(More info on the film's creation in the FLI interview: Suzy Shepherd on Imagining Superintelligence and "Writing Doom")

Correct link: https://www.youtube.com/watch?v=McnNjFgQzyc 

Another FLI-funded YouTube channel is https://www.youtube.com/@Siliconversations, which has ~2M views on AI Safety

Posts on this topic that I liked:

• Pain is not the unit of Effort
• “I would summarize a lot of effective altruism as ‘pretending to actually try’”
  • And this response: In praise of pretending to really try (although I'm not sure I agree, I think it's an interesting point)
• Altruism sharpens altruism (I always misremember this as "altruism begets altruism", which I think is catchier)

I fairly strongly disagree with "be honest about your counterfactual impact—most people overestimate it.", and on only working at a nonprofit you consider effective if you think you're ~10x better than the counterfactual hire or "irreplaceable."

As an example, I'm confident that there are software developers who would have been significantly more impactful than me at my role at GWWC, but didn't apply, and the extra ~$/year that they are donating (if they are actually donating more in practice than what they would have) does not compensate for that.
I also think that there's a good chance that I would have done other vaguely impactful work, or donated more myself, if they had been hired instead of me, largely compensating for their missed donations.

I remember wondering the same a few years ago, and I came to the opposite conclusion. I think the biggest differences in my reasoning were:

1. I think in practice it takes much more than 30 minutes on average to write a will, even more so if it's a significant amount of wealth (like $100k)
2. I think the annualized chance of death for someone worth $100k at 25 is significantly lower than the population average
3. People with no risk factors (e.g. heart disease, cancer) have a significantly lower chance of death, and if someone discovers a risk factor they can think about a will after that discovery

Also quickly noting that you're using the annualized chance of death for males in the US, but a significant percentage of EA Forum readers are women, so have less than half the mortality rate between 15 and 37, and/or live in countries with a much lower youth mortality risk (e.g.in the UK it's 0.6 per 1,000 25 y/o males, in Italy 0.4, in the Netherlands 0.4 if I'm interpreting this correctly, I expect Germany and other european countries to be similar, Canada 0.97, Australia 0.6)

The community tag was originally introduced as a way to separate out FTX-scandal related tags.

I don't think that's true, based on what CEA staff were posting publicly and some conversations I had at the time.

Some relevant posts and comment threads:
1. https://forum.effectivealtruism.org/posts/wvBfYnNeRvfEXvezP/moving-community-discussion-to-a-separate-tab-a-test-we#Why_consider_doing_this_at_all_

2. https://forum.effectivealtruism.org/posts/dDudLPHv7AgPLrzef/karma-overrates-some-topics-resulting-issues-and-potential

3. https://forum.effectivealtruism.org/posts/2jYDXwqSj87ZjLtwy/follow-and-filter-topics-and-an-update-to-the-community#3__The__Community__tag_and_topic

4. https://forum.effectivealtruism.org/posts/irhgjSgvocfrwnzRz/should-the-forum-be-structured-such-that-the-drama-of-the#GNLKxKvcijjcxSiRG 

When I was a moderator, my understanding was that the community tag was more about separating posts related to EA as in "doing good" from posts related to EA as in "a specific community of people". E.g. People uninterested in the community but still interested in AI Safety would still be the target audience of a post on "AI safety talent development"

That said, there were plenty of ambiguous cases, and users can tag any of their own posts as community when posting, so I agree that it's somewhat inconsistently applied.

Thank you for running the numbers!

I'm not sure about using these results to update your estimates of ň (as there are too many other differences between the US and LMICs, e.g. access to hospitals, no tubercolosis). But it does seem that reasonable values of ň would explain most of the lack of effects, especially for the study where mothers received "just" $333/month and similar ones.

I haven't run the numbers, and this is not my field so the below is very low confidence, but now that you mention it I wouldn't be surprised if isoelastic utility would be enough to explain the lack of results.

LLMs claim that if the effect size is 10 times smaller, you need a sample size 100x larger to have the same statistical significance (someone correct me if this is wrong)

So if a GiveDirectly RCT in Kenya needs a sample size of 2,000 individuals to detect a statistically significant effect; an RCT in the US where you expect the effect to be 10x smaller would need 200,000 individuals, which is intractable^[1].

Another intuition is that the effects of cash transfers in LMIC are significant but not huge, and iirc many experts claim that after ~5 years from the transfer there are negligible effects on subjective well-being, so it wouldn't take that much for the effect to become undetectable.

But again, this is all an uninformed vague guess.

1. ^{^}

   Edit: Gemini raises a good point that variance could also be higher in the US. If the standard deviation of wellbeing for beneficiaries in the US is 2x larger than in Kenya, and the effect is 10x smaller, I think you'd need a 400x larger sample size, not "just" 100x

I think these studies are just more evidence on the difference between US poverty and global poverty.

Some comments from the post:

Kelsey Piper wrote a nice article on recent results of cash transfers in the US: Giving people money helped less than I thought it would

If you give a new mom [in the US] a few hundred dollars a month or a homeless man one thousand dollars a month, that's gotta show up in the data, right?

Alas.

A few years back we got really serious about studying cash transfers, and rigorous research began in cities all across America. Some programs targeted the homeless, some new mothers and some families living beneath the poverty line. The goal was to figure out whether sizable monthly payments help people lead better lives, get better educations and jobs, care more for their children and achieve better health outcomes.

Many of the studies are still ongoing, but, at this point, the results aren’t “uncertain.” They’re pretty consistent and very weird. Multiple large, high-quality randomized studies are finding that guaranteed income transfers do not appear to produce sustained improvements in mental health, stress levels, physical health, child development outcomes or employment. Treated participants do work a little less, but shockingly, this doesn’t correspond with either lower stress levels or higher overall reported life satisfaction.

Homeless people, new mothers and low-income Americans all over the country received thousands of dollars. And it's practically invisible in the data. On so many important metrics, these people are statistically indistinguishable from those who did not receive this aid.

I cannot stress how shocking I find this and I want to be clear that this is not “we got some weak counterevidence.” These are careful, well-conducted studies. They are large enough to rule out even small positive effects and they are all very similar. This is an amount of evidence that in almost any other context we’d consider definitive.

[...]

Overall, the larger and more credible studies in this space have tended to find worse effects.

While it's sad that the lives of people in those studies didn't improve, I think this is some evidence that Giving isn’t demanding, and that giving 10% wouldn't worsen the life of a median person in the US in a measurable way.

Someone just mentioned that they accidentally pledged twice in 2015 and 2022, so we merged the accounts and we're back at 10,000 😅

Congratulations on indeed being the 10,000th active pledger!