RyanCarey
Bio
Researching Causality and Safe AI at Oxford.
Previously, founder (with help from Trike Apps) of the EA Forum.
Discussing research etc at https://twitter.com/ryancareyai.
Posts 72
Comments1269
Topic contributions6
It's not a snowball situation. You'll be less and less behind over time, and eventually, you might get to 90% of the aptitude that you would've achieved. Which is fine, except for certain evaluations that happen n years after your PhD - it's going to be extremely difficult to get on any pathway to professorship this way.
As for the issue of how to choose a direction, it's useful to know that you've ruled out clinical activity, and are set on some kind of research. Not everyone would agree with you that interpretability will do much for the paramount risks from AI, but let's take as given that interp is where you want to go. Then a neuroscience PhD that includes wetlab work is going to spend 3-6 years of your life only moving very slowly, and diagonally toward this destination. Comp neuro is not a great idea either, because it's quite remote from your current experience in medicine. There are areas of research that are simultaneously closer to interp, and to medicine. Specifically, researching the interpretability of medical decision-making models, or AI used in medical devices. Basically this kind of stuff: (1) (2) (3). There are probably a dozen CS professors and medical professors who are especially strong at applying modern AI systems and interpretability to medical applications. Consider asking Claude exactly who those professors are. Why not read some of their work? You could try to replicate, or extend one of their studies, or look for an opportunity to do research assisting with one of them, or to study with them.
Hey Valerio,
It's very much possible. What I would say is that you should 1. be realistic about the tradeoffs, 2. Think about your personal fit for AI, and 3. think about what version of this transition is best for you.
Tradeoffs.
Career progression. To start with, as with any career switch, you'll be stepping back a couple of years in seniority in order to make it. For the next few years, you'll obviously find it harder to get a job in AI than a job in medicine, even if you study hard. If you want to be a professor, your chance of doing this in AI is simply much lower than your chance of doing this through medicine +/- neuroscience, because you are currently much stronger at the latter (vis CS students who may be writing AI papers since the middle of undergrad), and furthermore, the latter is less competitive, due to medicine being a largely professional field.
What AI roles look like. In the long-run, working in AI can give you a perfectly competitive salary, compared to medicine, and in some cases a higher one. But the very best roles tend to be in the US, or sometimes the UK, requiring visa applications and waiting periods that can be extremely inconvenient. Also, they are often less secure than a career in medicine. You should also be aware of a common blind spot: as people get older, they care more about the security of their job, and living near their family, or living near where they grew up.
Clear advantages. AI research is far more interesting than being a doctor. The work is much more varied, and people work somewhat less hard. And if you are interested in existential risk, then obviously AI safety research can potentially do something to mitigate this risk in a way that medicine absolutely can't.
Personal fit
The variance in outcomes in an AI career is much more than in medicine. In medicine, it helps a little to be smarter, more conscientious, and more senior, of course. Compared to that, you will notice that in AI (and tech generally), that problem-solving and coding ability is massively important, and can lead to varying outcomes that are way larger than you see in medicine. So before moving into AI, you need to try to be objective and think about it: are you already writing research papers as an undergrad? How is your academic performance? Have you been a mathematics competition kid? Do you want to be in a field that is highly intellectually competitive? Do you find it easy to learn programming, and to build useful demos? Another big element of personal fit is motivation: how much do you care about being involved in interpretability: does it feel like potentially your life cause, or just something that could be nice and interesting. Personally, when I got interested in AI safety, I felt like the former - there was nothing in the world that could convince me away from trying to do something about existential risk. I wanted to try and try until I'd definitively reached a point of failure. If you feel strongly motivated like that, I would definitely recommend you do it, and that's a big consideration.
Thinking through the transition.
If you want to do it, the sooner you transition, the better you are likely to do. The problem with medicine is that there are always a few more years that you can study, to get slightly more money, and slightly more secure, that realistically will take up a lot of your time, and take away a lot of your ability to do outside activities.
I would say the most plausible options are as follows, in decreasing order of urgency:
- Quit as soon as you are accepted to a full-time CS degree or AI/EA job
- Reach a "save-point" at the end of your degree or internship that you can return to, and then (1).
- MD-> PhD (ideally in something more technical) -> research role
- (Don't do it)
Although options (2-3) would seem much more secure than (1), they do have their problems. In regard to the save-point, the issue is that I don't know of anyone who actually returned to medicine from EA/AIS work. And the mere option of a high-paying doesn't necessarily make your life all that stable unless you actually do work as a doctor, which will take time. Whereas you could perhaps be making your life more stable by working straight away. So you can argue that (2) it's just wasting time. The issue with (3) is that it's hard to get into a PhD in a strong CS or stats department. The supervisor that you find may be of more of a mixed background, which may be less impressive to AI people. Then again you need to be realistic about where this all ends up: if you become a professor, this is mostly likely to happen at a medical school. If you become a researcher, the most common outcome would not be that you get to work at a frontier lab. Rather, the most lucrative roles that are achievable would more likely be in startups that apply AI to medicine. And, the most impactful and interesting roles that are available are often more research-adjacent, like being a research manager, a policy advisor, or something like that.
Overall thoughts
As you can see, this is not such a simple question to answer. Transitioning from medicine to AI is a complicated judgment. I'm very happy I did it, but for someone in your place, I can only recommend that you understand and weigh the different considerations, and try to arrive at a decision that is best for you.
Yeah, the cost of cheap shared housing is something like $20k/yr of 2026 dollars, whereas your impact would be worth a lot more than that, either because you are making hundreds of thousands of post-tax dollars per year, or because you're foregoing those potential earnings to do important research or activism. Van-living is usually penny-wise, but pound-foolish.
Any updates on this, now that a couple of years have passed? Based on the website, I guess you decided not to hire a chair in the end? Also, was there only $750k granted in 2025?
Another relevant comment:
Overall a nice system card for Opus 4! It's a strange choice to contract Apollo to evaluate sabotage, get feedback that "[early snapshot] schemes and deceives at such high rates that we advise against deploying this model...." and then not re-contracting Apollo for final evals
I think we should keep our eye the most important role that online EA (and adjacent) platforms have played historically. Over the last 20 years, there has always been one or two key locations for otherwise isolated EAs and utilitarians to discover like-minds online, get feedback on their ideas, and then become researchers or real-life contributors. Originally, it was (various incarnations of) Felicifia, and then the EA Forum. The rationalist community benefited in similar ways from the extropians mailing list, SL4, Overcoming Bias and LessWrong. The sheer geographical coverage, and the element of in-depth intellectual engagement aren't practically replaceable by other community-building efforts.
I think that fulfilling this role is a lot more important than growing the EA community, and other goals that the EA Forum might have, and that it is worth doing until a better new venue comes along. Currently, I don't think a better venue exists. I don't think r/effectivegiving or LessWrong would be a great successor. You could make a case for Substack+Twitter, but that may flip to something else in a few years time, how people want to connect online can change completely on that kind of timescale. Overall, I think it important to keep things running for the next 5-10 as the future of EA and the future of online discussion declare themselves.
Of course, this role could be performed without a lot of new technology.
The other thing I wonder is: if the online team stopped stewarding the EA Forum's content, would it really turn into a mere bulletin board? I'm not so sure. I can imagine that plenty of people might continue to use the Forum to discuss EA matters and to post original research. If so, then this might be another way to cut costs with less change to the forum's core role, compared to declaring it a bulletin board or moving conversation to a different platform.
Agreed. EA doesn't feel intellectually lively anymore. I do think things like forethought foundation and some discussions on AI twitter still are though. What's tough is that this community used to be associated with trust and intellectual life. You could get it all in one package deal. Whereas now we have to find our sense of belonging in one place, and our intellectual life in another, and probably neither of those is the current EA.
I guess the professionalization of EA/AIS meant that this separation would happen anyway, but it just happened in a particularly demoralizing manner.