uncertainty about tractability makes identifying cost-effective interventions particularly challenging. While much of the current WAW work rightly focuses on academic field-building (see Elmore & McAuliffe, 2024), it is worth determining whether there are viable, near-term interventions that are already available or close to implementation.
With this goal in mind, we have developed the Wild Animal Welfare Intervention Database (WAWID). This project evaluates an array of interventions that may be promising for improving WAW in the (relatively) near term, evaluating them relative to criteria of interest to funders, advocates, researchers, and potential implementers across the WAW space.
The WAWID is available here:
Wild Animal Welfare Intervention DatabaseThe landing page includes a full list of the interventions and evaluation criteria. This report explains how we developed the WAWID, what we think you can learn from it, and suggest some future directions for this work (conditional on funding). A future report will provide some descriptive statistics.
How we developed the WAWID
We launched this project in [...]
---
Outline:
(01:34) How we developed the WAWID
(08:14) Initial observations
(13:13) Limitations
(15:20) Future Directions
(16:48) Acknowledgements
---
First published:
Source:
---
Narrated by TYPE III AUDIO.
---
Images from the article:
Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
This post was written by Simon Eckerström Liedholm, Strategy Researcher at Wild Animal Initiative, and it is a contribution to the AGI & Animals Debate Week. It partially builds on an earlier exploration of transformative AI and wild animals written by Mal Graham. Disclaimer: I (Simon) would describe myself primarily as a biologist. While I have a long-standing interest in AI safety, I'm by no means an expert on the subject.
Executive SummaryThis post asks whether, if AGI goes well for humans, it will also go well for animals, with a particular focus on wild animals. I estimate a ~30% probability that it would.
My prediction rests on several considerations. First, I think the most likely path to AGI “going well” for humans probably involves aligning the AGI with something roughly similar to the values of an average present-day human, values that currently permit a lot of suffering among farmed animals. Second, if current values are mostly locked in, which seems like a plausible scenario, I don't think it would lead to meaningful improvements of animal welfare.
I also conclude with some tentative suggestions for animal welfare advocates, and highlight potential backfire risks.
Definitions and assumptionsWhat question [...]---
Outline:
(00:41) Executive Summary
(01:31) Definitions and assumptions
(01:35) What question is this post meant to answer?
(02:19) Animals
(02:39) AGI
(03:16) Go well
(04:44) Considerations
(05:20) AGI timelines
(05:24) AGI timelines probably matter for animal welfare outcomes
(06:06) When should do I expect AGI to be developed?
(07:55) Working on safeguarding the welfare of future wild animals seems particularly important
(08:51) Future values
(08:54) Predicting future values based on current values
(11:39) Values of future decision makers
(13:01) An animal-welfare-indifferent AGI may be especially bad from a suffering-focused perspective
(14:40) Hard-coding animal welfare concerns (and associated risks)
(15:40) How likely is it that, if AGI goes well for humans, it will go well for animals?
(16:46) Conclusions
(16:49) General thoughts
(18:48) What would I recommend animal welfare advocates do?
(18:53) Risks of accidental harm
(21:42) Tentative suggestions
(23:45) What would make me change my mind?
(25:07) Acknowledgments
---
First published:
Source:
---
Narrated by TYPE III AUDIO.
TLDR: In February, I moved to Da Nang, Vietnam, to live and work with a group of EA people I mostly hadn't met. See our website and original forum post here. We're now extending until July, and we have room for more people[1]. If you're working on something impact-focused and want to spend a few weeks or months doing it surrounded by cool people, then come stay with us and join our Discord! We're really excited to grow our community. The rest of this post is my honest account of why this has been one of the better decisions I've made.
Come stay with us!I'm bad at networking
I've always struggled with conference 1-on-1s. Luckily, I think trust is built more through many small interactions in different contexts. At a coliving hub, you have many shots. You see the same people at dinner. You overhear them on calls. You watch how they react when their code doesn't work. You see how they deal with unique and stressful situations. You watch them change their mind about something. After three weeks of this, you know whether you'd want to work with them in a way that no 30-minute conversation could ever [...]
---
Outline:
(00:47) Im bad at networking
(01:41) Accountability
(02:28) Flying across the world puts you in a different headspace
(02:58) I cant talk to my family about my job
(03:40) Quick feedback loops
(04:19) In-person coordination is fast
(05:02) Sounds like a bubble
(06:00) Its fun
(06:44) Were extending until July
---
First published:
Source:
---
Narrated by TYPE III AUDIO.
---
Images from the article:
Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
It's called 80,000 Hours: How to Have a Fulfilling Career That Does Good. It covers all of our best advice for having a big, positive impact with your career.
It's being published by Penguin Random House on the 26th May in the US, and the 28th May in the UK.
Why did we decide to publish this book?According to the EA Survey, 80,000 Hours has been the biggest single entry point into effective altruism, and our online career guide has consistently had an outsized impact compared to our other programmes.[1] We think it's probably the best content we’ve ever published [...]
---
Outline:
(00:11) Summary
(00:52) The new book
(01:13) Why did we decide to publish this book?
(02:11) Whats new in this edition?
(03:47) How you can help
(04:45) FAQs
(10:48) Thank you
---
First published:
Source:
---
Narrated by TYPE III AUDIO.
---
Images from the article:
Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
This piece makes the following three points (which match the numbered sections):
We have, I argue, no good reason to presume that reducing existential risks from AI, which is explicitly intended to help humans,[1] is good for other animals. This is because animal farming would not exist without empowered humans, and assuming that these same empowered humans will outweigh this by sufficiently increasing wild animal welfare seems untenable. And to the extent that x-risk reduction appears as the dominant consequence of most (if not all) AI safety work, this refutes the case for AI x-risk reduction and AI safety being robustly good for all sentient beings. Importantly, while I therefore reject the position that reducing AI safety is net good for animals, this post does not take a stance on “it is net bad” vs “we should be agnostic” (although I preach for the latter elsewhere).
---
Outline:
(00:12) 0. Summary
(02:12) 1. AI safety may not help everyone
(03:36) 1.1. Saving humans means saving animal farming for at least some time
(04:25) 1.2. Saving humans has unclear implications for wild animal welfare
(08:59) 1.3. Trading off farmed and wild animal welfare
(10:28) 1.4. What all this does and does not imply
(12:17) 2. Can we defend AI safety regardless?
(12:22) 2.1. Background on why cross-species robustness would have been convenient
(13:49) 2.2. Three ways of doing without cross-species robustness
(17:16) 3. Conclusion: The relevance of off-target effects
(18:16) Acknowledgments
---
First published:
Source:
---
Narrated by TYPE III AUDIO.
In 2022, two of Anthropic's co-founders, Daniela and Dario Amodei described the company as a small group leaving OpenAI in late 2020 and early 2021 during the pandemic, meeting masked in backyards, because they wanted to make what they called a "focused research bet" with a tightly aligned team. The bet was on building AI systems that are "helpful, honest, and harmless". On race dynamics, Dario was explicit that Anthropic should build models close to the state of the art (because you need frontier models to study frontier safety problems), but that they shouldn't race ahead or build models bigger than other orgs, and shouldn't ramp up excitement or hype about giant models. Fast forward to today: Anthropic is a frontier for-profit company valued at $380 billion as of Feb 2026, is pursuing agentic coding on a technical trajectory that approaches autonomous recursive self-improvement, Dario has among the shortest AGI/TAI timelines of any major AI lab CEOs, their recently revised RSP has drawn criticism for dropping what many in the community interpreted as a commitment to pause development under certain risk thresholds, and now, what this piece is primarily about, the "~oversight" organisations it once called for will apparently [...]
---
Outline:
(02:37) A note on framing:
(03:21) The causal chain
(06:00) Selection mechanisms
(07:29) Counterforces
(08:52) Value drift and (perverse? ) incentive structure
(10:27) Where EA cant measure its own success
(11:36) All this can be testable
---
First published:
Source:
---
Narrated by TYPE III AUDIO.
This week, we are discussing the statement: “If AGI goes well for humans, it’ll go well for animals”. The announcement post, with a bit more info and a reading list, is here.
What is this thread for?
General discussions about and reactions to the debate statement.
Some of the comments on this thread will be populated directly from the debate banner on the homepage — these will mostly be people explaining why they voted the way they did.
However, you’re also welcome to comment on here directly, with any considerations you'd like to share, or questions you'd like to ask.
How should I understand the debate statement?
Again, our statement is: “If AGI goes well for humans, it’ll go well for animals”
The statement will ultimately mean whatever people interpret it to mean. The key is to explain how you are interpreting the statement in the comment that you attach to your vote. However, I can share a few notes which might pre-empt your questions:
---
Outline:
(00:23) What is this thread for?
(00:47) How should I understand the debate statement?
---
First published:
Source:
---
Narrated by TYPE III AUDIO.
---
Images from the article:
Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
0. Intro
Due to Claude's Constitution and OpenAI's model spec, the issue of AI character has started getting more attention, particularly concerning whether we want AI systems to be “obedient” or “ethical”.[1] But we think it's still not nearly enough.
AI character (e.g. how obedient, honest, cooperative, or altruistic AIs are, and in what circumstances) will have a big effect on society, and on how well the future goes. We think that figuring out what characters AI systems should have, and getting companies to actually build them that way, is among the most valuable things that people can do today.
The core argument for the importance of AI character is that it will meaningfully impact:
In this note, we present this core argument and discuss the core counterargument: that we should expect any character-related decisions we make today to get washed out by competitive pressures.
By “character” we mean [...]
---
Outline:
(00:14) 0. Intro
(02:17) 1. The core argument
(08:23) 1.1. Pathways to impact
(10:34) 1.2. Affecting takeover
(11:52) 1.3. Effects on superintelligence
(12:22) 2. The core counterargument
(14:41) 3. Rejoinders to the core counterargument
(15:02) 3.1. Loose constraints
(17:02) 3.2. Low-cost but high-benefit changes
(17:48) 3.3. Path-dependence
(20:14) 3.4. Smoothing the transition
(21:49) 3.5. Overall
(22:20) 4. Conclusion
(23:38) Appendix 1: Additional high-stakes scenarios
(26:55) Appendix 2: Pathways to impact
---
First published:
Source:
Linkpost URL:
https://www.forethought.org/research/the-importance-of-ai-character
---
Narrated by TYPE III AUDIO.
I was released from Dane County jail this week where I was held for two nights for my participation in Sunday's mass rescue at Ridglan Farms in Wisconsin. I wrote post in jail and posted it on my Substack shortly after.
It is freezing in the rain, with bits of hail coming down on me. I am surrounded by police, accompanied by over twenty activists at Ridglan Farms, a notorious dog testing and breeding facility. The police tell us if we leave the facility, they won’t arrest us. We are not sure what to do.
Two days ago, I joined nearly a hundred activists in walking into the second largest dog breeding and research facility in the United States. We walked onto the property in broad daylight to rescue the animals inside. Ridglan Farms has been a site of public controversy for years for confining thousands of beagles for cruel experiments, including force feeding them commercial laundry detergent and surgically ripping out their eye glands without anesthesia. The abuses are so bad that a local court ordered them to be prosecuted for felony animal abuse.
Under [...]
---
First published:
Source:
Linkpost URL:
https://urbananimal.substack.com/p/today-i-was-released-from-jail-for
---
Narrated by TYPE III AUDIO.
---
Images from the article:
Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
While my account is only 1 year old, I've been a lurker in the EA community long before that. I enjoy going through posts and comments every day, and I appreciate how proactive everybody is in tackling and discussing different problems. Generally, my experience with the online community has ranged from neutral to positive.
But there is one thing that has been bothering me, and that is the writing quality / style in EA spaces. Even though there have been enough discussions about this before, I want to highlight that dense, obscuring writing can do a lot of harm, and that's something we should solve as an efficiency-oriented community.
It might sound like I'm talking about personal aesthetic preferences, but this is a genuine issue. EA prides itself on rigorous epistemics, but dense, obscuring writing is often the opposite of rigorous.
What is dense or obscuring writing?
To be straightforward: dense writing isn't the same as complex writing. A post can cover a truly difficult theme and still be clear. Dense writing is what happens when the manner of writing hides the point of the post rather than communicating it.
Dense writing tends to show up like this:
---
Outline:
(01:00) What is dense or obscuring writing?
(02:00) Why might it be harmful?
(03:17) Intellectual honesty vs. intellectual dishonesty in writing
(04:41) Are there any benefits to dense writing?
(05:32) Are you interested in developing a straightforward and clear writing?
---
First published:
Source:
---
Narrated by TYPE III AUDIO.
From the publisher's feed