TLDR: No.

Confidence: Somewhat likely.

Summary

Some philanthropists discount the future much less than normal people. For philanthropists with low discount rates, does this change how they invest their money? Can they do anything to take advantage of other investors’ high time discounting?

We can answer this question in two different ways.

Should low-discount philanthropists invest differently in theory? No. [More]

Should low-discount philanthropists invest differently in practice? The real world differs from the standard theoretical approach in a few ways. These differences suggest that low-discount philanthropists should favor risky and illiquid investments slightly more than high-discount investors do. But the difference is too small to matter in practice. [More]

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Summary

Confidence: Likely. [More]

Some people have asked, “should we invest in companies that are likely to do particularly well if transformative AI is developed sooner than expected?”¹

In a previous essay, I developed a framework to evaluate mission-correlated investing. Today, I’m going to apply that framework to the cause of AI alignment.

(I’m specifically looking at whether to mission hedge AI, not whether to invest in AI in general. [More])

Whether to mission hedge crucially depends on three questions:

1. What is the shape of the utility function with respect to AI progress?
2. How volatile is AI progress?
3. What investment has the strongest correlation to AI progress, and how strong is that correlation?

I came up with these answers:

1. Utility function: No clear answer, but I primarily used a utility function with a linear relationship between AI progress and the marginal utility of money. I also looked at a different function where AI timelines determine how long our wealth gets to compound. [More]
2. Volatility: I looked at three proxies for AI progress—industry revenue, ML benchmark performance, and AI timeline forecasts. These proxies suggest that the standard deviation of AI progress falls somewhere between 4% and 20%. [More]
3. Correlation: A naively-constructed hedge portfolio would have a correlation of 0.3 at best. A bespoke hedge (such as an “AI progress swap”) would probably be too expensive. An intelligently-constructed portfolio might work better, but I don’t know how much better. [More]

Across the range of assumptions I tested, mission hedging usually—but not always—looked worse on the margin² than investing in the mean-variance optimal portfolio with leverage. Mission hedging looks better if the hedge asset is particularly volatile and has a particularly strong correlation to AI progress, and if we make conservative assumptions for the performance of the the mean-variance optimal portfolio. [More]

The most obvious changes to my model argue against mission hedging. [More] But there’s room to argue in favor. [More]

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Summary

TLDR: According to my preliminary model, the altruistic investing portfolio should ultimately allocate 5–20% on a risk-adjusted basis to mission-correlated investing. But for the current EA portfolio, it’s better on the margin to increase its risk-adjusted return than to introduce mission-correlated investments.

Last updated 2022-04-06.

The purpose of mission-correlated investing is to earn more money in worlds where your money matters more. For instance, if you’re working to prevent climate change, you could buy stock in oil companies. In worlds where oil companies are more successful and climate change gets worse, you make more money.

Previous work by Roth Tran (2019)¹ proved that, under certain weak assumptions, philanthropists should invest more in so-called “evil” companies than they would from a pure profit-making standpoint. This result follows from the assumption that a philanthropist’s actions become more cost-effective when the world gets worse along some dimension.

That’s an interesting result. But all it says is altruists should invest more than zero in mission hedging. How much more? Am I supposed to allocate 1% of my wealth to mission-correlated assets? 5%? 100%?

To answer this question, I extended the standard portfolio choice problem to allow for mission-correlated investing. This model makes the same assumptions as the standard problem—asset prices follow lognormal distributions, people experience constant relative risk aversion, etc.—plus the assumption that utility of money increases linearly with the quantity of the mission target, e.g., because the more CO2 there is in the atmosphere, the cheaper it is to extract.

I used this model to find some preliminary results. Future work should further explore the model setup and the relevant empirical questions, which I discuss further in the future work section.

Here are the answers the model gives, with my all-things-considered confidence in each:

• Given no constraints, philanthropists should allocate somewhere between 2% and 40% to mission hedging on a risk-adjusted basis,² depending on what assumptions we make. Confidence: Somewhat likely. [More]
• Given no constraints, and using my best-guess input parameters:
  • Under this model, a philanthropist who wants to hedge a predictable outcome, such as CO2 emissions, should allocate ~5% (risk-adjusted) to mission hedging.
  • Under this model, a philanthropist who wants to hedge a more volatile outcome, for example AI progress, should allocate ~20% to mission hedging on a risk-adjusted basis.
• If you can’t use leverage, then you shouldn’t mission hedge unless mission hedging looks especially compelling. Confidence: Likely. [More]
• If you currently invest most of your money in a legacy investment that you’d like to reduce your exposure to, then it’s more important on the margin to seek high expected return than to mission hedge. Confidence: Likely. [More]
• The optimal allocation to mission hedging is proportional to: (Confidence: Likely)
  1. the correlation between the hedge and the mission target being hedged;
  2. the standard deviation of the mission target;
  3. your degree of risk tolerance;
  4. the inverse of the standard deviation of the hedge.

Cross-posted to the Effective Altruism Forum.

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To make informed investing decisions, I want to estimate the future expected return of my portfolio. Markets are unpredictable, and future returns will likely significantly deviate from estimates—AQR believes there’s a 50% chance that 10-year realized equity returns will differ from their predictions by more than 3% per year. Still, it’s helpful to come up with a median guess.

In this post, I explain the projections that I use for my own financial planning.

Last updated 2022-06-16.

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Confidence: Highly likely.

In some sense, altruists and traditional investors have the same investing goals—they want to own the portfolio with the best balance of return and risk. But self-interested people only care about their own portfolios. If you’re a philanthropist, you also care about other (value-aligned) philanthropists’ portfolios.

When the market goes up, you have more money, and you can donate more to charity. But other altruists also have more money, and they can donate more to charity, so your money isn’t as valuable. Conversely, when markets go down, you have less money to donate at the exact time when charities need funding the most.

That means you should not (necessarily) invest your money in the best overall portfolio. Instead, you should use your investments to move the pool of altruistic money in the direction of optimal.

An illustration:

Alice and Bob both donate to the Against Malaria Foundation (AMF). (For simplicity, let’s say they’re the only two donors.) AMF has diminishing marginal utility of money—once it distributes malaria nets in all the best places, the next round of nets won’t save quite as many lives. So Alice and Bob prefer to invest in a way that will earn good returns but without too much risk. Ideally, they’d both hold something like the total world stock market.

Bob lives in the United States, and he invests all his money in US stocks. Alice could simply buy the global stock portfolio, which is roughly 50% US stocks and 50% international stocks. But that would put their aggregate portfolio at 75% US stocks, 25% international stocks (assuming Alice and Bob have the same amount of money). So AMF is being funded by an investment portfolio that’s overweighted toward US stocks, which adds risk without any reward to compensate.

Alice can fix this by investing her entire portfolio in non-US stocks. Now the aggregate portfolio of AMF donors is 50% US stocks, 50% international stocks, just as it should be. It wouldn’t make sense for someone to hold 0% US stocks in their personal retirement portfolio, but this strategy works for Alice because she’s a philanthropist.

(Of course, Alice could also talk to Bob and persuade him to diversify his investments, which might be an even better idea!)

Now, I’m not trying to say that the global stock market is the best investment, or that Alice did the exact right thing in this scenario. This is just an illustration of a broader point: for an individual altruistic donor, the best investment portfolio on the margin might not be the same thing as the best overall portfolio. And altruists should pick the portfolio that’s best on the margin.

(I wrote this post to provide an easy reference for this concept. The concept is not original to me—I originally heard it from Paul Christiano’s Risk aversion and investment (for altruists).)

Summary

Confidence: Likely.

Cross-posted to the Effective Altruism Forum.

Effective altruist earners-to-give might be able to donate more money if, instead of working at big companies for high salaries, they work at startups and get paid in equity. Startups are riskier than big companies, but EAs care less about risk than most people.

Working at a startup is easier than starting one. It doesn’t pay as well, but based on my research, it looks like EA startup employees can earn more than big company employees in expectation.

Does the optimal EA investment portfolio include a significant allocation to startups? To answer that question, I estimated the expected return and risk of startups by adding up the following considerations:

1. Find a baseline of startup performance by looking at historical data on VC firm returns.
2. VC performance is somewhat persistent. EAs can beat the average by working at startups that the top VC firms invest in.
3. Startup employees get worse equity terms than VCs, but they also don’t have to pay management fees, and they get meta-options. Overall, employees come out looking better than VCs.
4. Current market conditions suggest that future performance will be worse than past performance.
5. Startups are much riskier than publicly-traded stocks, and the startup market is moderately correlated with stocks (r=0.7).

All things considered, my best guess is that more earners-to-give should consider working at startups.

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Last updated 2022-11-18.

Many investors, even professionals, are ignorant about obvious facts that they really should know. When I say a fact is “obvious”, what I mean is that you can easily observe it by looking at widely-available data using simple statistical tools.

A list of obvious but underappreciated facts:

Fact 1. A single large-cap stock is about 2x as volatile as the total stock market. A small-cap stock is about 4x as volatile. It’s common knowledge that individual stocks are risky, but most people don’t know how to quantify the risk, and I believe they tend to underestimate it. I wrote a whole essay about this because I think it’s the most important underappreciated investing fact.

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Last updated 2021-10-20.

People who talk about talent/funding/capacity constraints mostly talk about what’s the biggest constraint right now. But it also matters what the constraints will be later.

Right now, the EA community holds a lot of wealth—more wealth than it can productively spend in the next few years, at least on smaller cause areas such as AI safety, cause prioritization research, and wild animal welfare. Those newer fields need time to scale up so they can absorb more funding.

That doesn’t mean EAs should stop earning to give. Maybe most EAs could do more good this year with their direct efforts than with their donations. But perhaps 10 years from now, the smaller causes will have scaled up a lot, and they’ll be able to deploy much more money. Earners-to-give can invest their money for a while, and then deploy it once top causes develop enough spending capacity.

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Summary: Mission hedgers want to hedge the quantity of a good, but can only directly hedge the price. As a motivating example, can we mission hedge climate change using oil futures or oil company stock? Based on a cursory empirical analysis, it appears that we can, and that oil stock makes for the better hedge. But this answer relies on some questionable data (either that, or my methodology is bad).

Introduction

The purpose of mission hedging is to earn more money in worlds where your money matters more. For instance, if you’re working to prevent climate change, you could buy stock in oil companies. In worlds where oil companies are more successful and climate change is worse, you make more money—at least in theory. But it might be hard in practice to find a good way to hedge climate change.

Let’s start with a simple example. Suppose you’re considering hedging climate change by buying oil futures. Does that work?

If people burn more oil, that directly contributes to climate change. You’d like to make money as the quantity of oil goes up. If you buy oil futures, you will make money as the price of oil goes up. The problem is, the quantity and price of oil aren’t necessarily related.

Before we talk about the economics of oil prices, let’s clarify some terminology.

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Cross-posted to the Effective Altruism Forum.

How do AI timelines affect the urgency of working on AI safety?

It seems plausible that, if artificial general intelligence (AGI) will arrive soon, then we need to spend quickly on AI safety research. And if AGI is still a way off, we can spend more slowly. Are these positions justified? If we have a bunch of capital and we’re deciding how quickly to spend it, do we care about AI timelines? Intuitively, it seems like the answer is yes. But is it possible to support this intuition with a mathematical model?

TLDR: Yes. Under plausible model assumptions, there is a direct relationship between AI timelines and how quickly we should spend on AI safety research.

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