Confidence: Likely.

I am here to clear fruit’s name against the accusations that have been made. Fruit is one of the healthiest types of foods—perhaps the healthiest food group—and we should bestow upon it the shining reputation it deserves.

On many occasions, I edit old posts to make additions, correct mistakes, etc. But there’s no way to know about updates unless you go digging through the archives. So I’m going to start publishing regular (perhaps quarterly) digests of the significant updates I’ve made to old posts.

Confidence: Almost certain.

I kept telling myself I wouldn’t write this post because it doesn’t matter. But I’ve seen one too many smart people speaking favorably about ergodicity economics. I believe the concept of ergodicity has essentially nothing going for it, and in this post I will explain why.

Ergodicity economics is one of those rare theories that somehow manages to be both unfalsifiable and false.

I originally wrote that sentence as a joke, then I deleted it, then I re-wrote it because I realized it’s actually true. Ergodicity economics is sufficiently vague in general that it can’t be falsified, but it is commonly interpreted as making specific falsifiable claims that are, in fact, false.

Edited 2025-05-26 to correct an inaccuracy—originally I said butter goes in the Dairy group but actually it goes in the Fats, Oils & Sweets group.

The original 1992 version of the USDA Food Pyramid was bad. So bad that people who scrupulously followed the guidelines were barely healthier than the people who ignored them.¹

But the Food Pyramid was not just wrong: it was marvelously wrong. It was wrong in many ways simultaneously. It achieved levels of wrongness hitherto undreamed of.

What was wrong about it? I will start with the obvious answers, and move into the philosophical.

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?

Many plans for how to safely build superintelligent AI have a critical section that goes like this:

1. Develop AI that’s powerful enough to do AI research, but not yet powerful enough to pose an existential threat.
2. Use it to assist with alignment research, thus greatly accelerating the pace of work—hopefully enough to solve all alignment problems.

You could call this process “alignment bootstrapping”.

This is a central feature of DeepMind’s plan (see “Amplified oversight”), Anthropic’s plan (see “Scalable Oversight”), and independent plans written by Sam Bowman (an AI safety manager at Anthropic), Joshua Clymer (a researcher at Redwood Research), and Marius Hobbhahn (CEO of Apollo Research).

There are various reasons why alignment bootstrapping could fail¹ even if implemented well, and some of those plans acknowledge this. But I’m also concerned about whether alignment bootstrapping will be implemented at all.

When the time comes, will AI companies actually spend their resources on alignment bootstrapping?

When AI companies have human-level AI systems, will they use them for alignment research, or will they use them (mostly) to advance capabilities instead?

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.

In this post I will explain the most impressive argument I heard in 2024.

First, some context:

There is an ongoing debate in the bodybuilding/strength training community about how much protein you should eat while losing weight.

Some say you should eat more protein if you’re losing weight:

If you’re eating less, your body is under extra pressure to cannibalize your muscles. Therefore, you should eat more protein to cancel this out.

The standard rebuttal:

Experimental trials have found that muscle gains max out when subjects eat 0.7–0.8 grams of protein per pound of bodyweight, and that’s true both when participants are maintaining weight and when they’re losing weight. There doesn’t appear to be a difference.

And the counter-rebuttal:

Almost all research looks at novice lifters. Experienced athletes have a more difficult time gaining muscle,¹ so losing weight will have a bigger negative impact on them, and therefore they need to eat more protein.

I used to believe this. Then I heard the most impressive argument of 2024.

I heard the argument in a YouTube video by Menno Henselmans:

It’s possible that in trained individuals there is a triple interaction effect, because that’s what you’re arguing here. If you’re saying that protein requirements increase in an energy deficit, but only in strength-trained individuals, then you are arguing for a triple interaction effect. […] That is very, very, very rare. Triple interaction effects, biologically speaking, simply do not occur much.

I didn’t understand what he was talking about. I spent two days pondering what it meant. On the third day, it finally clicked and I realized he was right.

To claim that trained lifters should eat more protein on an energy deficit, you’d need to believe that:

1. Above a certain level of protein intake (0.7–0.8 grams per pound), additional protein has no effect on muscle growth.²
2. Most of the time, trained athletes don’t need more protein than novices.
3. Novices don’t need more protein while losing weight than while maintaining/gaining weight.
4. HOWEVER, (a) among trained individuals who are (b) losing weight, the ones (c) who eat more protein (beyond 0.7–0.8 g/lb) gain more muscle.

The first variable (protein intake) has no interaction with muscle growth.

The second variable (trained vs. untrained) has no interaction with muscle growth.

The third variable (losing vs. maintaining weight) has no interaction with muscle growth.

The first and second variables together (protein intake + trained/untrained) have no interaction with muscle growth.

The first and third variables together (protein intake + losing/maintaining weight) have no interaction with muscle growth.

HOWEVER, when you put all three variables together, an interaction suddenly appears—a triple interaction effect.

This is a very strange claim. If all three variables together affect muscle growth, then you would expect each variable individually to affect muscle growth. And at least you would expect two out of three variables together to affect muscle growth.

(In fact, it is mathematically impossible to construct a differentiable function f(x, y, z) that is constant with respect to x, constant with respect to y, and constant with respect to z, but not constant overall. Although you could have a function f(x, y, z) where the slope with respect to each individual variable is close to 0, but not quite 0.)

Not to say a triple interaction effect can’t occur in the real world. It could be that muscle growth does depend on each of (protein intake, training experience, calorie deficit), but the relationships are so weak that the studies failed to pick them up.

But if you believe the studies’ results are correct, then it seems difficult—maybe even impossible—to still believe that trained lifters need to eat more protein while on a calorie deficit.

This was the best argument I heard in 2024 because:

• If you think about it, it’s obviously correct. It changed my mind as soon as I understood it.
• It’s difficult to come up with. (I’ve never heard anyone else make this argument.)

Posted on Apr 10, 2025

Notes

Since 2015, I have been taking notes on most articles I read. I figured other people might find them useful, so I cleaned them up and published them on my website. You can find them via the new “Notes” tab.

I will update the page every once in a while as I read more articles and take more notes.

I also have notes on every educational book I’ve read since 2015, but the notes are on physical paper (can you believe it?). I might digitize them at some point.

Posted on Mar 31, 2025

David J. Balan once proposed that there are two kinds of “no evidence”:

1. There have been lots of studies directly on this point which came back with the result that the hypothesis is false.
2. There is no evidence because there are few or no relevant studies.

I propose that there are three kinds of “no evidence”:

1. The hypothesis has never been studied.
2. There are studies, the studies failed to find supporting evidence, but they wouldn’t have found supporting evidence even if the hypothesis were true.
3. There are studies, the studies should have found supporting evidence if the hypothesis were true, and they didn’t.

Example of type 1: A 2003 literature review found that there were no studies¹ showing that parachutes could prevent injury when jumping out of a plane.

Example of type 2: In 2018, there was finally a randomized controlled trial² on the effectiveness of parachutes, and it found no difference between the parachute group and the control group. However, participants only jumped from a height of 0.6 meters (~2 feet). I don’t know about you, but this result does not make me want to jump out of a plane without a parachute.

Like in the parachute example, you see type-2 “no evidence” whenever the conditions of a study don’t match the real-world environment. You also see type-2 “no evidence” when an experiment is underpowered. Say you want to test the hypothesis that boys are taller than girls. So you go find your niece Sally and your neighbor’s son James and it turns out Sally is an inch taller than James. Your methodology was valid—you can indeed test the hypothesis by finding some people and measuring their heights—but your sample size was too small.

(The difference between type 2 and type 3 can be a matter of degree. The more powerful a study is, the stronger its “no evidence” if it fails to find an effect.)

Posted on Mar 03, 2025

Notes