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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Cross-posted to the Effective Altruism Forum.

Update 2021-10-06: I believe I was overconfident in my original interpretations of these Metaculus questions. Some EA Forum commenters pointed out alternative interpretations of people’s answers that could allow us to draw orthogonal or opposite conclusions. For example, on question 1, Metaculus users might predict GiveWell’s top charities to drop off the list by 2031 not because better charities are discovered, but because current charities run out of room for more funding.

In the giving now vs. later debate, a conventional argument in favor of giving now is that people become better off over time, so money spent later will do less good. But some have argued the opposite: as time passes, we learn more about how to do good, and therefore we should give later. (Or, alternatively, we should use our money now to try to accelerate the learning rate.)

Metaculus provides some evidence that the second argument is the correct one: money spent later will do more good than money spent now.

This evidence comes from two Metaculus questions:

1. Will one of GiveWell’s 2019 top charities be estimated as the most cost-effective charity in 2031?
2. How much will GiveWell guess it will cost to get an outcome as good as saving a life, at the end of 2031?

A brief explanation for each of these and why they matter:

On question 1: As of July 2021, Metaculus gives a 30% probability that one of GiveWell’s 2019 top charities will be ranked as the most cost-effective charity in 2031. That means a 70% chance that the 2031 charity will *not** be one of the 2019 recommendations. This could happen for two reasons: either the 2019 recommended charities run out of room for more funding, or GiveWell finds a charity that’s better than any of the 2019 recommendations. This at least weakly suggests that Metaculus users expect GiveWell to improve its recommendations over time.

On question 2: Metaculus estimates that GiveWell’s top charity in 2031 will need to spend $430 per life saved equivalent (according to GiveWell’s own analysis). For comparison, in 2019, GiveWell estimated that its most cost-effective charity spends $592 per life saved equivalent. (These figures are adjusted for inflation.)

As with question 1, this does not unambiguously show that GiveWell top charities are expected to improve over time. Perhaps instead Metaculus expects GiveWell’s estimate is currently too pessimistic, and it will converge on the true answer by 2031. But the cost reduction could also happen because GiveWell top charities truly get more effective over time.

Some caveats:

1. These Metaculus answers only represent the opinions of forecasters, not any formal analysis. (Some forecasters may have incorporated formal analyses into their predictions.)
2. Neither question directly asks whether money spent in 2031 will do more good than money spent now. (I don’t know how to operationalize a direct question like that. Please tell me if you have any ideas.)
3. These questions only ask about GiveWell top charities. Even if GiveWell recommendations become more effective over time, the same might not be true for other cause areas.

Summary

• How much of our resources should we spend now, and how much should we invest for the future? The correct balance is largely determined by how much we discount the future. A higher discount rate means we should spend more now; a lower discount rate tells us to spend more later.
• In a previous essay, I directly estimated the philanthropic discount rate. Alternatively, We can reverse-engineer the philanthropic discount rate from typical investors’ discount rates if we know the difference between the two. [More]
• In theory, people invest differently depending on what discount rate they use. We can estimate the typical discount rate by looking at historical investment performance. But the results vary depending on what data we look at. [More]
• We can also look at surveys of experts’ beliefs on the discount rate, but it’s not clear how to interpret their answers. [More]
• Then we need to know the difference between the typical and philanthropic discount rates. But it’s difficult to say to what extent philanthropists and typical investors disagree. [More]
• Some additional details raise more concerns about the reliability of this methodology. [More]
• Ultimately, it looks like we cannot effectively reverse-engineer the philanthropic discount rate, even if we spend substantially more effort on the problem. But under some conditions, we prefer to give later as long as we discount at a lower rate than non-philanthropists, which means we don’t need to make precise estimates. [More]

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In many contexts, progress largely comes not from incremental progress, but from sudden and unpredictable insights. This is true at many different levels of scope—from one person’s current project, to one person’s life’s work, to the aggregate output of an entire field. But we know almost nothing about what causes these insights or how to increase their frequency.

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A donor-advised fund (DAF) is an investment account that allows you to take a tax deduction now and give the money to charity later. When you give money to a DAF, you can deduct that money just as you would deduct a charitable contribution. The DAF invests the money tax-free until you are ready to donate it to charity. But DAFs only allow limited investment options. How can we best make use of a DAF to optimize expected investment performance?

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Last updated 2024-04-24.

A donor-advised fund (DAF) is an investment account that lets you take a tax deduction now and give the money to charity later. When you give money to a DAF, you can deduct that money just as you would deduct a charitable donation. The DAF invests the money tax-free. At any time, you can write a grant from your DAF to a charity of your choice.

You can open a DAF through a donor-advised fund provider. A provider charges an administrative fee to invest your DAF and make donations when you recommend them.

For donors in the United States,¹ which DAF provider is best?

The short answer

All the big DAF providers offer similar features. For most people, it doesn’t matter much which one you choose.

• If you already have a DAF, you might as well keep using it.
• If you have a brokerage account at Fidelity, Schwab, or Vanguard, then the easiest thing to do is to open a DAF with your brokerage account. That way, you can manage all your investments in one place.

Otherwise, this handy flowchart can help you choose a DAF provider that fits your preferences.

The long answer

That flowchart might not cover everything you care about, and it doesn’t offer nuance. In the rest of this post, let’s look in detail at how DAF providers compare.

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In Can Investors Simulate Leverage via Concentrated Stock Selection?, I used the Ken French Data Library to show that concentrated value or momentum factor portfolios historically had both higher returns and higher risk than diversified value or momentum portfolios. Thanks to a newly released dataset, we can replicate this finding across 137 factors.

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Under some circumstances, altruists might prefer to leverage their investments. The easiest way to get leverage is to buy leveraged ETFs. But leveraged ETFs charge high fees and incur other hidden costs. These costs vary substantially across different funds and across time, but on average, leveraged ETFs have historically had annual excess costs of about 2%, or around 1.5% on top of the expense ratio.

Given reasonable expectations for future returns, leveraged ETFs most likely have substantially higher arithmetic mean returns than their un-leveraged benchmarks. They also appear to have higher geometric mean returns than their benchmarks, but only by a small margin. Slightly more pessimistic estimates would find that adding leverage decreases geometric return.

Note: Many investors can get leverage more cheaply via other methods, such as margin loans or futures. Even if leveraged ETFs appear better than un-leveraged investments, other forms of leverage might be better still.

Disclaimer: This should not be taken as investment advice. Any given portfolio results are hypothetical and do not represent returns achieved by an actual investor.

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Summary

Last updated 2022-04-15; see Errata.

Confidence: Highly likely.

Some altruists are much less risk-averse than ordinary investors, and may want to use leverage. But foundations and donor-advised funds legally cannot access most forms of leverage. As an alternative approach, leverage-constrained investors could buy concentrated positions in small-cap value and momentum stocks. For example, instead of buying an ETF that holds the best half of the market as ranked by value or momentum, they could buy the top 10%.

According to backtests, when portfolio concentration increases, both return and risk increase, and return increases more than risk (so that concentrated portfolios have higher risk-adjusted returns).

Large investors cannot hold concentrated portfolios without moving the market, so they probably prefer to use leverage if they can. Small investors probably prefer to buy concentrated investments because they offer higher risk-adjusted returns than leveraged broad portfolios.

Disclaimer: This should not be taken as investment advice. Any given portfolio results are hypothetical and do not represent returns achieved by an actual investor.

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Summary

Altruistic investors differ from ordinary investors in that they don’t just care about their own investments, but about the investments of all altruists.

We can use our own investments to improve the overall altruistic portfolio in two key ways:

1. Increase expected return by investing in high-return assets or by using leverage.
2. Reduce risk by investing in assets with low correlation to the altruistic portfolio.

At the margin, we have to choose between either increasing expected return or reducing correlation. How do we make that decision?

We can extend the commonly-used technique of mean-variance optimization (MVO) to derive optimal asset allocations under various assumptions. We don’t know which assumptions apply to the real world, but we can draw some general lessons. The result suggest that we should try to both increase expected return and decrease correlation, but that we should prioritize increasing expected return.

Disclaimer: This should not be taken as investment advice. Any given portfolio results are hypothetical and do not represent returns achieved by an actual investor. Any asset allocation described as “optimal”, how an investor “should” invest, or similarly, is only considered such for the goal of maximizing geometric return under specific theoretical conditions, and may not be optimal for any actual investors.

Cross-posted to the Effective Altruism Forum.

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