Recently, I've encountered an increasing number of misconceptions, in rationalist and effective altruist spaces, about what Open Philanthropy's Global Catastrophic Risks (GCR) team does or doesn't fund and why, especially re: our AI-related grantmaking. So, I'd like to briefly clarify a few things:

• Open Philanthropy (OP) and our largest funding partner Good Ventures (GV) can't be or do everything related to GCRs from AI and biohazards: we have limited funding, staff, and knowledge, and many important risk-reducing activities are impossible for us to do, or don't play to our comparative advantages.
  • Like most funders, we decline to fund the vast majority of opportunities we come across, for a wide variety of reasons. The fact that we declined to fund someone says nothing about why we declined to fund them, and most guesses I've seen or heard about why we didn't fund something are wrong. (Similarly, us choosing to fund someone doesn't mean we endorse everything about them or their work/plans.)
  • Very often, when we decline to do or fund something, it's not because we don't think it's good or important, but because we aren't the right team or organization to do or fund it, or we're prioritizing other things that quarter.
  • As such, we spend a lot of time working to help create or assist other philanthropies and organizations who work on these issues and are better fits for some opportunities than we are. I hope in the future there will be multiple GV-scale funders for AI GCR work, with different strengths, strategies, and comparative advantages — whether through existing large-scale philanthropies turning their attention to these risks or through new philanthropists entering the space.
• While Good Ventures is Open Philanthropy's largest philanthropic partner, we also regularly advise >20 other philanthropists who are interested to hear about GCR-related funding opportunities. (Our GHW team also does similar work partnering with many other philanthropists.) On the GCR side, we have helped move tens of millions of non-GV money to GCR-related organizations in just the past year, including some organizations that GV recently exited. GV and each of those other funders have their own preferences and restrictions we have to work around when recommending funding opportunities.
  • Among the AI funders we advise, Good Ventures is among the most open and flexible funders.
  • We're happy to see funders enter the space even if they don’t share our priorities or work with us. When more funding is available, and funders pursue a broader mix of strategies, we think this leads to a healthier and more resilient field overall.
• Many funding opportunities are a better fit for non-GV funders, e.g. due to funder preferences, restrictions, scale, or speed. We've also seen some cases where an organization can have more impact if they're funded primarily or entirely by non-GV sources. For example, it’s more appropriate for some types of policy organizations outside the U.S. to be supported by local funders, and other organizations may prefer support from funders without GV/OP’s past or present connections to particular grantees, AI companies, etc. Many of the funders we advise are actively excited to make use of their comparative advantages relative to GV, and regularly do so.
• We are excited for individuals and organizations that aren't a fit for GV funding to apply to some of OP’s GCR-related RFPs (e.g. here, for AI governance). If we think the opportunity is strong but a better fit for another funder, we'll recommend it to other funders.
  • To be clear, these other funders remain independent of OP and decline most of our recommendations, but in aggregate our recommendations often lead to target grantees being funded.
• We believe reducing AI GCRs via public policy is not an inherently liberal or conservative goal. Almost all the work we fund in the U.S. is nonpartisan or bipartisan and engages with policymakers on both sides of the aisle. However, at present, it remains the case that most of the individuals in the current field of AI governance and policy (whether we fund them or not) are personally left-of-center and have more left-of-center policy networks. Therefore, we think AI policy work that engages conservative audiences is especially urgent and neglected, and we regularly recommend right-of-center funding opportunities in this category to several funders.
• OP's AI teams spend almost no time directly advocating for specific policy ideas. Instead, we focus on funding a large ecosystem of individuals and organizations to develop policy ideas, debate them, iterate them, advocate for them, etc. These grantees disagree with each other very often (a few examples here), and often advocate for different (and sometimes ~opposite) policies.
• We think it's fine and normal for grantees to disagree with us, even in substantial ways. We've funded hundreds of people who disagree with us in a major way about fundamental premises of our GCRs work, including about whether AI poses GCR-scale risks at all (example).
• I think frontier AI companies are creating enormous risks to humanity, I think their safety and security precautions are inadequate, and I think specific reckless behaviors should be criticized. AI company whistleblowers should be celebrated and protected. Several of our grantees regularly criticize leading AI companies in their official communications, as do many senior employees at our grantees, and I think this happens too infrequently.
• Relatedly, I think substantial regulatory guardrails on frontier AI companies are needed, and organizations we've directed funding to regularly propose or advocate policies that ~all frontier AI companies seem to oppose (alongside some policies they tend to support).
• I'll also take a moment to address a few misconceptions that are somewhat less common in EA or rationalist spaces, but seem to be common elsewhere:
  • Discussion of OP online and in policy media tends to focus on our AI grantmaking, but AI represents a minority of our work. OP has many focus areas besides AI, and has given far more to global health and development work than to AI work.
  • We are generally big fans of technological progress. See e.g. my post about the enormous positive impacts from the industrial revolution, or OP's funding programs for scientific research, global health R&D, innovation policy, and related issues like immigration policy. Most technological progress seems to have been beneficial, sometimes hugely so, even though there are some costs and harms along the way. But some technologies (e.g. nuclear weapons, synthetic pathogens, and superhuman AI) are extremely dangerous and warrant extensive safety and security measures rather than a "move fast and break [the world, in this case]" approach.
  • We have a lot of uncertainty about how large AI risk is, exactly which risks are most worrying (e.g. loss of control vs. concentration of power), on what timelines the worst-case risks might materialize, and what can be done to mitigate them. As such, most of our funding in the space has been focused on (a) talent development, and (b) basic knowledge production (e.g. Epoch AI) and scientific investigation (example), rather than work that advocates for specific interventions.

I hope these clarifications are helpful, and lead to fruitful discussion, though I don't expect to have much time to engage with comments here.

Re: why our current rate of spending on AI safety is "low." At least for now, the main reason is lack of staff capacity! We're putting a ton of effort into hiring (see here) but are still not finding as many qualified candidates for our AI roles as we'd like. If you want our AI safety spending to grow faster, please encourage people to apply!

I'll also note that GCRs was the original name for this part of Open Phil, e.g. see this post from 2015 or this post from 2018.

Holden has been working on independent projects, e.g. related to RSPs; the AI teams at Open Phil no longer report to him and he doesn't approve grants. We all still collaborate to some degree, but new hires shouldn't e.g. expect to work closely with Holden.

We fund a lot of groups and individuals and they have a lot of different (and sometimes contradicting) policy opinions, so the short answer is "yes." In general, I really did mean the "tentative" in my 12 tentative ideas for US AI policy, and the other caveats near the top are also genuine.

That said, we hold some policy intuitions more confidently than others, and if someone disagreed pretty thoroughly with our overall approach and they also weren't very persuasive that their alternate approach would be better for x-risk reduction, then they might not be a good fit for the team.

Echoing Eli: I've run ~4 hiring rounds at Open Phil in the past, and in each case I think if the top few applicants disappeared, we probably just wouldn't have made a hire, or made significantly fewer hires.

Indeed. There aren't hard boundaries between the various OP teams that work on AI, and people whose reporting line is on one team often do projects for or with a different team, or in another team's "jurisdiction." We just try to communicate about it a lot, and our team leads aren't very possessive about their territory — we just want to get the best stuff done!

The hiring is more incremental than it might seem. As explained above, Ajeya and I started growing our teams earlier via non-public rounds, and are now just continuing to hire. Claire and Andrew have been hiring regularly for their teams for years, and are also just continuing to hire. The GCRCP team only came into existence a couple months ago and so is hiring for that team for the first time. We simply chose to combine all these hiring efforts into one round because that makes things more efficient on the backend, especially given that many people might be a fit for one or more roles on multiple teams.

The technical folks leading our AI alignment grantmaking (Daniel Dewey and Catherine Olsson) left to do more "direct" work elsewhere a while back, and Ajeya only switched from a research focus (e.g. the Bio Anchors report) to an alignment grantmaking focus late last year. She did some private recruiting early this year, which resulted in Max Nadeau joining her team very recently, but she'd like to hire more. So the answer to "Why now?" on alignment grantmaking is "Ajeya started hiring soon after she switched into a grantmaking role. Before that, our initial alignment grantmakers left, and it's been hard to find technical folks who want to focus on grantmaking rather than on more thoroughly technical work."

Re: the governance team. I've lead AI governance grantmaking at Open Phil since ~2019, but for a few years we felt very unclear about what our strategy should be, and our strategic priorities shifted rapidly, and it felt risky to hire new people into a role that might go away through no fault of their own as our strategy shifted. In retrospect, this was a mistake and I wish we'd started to grow the team at least as early as 2021. By 2022 I was finally forced into a situation of "Well, even if it's risky to take people on, there is just an insane amount of stuff to do and I don't have time for ~any of it, so I need to hire." Then I did a couple non-public hiring rounds which resulted in recent new hires Alex Lawsen, Trevor Levin, and Julian Hazell. But we still need to hire more; all of us are already overbooked and turning down opportunities for lack of bandwidth constantly.

Cool stuff. Do you only leverage prediction markets, or do you also leverage prediction polls (e.g. Metaculus)? My sense of the research so far is that they tend to be similarly accurate with similar numbers of predictors, with perhaps a slight edge for prediction polls.