My Career Decision-Making Process

ShayBenMoshe

My Career Decision-Making Process

ShayBenMoshe

37 min readJan 21, 2021

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Michelle_Hutchinson

Thank you very much for this post! As you say, it's great to have examples of how people think through their careers, what options they chose and why. Useful for others to learn from and also help each other feel less alone in making these hard decisions and going through the frustration of applications.

I'd be particularly interested in hearing more about why you don't see cybersecurity and formal verification as promising: in particular whether your view is that EAs should be aiming to build up expertise in these, or whether you think they are useful skills for a number of EAs to have, it's just that their use will come in the future (or in a country other than Israel).

ShayBenMoshe

Thanks for your comment Michelle! If you have any other comments to make on my process (both positive and negative), I think that would be very valuable for me and for other readers as well.

Important Edit: Everything I wrote below refers only to technical cyber-security (and formal verification) roles. I don't have strong views on whether governance, advocacy or other types of work related to those fields could be impactful. My intuition is that these are indeed more promising than technical roles.

I don't see any particularly important problem that can be addressed using cyber-security or formal verification (now or in the future), which is not already being addressed by the private or public sector. Surely these areas are important for the world, and therefore are utilized and researched outside of EA. For example, (too) many cyber-security companies provide solutions for other organizations (including critical organizations such as hospitals and electricity providers) to protect their data and computer systems. Another example is be governments using cyber-security tools for intelligence operations and surveillance. Both examples are obviously important, but not at all neglected.

One could argue that EA organizations need to protect their data and computer systems as well, which is definitely true, but can easily be solved by purchasing the appropriate products or hiring infosec officers, just like in any other organization. Other than that I didn't find any place where cyber-security can be meaningfully applied to assist EA goals.

As for formal verification, I believe that the case is similar - these kinds of tools are useful for certain (and very limited) problems in the software and hardware industry, but I am unaware of any interesting applications for EA causes. One caveat is that I believe that it is plausible (but not very probable) that formal verification can be used for AI alignment, as I outlined in this comment.

My conclusion is that, right now, I wouldn't recommended people in EA to build skills in any of these areas for the sake of having direct impact (of course cyber-security is a great industry for EtG). To convince me otherwise, someone would have to come up with a reasonable suggestions where these tools could be applied. If anyone has any such ideas (even rough ideas), I would love to hear them!

aog

Really great post, thank you! You discuss the possibility of "part-time earning to give while simultaneously running side projects" and note that you've chosen to work part-time on a PhD in Computational Healthcare while also working a separate part-time job for earning to give.

Part-time earning to give seems like an interesting possibility I hadn't considered before, mainly because I assumed there are very few part-time jobs that pay well. What has been your experience here? Do you have a unique opportunity that allows you to earn a lot part-time? Perhaps you've worked as an consultant or independent contractor who sets their own hours? What jobs have you considered here? More broadly, would you expect most college-educated people to be able to find part-time work that pays proportionally as well as what they'd earn working full-time? (Not looking for any definitive conclusion on the topic, just your off-the-cuff impressions)

Thanks again, and good luck with your new career plans!

ShayBenMoshe

This is a very good question and I have some thoughts about it.

Let me begin by answering about my specific situation. As I said, I have many years of experience in programming and cyber security. Given my background and connections (mostly from the army) it was fairly easy for me to find multiple companies I could work for as a contractor/part-time employee. In particular, in the past 3 years I have worked part-time in cyber security and had a lot of flexibility in my hours. Furthermore, I am certain that it is also possible to find such positions in more standard software development areas. In fact, just before I finished high school, I took a part-time front-end development position in some Israeli startup.

As for other people, it is harder for me to say. I imagine that it will not be so easy for someone who just graduated to find a high paying part-time job, but that highly depends on location, domain, and past experience. Generally, I believe that this path mostly suits people who already have some experience in their field or are willing to work as freelancers and face a slower progress in this part of their careers. For example, it can work very well for people already pursuing (ordinary) EtG or who are at later stages of their careers and want to switch to a different career path.

Edit - If this is something people are interested in, I can write a more detailed post about this idea specifically, where we can also have a longer discussion in the comments.

aog

Thanks, that makes sense. Freelancing in software development and tech seems to me like a reasonable path to a well-paid part-time gig for many people. I wonder what other industries or backgrounds lend themselves towards these kinds of jobs.

While this is fascinating, I’d be most interested in your views on AI for Good, healthcare, and the intersection between the two, as potential EA cause areas.

Your views, as I understand them (and please correct me where I’m wrong): You see opportunity for impact in applying AI and ML techniques to solve real-world problems. Examples include forecasting floods and earthquakes, or analyzing digital data on health outcomes. You’re concerned that there might already be enough talented people working on the most impactful projects, thereby reducing your counterfactual impact, but you see opportunities for outsize impact when working on a particularly important problem or making a large counterfactual contribution as an entrepreneur.

Without having done a fraction of the research you clearly have, I’m hopeful that you’re right about health. Anti-aging research and pandemic preparedness seem to be driving EA interest into healthcare and medicine more broadly, and I’m wondering if more mainstream careers in medical research and public health might be potentially quite impactful, if only from a near-term perspective. Would be interested in your thoughts on which problems are high impact, how to identify impactful opportunities when you see them, and perhaps the overall potential of the field for EA — as well as anything anyone else has written on these topics.

AI for Good seems like a robustly good career path in many ways, especially for someone interested in AI Safety (which, as you note, you are not). Your direct impact could be anywhere from “providing a good product to paying customers” to “solving the world’s most pressing problems with ML.” You can make a good amount of money and donate a fraction of it. You’ll meet an ambitious network of people, learn the soft skills of business, and receive a widely respected credential — valuable capital for any career. Crucial from my perspective, you’d learn how to develop and deploy AI in the real-world, which I think could be very helpful when transitioning to a career in AI technical safety research or AI policy. (AI Safety people, agree or disagree that this experience would be useful?)

Do you have further thoughts about how do have an impactful career doing AI for Good? Where are the highest impact positions? How do you enter the field, what qualifications and skills do you need? How can someone judge for themselves the opportunity for impact in a particular role?

Thank you! It’s inspiring and informative to see someone doing such thorough and independent cause prioritization research for their own career.

ShayBenMoshe

Thanks for spelling out your thoughts, these are good points and questions!

With regards to potentially impactful problems in health. First, you mentioned anti-aging, and I wish to emphasize that I didn't try to assess it at any point (I am saying this because I recently wrote a post linking to a new Nature journal dedicated to anti-aging). Second, I feel that I am still too new to this domain to really have anything serious to say, and I hope to learn more myself as I progress in my PhD and work at KSM institute. That said, my impression (which is mostly based on conversations with my new advisor) is that there are many areas in health which are much more neglected compared to others, and in particular receive much less attention from the AI and ML community. From my very limited experience, it seems to me that AI and ML techniques are just starting to be applied to problems in public health and related fields, at least in research institutes outside of the for-profit startup scene. I wish I had something more specific to say, and hopefully I will have in a year or two from now.

I completely agree with your view on AI for good being "a robustly good career path in many ways". I would like mention once more that in order to have a really large impact in it though, one needs to really optimize for that and avoid the trap of lower counterfactual impact (at least in later stages of the career, after they have enough experience and credentials).

It is very hard for me to say where the highest impact position are, and this is somewhat related to the view that I express at the subsection Opportunities and Cause Areas. I imagine that the best opportunities for someone in this field highly depend on their location, connections and experience. For example, in my case it seemed that joining the floods predictions efforts at Google, and the computational healthcare PhD, are significantly better options than the next options in the AI and ML world.

With regards to entering the field, I am super new to this, so I can't really answer. In any case, I think that entering to the fields of AI, ML and data science is no different for people in EA than others, so I would follow the general recommendations. In my situation, I had enough other credentials (background in math and in programming/cyber-security) to make people believe that I can become productive in ML after a relatively short time (though at least one place did reject me for not having background in ML), so I jumped right in to working on real-world problems rather than dedicating time to studying.

As to estimating impact of a specific role or project, I think it is sometimes fairly straightforward (when the problem is well-defined and the probabilities are fairly high, you can "just do the math" [don't forget to account for counterfactuals!]), while in other cases it might be difficult (for example more basic research or things having more indirect effects). In the latter case, I think it is helpful to have a rough estimate - understand how large the scope is (how many people have a certain disease or die from it every year?), figure out who is working on the problem and which techniques they use, try to estimate how much of the problem you imagine you can solve (e.g. can we eliminate the disease? [probably not.] how many people can we realistically reach? how expensive is the solution going to be?). All of this together can help you in figuring out the orders of magnitudes you are talking about. Let me give a very rough example of an outcomes of these estimates: A project will take roughly 1-3 years, seems likely to succeed, and if successful, will significantly improve the lives of 200-800 people suffering from some disease every year, and there's only one other team working on the exact same problem. This sounds great! Changing the variables a little might make it seem much less attractive, for example if only 4 people will be able to pay for the solution (or suffer from it to being with), or if there are 15 other teams working on exactly the same problem, in which case your impact will probably be much lower. One can also imagine projects with lower chances of success, which if successful will have a much larger effect. I tend to be cautious in these cases, because I think that it is much easier to be wrong about small probabilities (I can say more about this).

Let me also mention that it possible to work on multiple projects at the same time, or over a few years, especially if each one consist of several steps in which gain more information and you can re-evaluate them along the way. In such cases, you'd expect some of the projects to succeed, and learn how to calibrate your estimates over time.

Lastly, with regards to your description of my views, that's almost right, except that I also see opportunities for high impact not only on particularly important problems but also on smaller problems which are neglected for some reason (e.g. things that are less prestigious or don't have economic incentives). I'd also add that at least in my case in computational healthcare I also intend to apply other techniques from computer science besides AI and ML (but that's really a different story than AI for good).

This comment already becomes way too long, so I will stop here. I hope that it is somewhat useful, and, again, if someone wants me to write more about a specific aspect, I will gladly do so.

Arden Koehler

Hey shaybenmoshe, thanks for this post! I work at 80,000 Hours, so I'm especially interested in it from a feedback perspective. Michelle has already asked for your expended thoughts on cybersecurity and formal verification, so I'll skip those -- would you also be up for expanding on why the Key Ideas page seems less helpful to you vs. the older career guide?

ShayBenMoshe

Hey Arden, thanks for asking about that. Let me start by also thanking you for all the good work you do at 80,000 Hours, and in particular for the various pieces you wrote that I linked to at 8. General Helpful Resources.

Regarding the key ideas vs old career guide, I have several thoughts which I have written below. Because 80,000 Hours' content is so central to EA, I think that this discussion is extremely important. I would love to hear your thoughts about this Arden, and I will be glad if others could share their views as well, or even have a separate discussion somewhere else just about this topic.

Content

I think that two important aspects of the old career guide are much less emphasized in the key ideas page: the first is general advice on how to have a successful career, and the second is how to make a plan and get a job. Generally speaking, I felt like the old career guide gave more tools to the reader, rather than only information. Of course, the key ideas page also discusses these issues to some extent, but much less so than the previous career guide. I think that these were very good career advice which could potentially have a large effect on your readers' careers.

Another important point is that I don't like, and disagree with the choice of, the emphasis on longtermism and AI safety. Personally, I am not completely persuaded by the arguments for choosing a career by a longtermist view, and even less by the arguments for AI safety. More importantly, I had several conversations with people in the Israeli EA community and with people I gave career consultation to, who were alienated by this emphasis. A minority of them felt like me, and the majority understood it as "all you can meaningfully do in EA is AI safety", which was very discouraging for them. I understand that this is not your only focus, but people whose first exposure to your website is the key ideas page might get that feeling, if they are not explicitly told otherwise.

Another point is that the "Global priorities" section takes a completely top-to-bottom approach. I do agree that it is sometimes a good approach, but I think that many times it is not. One reason is the tension between opportunities and cause areas which I already wrote about. The other is that some people might already have their career going, or are particularly interested in a specific path. In these situations, while it is true that they can change their careers or realize that they can enjoy a broader collection of careers, it is somewhat irrelevant and discouraging to read about rethinking all of your basic choices. Instead, in these situations it would be much better to help people to optimize their current path towards more important goals. Just to give an example, someone who studies law might get the impression that his choice is wrong and not beneficial, while I believe that if they tried they could find highly impactful opportunities (for example the recently established Legal Priorities Project looks very promising).

I think that these are my major points, but I do have some other smaller reservations about the content (for example I disagree with the principle of maximizing expected value, and definitely don't think that this is the way it should be phrased as part of the "the big picture").

Writing Style

I really liked the structure of the previous career guide. It was very straightforward to know what you are about to read and where you can find something, since it was so clearly separated into different pages with clear titles and summaries. Furthermore, its modularity made it very easy to read the parts you are interested in. The key ideas page is much more convoluted, it is very hard to navigate and all of the expandable boxes are not making it easier.

Arden Koehler

Thanks for this quick and detailed feedback shaybenmoshe, and also for your kind words!

I think that two important aspects of the old career guide are much less emphasized in the key ideas page: the first is general advice on how to have a successful career, and the second is how to make a plan and get a job. Generally speaking, I felt like the old career guide gave more tools to the reader, rather than only information.

Yes. We decided to go "ideas/information-first" for various reasons, which has upsides but also downsides. We are hoping to mitigate the downsides by having practical, career-planning resources more emphasised alongside Key Ideas. So in the future the plan is to have better resources on both kinds of things, but they'll likely be separated somewhat -- like here are the ideas [set of articles], and here are the ways to use them in your career [set of articles]. We do plan to introduce the ideas first though, which we think are important for helping people make the most of their careers. That said, none of this is set in stone.

Another important point is that I don't like, and disagree with the choice of, the emphasis on longtermism and AI safety. Personally, I am not completely persuaded by the arguments for choosing a career by a longtermist view, and even less by the arguments for AI safety. More importantly, I had several conversations with people in the Israeli EA community and with people I gave career consultation to, who were alienated by this emphasis. A minority of them felt like me, and the majority understood it as "all you can meaningfully do in EA is AI safety", which was very discouraging for them. I understand that this is not your only focus, but people whose first exposure to your website is the key ideas page might get that feeling, if they are not explicitly told otherwise.

We became aware of the AI safety problem last year -- we've tried to deemphasie AI Safety relative to other work since to make it clearer that, although it's our top choice for most pressing problem and therefore what we'd recommend people work on if they could work on anything equally successfully, that doesnt' mean that it's the only or best choice for everyone (by a long shot!). I'm hoping Key Ideas no longer gives this impression, and that our lists of other problems and paths might help show that we're excited about people working on a variety of things.

Re: Longtermism, I thnk our focus on that is just a product of most people at 80k being more convinced of longtermism's truth/importance, so a longer conversation!

Another point is that the "Global priorities" section takes a completely top-to-bottom approach. I do agree that it is sometimes a good approach, but I think that many times it is not. One reason is the tension between opportunities and cause areas which I already wrote about. The other is that some people might already have their career going, or are particularly interested in a specific path. In these situations, while it is true that they can change their careers or realize that they can enjoy a broader collection of careers, it is somewhat irrelevant and discouraging to read about rethinking all of your basic choices. Instead, in these situations it would be much better to help people to optimize their current path towards more important goals.

I totally agree with this and think it's a problem with Key Ideas. We are hoping the new career planning process we've released can help with this, but also know that it's not the most accessible right now. Other things we might do: improve our 'advice by expertise' article, and try to make clear in the problems section (similar to the point about ai safety above) that we're talking about what is most pressing and therefore best to work on for the person who could do anything equally successfully, but that career capital and personal fit mean that's not going to be true of the reader, so while we think the problems are important for them to be aware of and an important input to their personal prioritisation, it's not the end of it.

I disagree with the principle of maximizing expected value, and definitely don't think that this is the way it should be phrased as part of the "the big picture".

Similar to longtermism (and likely related) - it's just our honest best guess at what is at least a good decision rule, if not the decision rule.

I really liked the structure of the previous career guide. It was very straightforward to know what you are about to read and where you can find something, since it was so clearly separated into different pages with clear titles and summaries. Furthermore, its modularity made it very easy to read the parts you are interested in. The key ideas page is much more convoluted, it is very hard to navigate and all of the expandable boxes are not making it easier.

Mostly agree with this. We're planning to split key ideas into several articles that are much easier to navigate, but we're having trouble making that happen as quickly as we would like. One thing is that lots of people skipped around the career guide, so we think many readers prefer a more 'shopping'-like experience (like a newspaper) than the career guide had anyway. We're hoping to go for a hybrid in the future.

ShayBenMoshe

Thanks for detailing your thoughts on these issues! I'm glad to hear that you are aware of the different problems and tensions, and made informed decisions about them, and I look forward to seeing the changed you mentioned being implemented.

I want to add one comment about to the How to plan your career article, if it's already mentioned. I think it's really great, but it might be a little bit too long for many readers' first exposure. I just realized that you have a summary on the Career planning page, which is good, but I think it might be too short. I found the (older) How to make tough career decisions article very helpful and I think it offers a great balance of information and length, and I personally still refer people to it for their first exposure. I think it will be very useful to have a version of this page (i.e. of similar length), reflecting the process described in the new article.

With regards to longtermism (and expected values), I think that indeed I disagree with the views taken by most of 80,000 hours' team, and that's ok. I do wish you offered a more balanced take on these matters, and maybe even separate the parts which are pretty much a consensus in EA from more specific views you take so that people can make their own informed decisions, but I know that it might be too much to ask and the lines are very blurred in any case.

Kirsten

Strong upvoted this as I feel almost exactly the same way! I've tried the new 80k Google doc but looked the old career guide and career decision making tool a lot better.

Shargil

Wow!!!

I am in this cross road right now, having similar background and also from Israel.
Just started a doc similar to what you described lately. Added many options there while reading this post.
I loved the fact that you got very specific about different options\ names and didn't just stay in the problem areas. I agree that reading about actual career decision case studies can really help, and problem areas is important but can stay a bit vague.

Found it super helpful thanks!

P.S I have a bit too many tabs opened after reading this post :)

jared_m

Regarding this valuable excerpt:

Working part-time might be more profitable than one might intuitively think. This is due to two factors: the first is that, at least in Israel, the income tax is progressive, making the marginal earning smaller; the second is that in some circumstances a freelancer or a consultant can earn even more per hour than a full-time employee.

Others thinking about this very sensible path of "part-time EtG" might scan a book like this one. The author (a long-term independent consultant who also helps part- and full-time expert contractors set up their practices) validates your approach from a slightly different angle. In a section that reads Make Certain You Charge Enough, Katcher writes that many new consultants think it is better to have a low-paying client than no client at all, which he describes as a "mistaken belief." He advocates for protecting a consultant's personal time and perceived value in the marketplace.

Potential part-time EtG practitioners might think about the demand for their time as a labor supply-demand curve in miniature. Given consulting clients 1, 2, and 3 below, someone looking at EtG might decline to serve client 3 in order to free up those 15 hours of workday support a week for self-study, formal study, or other rewarding independent projects (exercise, cooking, writing, etc.):

Client that requires ~15 hours a week of support and is willing to pay $150 (or €150/£150/etc.) an hour, with limited career capital benefits for the consultant
Client that requires ~10 hours a week of support and is willing to pay $75 (or €75/£75/etc. ) an hour, with significant career capital benefits for the consultant
Client that requires ~15 hours a week of support and is willing to pay $75 (or €75/£75/etc. ) an hour, with limited career capital benefits for the consultant

Declining client 3 can also provide the part-time EtG contractor more workday time to identify and pitch to prospective clients who are even more profitable or desirable from a career capital perspective than clients 1 and 2. (Of course, individuals might want to mentally reorder those in terms of price points, hours, etc., if helpful to think ex ante about the shadow price of one's time and what categories of career capital one might value.) Thanks for this thoughtful write-up, and best wishes for your new PhD studies and set of projects!

aog

Hi Shay. We had a great conversation about your career writeup last year, and I've been trying to build a more object-level view of how I'll achieve impact in my career as a result. I've just written up some thoughts about a career path to improve existential safety: nuclear weapons security. Interested in any feedback if you end up with the time to read it. Either way, hope you're doing good and doing well!

Miranda_Zhang

Thank you for this - very well-organized, clear, & relatable as someone struggling to figure out their first foray into the professional world.

Definitely agree that having more posts like this would be helpful considering the selectivity + relatively low supply of formal EA mentorship programs.

T_W

Hey! This is easily the most detailed career decision I've come across on the forum, so it's super helpful to walk through. One question I had is how you made your decisions earlier on in your career? Like did you apply the same framework to decide to follow out cybersecurity earlier? And how did you decide to do the masters? As someone earlier on than you, this part is of great interest to me, and I'd love to hear more!

ShayBenMoshe

Glad to hear this was helpful! In short, the answer is no, it was much less structured (though also thought of).

These decisions were made under very different circumstances, and I'm not sure there is much value in explaining how I arrived at them. Very quickly though - I chose math because I highly enjoyed it and had the time (I did consider alternatives, such as physics and CS); cyber-security was part of my military service, which did involve some choice, but it is a very different situation and would drag me into a long digression.

Apologies if this doesn't help much. Feel free to ask any other questions (publicly or privately).

Comments

My Career Decision-Making Process

My Career Decision-Making Process

Summary

Table of Contents

Acknowledgements

1. Goals of this Post

2. My Background

3. Preferences and Constraints

4. Methodology

Opportunities and Cause Areas

5. Narrowing Down the Long-List

6. Making a Final Decision

Impact

Career Capital

Personal Preferences

7. Final Plan

8. General Helpful Resources

9. Options Considered

AI for Good

AI Safety

Alternative Proteins

Biorisks Reduction

Climate Venture Capitals

Computational Healthcare PhD

Earning to Give and Part-time EtG

Entrepreneurship

Flood Forecasting at Google

Geoengineering and Carbon Removal

Health

Information Security and Formal Verification

Meta-Science

Nuclear Fission Energy

Nuclear Fusion Energy

Physics PhD

Research at EA Organizations