I’m a 19-year-old from Taiwan whose long-term career goal is to reduce AI-related s-risks.
Recently, while struggling with my college major decision(if you’re intersted, please see this post: https://reurl.cc/XamZdD), I started thinking much more seriously about my career path—specifically, how to estimate the value of (1) earning to give, (2) contribuing to reducing AI s-risks directly in an EA organization, and (3) contributing indirectly in a non-EA organization.
I found that this question isextremely difficult. It seems much closer to a complex economics/decision-theory problem about the entire EA ecosystem than to the kind of simple heuristics usually discussed online.
Honestly, I feel that someone could probably writea 300-page book on how to rigorously evaluate “Is earning to give the optimal choice for someone with my background?” It seems far deeper than what is currently available.
After conversations with a few more experienced EAs, I also realized that my current thinking framework for “how to make career impact” still has many flaws. I’ve already read some intrductory books such as Doing Good Better, most of the 80,000 Hours articles, posts about talent constraints and ETG on the EA Forum, and several scattered pieces by writers such as Brian Tomasik. These were all very helpful, but still feel far from enough for developing a robust framework.
It seems like many senior EAs know an advanced internal reasoning process (for example, how to estimate when earning to give is actually worth it)in their brain that I haven’t yet learned. This worries me, because without these tools my independent career reasoning may be systematically inaccurate, and I would need frequent corrections from more EA people. However, I currently don’t know many EAs, and the ones I do know are often very busy.
My main question is: I’m wondering if there are other learning methods/resources I’ve missed—something beyond reading published EA books, the EA Forum, LessWrong, 80k’s articles/podcasts, and EA Global talks. If there are ways to learn the “thinking framework” behind EA career reasoning that I haven’t considered, I would really appreciate suggestions.
It seems like many senior EAs know an advanced internal reasoning process (for example, how to estimate when earning to give is actually worth it)in their brain that I haven’t yet learned. This worries me, because without these tools my independent career reasoning may be systematically inaccurate, and I would need frequent corrections from more EA people. However, I currently don’t know many EAs, and the ones I do know are often very busy.
No, these problems are fundamentally unsolvable, and everybody is just as confused and as uncertain as you. There is simply no way to know or predict these kinds of things. All you can do is make your best guess.
Thanks for your replying a lot. I’m really grateful for this.
Yeah, I agree most senior EAs don’t have like a completely accurate math model to calculate how worth is earn to give. However, I believe they have a thinking framework which can have an approximate answer that’s way more accurate than my own thoughts. Which means, there should be a guessing framework(on value of earn to give, which is probably complicated)to make a best guess rather than random guessing.
I don’t know if I believe that. It seems like there’s a whole lot of random guessing going on among the most senior people in EA.
You might be better served to look into general psychology/social science research and advice into how to make difficult personal decisions under conditions of high irreducible uncertainty. There are a few tricks I like. One is the regret minimization trick: which path would you regret least, even if it didn’t work out? Another trick is the coin flip or random number generator trick: randomly pick an option as if the coin flip (or other random event) is going to settle the decision for you. Notice whether you feel good or bad about that.
I think we all wish there was some kind of scientific formula we could use to make career decisions and so on. But I think there’s an inevitable mix of things like intuition, gut feeling, traditional wisdom, advice from people you know and trust, and so on.
[Question] How should beginner EAs learn the advanced reasoning frameworks behind career-impact decisions?
I’m a 19-year-old from Taiwan whose long-term career goal is to reduce AI-related s-risks.
Recently, while struggling with my college major decision(if you’re intersted, please see this post: https://reurl.cc/XamZdD), I started thinking much more seriously about my career path—specifically, how to estimate the value of (1) earning to give, (2) contribuing to reducing AI s-risks directly in an EA organization, and (3) contributing indirectly in a non-EA organization.
I found that this question is extremely difficult. It seems much closer to a complex economics/decision-theory problem about the entire EA ecosystem than to the kind of simple heuristics usually discussed online.
Honestly, I feel that someone could probably write a 300-page book on how to rigorously evaluate “Is earning to give the optimal choice for someone with my background?” It seems far deeper than what is currently available.
After conversations with a few more experienced EAs, I also realized that my current thinking framework for “how to make career impact” still has many flaws. I’ve already read some intrductory books such as Doing Good Better, most of the 80,000 Hours articles, posts about talent constraints and ETG on the EA Forum, and several scattered pieces by writers such as Brian Tomasik. These were all very helpful, but still feel far from enough for developing a robust framework.
It seems like many senior EAs know an advanced internal reasoning process (for example, how to estimate when earning to give is actually worth it)in their brain that I haven’t yet learned. This worries me, because without these tools my independent career reasoning may be systematically inaccurate, and I would need frequent corrections from more EA people. However, I currently don’t know many EAs, and the ones I do know are often very busy.
My main question is: I’m wondering if there are other learning methods/resources I’ve missed—something beyond reading published EA books, the EA Forum, LessWrong, 80k’s articles/podcasts, and EA Global talks. If there are ways to learn the “thinking framework” behind EA career reasoning that I haven’t considered, I would really appreciate suggestions.
Thanks very much for reading.
No, these problems are fundamentally unsolvable, and everybody is just as confused and as uncertain as you. There is simply no way to know or predict these kinds of things. All you can do is make your best guess.
Thanks for your replying a lot. I’m really grateful for this.
Yeah, I agree most senior EAs don’t have like a completely accurate math model to calculate how worth is earn to give. However, I believe they have a thinking framework which can have an approximate answer that’s way more accurate than my own thoughts. Which means, there should be a guessing framework(on value of earn to give, which is probably complicated)to make a best guess rather than random guessing.
I don’t know if I believe that. It seems like there’s a whole lot of random guessing going on among the most senior people in EA.
You might be better served to look into general psychology/social science research and advice into how to make difficult personal decisions under conditions of high irreducible uncertainty. There are a few tricks I like. One is the regret minimization trick: which path would you regret least, even if it didn’t work out? Another trick is the coin flip or random number generator trick: randomly pick an option as if the coin flip (or other random event) is going to settle the decision for you. Notice whether you feel good or bad about that.
I think we all wish there was some kind of scientific formula we could use to make career decisions and so on. But I think there’s an inevitable mix of things like intuition, gut feeling, traditional wisdom, advice from people you know and trust, and so on.
Update: After some self-thinking, I think maybe reading the basics of economics would be helpful given currently I bascially don’t know any economics