Hide table of contents

I’m looking to compile a list of principles or rules-of-thumb that can help determine whether a project intended to improve the long-term could be valuable.

Given a reasonably fleshed-out project idea, what characteristics (besides estimated expected value) indicate that the project is likely worth putting time or money behind? Similarly, what red flags suggest that a project is probably not worth doing?

Another twist: given a list of longtermist project ideas, what criteria could be used to rank those ideas? Note here that “project” is intentionally an ambiguous term here; it could be a short, self-contained side project, or an entirely new organization. Assume, though, that each project has a clearly-defined deliverable/goal.

Any suggestions or links are helpful! I’m assuming OpenPhil, EA Funds, and other longtermist funders have some internal criteria that they use to assess grant applications, so information about those criteria would be particularly useful.

Thanks!

13

0
0

Reactions

0
0
New Answer
New Comment


1 Answers sorted by

Some quick thoughts. I work at Rethink Priorities and also started recently as a (very) part-time guest manager at the Long-Term Future Fund, but all thoughts are my own. Note that I'm actively thinking about this right now, so these are just my working thoughts, and they're subject to change in months or even days.

Here are factors I use to identify whether a project is worth doing (keeping personnel factors constant, which is true if I'm advising someone/myself whether to do a project, and not for e.g. funding decisions). 

Note that I'm less rigorous/principled than the below list will suggest in practice, as sometimes a single factor may dominate (e.g., if a project has really strong learning value, or the story for decreasing x-risk is unusually clear, or if the researcher just seems so viscerally excited about the project that it'd be hard to convince them to do anything else).

  1. Does this project have a strong direct line/clean story for improving the long-term future (especially via reducing existential risk), and/or multiple fuzzier stories?
  2. Does it have leverage/scalability potential?
    1. E.g. at similar levels of cost-effectiveness, I'm much more excited about projects that can scale to >$100M of spending, without losing (much) cost-effectiveness, than  projects that "top out" at $100k or $1M.
  3. Relatedly, what does the learning/exploration value of this project look like?
    1. To the researcher/entrepreneur?
    2. To the institution? (if they're working in an EA-institutional context)
    3. To the EA or longtermist ecosystem as a whole?
  4. Does the researcher/entrepreneur working at this project seem viscerally excited about this? Are they likely to pursue it with passion and vigor?
  5. Am I personally excited about this project? Can I imagine working on the project myself, or spending multiple hours a week advising this project?
  6. Do other people I respect like this project (or other projects like it), and want there to be more of this project in the world?
    1. Though I try to be careful about what my own independent impressions are, and not contribute to information cascades.
  7. Does the project have fast ways to fail? Can you de-risk your investment by realizing the project is not worth it with <10% of the time/money investment?
  8. What does the project look like in terms of explicit cost-effectiveness?
    1. This matters much more for larger projects than smaller ones, and more for projects where the learning/exploration value is low than ones where the exploration value is high.
    2. By cost-effectiveness, I mean something like "At scale, how many dollars or equivalent in human capital do you need to avert one microextinction/microdoom or basis point in x-risk"

Thanks Linch! This list is really helpful. One clarifying question on this point: 

Relatedly, what does the learning/exploration value of this project look like?

  1. To the researcher/entrepreneur?
  2. To the institution? (if they're working in an EA-institutional context)
  3. To the EA or longtermist ecosystem as a whole?

For 1) and 2), I assume you're referring to the skills gained by the person/institution completing the project, which they could then apply to future projects. 

For 3), are you referring to the possibility of "ruling out intervention X as a feas... (read more)

2
Linch
Thanks for the question! Hmm, I don't think there's a hard cutoff of person/institution vs. ecosystem. For 3), skills learned from completing a project (or trying to complete a project) might also be generalizable elsewhere (so there's value other than ruling out specific interventions).  For example, learning how to do a biosecurity ballot initiative in California can be useful for doing future biosecurity ballot initiatives in California, or AI safety ones. Some of the skills and knowledge acquired here can be passed on to other individuals or orgs.
Curated and popular this week
LintzA
 ·  · 15m read
 · 
Cross-posted to Lesswrong Introduction Several developments over the past few months should cause you to re-evaluate what you are doing. These include: 1. Updates toward short timelines 2. The Trump presidency 3. The o1 (inference-time compute scaling) paradigm 4. Deepseek 5. Stargate/AI datacenter spending 6. Increased internal deployment 7. Absence of AI x-risk/safety considerations in mainstream AI discourse Taken together, these are enough to render many existing AI governance strategies obsolete (and probably some technical safety strategies too). There's a good chance we're entering crunch time and that should absolutely affect your theory of change and what you plan to work on. In this piece I try to give a quick summary of these developments and think through the broader implications these have for AI safety. At the end of the piece I give some quick initial thoughts on how these developments affect what safety-concerned folks should be prioritizing. These are early days and I expect many of my takes will shift, look forward to discussing in the comments!  Implications of recent developments Updates toward short timelines There’s general agreement that timelines are likely to be far shorter than most expected. Both Sam Altman and Dario Amodei have recently said they expect AGI within the next 3 years. Anecdotally, nearly everyone I know or have heard of who was expecting longer timelines has updated significantly toward short timelines (<5 years). E.g. Ajeya’s median estimate is that 99% of fully-remote jobs will be automatable in roughly 6-8 years, 5+ years earlier than her 2023 estimate. On a quick look, prediction markets seem to have shifted to short timelines (e.g. Metaculus[1] & Manifold appear to have roughly 2030 median timelines to AGI, though haven’t moved dramatically in recent months). We’ve consistently seen performance on benchmarks far exceed what most predicted. Most recently, Epoch was surprised to see OpenAI’s o3 model achi
Dr Kassim
 ·  · 4m read
 · 
Hey everyone, I’ve been going through the EA Introductory Program, and I have to admit some of these ideas make sense, but others leave me with more questions than answers. I’m trying to wrap my head around certain core EA principles, and the more I think about them, the more I wonder: Am I misunderstanding, or are there blind spots in EA’s approach? I’d really love to hear what others think. Maybe you can help me clarify some of my doubts. Or maybe you share the same reservations? Let’s talk. Cause Prioritization. Does It Ignore Political and Social Reality? EA focuses on doing the most good per dollar, which makes sense in theory. But does it hold up when you apply it to real world contexts especially in countries like Uganda? Take malaria prevention. It’s a top EA cause because it’s highly cost effective $5,000 can save a life through bed nets (GiveWell, 2023). But what happens when government corruption or instability disrupts these programs? The Global Fund scandal in Uganda saw $1.6 million in malaria aid mismanaged (Global Fund Audit Report, 2016). If money isn’t reaching the people it’s meant to help, is it really the best use of resources? And what about leadership changes? Policies shift unpredictably here. A national animal welfare initiative I supported lost momentum when political priorities changed. How does EA factor in these uncertainties when prioritizing causes? It feels like EA assumes a stable world where money always achieves the intended impact. But what if that’s not the world we live in? Long termism. A Luxury When the Present Is in Crisis? I get why long termists argue that future people matter. But should we really prioritize them over people suffering today? Long termism tells us that existential risks like AI could wipe out trillions of future lives. But in Uganda, we’re losing lives now—1,500+ die from rabies annually (WHO, 2021), and 41% of children suffer from stunting due to malnutrition (UNICEF, 2022). These are preventable d
Rory Fenton
 ·  · 6m read
 · 
Cross-posted from my blog. Contrary to my carefully crafted brand as a weak nerd, I go to a local CrossFit gym a few times a week. Every year, the gym raises funds for a scholarship for teens from lower-income families to attend their summer camp program. I don’t know how many Crossfit-interested low-income teens there are in my small town, but I’ll guess there are perhaps 2 of them who would benefit from the scholarship. After all, CrossFit is pretty niche, and the town is small. Helping youngsters get swole in the Pacific Northwest is not exactly as cost-effective as preventing malaria in Malawi. But I notice I feel drawn to supporting the scholarship anyway. Every time it pops in my head I think, “My money could fully solve this problem”. The camp only costs a few hundred dollars per kid and if there are just 2 kids who need support, I could give $500 and there would no longer be teenagers in my town who want to go to a CrossFit summer camp but can’t. Thanks to me, the hero, this problem would be entirely solved. 100%. That is not how most nonprofit work feels to me. You are only ever making small dents in important problems I want to work on big problems. Global poverty. Malaria. Everyone not suddenly dying. But if I’m honest, what I really want is to solve those problems. Me, personally, solve them. This is a continued source of frustration and sadness because I absolutely cannot solve those problems. Consider what else my $500 CrossFit scholarship might do: * I want to save lives, and USAID suddenly stops giving $7 billion a year to PEPFAR. So I give $500 to the Rapid Response Fund. My donation solves 0.000001% of the problem and I feel like I have failed. * I want to solve climate change, and getting to net zero will require stopping or removing emissions of 1,500 billion tons of carbon dioxide. I give $500 to a policy nonprofit that reduces emissions, in expectation, by 50 tons. My donation solves 0.000000003% of the problem and I feel like I have f
Relevant opportunities