Is it a good use of time to call or write your representatives to advocate for issues you care about? I did some research, and my current (weakly-to-moderately-held) belief is that messaging campaigns are very cost-effective.

In this post:

• I look at evidence from randomized experiments, surveys of legislators’ opinions, and observational evidence. All lines of evidence suggest that messaging campaigns are effective, but none of the evidence is strong.
• I write an estimate of how many messages it takes to get a bill to pass in expectation, and how much that costs. According to my model, changing a vote outcome takes 17,000 messages for the Michigan state legislature and 2.2 million messages for US Congress.
• I provide links to resources on how to participate in messaging campaigns for animal welfare, AI safety, and global poverty.

Continue reading

TLDR: The available evidence is weak. It looks like small protests may be effective at garnering support among the general public. Policy-makers appear to be more sensitive to protest size, and it’s not clear whether small protests have a positive or negative effect on their perception.

Previously, I reviewed evidence from natural experiments and concluded that protests work (credence: 90%).

My biggest outstanding concern is that all the protests I reviewed were nationwide, whereas the causes I care most about (AI safety, animal welfare) can only put together small protests. Based on the evidence, I’m pretty confident that large protests work. But what about small ones?

I can see arguments in both directions.

On the one hand, people are scope insensitive. I’m pretty sure that a 20,000-person protest is much less than twice as impactful as a 10,000-person protest. And this principle may extend down to protests that only include 10–20 people.

On the other hand, a large protest and a small protest may send different messages. People might see a small protest and think, “Why aren’t there more people here? This cause must not be very important.” So even if large protests work, it’s conceivable that small protests could backfire.

What does the scientific literature say about which of those ideas is correct?

Continue reading

Last year I did a caffeine cycling self-experiment and I determined that I don’t get habituated to caffeine when I drink coffee three days a week. I did a follow-up experiment where I upgraded to four days a week (Mon/Wed/Fri/Sat) and I found that I still don’t get habituated.

For my current weekly routine, I have caffeine on Monday, Wednesday, Friday, and Saturday. Subjectively, I often feel low-energy on Saturdays. Is that because the caffeine I took on Friday is having an aftereffect that makes me more tired on Saturday?

When I ran my second experiment, I took caffeine four days, including the three-day stretch of Wednesday-Thursday-Friday. I found that my performance on a reaction time test was comparable between Wednesday and Friday. If my reaction time stayed the same after taking caffeine three days in a row, that’s evidence that I didn’t develop a tolerance over the course of those three days.

But if three days isn’t long enough for me to develop a tolerance, why is it that lately I feel tired on Saturdays, after taking caffeine for only two days in a row? Was the result from my last experiment incorrect?

So I decided to do another experiment to get more data.

This time I did a new six-week self-experiment where I kept my current routine, but I tested my reaction time every day. I wanted to test two hypotheses:

1. Is my post-caffeine reaction time worse on Saturday than on Mon/Wed/Fri?
2. Is my reaction time worse on the morning after a caffeine day than on the morning after a caffeine-free day?

The first hypothesis tests whether I become habituated to caffeine, and the second hypothesis tests whether I experience withdrawal symptoms the following morning.

The answers I got were:

1. No, there’s no detectable difference.
2. No, there’s no detectable difference.

Therefore, in defiance of my subjective experience—but in agreement with my earlier experimental results—I do not become detectably habituated to caffeine on the second day.

However, it’s possible that caffeine habituation affects my fatigue even though it doesn’t affect my reaction time. So it’s hard to say for sure what’s going on without running more tests (which I may do at some point).

Continue reading

Is moral offsetting a good idea? Is it ethical to spend money on something harmful, and then donate to a charity that works to counteract those harms?

I’m not going to answer that question. Instead I’m going to ask a different question: if you use an LLM, how much do you have to donate to AI safety to offset the harm of using an LLM?

I can’t give a definitive answer, of course. But I can make an educated guess, and my educated guess is that for every $1 spent on an LLM subscription, you need to donate $0.87 to AI safety charities.

Continue reading

I wrote a report giving a high-level review of what work people are doing in AI safety. The report specifically focused on two areas: AI policy/advocacy and non-human welfare (including animals and digital minds).

You can read the report below. I was commissioned to write it by Rethink Priorities, but beliefs are my own.

Continue reading

TLDR: A meta-analysis allegedly showed that a 500-calorie deficit is the sweet spot to avoid losing lean mass, but the interpretation of the data was wrong and actually it didn’t show that. When interpreted correctly, the data provides weak (insignificant) evidence that any deficit will result in a loss of lean mass.

If you’re losing weight, does lifting weights reduce how much muscle you lose? Is it possible to entirely prevent muscle loss (or even gain muscle)?

Murphy & Koehler (2021)¹ did a meta-analysis on this question. They collected experiments where the experimental groups did resistance training while eating at an energy deficit (RT+ED), and the control groups did resistance training while eating a normal amount of food (RT+CON).

They found a strong association between change in lean mass and the magnitude of the energy deficit (slope = –0.325, p = 0.001). The meta-analysis predicts that you can eat at a deficit of 500 calories per day without losing any lean mass, but you will lose mass at a larger deficit.

(The meta-analysis also reported that participants gained strength in almost every study, even with larger calorie deficits. That’s useful to know, but I will focus on lean mass for this post.)

I should mention that what we actually care about is muscle loss, not lean mass loss. Lean mass includes anything that isn’t fat—muscle fibers, organs, glycogen, etc. Muscle mass is harder to measure. We don’t know what happened to study participants’ muscle, only their total lean mass.

Let’s set that aside and assume lean mass is a useful proxy for muscle mass.

The authors showed a plot of every individual study’s experimental group (RT+ED) and control group (RT+CON), along with a regression line predicting lean mass change as a function of energy deficit:²

But…does this regression line look a little odd to you?

Continue reading

James Özden and Sam Glover at Social Change Lab wrote a literature review on protest outcomes¹ as part of a broader investigation² on protest effectiveness. The report covers multiple lines of evidence and addresses many relevant questions, but does not say much about the methodological quality of the research. So that’s what I’m going to do today.

I reviewed the evidence on protest outcomes, focusing only on the highest-quality research, to answer two questions:

1. Do protests work?
2. Are Social Change Lab’s conclusions consistent with the highest-quality evidence?

Here’s what I found:

Do protests work? Highly likely (credence: 90%) in certain contexts, although it’s unclear how well the results generalize. [More]

Are Social Change Lab’s conclusions consistent with the highest-quality evidence? Yes—the report’s core claims are well-supported, although it overstates the strength of some of the evidence. [More]

Cross-posted to the Effective Altruism Forum.

Continue reading

This research piece is not as rigorous or polished as usual. I wrote it quickly in a stream-of-consciousness style, which means it’s more reflective of my actual reasoning process.

My understanding of HIIT (high-intensity interval training) as of a week ago:

1. VO2max is the best fitness indicator for predicting health and longevity.
2. HIIT, especially long-duration intervals (4+ minutes), is the best way to improve VO2max.
3. Intervals should be done at the maximum sustainable intensity.

I now believe those are all probably wrong.

Continue reading

Conventional wisdom says charity cost-effectiveness obeys a power law. To my knowledge, this hypothesis has never been properly tested.¹ So I tested it and it turns out to be true.

(Maybe. Cost-effectiveness might also be log-normally distributed.)

• Cost-effectiveness estimates for global health interventions (from DCP3) fit a power law (a.k.a. Pareto distribution) with \(\alpha = 1.11\). [More]
• Simulations indicate that the true underlying distribution has a thinner tail than the empirically observed distribution. [More]

Continue reading

Summary

Last updated 2025-04-25.

It’s been a while since I last put serious thought into where to donate. Well I’m putting thought into it this year and I’m changing my mind on some things.

I now put more priority on existential risk (especially AI risk), and less on animal welfare and global priorities research. I believe I previously gave too little consideration to x-risk for emotional reasons, and I’ve managed to reason myself out of those emotions.

Within x-risk:

• AI is the most important source of risk.
• There is a disturbingly high probability that alignment research won’t solve alignment by the time superintelligent AI arrives. Policy work seems more promising.
• Specifically, I am most optimistic about policy advocacy for government regulation to pause/slow down AI development.

In the rest of this post, I will explain:

1. Why I prioritize x-risk over animal-focused longtermist work and global priorities research.
2. Why I prioritize AI policy over AI alignment research.
3. My beliefs about what kinds of policy work are best.

Then I provide a list of organizations working on AI policy and my evaluation of each of them, and where I plan to donate.

Cross-posted to the Effective Altruism Forum.

Continue reading