I got "0 conflicts, 1 bullet bitten", with the bullet being the repugnant conclusion. 

Does this mean that you have 6 bullets you'd prefer over having to bite the repugnant conclusion? :) 

Hi Vasco, 
Thanks for the question and sorry for the slow reply. 

The overall process was to combine two estimates of THL's cost-effectiveness through weighting them in a Guesstimate model, along with some assumptions about the speed-up time of the intervention and the marginal cost-effectiveness relative to the past average cost-effectiveness. 
The more detailed steps were: 
1. Gather two estimates: THL review by ACE from 2025, wherein cage-free work has an estimated 2 to 44 hens affected per dollar (best guess of 11); THL self-published report from 2025, which reports 2 hens freed per dollar spent between 2015 and 2024. 
2. Make assumptions: The THL self-published report was conservative in that it didn't include a counterfactual speed up assumption. So, we assumed that the interventions THL carried out each affected 6 to 15x as many hens than reported on average through speeding up the timelines to hens becoming cage-free. Then, since the marginal cost-effectiveness in 2026 is probably much less than the average cost-effectiveness from 2015-2024, we discounted the resulting THL self-reported estimate by 70%. This is a rough guess.
3. In a Guesstimate model, I combined the two estimates (giving equal weight to each) to get an overall estimate of THL's cost-effectiveness on the margin (caveat below). (The ACE estimate was encoded as a lognormal distribution between 2 and 44 hens/$, which had a mean of 14 which is slightly better than their best-guess, which could be a median or mean, I'm not sure). This produced an estimate of 10 (3.6 to 29) hens/$. The method isn't perfect, since these estimates might not be independent so the variance could be artificially reduced, but for our initial version, we felt it was a good enough estimate (other factors matter more to the CCF). 

Upon looking back, I realize that I made a typo in the methodology -- it should be 3.6 to 29 chickens per dollar spent, not 3.6 to 22. So thanks for pointing my attention to this! I've now fixed it.

On the more important question, I agree that the marginal cost-effectiveness is definitely the important question, and I'd like to have this. However, the best estimates we could find for the first version of the CCF were for average cost-effectiveness of past spending. We plan to improve upon these estimates in the future, such as through considering more evidence. If you know of any good marginal cost-effectiveness estimates, or other cost-effectiveness estimates, please do link them below!

Hope this is helpful! Also, I'll look into how to change the settings so you can copy text, I'm not sure why it's like that

I'm not a student, but I'm surprised UC Law SF(formerly UC Hastings) doesn't have an EA club or AI safety club. Seems like an interesting spot for people interested in AI governance work that could be well-served by UC Berkeley or Stanford pitching people to join their club meetings.  

Some hope for you if you're an EA with a chronic illness.

I’ve been reading the biographies of moral heroes, and I’d guess ~50% of them struggled with ongoing health issues.

Being sick sucks, but it doesn’t necessarily mean you won’t be able to do a ton of good

• Florence Nightingale
• Benjamin Franklin
• William Wilberforce
• Alexander Hamilton
• Helen Keller

It’s not everybody, but it’s a surprising percentage of them.

I myself struggle with a chronic mystery ailment and I find it inspiring to hear about all of these people who still managed to do great things, even though their bodies were not always so cooperative.

(Also, just as an aside, the book Stop Being Your Symptoms, Start Being Yourself didn’t cure my illness, but it did reduce its effects on my life by about 85%, and I can’t recommend it enough. It’s basically CBT/psychology stuff applied to chronic illness)

I'm early-ish in the application pipeline for a non-EA job and they're asking me to do a 20-HOUR UNPAID work test where they explicitly want to own and use the product of my "work test" ....

Thank you for writing this, Tom and Rocky. I think this is an important caution, and I appreciate the way you are framing it.

As someone directly involved in a welfare-tech-style intervention, I actually agree with much of your core argument. I also strongly believe that good tech interventions can be extremely impactful, partly for some of the reasons you outline: they can sometimes bypass certain barriers that exist for other welfare work, and they can make it possible to help very large numbers of animals relatively quickly. But I also agree that, when looked at from the outside, they can be falsely perceived as silver bullets, so I want to share three insights from SWP about electrical stunning for shrimp.

I’ve also posted a more direct response to “Animal Welfare Has an Evidence Problem,” which you refer to in the post, to clarify some of these points. 

First, at SWP, our work on shrimp stunning has been much more complex than simply deploying stunners at scale. The Humane Slaughter Initiative does involve getting electrical stunning equipment used on farms, but in practice, we are doing much more than that: coordinating and funding welfare research and R&D, collecting field data, working with scientists, developing and refining slaughter protocols, iterating on the technology with existing equipment providers, negotiating with industry, engaging corporate stakeholders, building and maintaining producer relationships, training staff, educating farmers… Much of this work is not visible from the surface, but it’s central to making the intervention possible. The tech matters, but it does not operate in a vacuum. 

Second, on shrimp stunning specifically, I think you captured something important. SWP entered this space aware that there were uncertainties, and we’ve been proactively trying to reduce them. Some of that work is public, but much of it happens through industry collaborations, field implementation, and producer relationships, which are often necessarily behind closed doors or covered by NDAs. That can make the work less visible from the outside, even though it is central to what we are doing.

This is one reason I think treating our intervention too simplistically, as merely putting stunners on farms, can lead to exactly the kind of disillusionment and backlash you describe.

Third, I feel some of the issues you describe are not limited to tech interventions. Many ambitious attempts to change existing systems seem to have an early phase where progress looks relatively fast, followed by a harder phase where problems become more complex, uncertainty can increase, coordination costs become bigger, and resistance from affected stakeholders becomes stronger. That does not necessarily mean the intervention – tech or otherwise – was a mistake; it can be a natural progression of an ambitious project. 

Thanks again for writing this! It’s an important reminder that welfare tech can be genuinely promising and, in some cases, extremely impactful, but it should not be treated as a silver bullet.