Career Choice: Becoming a Researcher in a Non-EA-Priority Field vs Donating $100k / Year to EA Research?
By themasterchief166 @ 2026-06-11T13:53 (+8)
Mechanical + electrical engineering graduate who likes research and whose goal is to maximize impact. To this end, I am currently deciding between two career paths:
- Become a professor / researcher who spends their career identifying, tackling, and pivoting between neglected scientific problems that are not among 80,000 Hours' main recommended paths (e.g., advanced manufacturing, alternative energy storage, cryptography, etc.).
- Take a higher-paying career with decent work-life balance (say, ML engineer earning $400k / yr) and donate around $100k/year to support researchers working on cause areas that EA generally considers most important (e.g., biosecurity, AI policy, animal welfare).
Note: Though I want to tackle and pivot between neglected scientific problems through research, I'm not interested in the major EA cause areas at the moment, nor do I expect to be in the near future. Also, I would care a lot about WLB if I went the non-researcher route, so taking on a higher paying career would not be an option in that case.
Any resources or thoughts that one should keep in mind when comparing the two career paths?
One way I've tried to think about it is whether I could earn and donate enough to "replace" the impact I might have had as a researcher. After all, $100k / yr is probably enough to fund an additional PhD student, but there are other factors to consider (funded student may not become a professor / work on neglected problems for instance). More importantly, this way of thinking doesn't seem quite right, since the funded researcher would not be a direct replacement for me -- the tradeoff seems closer to:
- Contributing directly to a potentially neglected but non-EA-priority field, versus
- Helping fund one additional researcher working in a major EA cause area
TL;DR: If anybody has any resources or insights, would deeply appreciate hearing them.
James Brobin @ 2026-06-11T21:38 (+3)
It's difficult to give helpful advice since I don't know much about you. That said, I think you're highly overestimating how much of a salary you could get if you went into the private sector. For instance, you mention wanting to work as a ML engineer, but I have to assume that it is an extremely competitive field right now. And, if you got a job in EE or ME, it seems like your salary would be far less than 400k a year.
Setting that aside, I think that one of the primary motivators for working on something for many years (i.e. getting a PhD) is that you genuinely believe that it's the most important thing you could be doing with your life. This may not be the case for you, but, if it is, it would probably be worth spending a lot of time looking into what you really think are the most important topics you could be researching and pursuing those.
Additionally, if you're really uncertain, I'd wait for a bit before committing to additional education since education is expensive.
It seems like a lot of EA stuff is much more talent constrained than money constrained, so research is probably a safer bet in terms of EV.
You could try advising with 80,000 Hours or Probably Good.
You might also benefit from really diving into 80,000 Hours site. I'm assuming you've already read their career guide, but their career profiles and other blog posts are also really helpful.
Hopefully that was helpful.
Mo Putera @ 2026-06-12T06:11 (+2)
What about systematically fit-testing with cheap tests, per 80K's advice? This is the "be Alice, don't be Bob" approach; the information you learn from acting -> getting feedback -> sense-making -> reorienting -> acting ... is both much richer and more personally decision-relevant. Either way you shouldn't sacrifice WLB ("jog, don't sprint").
Peter Drotos 🔸 @ 2026-06-17T12:16 (+1)
Hey! Great to hear you are considering impact for your career decision!
EE definitely has an overlap with 80k priorities, e.g. https://80000hours.org/career-reviews/ai-hardware/ (which includes security/cryptography). And certain ML work can also be high-impact.
Regarding ME, I’d suggest reaching out to https://high-impact-engineers.github.io/
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 you’ll have much better chances of success so it’s worth running some cheap tests first unless entry to the field is already highly competitive.
Also, it’s 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).
Semi Hayat @ 2026-06-12T08:25 (+1)
I would concentrate on comparative advantage and replaceability.
If you are particularly suited to research and can find truly neglected problems, your direct contribution may be harder to replace than your donations. Funding is important, but you can always find another donor more easily than a researcher with your unique skills and interests.
However, if the higher-paying career lets you reliably give large amounts over decades, that could add up to a lot of impact, especially in highly effective cause areas.
Comparing your impact to the cost of funding another PhD student may be a less useful perspective to adopt.