I'm a social entrepreneur and product manager that's been involved in EA since 2013! Right now, my interests lie in the areas of self/life improvement and societal transformation.

I'm the COO of Roote, an educational hub and startup studio focused on systems change to ensure humanity has a bright future. This includes reducing human and animal suffering as well as x-risks (see Roote's article on meta existential risks).

I'm also the founder of Better, a research organization and startup studio that is working on improving well-being and well-doing. We're specifically operating in the space of evidence-based self-improvement. Our theory of change is that recommendations we make can significantly amplify the efforts of EAs and EA organizations as well as improve people's lives in a highly cost effective manner.

Topic Contributions


Project: A web platform for crowdsourcing impact estimates of interventions.

I think that’s a good idea to reduce groupthink! Also, I think it can be helpful to uncover if specific individuals and sub-groups think a proposal is promising based on their estimates, since rarely will an entire group view something similarly. This could bring individuals together to further discuss and potentially support/execute the idea.

Project: A web platform for crowdsourcing impact estimates of interventions.

I think this is an excellent idea and one that I’ve wanted to exist for quite a few years now! My interest in this area stems from wanting to surface compelling strategies and projects that don’t receive sufficient attention because they’re not currently “trending” in the movement. By explicitly laying out theories of change and crowdsourcing estimates for each part of those theories of change, this makes it much easier for people to to understand proposals, identify how various people and groups in the community think about a proposal, and compare the expected impact of various strategies and projects against each other.

Right now, people submit ideas and projects on the EA Forum, but that doesn’t clearly translate to action. But if it’s pretty clear that specific individuals, sub-groups in the community, or the community at large think a theory of change is promising, I think this has the potential to greatly increase awareness and the likelihood of execution of promising proposals.

Ideal governance (for companies, countries and more)

I've been interested in the area of improving governance for a long time! On a societal level, there are some organizations and efforts in the space like One Project and RadicalXChange. Unfortunately I'm not aware of research that evaluates the efficacy of various governance models. I've been thinking about doing that for quite a few years. Doing that research is a high priority on Roote's backlog. We haven't tried getting funding for it yet, but it is somewhat related to our funded Civic Abundance and Web3 & Society initiatives. For example, for Web3 & Society, given the proliferation of alternative governance methods in Web3 as well as the transparent nature of decisions and performance results with DAOs, we can directly assess the efficacy of various DAO governance models.

What general financial advising advice would you give to EAs?

There are a few articles and Q&As on the EA Forum, like Investing to Give Beginner Advice? There are also blog posts on EA personal blogs, in particular Brian Tomasik's Assorted tips on personal finance and Ben Kuhn's Giving away money: a guide. Appendix B of an article I wrote on improving nonprofit treasury management also covers investments. The 80,000 Hours article I linked to in Appendix B, Common investing mistakes in the effective altruism community, is also pretty good.

My opinionated TL;DR is that "investing to give" is useful for various cases including donating once-a-year on Giving Tuesday, longtermist investment funds, and of course standard personal wealth accumulation. The standard investment advice is to use low-cost ETFs to create diversified portfolios. The main tax tip applicable to EAs is to donate appreciated securities (assume someone is donating their earnings). In addition to standard investment advice, more advanced investors will use leverage/margin to increase the returns of low-volatility portfolios as Tomasik and the 80K article mention. IMO the best strategy, which no one really uses, involves using various evidence-based factors to select asset classes, which the 80K article briefly alludes to at the end. That's what sparked my initial interest in the investment space and why I launched an investment firm (I also do more standard stuff like optimal high-interest bank accounts and passive asset selection by popular demand).

I run Antigravity Investments, which is an EA-aligned investing firm. I've mostly been advising charities, but now that better technology is available like Altruist, I'm in the process of expanding to once again directly advise individuals (that's why the website has a temporary placeholder at the moment). Robo-advisors are good for people that want something standard, and there are a few firms doing evidence-based asset class selection. Alpha Architect is one such firm.

Predicting for Good: Charity Prediction Markets

Sweet! So originally I was trying a non-CFTC mechanism to launch a real-money prediction market, but then Kalshi got CFTC approval, and then it felt less impactful to launch a second real-money market whether via the CFTC or via other methods I was considering. Although Kalshi might not be launching markets around social impact questions so there’s probably still a social impact opportunity there.

Also, I looked into the costs and complexity, and it seemed pretty high. I wasn’t sure if I wanted to commit to doing it and ended up deciding on working on other projects that also seemed impactful like “GiveWell for Impact Investing.”

Issues with centralised grantmaking

This is about intangible characteristics that seem really important in a grantee. 

To give intuition, I guess one analogy is hiring. You wouldn't hire someone off a LinkedIn profile, there's just so much "latent" or unknown information and fit that matters. To solve this problem, people often have pretty deep networks and do reference checks on people.

This is important because if you went in big for another CSET, or something that had to start in the millions, you better know the people, the space super well. 

I think this means you need to communicate well with other grant makers. For any given major grant, this might be a lot easier with 3-5 close colleagues, versus a group of 100 people. 

I see! Interestingly there are organizations, like DAOs, that do hiring in a decentralized manner (lots of people deciding on one candidate). There probably isn't much efficacy data on that compared to more centralized hiring, but it's something I'm interested in knowing.

I think there are ways to assess candidates that can be less centralized, like work samples, rather than reference checks. I mainly use that when hiring, given it seems some of the best correlates of future work performance are present and past work performance on related tasks.

If sensitive info matters, I can see smaller groups being more helpful, I guess I'm not sure the degree to which that's necessary. Basically I think that public info can also have pretty good signal.

So it doesn't matter how large your team is, there's no value getting 1000 grantmakers if you only need to know 200 experts in the space.

That's a good point! Hmm, I think that does go into interesting and harder to answer questions like whether experts are needed/how useful they are, whether having people ask a bunch of different subject matter experts that they are connected with (easier with a more decentralized model) is better than asking a few that a funder has vetted (common with centralized models), whether an expert interview that can be recorded and shared is as good as interviewing the expert yourself, etc., some of which may be field-by-field.

Someone has to kibosh this, and a set of unified grant makers could do this.

Is there a reason a decentralized network couldn't also do this? If it turns out that there are differing views, it seems that might be a hard judgement to make, whether in a centralized model or not.

Issues with centralised grantmaking

I agree! I was trying to highlight that because we're not sure that centralized funding is better or not, it would be a high priority to test other mechanisms, especially if there's reason to believe other mechanisms could result in significantly different outcomes.

Issues with centralised grantmaking

I recall reading that top VC's are able to outperform the startup investing market, although it may have a causal relationship going the other way around.

Yep, there's definitely return persistence with top VCs, and the last time I checked I recall there was uncertainty around whether that was due to enhanced deal flow or actual better judgement.

That being said, the very fact that superforecasters are able to outperform prediction markets should signal that there are (small groups of) people able to outperform the average, isn't it?

I think that just taking the average is one decentralized approach, but certainly not representative of decentralized decision making systems and approaches as a whole.

Even the Good Judgement Project can be considered a decentralized system to identify good grantmakers. Identifying superforecasters requires having everyone do predictions and then find the best forecasters among them, whereas I do not believe the route to become a funder/grantmaker is that democratized. For example, there's currently no way to measure what various people think of a grant proposal, fund that regardless of what occurs (there can be rules about not funding downside risk stuff, of course), and then look back and see who was actually accurate.

There haven't actually been real prediction markets implemented at a large scale (Kalshi aside, which is very new), so it's not clear whether that's true. Denise quotes Tetlock mentioning that objection here.

I also think that determining what to fund requires certain values and preferences, not necessarily assessing what's successful. So viewpoint diversity would be valuable. For example, before longtermism became mainstream in EA, it would have been better to allocate some fraction of funding towards that viewpoint, and likewise with other viewpoints that exist today. A test of who makes grants to successful individuals doesn't protect against funding the wrong aims altogether, or certain theories of change that turn out to not be that impactful. Centralized funding isn't representative of the diversity of community views and theories of change by default (I don't see funding orgs allocating some fraction of funding towards novel theories of change as a policy).

Issues with centralised grantmaking

There's evidence to suggest that decentralized decision making can outperform centralized decision making; for example with prediction markets and crowdsourcing. I think it's problematic in general to assume that centralized thinking and institutions are better than decentralized thinking and institutions, especially if that reasoning is based on the status quo. I was asking this series of questions because by wording that centralized funding was a "hypothesis," I thought you would support testing other hypotheses by default.

Predicting for Good: Charity Prediction Markets

Cool, I considered a project in this space before in 2020! You mention "Prediction markets can allow traders to legally put in real money." Are you aware that the CFTC has permitted Kalshi to operate a real-money prediction market? I ended up considering launching a second version of Kalshi for real-money forecasting for impactful areas (including various paths to either go through the CFTC or bypas it with various mechanisms), so people would have a direct financial incentive to participate. This mechanism is one of several I considered if someone wanted to get a real-money prediction market spun up with greater ease than going through the CFTC, and subject to less restrictions. I am currently working on other projects, but if anything related to real-money prediction markets is something anyone is interested in discussing, feel free to reach out!

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