I sometimes say, in a provocative/hyperbolic sense, that the concept of "neglectedness" has been a disaster for EA. I do think the concept is significantly over-used (ironically, it's not neglected!), and people should just look directly at the importance and tractability of a cause at current margins.

Maybe neglectedness useful as a heuristic for scanning thousands of potential cause areas. But ultimately, it's just a heuristic for tractability: how many resources are going towards something is evidence about whether additional resources are likely to be impactful at the margin, because more resources mean its more likely that the most cost-effective solutions have already been tried or implemented. But these resources are often deployed ineffectively, such that it's often easier to just directly assess the impact of resources at the margin than to do what the formal ITN framework suggests, which is to break this hard question into two hard ones: you have to assess something like the abstract overall solvability of a cause (namely, "percent of the problem solved for each percent increase in resources," as if this is likely to be a constant!) and the neglectedness of the cause.

That brings me to another problem: assessing neglectedness might sound easier than abstract tractability, but how do you weigh up the resources in question, especially if many of them are going to inefficient solutions? I think EAs have indeed found lots of surprisingly neglected (and important, and tractable) sub-areas within extremely crowded overall fields when they've gone looking. Open Phil has an entire program area for scientific research, on which the world spends >$2 trillion, and that program has supported Nobel Prize-winning work on computational design of proteins. US politics is a frequently cited example of a non-neglected cause area, and yet EAs have been able to start or fund work in polling and message-testing that has outcompeted incumbent orgs by looking for the highest-value work that wasn't already being done within that cause. And so on.

What I mean by "disaster for EA" (despite the wins/exceptions in the previous paragraph) is that I often encounter "but that's not neglected" as a reason not to do something, whether at a personal or organizational or movement-strategy level, and it seems again like a decent initial heuristic but easily overridden by taking a closer look. Sure, maybe other people are doing that thing, and fewer or zero people are doing your alternative. But can't you just look at the existing projects and ask whether you might be able to improve on their work, or whether there still seems to be low-hanging fruit that they're not taking, or whether you could be a force multiplier rather than just an input with diminishing returns? (Plus, the fact that a bunch of other people/orgs/etc are working on that thing is also some evidence, albeit noisy evidence, that the thing is tractable/important.) It seems like the neglectedness heuristic often leads to more confusion than clarity on decisions like these, and people should basically just use importance * tractability (call it "the IT framework") instead.

Consider whether you're comparatively advantaged to give to non-tax-deductible things.

(Not financial advice.) I think people -- especially donors who are giving >$100k/year -- often default to thinking that they should stick to tax-deductible giving, because they have an unusually high "501c3 multiplier" due to high marginal income tax rates or low cost basis for capital gains taxes. I claim this is a mistake for some donors, because what matters is whether your 501c3 multiplier is unusually high relative to the average dollar in the donor mix, which is usually coming from other people in very high tax brackets.

People who do have unusually high "501c3 multipliers" include those with employer matches to 501c3 donations. For a 1:1 match for cash donations, I think the multiplier is something like 3.5x, and even higher if you're donating appreciated assets like equity.^[1] I would guess that you need to have a multiplier at least that good to actually be comparatively advantaged [ETA: because I think lots of the dollars from individual donors in the EA giving space come from people with 1:1 or better employer matches, like Google or Anthropic].^[2]

The reason this matters is that if too many people think they're comparatively advantaged for tax-deductible giving, then non-tax-deductible opportunities (e.g. 501c4 advocacy, political giving, even future 501c3s awaiting their 501c3 determination) will unduly struggle to fundraise, so the best marginal opportunities are often going to be in that category.

1. ^{^}

   If your donation budget is $10,000 (of post-tax income) and you're, say, a single San Franciscan making $500k (and therefore paying a 42.53% marginal tax rate, per SmartAsset), I think this means you could donate ~$17,400 in cash (a 1.74x multiplier) and deduct that from your income, reducing your tax burden by $7,400 = $10,000 from your post-tax income. Then your 1:1 employer match means the charity gets double that, or $34,800 (a 3.48x multiplier). If you're donating assets that have appreciated, rather than cash, you also avoid paying taxes on those assets, which drives the multiplier up further.

2. ^{^}

   Some people don't have an employer match but are giving equities instead of cash, so you might think they'd have an unusually high 501c3 multiplier because their donations both mean they don't have to pay taxes from selling the assets and they can write off the deduction. By my math/in my understanding of how taxes work, it's pretty hard for this to get to 3.5x, because:

   1. I believe you can also give (some kinds of?) appreciated assets to 501c4s without the 501c4 paying tax on that, unless the 501c4 also engages in certain political activities. So the first half of the logic -- "they don't have to pay taxes from selling the assets" -- also applies to (some) non-tax-deductible donations, and you're left with the writeoff, which is just a ~1.74x multiplier.
   2. I believe that when you donate short-term-appreciated assets, you can only write off the cost basis, not the fair market value, and the long-term-appreciated tax rate is low enough (20% for the highest tax brackets) that I don't think it can get you to 3.5x. But I haven't totally crunched the numbers here.

Having a savings target seems important. (Not financial advice.)

I sometimes hear people in/around EA rule out taking jobs due to low salaries (sometimes implicitly, sometimes a little embarrassedly). Of course, it's perfectly understandable not to want to take a significant drop in your consumption. But in theory, people with high salaries could be saving up so they can take high-impact, low-paying jobs in the future; it just seems like, by default, this doesn't happen. I think it's worth thinking about how to set yourself up to be able to do it if you do find yourself in such a situation; you might find it harder than you expect.

(Personal digression: I also notice my own brain paying a lot more attention to my personal finances than I think is justified. Maybe some of this traces back to some kind of trauma response to being unemployed for a very stressful ~6 months after graduating: I just always could be a little more financially secure. A couple weeks ago, while meditating, it occurred to me that my brain is probably reacting to not knowing how I'm doing relative to my goal, because 1) I didn't actually know what my goal is, and 2) I didn't really have a sense of what I was spending each month. In IFS terms, I think the "social and physical security" part of my brain wasn't trusting that the rest of my brain was competently handling the situation.)

So, I think people in general would benefit from having an explicit target: once I have X in savings, I can feel financially secure. This probably means explicitly tracking your expenses, both now and in a "making some reasonable, not-that-painful cuts" budget, and gaming out the most likely scenarios where you'd need to use a large amount of your savings, beyond the classic 3 or 6 months of expenses in an emergency fund. For people motivated by EA principles, the most likely scenarios might be for impact reasons: maybe you take a public-sector job that pays half your current salary for three years, or maybe you'd need to self-fund a new project for a year; how much would it cost to maintain your current level of spending, or a not-that-painful budget-cut version? Then you could target that amount (in addition to the emergency fund, so you'd still have that at the end of the period); once you have that, you could feel more secure/spend less brain space on money, donate more of your income, and be ready to jump on a high-impact, low-paying opportunity.

Of course, you can more easily hit that target if you can bring down your expenses -- you both lower the required amount in savings and you save more each month. So, maybe some readers would also benefit from cutting back a bit, though I think most EAs are pretty thrifty already.

(This is hardly novel -- Ben Todd was publishing related stuff on 80k in 2015. But I guess I had to rediscover it, so posting here in case anyone else could use the refresher.)

I think some of the AI safety policy community has over-indexed on the visual model of the "Overton Window" and under-indexed on alternatives like the "ratchet effect," "poisoning the well," "clown attacks," and other models where proposing radical changes can make you, your allies, and your ideas look unreasonable.

I'm not familiar with a lot of systematic empirical evidence on either side, but it seems to me like the more effective actors in the DC establishment overall are much more in the habit of looking for small wins that are both good in themselves and shrink the size of the ask for their ideal policy than of pushing for their ideal vision and then making concessions. Possibly an ideal ecosystem has both strategies, but it seems possible that at least some versions of "Overton Window-moving" strategies executed in practice have larger negative effects via associating their "side" with unreasonable-sounding ideas in the minds of very bandwidth-constrained policymakers, who strongly lean on signals of credibility and consensus when quickly evaluating policy options, than the positive effects of increasing the odds of ideal policy and improving the framing for non-ideal but pretty good policies.

In theory, the Overton Window model is just a description of what ideas are taken seriously, so it can indeed accommodate backfire effects where you argue for an idea "outside the window" and this actually makes the window narrower. But I think the visual imagery of "windows" actually struggles to accommodate this -- when was the last time you tried to open a window and accidentally closed it instead? -- and as a result, people who rely on this model are more likely to underrate these kinds of consequences.

Would be interested in empirical evidence on this question (ideally actual studies from psych, political science, sociology, econ, etc literatures, rather than specific case studies due to reference class tennis type issues).

Biggest disagreement between the average worldview of people I met with at EAG and my own is something like "cluster thinking vs sequence thinking," where people at EAG are like "but even if we get this specific policy/technical win, doesn't it not matter unless you also have this other, harder thing?" and I'm more like, "Well, very possibly we won't get that other, harder thing, but still seems really useful to get that specific policy/technical win, here's a story where we totally fail on that first thing and the second thing turns out to matter a ton!"

I notice a pattern in my conversations where someone is making a career decision: the most helpful parts are often prompted by "what are your strengths and weaknesses?" and "what kinds of work have you historically enjoyed or not enjoyed?"

I can think of a couple cases (one where I was the recipient of career decision advice, another where I was the advice-giver) where we were kinda spinning our wheels, going over the same considerations, and then we brought up those topics >20 minutes into the conversation and immediately made more progress than the rest of the call to that point.

Maybe this is because in EA circles people have already put a ton of thought into considerations like "which of these jobs would be more impactful conditional on me doing a 8/10 job or better in them" and "which of these is generally better for career capital (including skill development, networks, and prestige)," so it's the conversational direction with the most low-hanging fruit. Another frame is that this is another case of people underrating personal fit relative to the more abstract/generally applicable characteristics of a job.

Are you a US resident who spends a lot of money on rideshares + food delivery/pickup? If so, consider the following:

• Costco members can buy up to four Uber gift cards of $50 value every two weeks (that is, 2 packs of 2 $50 gift cards). Now, and I think typically, these sell at 20% off face value.
• Costco membership costs $65/year.
• It takes ~2 minutes per gift card all-in.
• You can use them on rides, scooters, and Uber Eats.
• According to o3-mini-high, this means it's worth it if you spend $1625 / (5 - how much you value your marginal minute) per year on these services, if you get no other use out of the Costco membership. (If you do, this number goes down, of course.)
• Hooray, you now have more money for donations, consumption, savings, or investment for a small time cost!
• I was not paid by Costco or Uber to say this, I swear.

Giving now vs giving later, in practice, is a thorny tradeoff. I think these add up to roughly equal considerations, so my currently preferred policy is to split my donations 50-50, i.e. give 5% of my income away this year and save/invest 5% for a bigger donation later. (None of this is financial/tax advice! Please do your own thinking too.)

In favor of giving now (including giving a constant share of your income every year/quarter/etc, or giving a bunch of your savings away soon):

• Simplicity.
• The effects of your donation might have compounding returns, e.g. field-building gets more people doing great stuff, this can in turn build the field, etc., or be path-dependent, e.g. someone does some writing that establishes better concepts for the field.
• Value drift: maybe you don't trust your future self to give as much, or to be as good at picking good stuff. (Some commitment mechanisms exist for this, like DAFs, but that really only fixes the "give as much" problem, and there are lots of opportunities that DAFs can't fund, such as 501c4 advocacy organizations, individuals, political campaigns, etc.)
• Expropriation risk: you might lose the money, including via global catastrophe.

In favor of giving later:

• Value of information: especially in a fast-changing field like AI, we'll continue learning more about what kinds of interventions work as time goes on.
• Philanthropic learning: basically the opposite of value drift: you specifically might become a wiser donor, especially if you're currently young and/or new to the field.
• Returns to scale: it's probably better to make e.g. a single $150k donation than ten donations averaging $15k, because orgs can act pretty decisively with an amount like that, like hire somebody or run a program. (Eventually you hit diminishing returns, but not for most individual donors.)
• Compounding returns on investment.
• Tax bunching (only applies to donations that you can write off): in my understanding, at least in the US, there's a threshold below which you effectively can't write off donations (the standard deduction), so there's effectively a fixed cost in any year that you make donations. This makes donating a fixed amount every year a pretty suboptimal strategy, other things equal; if you're donating an amount below or not that far above the standard deduction to c3 orgs every year, you might be able to save or donate significantly more if you instead donate once every few years.

A technique I've found useful in making complex decisions where you gather lots of evidence over time -- for example, deciding what to do after your graduation, or whether to change jobs, etc., where you talk to lots of different people and weigh lots of considerations -- is to make a spreadsheet of all the arguments you hear, each with a score for how much it supports each decision.

For example, this summer, I was considering the options of "take the Open Phil job," "go to law school," and "finish the master's." I put each of these options in columns. Then, I'd hear an argument like "being in school delays your ability to take a full-time job, which is where most of your impact will happen"; I'd add a row for this argument. I thought this was a very strong consideration, so I gave the Open Phil job 10 points, law school 0, and the master's 3 (since it was one more year of school instead of 3 years). Later, I'd hear an argument like "legal knowledge is actually pretty useful for policy work," which I thought was a medium-strength consideration, and I'd give these options 0, 5, and 0.

I wouldn't take the sum of these as a final answer, but it was useful for a few reasons:

• In complicated decisions, it's hard to hold all of the arguments in your head at a time. This might be part of why I noticed a strong recency bias, where the most recent handful of considerations raised to me seemed the most important. By putting them all in one place, I could feel like I was properly accounting for all the things I was aware of.
• Relatedly, it helped me avoid double-counting arguments. When I'd talk to a new person, and they'd give me an opinion, I could just check whether their argument was basically already in the spreadsheet; sometimes I'd bump a number from 4 to 5, or something, based on them being persuasive, but sometimes I'd just say, "Oh, right, I guess I already knew this and shouldn't really update from it."
• I also notice a temptation to simplify the decision down to a single crux or knockdown argument, but usually cluster thinking is a better way to make these decisions, and the spreadsheet helps aggregate things such that an overall balance of evidence can carry the day.