Trying to articulate a difficulty I have with the effective altruism community:

One thing that I, and I think many others, value about EA is that it's an antidote to using motivated reasoning to justify whatever you're currently doing for non-altruistic reasons as high-impact.
For this reason, people care a lot about what activities "count" as effective.

Periodically, people try to convince EAs that their "pet cause" is effective. In order to justify excluding those people, EA wants very objective standards for what "counts."
The original EA arguments satisfied this. They were simple, highly legible cases where it was easy to be objective—eg "guide dogs cost 100x more than trachoma surgery," "bednets save a life for $3500."
For this reason, many people in effective altruism got really excited about a set of simple, highly legible cognitive tools you can use to make arguments like this: back of the envelope calculations, expected-value reasoning, thinking about counterfactuals...
In some sense, this was an important part of the vision of the effective altruism community / the case for why it made sense: even if people disagreed about the object level, they had a shared commitment to a set of rigorous epistemic standards.
But: it turns out most of the highest-impact things one can do (probably) aren't amenable to these tools because they're too complicated. The uncertainty intervals on the relevant numbers span orders of magnitude and even figuring out the sign of a lot of things is hard.
Instead, the best arguments for what many EAs think are the most effective causes involve hundreds of pages of reasoning spanning many different lines of evidence, some of which is quantitative but a lot of which isn't. They require a huge amount of context to understand + judge.
Because of this, it's hard to define what "counts" as effective altruism in any way other than "stuff this set of cool kids thinks is important." There's no set of epistemic principles that I can point to and be like, "follow these to make a legitimate EA argument."
This also makes it a lot harder to justify the community's implicit claim to specialness or unusual effectiveness. If someone were to ask me why I pay special attention to the EA community, there's nothing in particular I can point to that seems like a unique insight or process.
And, it makes it harder to do the "antidote to motivated reasoning" thing I mentioned earlier. If someone tries to persuade me that their pet cause is the best possible thing to do, I can't just be like "come back with a back of the envelope calculation and we'll talk."
I don't know why this is so unsatisfying though. Maybe I just miss the days where it felt like you could figure out how to save the world by arguing about math on the internet.
(Lots of great replies here that Twitter's UI is making it very hard to navigate since they're spread across the entire thread. Sorry if I miss yours because of that!)

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More from @benskuhn

9 Nov
I'm hiring for a ton of high-leverage eng leadership roles that will work closely with me!
- Director of Eng (x3)
- Eng Manager, Database
- Head of Technical Recruiting
If interested, or you know a great candidate, DM me—you'll have a big impact on Wave + the world!

Details:
First, why work at Wave?
1. Banking the unbanked has a huge impact: if we succeed we'll likely help 10m+ people escape extreme poverty
2. We're growing quickly, so you'll learn and grow a ton
3. It's the most beloved product I know of:
Onto the roles:
1. Director of Eng: I'm hiring for up to 3 people to support groups of eng teams, 2 in product and 1 in infra/platform. These will be crucial to growing + mentoring our engineering team so we can build out everything our users want!

JD: wave.com/en/careers/job…
Read 8 tweets
9 Nov
One thing that keeps surprising me about Wave is how insanely important good customer support is. If you ask people why they love Wave the top 3 answers are:
- it's cheap
- it's fast
- if you call support, they solve your problem *while you're on the phone* (omg!!!)
I thought the 3rd one was kind of the minimal bar for functioning customer support, but for certain other relevant companies, it's more like you call support and then they solve your problem a week later. Hopefully.
Unfortunately it's easy to quantify the costs of customer support and hard to quantify the benefits, so I suspect a lot of companies systematically under-invest in good support, especially if they rely a lot on hard/quantifiable data instead of free-form talking to tons of users.
Read 5 tweets
25 Jul
I had a fascinating conversation yesterday with a former roommate who's now a postdoc in number theory. I learned a lot about how math research works! Some stuff I found interesting:
Most big number theory results are apparently 50-100 page papers where deeply understanding them is ~as hard as a semester-long course.

Because of this, ~nobody has time to understand all the results they use—instead they "black-box" many of them without deeply understanding.
(Probably worth noting that mathematicians have an extremely high bar for "deeply understanding." It's not like they're being dumb, the impression that I got is that making progress in number theory is actually just close to the limit of humanity's collective cognitive ability)
Read 22 tweets
2 Jul
Thread of amazing Wave videos/stories/memes/etc. Hype levels in Senegal/Cote d’Ivoire are increasing rapidly and the results are awesome:
Some distributors (people who go around to markets etc getting users signed up) wrote us a song!!! Really impressive production quality too.

Chorus (translated): “Deposit?” “It’s free!” “Withdraw? “It’s free! “Transfer?” “It’s super cheap!” “What is it again?” “It’s Wave Money!”
A team of distributors ran into their counterparts from a competitor, so obviously there had to be a dance battle:
Read 6 tweets
23 Jun
Six (!!) years ago today was my first day at Wave! This has now surpassed elementary school to become the longest period of time I've *ever* spent at the same place. (For some value of "the same" and "place".)

To celebrate, 1 like = 1 thing I learned:
1. important problems >>> "hard problems"

I learned way more by building a simple UI around some arithmetic—that mattered bc people loved it—than out of my previous job building ML models and reading stats papers.
2. the ceiling for "trying hard" is ~2 orders of magnitude higher than you think. If you haven't spent entire years failing, you are trying medium-hard at best.
Read 48 tweets
14 May
More late-stage companies are switching from four-year vesting to one-year grants. Stripe, Lyft, and now Coinbase (blog.coinbase.com/how-coinbase-i…).

Seems like a good time to talk about why being an early startup employee is (used to be?) a much better deal than it might seem:
1. Picking winning startups is actually not *that* hard: benkuhn.net/vc-emh/ It's not *easy*, but some VCs, and TechCrunch award voters, do it regularly.

"If it's not hard then why aren't you rich?" Because plebs can't invest at those valuations, only name-brand VCs can.
("Why do name-brand VCs get a good deal?" Because many companies don't optimize very hard for valuation when fundraising. Instead they try to minimize distraction, risk of down rounds, etc. See post for a more fleshed-out argument.)
Read 10 tweets

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