# Three positive updates I made about technical grantmaking at Coefficient Giving (fka Open Phil)

~~Open Philanthropy’s ~~Coefficient Giving’s Technical AI Safety team is [hiring grantmakers](https://jobs.ashbyhq.com/openphilanthropy/4e210168-34c5-43c5-9f97-8767834f8c42). I thought this would be a good moment to share some positive updates about the role that I’ve made since I joined the team a year ago.

tl;dr: I think this role is more impactful and more enjoyable than I anticipated when I started, and I think more people should consider applying.

It’s not about the “marginal” grants
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Some people think that being a grantmaker at Coefficient means sorting through a big pile of grant proposals and deciding which ones to say yes and no to. As a result, they think that the only impact at stake is how good our decisions are about marginal grants, since all the excellent grants are no-brainers.

But grantmakers don’t just evaluate proposals; we elicit them. I spend the majority of my time trying to figure out how to get better proposals into our pipeline: writing RFPs that describe the research projects we want to fund, or pitching promising researchers on AI safety research agendas, or steering applicants to better-targeted or more ambitious proposals.

Maybe more importantly, cG’s technical AI safety grantmaking strategy is currently underdeveloped, and even junior grantmakers can help develop it. If there's something you wish we were doing, there's a good chance that the reason we're not doing it is that we don't have enough capacity to think about it much, or lack the right expertise to tell good proposals from bad. If you join cG and want to prioritize that work, there's a good chance you'll be able to make a lot of work happen in that area.

How this cashes out is: as our team has tripled headcount in the past year, we’ve also ~tripled the amount of grants we’re making, and we think the distribution of impact per dollar of our grantmaking has stayed about the same. That is, we’ve about tripled the amount of grant money we’ve moved towards the top end of the impact distribution as well as at the marginal end.

To be even more concrete, here’s one anecdote I can share. About a year ago, Jason Gross asked me for $10k for compute for an experiment he was running. I spoke to him a few times and encouraged him to make grander plans. The resulting conversations between him, me, and Rajashree Agrawal led to me giving them a $1M grant to try to found something ambitious in the formal software verification space (I’m reasonably excited about FSV as a def/acc + mitigating reward hacking play.) They eventually founded [Theorem](https://theoremlabs.com/), a startup focussed on formal software verification, which went on to be the first FSV startup accepted to YC, and they subsequently raised at one of the largest valuations in their cohort. Jason and Rajashree say that they would have been very unlikely to set their goals that big without my initial grant. Nothing about that seems marginal to me, yet it wouldn’t have happened had I not been here.

There is no counterfactual grantmaker
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When I was offered the job a little over a year ago, I was told that I was the only candidate still being considered for the role, and that there was no one left to make offers to if I didn’t accept. In our [current hiring round](https://jobs.ashbyhq.com/openphilanthropy/4e210168-34c5-43c5-9f97-8767834f8c42), we’d like to hire 3-4 technical AI safety grantmakers, but once again it’s far from obvious that we’ll find enough candidates that meet our bar. If you get an offer and don’t take it, the likeliest result is that we hire one fewer person.

Why is this? I think the main reason is that fewer people apply to our roles than you might expect (if you’ve already applied, thank you!). We are looking for people who could succeed in a research career, and most such people don’t want to leave research. It also helps for this role if you are well networked and have a lot of context on technical AI safety. Most people with a lot of context are settled in their roles and unlikely to apply. Separately, the set of skills required to be a good grantmaker includes some things that aren’t as important for being a good researcher, so occasionally strong researchers who apply have disqualifying qualities, even people who on paper seemed like they might be really good.

What this all means is that our top candidates end up being extremely counterfactual. Their acceptance or rejection of the role doesn't just improve outcomes very slightly relative to some other person we could have hired, but counterfactually causes tens of millions of dollars to move out the door to really impactful projects that wouldn't have otherwise been funded.

If we're so starved for grantmaker labor, why don't we lower our hiring bar? I think we’re going to have a slightly lower bar than we’ve had in the past; we really want to fill these roles. But also, we think there are diffuse long-term negative effects of seriously lowering our hiring bar. I acknowledge that perhaps we're making the wrong tradeoffs here.

(If you feel moved to apply by the counterfactual argument, but would drop out if it turns out that we think we have enough other good applicants, please feel free to indicate that in your application. If we get an unexpected windfall of strong applicants, such that we have more qualified candidates than we can hire, we’ll be happy to let you know, and there will be no hard feelings if you drop out.)

Grantmaking is more fun/motivating than I anticipated
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Before I joined OpenPhil, I was about as “research archetype” as they get. I spent most of my time thinking about [wacky](https://ui.adsabs.harvard.edu/abs/2023arXiv231006301C/abstract) [theory](https://arxiv.org/abs/2408.05451) [math](https://arxiv.org/abs/2405.10927) [ideas](https://arxiv.org/pdf/2501.14926). My work style was chaotic-academia: I went to bed at random times and worked at random times and in random places, mostly on whatever interested me at the time. 

Now I have a team and a manager, and I have lots of things that need to be done. I am not planning to have any papers with my name on them in the foreseeable future. But, I'm really enjoying it! So why am I enjoying it more than you might expect, and indeed indeed more than I expected going in? Some factors:

*   I do actually spend a decent fraction of my time thinking about object-level technical stuff. Mostly that looks like talking to top researchers, but it also looks like reading academic papers, and going to conferences to talk to people about their research or argue about AI safety. That can be a big part of the job if you want it to be.
    *   One thing that's nice about the technical roles at cG is that we have a lot of generalist grantmakers but not a lot of technical specialists. That means that people are focused on leveraging the technical expertise of those who have it efficiently, which means that I get to spend a larger than expected fraction of my time on technical stuff. For example, with large grants that have a significant technical and non-technical component to the investigations, I often pair with a generalist grantmaker who will take the non-technical aspects off my hands. I get to spend more time thinking about which research agendas are promising, and less time worrying about whether an organisation has a healthy board of directors etc, than I expected.
*   The work I'm doing feels important and tangible. I go to conferences and see people walking around and giving talks who wouldn’t be in the room without my grant. A promising junior person mentions they just got a job at an org I funded to grow its headcount. Maybe it's cliche, but I actually do think that seeing the effects of my work on the shape of the field, is pretty motivating.
*   I'm significantly more empowered than I expected to be when I joined. I've been given much more trust than I expected, and I've been empowered to make decisions based on my inside view. My manager is constantly pushing me to take on bigger projects, be more ambitious and creative, and be more agentic. As a result, I think I have become more ambitious and agentic, and noticing that in myself has been very motivating. I think if you think that a more agentic, ambitious version of yourself is someone you'd like to grow into, then this might be a good role for you, even if you're not sure how well that will go yet.
*   This role can be very sociable. I spend a lot of my time talking to researchers about the research they're doing and why. I don't get as much time to spend on getting into the low-level technicalities, at least not in my day-to-day, but I actually find that high-level strategic thinking which still interfaces with technical details can scratch much of the same itch as doing the research myself. I also think that thinking through strategic questions about technical AI safety and the future of AI are extremely interesting questions.

Please apply!
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If all this sounds appealing to you, you can **apply**[**here**](https://jobs.ashbyhq.com/openphilanthropy/4e210168-34c5-43c5-9f97-8767834f8c42) **by December 1st**! Our team funds a lot of great research – from scaling up research orgs like [Redwood](https://www.redwoodresearch.org/) or [Apollo](https://www.apolloresearch.ai/), to eliciting projects like those described in [our RFP](https://www.lesswrong.com/posts/26SHhxK2yYQbh7ors/research-directions-open-phil-wants-to-fund-in-technical-ai), to proactively seeding new initiatives. 

Last year, the Technical AI Safety team made $40 million in grants; this year it’ll be over $140 million. We want to scale further in 2026, but right now we only have three grant investigators on the team, so we’re often bottlenecked by our grantmaker bandwidth. If you think you might be a strong fit, your application could be the difference between us finding the right person or leaving a role unfilled. If you have more questions, you can dm me on LW or reach out to me at jake \[dot\] mendel \[at\] coefficientgiving \[dot\] org