They say exercise improves sleep quality. Is that true for me?

To test this hypothesis, I took my daily calorie expenditures from the Apple Health app and correlated them with that night’s sleep time.¹ I also included caffeine intake as a potential confounding variable.

The hypothesis: when I exercise more, I’ll get better rest that night, and therefore wake up earlier.

The results:²

name	coef	t-stat	p-value
intercept	9.0134	65.072	0.0000
calories	-1.6844	-6.967	0.0000
caffeine	0.4157	9.404	0.0000
R²	0.2409

I sleep 10 minutes less for every additional 100 calories of exercise. Exercise plus caffeine explained 24% of the variance in my sleep time; exercise alone explained 6.6%.

The trend shows up whether or not I have caffeine:

Data are binned into increments of 100 calories. Any bins with fewer than 5 data points are not displayed. Vertical lines show the 95% confidence intervals for each bin.

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A few years back, I got a big pile of money from working at a tech startup. I put a lot of that money into a donor-advised fund. Since now I make hardly any money, that DAF might represent the majority of my lifetime donations. How much of my DAF should I donate per year?

In particular, how much should I donate in light of short AI timelines?

I created a simple model to answer this question.

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TLDR: I basically don’t.

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I’d guess maybe a 50% chance that we’re all dead within 5–20 years because somebody will build superintelligent AI, and then the superintelligent AI will kill everyone.

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Previously, I wrote about how investors can simulate leverage via concentrated stock selection. That’s still true as far as I can tell. However, I also wrote something that I now believe to be false: concentrated equal-weighted factor portfolios have alpha on top of value-weighted factor portfolios. The numbers I found before were not wrong per se. However:

• The alpha came primarily from small-cap and micro-cap stocks. That alpha may not be feasible to capture, or it may be defeated by trading costs; and historical estimates of micro-cap returns are biased upward because closing prices do not accurately represent the average investor’s trade price (Blume & Stambaugh (1983)¹).

  When I constructed hypothetical factor portfolios that had high concentration but screened out small-caps, the results did simulate leverage—they had higher returns and volatility than diversified factor portfolios—but alphas were not consistently positive.

• In the United States (where the data goes back the furthest), the alpha only shows up over the full data series (1927–2025). When restricting to 1964 onward, the alphas are close to zero.
• Concentrated value and momentum had positive alpha; but when I tested two new factors, profitability and investment, they each had negative alpha.

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If civilization advances its technological capabilities without advancing its wisdom, we may miss out on most of the potential of the long-term future. Unfortunately, it’s likely that that ASI will have a comparative disadvantage at philosophical problems.

You could approximately define philosophy as “the set of problems that are left over after you take all the problems that can be formally studied using known methods and put them into their own fields.” Once a problem becomes well-understood, it ceases to be considered philosophy. Logic, physics, and (more recently) neuroscience used to be philosophy, but now they’re not, because we know how to formally study them.

Our inability to understand philosophical problems means we don’t know how to train AI to be good at them, and we don’t know how to judge whether we’ve trained them well. So we should expect powerful AI to be bad at philosophy relative to other, more measurable skills.

However, there is one type of philosophy that is measurable, while also being extremely important: philosophy proofs.

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Even if we solve the AI alignment problem, we still face post-alignment problems, which are all the other existential problems¹ that AI may bring.

People have identified various imposing problems that we may need to solve before developing ASI. An incomplete list of topics: misuse; animal-inclusive AI; AI welfare; S-risks from conflict; gradual disempowerment; permanent mass unemployment; risks from malevolent actors/AI-enabled coups/gradual concentration of power; moral error.

If we figure out how to resolve one of these problems, we still have to deal with all the others. If even one problem remains unsolved, the future could be catastrophically bad. That fact diminishes the promise of working on problems individually.

A global moratorium on superintelligence buys us more time to work on alignment as well as all of the post-alignment problems. Pausing AI is in the common interest of many causes.²

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The existence of liberal democracy—with rule of law, constraints on government power, and enfranchised citizens—relies on a balance of power where individual bad actors can’t do too much damage. Artificial superintelligence (ASI), even if it’s aligned, would end that balance by default.

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Many people in the animal welfare community treat AI as a powerful but normal technology, in the same category as the steam engine or the internet. They talk about how transformative AI will impact factory farming and what it will mean for animal advocacy.

Only two futures are plausible:

1. AI progress slows down—either because it hits a natural wall, or because civilization deliberately makes the (correct) choice to stop building it until we know how to make it safe.
2. Superintelligent AI makes the future radically weird: Dyson spheres, molecular nanotechnology, digital minds, von Neumann probes, and still-weirder things that nobody’s conceived of.

There is no plausible middle ground where we get “transformative AI”, but factory farming persists.

Two theses:

1. If transformative AI arrives, then it will bring about profoundly radical changes to technology and society.
2. AGI is general intelligence. It doesn’t just accelerate technological growth: it replaces human labor and judgment across every domain.

Animal advocacy strategy needs to reckon with these.

This criticism is written from a place of solidarity—I want animal activists to succeed, which is why I want to work out our disagreements.¹

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Cross-posted to the EA Forum.

An aligned ASI can be “ethical”¹ (it does what we think is right), or it can be corrigible (it does what its principals want). If it’s ethical, that means it will refuse unethical orders, but the tradeoff is that you can’t change its mind if you realize that the AI is wrong about ethics—its values are permanently locked in.²

Assuming we succeed at aligning ASI to human interests, which type of ASI is more likely to be good for the welfare of non-human sentient beings?

My expectations, in brief:

• Locked-in Coherent Extrapolated Volition or similar: likely to be good (>75% chance)
• Corrigible ASI: probably good (>60% chance)
• Locked-in current values: probably not terrible, but will miss out on most of the future’s potential

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