Working on computer chips driven by a childhood passion and donating the majority of my income. Recently transitioned to work on Compute Governance full-time.
Was seeking a community of people trying to optimize their impact and made the above decisions after engaging with EA core ideas.
I meant that if EtG is important to you, then why not just go all in on EtG? You might say that AI-related career advice assigns higher priority to research, but I think that assumes you only pick one of these and not split your attention. I think 80k would encourage focusing on EtG if it is important to you for some reason:
Overall, earning to give typically ends up seeming most attractive in the following cases: ā¦ Thereās a particular job you really want to do for other reasons thatās higher-earning than average, or that can be adapted into. This might look like Jeff working as a software engineer, or someone who really always wanted to be a doctor, or a teacher who does private tutoring and donates the extra. ā¦
I think Iād only encourage seriously doing two of these in parallel if both are important to you for some reason other than impact.
How do you expect to spread your effort across the two? What major sacrifices are you making (e.g., not attend a CS degree, work +N hours a week compared to average) that enable this, and what is your estimate on how it impacts your productivity in the long run?
Iām still sceptical about your plan on pursuing two very different careers in parallel. Itās not an issue if EtG is important to you but Iām not seeing what justifies doing two in parallel? Why is it better than one person doing EtG and another doing CS?
On the original question, I think itās very difficult to tell from the outside. You want to do projects to gain experience that lets you enter certain roles (e.g. entry AI safety roles). The people hiring for such roles are the ones who can tell what they are looking for and how people usually get there.
On immigration, I think the EA orgs are terrible entry examples for counterfactual impact. There is already large interest to enter them despite the small size of the field so youād probably replace a similarly good candidate who already had US work permit.
For AI companies, you could argue that youād likely replace someone who has potentially never thought about safety before. But the AI entry roles are also incredibly competitive so itās not that you directly replace someone, itās more like you change the candidate distribution from 1000:10 to 1000:11 (non-safety vs safety focused).
I think itās worth considering upskilling outside these bubles and then transfer as your profile becomes more scarse.
I considered ādoing work betterā to be included in ādoing work differently,ā but probably better to name these explicitly, thanks! Either the direction could be different (different goals) or the productivity (difference in effort/āfit/āetc).
AFAIK research suggests that people are terrible at predicting what they would enjoy. (Check 80k for this). But personal fit is a crucial thing, once you actually tried (seriously and with being as open as you can) and it does not feel right, you should move on and try something else. After a few diverse āsamplesā you should get a rough idea of your options and decide what to stick with.
Hey! Great to hear you are considering impact for your career decision!
EE definitely has an overlap with 80k priorities, e.g. https://āā80000hours.org/āācareer-reviews/āāai-hardware/āā (which includes security/āācryptography). And certain ML work can also be high-impact.
Regarding ME, Iād suggest reaching out to https://āāhigh-impact-engineers.github.io/āā
In general, people say that people with certain backgrounds are more of a bottleneck in priority areas than funding. (This does not mean that everyone is definitely a good fit for some high-impact direct work but rather, should be viewed as one factor when prioritizng what to explore next).
On your example, yes you could use money to fund more marginal candidates but if your fit is much better than marginal, then youāll have much better chances of success so itās worth running some cheap tests first unless entry to the field is already highly competitive.
Also, itās interesting that you are comparing the researcher path with ~$100k income vs ML with 400-100=$300k. Does the ~3x factor feel about right to compensate for taking the EtG path instead of your default? (You may end up similarly enyojing the EtG work and happy to live on $100k or slightly dislike it and require more reward to compensate for that).
I guess an alternative (probably more fortunate) framing is how much youād be willing to pay to save your own life using money that youād otherwise donate to high-impact causes. (assuming youād have that much money available to allocate)
Iād argue that if they would pay their employees a competitive for-profit salary in this case, the employeeās share of the input is 0. Maybe they arenāt motivated by impact, thatās why they took the full salary. They just ādo their jobā and produce value for their employer.
Iād push back on this. Maybe the career choice (decision to do the job at the given market rate instead of doing something else) of N employees on the market allows achieving the current total production at $2X total cost instead of, say $3X, if it was fully up to the market. (The decisions shifting the supply curve upwards so the market can buy more production at a given price)
In that case, the total funding required is $X less, so on average $X/āN for each impact focused employee. For comparison, if the market consists of, say, another N impact agnostic employees (2N employees total), then the individual market rate salary was also $X/āN. (Average funding need reduction comparable to the market salary.)
Now, the above numbers are obviously arbitrary, but I think this illustrates the effect that I (we?) hope to have from direct work.
I think location constraints (either visa or personal) should be an important factor for career choice. 80k recommends moving to a hub in general, but I think the importance varies across career paths. As a starting point, Iād try to factor such constraints into my exploration priority list. I.e., if Iām roughly equally excited about two paths overall and one fits my location constraints much better, then explore that path first.
I would definitely advise against doing a medical degree, just to then become an engineer in the US. I think itās not realistic to keep your motivation level sufficiently high.
However, it seems itās difficult to make impact to reducing AI risks outside of the USA/āUK. All of the frontier AI companies and EA organzations(like CLR, MIRI) are mostly in the USA(or the UK), and itās hard to get a remote job opportunity.
Frontier companies are highly competitive; they can afford not to bother with remote working. For EA/āAIS, I think most entry roles are similarly highly competitive, and for some roles, itās simply mandatory to be close to Tech/āPolicy hubs. But there are also some remote-first organizations, mostly ones that require scarce experienced profiles.
I think itās important to distinguish between the following two theories of change categories:
Doing important work that not enough people want to do
Doing important work differently from how others would do it
For #1, if the nature of the work itself does not require in-person presence, and there are no remote options, then maybe the current supply/ādemand gap is not as big as you originally thought, so the expected counterfactual impact might also be much lower. For #2, there is no such gap by definition, and filtering applicants for in-person presence is very likely.
Maybe we need to flip this around. Instead of tracking how much funding was allocated to a certain cause area, we should be tracking the expected marginal opportunity in each and comparing those. I.e., what was the expected result of a marginal $1M donated to each cause area on average in, say, a given year?
This does not incentivize for making the allocations secret since the decisions are made based on the current state of the āmarketā irrespective of any previous allocations.
Going back to the 100 fund managers example, I think Iād much prefer them to individually recognize that people preferring the alternative allocation are just as competent in the decision as they are (in an ideal case), and as a result, apply the uncertainty to their own preference (making it 75-25 instead of 100-0/ā50-50) rather than relying on an external mechanism.
I also recommend engaging with RPās new cross-cause work here. Curious to hear takes.
Maybe worth trying the same approach used for this other book (Iāve not tested it myself yet) until an official version is available:
https://āāforum.effectivealtruism.org/āāposts/āāD87rkNkNCtHC3X6Ee/āānew-book-compassionate-purpose-personal-inspiration-for-a
I think Patient philanthropy and Career capital are valid reasons (not sure about backfire risks).
Iād also highlight that the claim āfunding is not the main bottleneckā does not imply that more funding is useless.
And importantly, I think another key part of the 80k advice is to find something you are really good at. So I think the career path priority list should be treated as a starting point that you should first adjust to your own strengths and interests, and then explore through experiments. Start with cheap tests and donāt dismiss a great opportunity that is a few items below in the priority list.
If you are considering being a dentist (over alternative EtG paths), then you may already have signals that suggest youād do well as a dentist. If thatās the case, then I think you should also consider Biorisk as a career path. (Starting with reading about it /ā talking to people who think itās an important problem, if you have not done this already).
I think itās fair to say that entry into AI/āML is difficult. I just checked recently if 80k themselves acknowledge this, and they do!
Cons
Due to a shortage of managers, itās difficult to get jobs and might take you some time to build the required career capital and expertise
...
Any recommendation to the juniors on where/āhow to gain career capital with more capacity?
Also adding Rethink Prioritiesā pilot of personalized donation advice (signup form here) that I found in their 2025 Results, 2026 Plans and Funding Needs document (shared also on their website and in an EA forum post).
Iāve had a call with them this week, and they are working on establishing support for cause-neutral individual donors. Iād recommend reaching out to them if you are interested.
Adding OpenPhil (now Coefficieng Giving) new donor advising (from $250k/āyr):
I forwarded the post to Nina from HIP. But Iād recommend you also reach out to them directly: https://āāwww.highimpactprofessionals.org/āācontact-us
Donāt have specific ideas in mind but thereās definitly decent overlap. I think having a call about what they see as gaps and how HIE could fill these would be very valuable.
Great to see someone talking about this!
Would be very interested to see something tackling the same topic on AI Safety.
Please link if already exists.
My impression is that research in CS is substantially more competitive than engineering work. So while Iād encourage everyone to be ambitious and also aim for what seems to be a better personal fit, Iād be more excited about many EtG people succeeding in CS engineering and less in research, vs. many struggling in research and slightly more succeeding in it.
And you also need to keep in mind that AI might change a lot in this space, so people should probably think about diversification for better personal security.