I’m currently researching forecasting and epistemics as part of the Quantified Uncertainty Research Institute.
Ozzie Gooen
Karma: 12,420
Thanks for doing this! I found this video very interesting. This area seems pretty critical to get right, quite happy to have more insight on it.
I had a bunch of unpublished drafts and notes in Google Docs. I just released them here, using Claude to help organize and summarize.
https://priors.quantifieduncertainty.org/
I imagine some here would find them interesting! Most are fairly old, starting from 2017, when I joined FHI.
“I’d love to have a call and catch up in any case! I’m curious whether you already have an opinion on whether places like DeepMind will be interested in paying for evals like the two types mentioned here (character and backdoors).”
Quickly:
1. I’ll schedule a call sometime, once I understand my schedule a bit better.
2. I have incredibly little info on DeepMind right now. As you may imagine, personally I’m excited about a lot of these areas, but I can’t speak for DeepMind.
“Why did Guesstimate/Squiggle as for-profit not work out?”
I’ve been asked this question a bunch of times before, happy to give some quick thoughts here. Most of my answer is here.If it were the case that it seemed very easy to make these as startups, and ideally in which there were some clear cofounders who could handle the business side, I think the for-profit side would have been more promising.
In my case:
1. I realized this was a niche/small space, that getting a large market would likely mean having to pivot.
2. I wasn’t sure what might take off. I think Squiggle has a lot going for it, but am not sure if the value proposition is strong enough for a large company now, especially as LLMs have been getting much more powerful (changing the trade-offs around decision tools).
3. My main goal was helping effective altruist / rationalist researchers, and I suspected that focusing much more on commercialization would have hurt that.
4. Related to (3), I just found myself much more motivated to go after altruists/nonprofits than to go after standard enterprise deals
Hi Dawn!
I’d be happy to discuss this.
Quickly (and to give context to others reading this):I think that ‘taking over QURI’ makes the most sense for people who want to take over Guesstimate/Squiggle, and even then I’d default to being hesitant about that.
QURI doesn’t really have much to take over, especially for research. There’s a simple brand, there’s a niche community that knows its history. There aren’t staff or much IP at all. It’s unclear to me what the value is of doing related work ‘inside QURI’ vs. making a new org.
“$1m in funding over ~2 years” → I’d personally be excited to see what you do with this, but I’m unsure how easy it would be to raise this. In our experience fundraising was definitely not trivial, and we’ve mostly been asking for much less than $1m (though year by year). Most EA funders now are focused on other dedicated areas like technical AI safety or bio risk, I think our category of work has been more challenging. For a while the FTX Future Fund was interested in these areas, then later Coefficient Giving had a Forecasting fund, but both closed fairly quickly. It’s quite possible that Anthropic money and similar will change the situation, but I haven’t seen that yet. Related, I’d recommend very much deeply understanding the funding climate before making an incubator, as I think it’s easy to create a bunch of orgs that basically have no where to go.
In terms of incubators, some reading this might be interested in Surplus! It’s for-profit only, but definitely related to our work.
If you want a 501(c)(3) sponsor, I think there’s a lot of good options. QURI was itself sponsored by Rethink Priorities for much of its life.
I’d be happy to chat / help mentor either way.
Many congrats on the success! That seems fantastic.
Multipolar worlds will compete away >90% of net value that would otherwise be preserved
If they’re halfway-reasonable, they could use smart AIs to negotiate for them. Big question is who will control these worlds.
I think it’s likely humans will settle on AI solutions that lose 90% of the value vs. my optimal solution, but that’s very much a values question, not a multipolar vs. unipolar question.
AI alignment to humans will in practice avoid moral catastrophes to animals
I expect certain conservative/religious communities to lock-in values that could be really bad. But I’d expect that better tech can remove say ~90% of the damages? But this is very hand-wavy.
This seems like an very coarse take to me.
Shutting down one of these companies might cost, say, a trillion dollars and lost investor/employee value. And I think that the real risky ‘frontier AI’ might only be a portion of their work.
I could very much see an argument for them to stop a lot of the key frontier work and then move to more conservative engineering efforts, for instance. I think that there’s a large variety of space to do around AI development and AI safety, it seems easy for me to imagine large changes in direction that could still have some market value but much less risks.
I think there was one certain failure mode of “prediction markets will somehow both be legal, and also more legal than regular sports gambling, in the US”.
This combination of scenarios seemed very unlikely to me 4-6 years ago! I think this was seen universally as a tough combination, you can see this in the market prices / valuations of the related companies.
That said, there was an understanding a while back that US public prediction markets would likely be sleazy/predatory in ways similar to sports gambling. This is one reason I preferred prediction tournaments like Metaculus over financial prediction markets like Kalshi.
I’d also flag that there was, and still is, potential for real-money prediction markets to get adapted by large experienced financial authorities for more serious purposes like hedging. There are overall professionalized and useful ways to use these sorts of tools, though it is the case that much of the current market is quite miserable.
Thanks so much for this response! That’s really useful to know. I really appreciate the transparency and clarity here.
Hope that the team members of it are all doing well now.
I don’t mean to sound too negative on this—I did just say “a bit sad” on that one specific point.
Do I think that CE is doing worse or better overall? It seems like Coefficient has been making a bunch of changes, and I don’t feel like I have a good handle on the details. They’ve also been expanding a fair bit. I’d naively assume that a huge amount of work is going on behind the scenes to hire and grow, and that this is putting CE in a better place on average.
I would expect this (the GCR prio team change) to be some evidence that specific ambitious approaches to GCR prioritization are more limited now. I think there are a bunch of large projects that could be done in this area that would probably take a team to do well, and right now it’s not clear who else could do such projects.
Bigger-picture, I personally think GCR prioritization/strategy is under-investigated, but I respect that others have different priorities.
I had my Claude system do some brainstorming work on this.
https://www.longtermwiki.com/knowledge-base/models/intervention-models/anthropic-pledge-enforcement/
It generated some more specific interventions here.
I’ve been experimenting recently with a longtermist wiki, written fully with LLMs.
Some key decisions/properties:
1. Fully LLM-generated, heavily relying on Claude Code.
2. Somewhat opinionated. Tries to represent something of a median longtermist/EA longview, with a focus on the implications of AI. All pages are rated for “importance”.
3. Claude will estimates a lot of percentages and letter grades for things. If you see a percentage or grade, and there’s no citation, it might well be a guess by Claude.
4. An emphasis on numeric estimates, models, and diagrams. I had it generate many related models to different topics, some are better than others. Might later take the best ones and convert to Squiggle models or similar.
5. Still early & experimental. This is a bit in-between an official wiki and a personal project of interest now. I expect that things will become more stable over time. For now, expect pages to change locations, and terminology to be sometimes inconsistent, etc.
I overall think this space is pretty exciting right now, but it definitely brings challenges and requires cleverness.
https://www.longtermwiki.com/
https://www.longtermwiki.com/knowledge-base/responses/epistemic-tools/tools/longterm-wiki/
Recently I’ve been working on some pages about Anthropic and the OpenAI Foundation’s potentials for impact.
For example, see:
https://www.longtermwiki.com/knowledge-base/organizations/funders/anthropic-investors/
https://www.longtermwiki.com/knowledge-base/organizations/funders/openai-foundation/
There’s also a bunch of information on specific aspects of AI Safety, different EA organizations, and a lot more stuff.
It costs about $3-6 to add a basic page, maybe $10-$30 to do a nicer page. I could easily picture wanting even better later on. Happy to accept requests to add pages for certain organizations/projects/topics/etc that people here might be interested!
Also looking for other kinds of feedback!
I should also flag that one way to use it is through another LLM. Like, ask your local language model to help go through the wiki content for you and summarize the parts of interest.
I plan to write a larger announcement of this on the Forum later.
Quickly:
1. I think there’s probably good work to be done here!
2. I think the link you meant to include was https://www.longtermwiki.com/knowledge-base/organizations/funders/giving-pledge/
3. To be clear, I’m not directly writing this wiki. I’m using Claude Code with a bunch of scripts and stuff to put it together. So I definitely recommend being a bit paranoid when it comes to specifics!
That said, I think normally it does a decent job (and I’m looking to improve it!). On the 36%, that seems to have come from this article, which has a bit more, which basically reaffirms the point.
https://ips-dc.org/report-giving-pledge-at-15/
Also, to give Anthropic credit, I want to flag that a bunch of the employee donations are legally binding. Anthropic had a matching program which led to a good amount of money in Donor Advised Funds. https://www.longtermwiki.com/knowledge-base/organizations/funders/anthropic-investors/ (Note that this is also LLM-generated, so meant as a rough guess)
Deceased Pledger pledge fulfillmentWe calculate pledge fulfillment for deceased Pledgers as the amount of a Pledger’s charitable giving (either during their lifetime or through bequests from their estate) divided by the sum of their final net worth plus their charitable giving.
22 U.S. Pledgers have died, including 14 of the original 2010 signers. These 22 people were worth a combined $43.4 billion when they died.
Only one of the 22 deceased Pledgers — Chuck Feeney — gave his entire $8 billion fortune away before he died.
8 of the 22 deceased Pledgers fulfilled their pledges, giving away 50 percent or more of their wealth at death, either while they were living or in their estates.
The remaining 13 deceased Pledgers gave away less than 50 percent of their wealth, either while they were living or in their estates — although some of their estates are still being resolved.
A bit sad to find out that Open Philanthropy’s (now Coefficient Giving) GCR Cause Prioritization team is no more.
I heard it was removed/restructured mid-2025. Seems like most of the people were distributed to other parts of the org. I don’t think there were public announcements of this, though it is quite possible I missed something.
I imagine there must have been a bunch of other major changes around Coefficient that aren’t yet well understood externally. This caught me a bit off guard.
There don’t seem to be many active online artifacts about this team, but I found this hiring post from early 2024, and this previous AMA.
I’ve known and respected people on both sides of this, and have been frustrated by some of the back-and-forth on this.
On the side of the authors, I find these pieces interesting but very angsty. There’s clearly some bad blood here. It reminds me a lot of meat eaters who seem to attack vegans out of irritation more than deliberate logic. [1]
On the other, I’ve seen some attacks of this group on LessWrong that seemed over-the-top to me.
Sometimes grudges motivate authors to be incredibly productive, so maybe some of this can be useful.
It seems like others find these discussions useful form the votes, but as of now, I find it difficult to take much from them.
[1] I think there are many reasonable meat eaters out there, but there are also many who are angry/irrational about it.
Interesting analysis!
One hypothesis: animal advocacy is a frequent “second favorite” cause area. Many longtermists prefer animal work to global health, but when it comes to their own donations and career choices, they choose longtermism. This resembles voting dynamics where some candidates do well in ranked-choice but poorly in first-past-the-post.
Larks makes a good point—AI risk is also underfunded relative to survey preferences. The bigger anomaly is global health’s overallocation.
My very quick guess is that’s largely founder effects. I.E. GiveWell’s decade-long head start in building donor pipelines and mainstream legibility, while focusing on global health.
I find this pretty exciting. Would love to see FAR-UVC become more popular, and I think this seems like a smart move to help do that. Thanks for organizing and financing!
Sounds like arduous but important work. Good luck with the rest of it!