The Open Philanthropy Blog

Since 1900, the global economy has grown by about 3% each year, meaning that it doubles in size every 20–30 years. I’ve written a report assessing whether significantly faster growth might occur this century. Specifically, I ask whether growth could be ten times faster, with the global economy growing by 30% each year. This would mean it doubled in size every 2–3 years; I call this possibility ‘explosive growth’.

The report builds on the work of my colleague, David Roodman. Although recently growth has been fairly steady, in the distant past it was much slower. David developed a mathematical model for extrapolating this pattern into the future; after calibration to data for the last 12,000 years, the model predicts that the global economy will grow ever faster over time and that explosive growth is a couple of decades away! My report assesses David’s model, and compares it to other methods for extrapolating growth into the future.

At first glance, it might seem that explosive growth is implausible — that it is somehow absurd or economically naive. Contrary to this view, I offer three considerations from economic history and growth theory that suggest advanced AI could drive explosive growth. In brief:

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We’re excited to announce that Open Philanthropy co-founder Alexander Berger has been promoted to co-CEO!

For some time now, Alexander has been the primary leader for most of our work on causes focused on maximizing verifiable impact within our lifetimes, while I have increasingly focused on causes directly aimed at affecting the very long-run future. I felt that Alexander should be promoted to recognize this reality and formalize the division of labor at Open Philanthropy. I am confident that he is an excellent fit for this role.

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Since our last hiring update, we have had a lot of new staff join Open Philanthropy. I’d like to use this post to introduce the new members of our team. We’re excited to have them!

If you are interested in joining our team, check out open positions on our Working at Open Phil page.

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This post compares our progress with the goals we set forth a year ago, and lays out our plans for the coming year.

In brief:

  • We recommended over $200 million worth of grants in 2020. The bulk of this came from recommendations to support GiveWell’s top charities and from our major current focus areas: potential risks of advanced AI, biosecurity and pandemic preparedness, criminal justice reform, farm animal welfare, scientific research, and effective altruism. [More]
  • We completed and published a number of reports on the likelihood of transformative AI being developed within the next couple of decades and other topics relevant to our future funding priorities. We are now working on both publishing additional reports in this area and updating our internal views on certain key values that inform our “near-termist” giving. [More]
  • We’re interested in determining how quickly we should increase our giving. As a means of answering this question, we have developed a model to optimize our spending levels across time within “near-termist” causes, which we hope to share this year. [More]
  • We have also begun the process of investigating potential new areas for giving. This year, we hope to launch searches for program officers in multiple new focus areas. [More]
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One of Open Phil’s major focus areas is technical research and policy work aimed at reducing potential risks from advanced AI.

To inform this work, I have written a report developing one approach to forecasting when artificial general intelligence (AGI) will be developed. By AGI, I mean computer program(s) that can perform virtually any cognitive task as well as any human, for no more money than it would cost for a human to do it. The field of AI is largely understood to have begun in Dartmouth in 1956, and since its inception one of its central aims has been to develop AGI.1

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Open Philanthropy’s ability to give effectively to the world’s most important and neglected causes hinges on the collective strength and expertise of our team. As such, we’ve thought extensively about how we can identify those who could most meaningfully contribute to Open Philanthropy’s mission, and how we can craft a recruitment process that encourages them to apply.

Recruiting Manager Anya Hunt sat down with Communications Officer Michael Levine to talk about Open Philanthropy’s approach to recruiting, the role of work tests in the application process, and measures we are taking to diversify our pipeline and attract talent from different communities. The questions and answers have been edited lightly for clarity.

Generally speaking, how does Open Philanthropy approach the recruitment process?

There are parallels to the way we approach grantmaking. Each hire is an investment — we’re making a bet that this person will help Open Phil more effectively carry out our mission. In both cases, we’re trying to be evidence-based where we can, but we’re also trying to minimize bureaucracy. We try to hold ourselves to rigorous standards of decision-making, accounting for our biases wherever possible. We can only be as effective as the people we hire. So we’re willing to invest an unusual amount of time and energy into sourcing and vetting candidates.

How does that mindset impact the recruiting process?

Mainly, it causes us to put work tests at the center of the process. After some basic screening, the first thing candidates do, typically, before we interview them, before anything else happens, is to take at least one work test, and we’ll pay them an honorarium to complete it. Then we evaluate it blind and generally admit people to the next round only if they meet a certain preset standard. This results in an unusually long process — that’s both an upside and a downside. It’s a downside for the obvious reasons, but it’s an upside because we think making a hire, and accepting an offer, is a really important decision on both our end and the candidate’s. Because onboarding new staff can be very costly to both us and the new hire, we aim to be highly confident in every offer we make (though of course we do still make mistakes).

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We believe that every life has equal value — and that philanthropic dollars can go particularly far by helping those who are living in poverty by global standards. Currently, many of the best giving opportunities we’ve found in the Global Health and Development focus area are recommended by GiveWell, a nonprofit dedicated to finding outstanding giving opportunities and publishing its full analysis to help donors decide where to give. (Learn more about our relationship with GiveWell here.)

GiveWell recently announced its recommendations for giving, a list that focuses on programs with a strong track record and excellent cost-effectiveness, can use additional funding to expand their core programs, and are exceptionally transparent. We have allocated an additional $100 million for GiveWell top charities, GiveWell standout charities, and GiveWell Incubation Grants in the year-end period (beyond what we’ve already granted to GiveWell-recommended charities earlier this year).

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When the Soviet Union began to fracture in 1991, the world was forced to reckon with the first collapse of a nuclear superpower in history.1 The USSR was home to more than 27,000 nuclear weapons, more than one million citizens working at nuclear facilities, and over 600 metric tons of nuclear fissile materials.2 It seemed inevitable that some of these weapons, experts, and materials would end up in terrorist cells or hostile states,3 especially given a series of recent failed attempts at non-proliferation cooperation between the US and the USSR.

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Last year, the year before, the year before that, the year before that, and the year before that, we published a set of suggestions for individual donors looking for organizations to support. This year, we are repeating the practice and publishing updated suggestions from Open Philanthropy program staff who chose to provide them.

Similar caveats to previous years apply:

  • These are reasonably strong options in causes of interest, and shouldn’t be taken as outright recommendations (i.e., it isn’t necessarily the case that the person making the suggestion thinks they’re the best option available across all causes).
  • The recommendations below fall within the cause areas Open Philanthropy has chosen to focus on. While this list does not expressly include GiveWell’s top charities, we believe those organizations to be the most cost-effective, evidence-backed giving opportunities available to donors today, and expect that some readers of this post might want to give to them.
  • Many of these recommendations appear here because they are particularly good fits for individual donors - due to being able to make use of fairly arbitrary amounts of donations from individuals, and in some cases because the recommender thought they’d be particularly likely to appeal to readers. This shouldn’t be seen as a list of our strongest grantees overall (although of course there may be overlap).
  • Our explanations for why these are strong giving opportunities are very brief and informal, and we don’t expect individuals to be persuaded by them unless they put a lot of weight on the judgment of the person making the suggestion.

In addition, we’d add that these recommendations are made by the individual program officers or teams cited, and do not necessarily represent my (Holden’s) personal or Open Phil’s institutional “all things considered” view.

Note that we are no longer including “Why we haven’t fully funded it” information for each option. In most cases, these grants are coming from limited per-focus-area budgets.

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Open Philanthropy is interested in when AI systems will be able to perform various tasks that humans can perform (“AI timelines”). To inform our thinking, I investigated what evidence the human brain provides about the computational power sufficient to match its capabilities. I consulted with more than 30 experts, and considered four methods of generating estimates, focusing on floating point operations per second (FLOP/s) as a metric of computational power.

The full report on what I learned is here. This blog post is a medium-depth summary of some context, the approach I took, the methods I examined, and the conclusions I reached. The report’s executive summary is a shorter overview.

In brief, I think it more likely than not that 1015 FLOP/s is enough to perform tasks as well as the human brain (given the right software, which may be very hard to create). And I think it unlikely (21 FLOP/s is required.1 But I’m not a neuroscientist, and the science here is very far from settled.2 I offer a few more specific probabilities, keyed to one specific type of brain model, in the report’s appendix.

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