Early career EA's should consider joining fast-growing startups in emerging technologies

by billzito2 min read10th Oct 202112 comments

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Working at EA vs. non-EA orgsCareer choice
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Some early career EAs are able to find good direct work, but there aren’t enough opportunities for all the promising early career people. Another option that I don’t think gets considered enough is to work at a fast-growing startup in an emerging technology (e.g. robotics or NLP), where one can often have much faster skill development within a couple of years. By fast-growing startup, I mean a company that seems decently likely to be one of the top ~20 highest valued startups founded in a given 5 year period.

Claim: it would often be more valuable to work at a fast-growing startup than direct work that doesn’t seem likely to have high positive counterfactual impact or high potential for individual growth.

The three main benefits from fast-growing startups are that the company’s growth leads to greater responsibility earlier, you can work with very high-caliber people and develop a better bar for excellence, and that fast-growing startups often work on the edges of technological development, giving insights into how the world will change over the next decade.

Aurora, a >$10B self-driving car company, had ~30 employees when I joined, and ~300 when I left two years later. About 6 months after joining, I started leading a team of ~5 engineers on a high priority engineering project. That was mostly due to the company needing leaders to keep up with our growth, and my hustle and generalist skills making me well-suited for the role. That experience taught me a lot about leadership, management, and long-term engineering projects, and it seems like this type of experience is much more common in fast-growing startups. In contract, nonprofits often grow slowly or not at all.

An additional benefit is working with very high caliber people, and getting a sense of what a highly successful company looks like. It’s useful to have a well-calibrated bar for who you should work with in the future and who to hire -- I think it would be pretty valuable if more people in the community had well-defined standards of excellence. To quantify this, I think I probably worked with at least 5 of the best 100 people who have worked on self-driving cars in the past decade, and at least 15 people that could get hired to lead a team at basically any self-driving or robotics company.

It’s also useful to see trends in fast-growing startups to understand how the world is changing. At Aurora, I learned about how people are thinking about ML engineering and deploying ML products, and which parts of the ML industry were real or all-hype. Learnings on the front of developing technologies seem a lot more useful for doing impactful work later than learning about random web apps, because the learnings are more applicable for direct work (e.g. AI research).

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By fast-growing startup, I mean a company that seems decently likely to be one of the top ~20 highest valued startups founded in a given 5 year period.

This sounds more like "top startup" than "fast-growing"? Not trying to nitpick, the terms just seem pretty different to me.

I think the bar need not be that high for some of the benefits you mention. I had an experience that jibes with this:

About 6 months after joining, I started leading a team of ~5 engineers on a high priority engineering project. That was mostly due to the company needing leaders to keep up with our growth, and my hustle and generalist skills making me well-suited for the role. That experience taught me a lot about leadership, management, and long-term engineering projects, and it seems like this type of experience is much more common in fast-growing startups.

from joining a startup that was certainly not one of the top ~20 in a five-year period -- it was "just a TechStars company." I found this really valuable. Probably I got fewer of the other benefits you mention around working with the top people in a given industry (this was a "random webapp" startup, not an ML startup, so that didn't really apply.)

Top and (sustainably) fast-growing (over a long period of time) are roughly synonymous, but fast-growing is the upstream thing that causes it to be a good learning experience.

Note that billzito didn't specify, but the important number here is userbase or revenue growth, not headcount growth; the former causes the latter, but not vice versa, and rapid headcount growth without corresponding userbase growth is very bad.

People definitely can see rapidly increasing responsibility in less-fast-growing startups, but it's more likely to be because they're over-hiring rather than because they actually need that many people, in which case:

  • You'll be working on less important problems that are more likely to be "fake" or busywork
  • There will be less of a forcing function for you to be very good at your job (because it will be less company-threatening if you aren't)
  • There will be less of a forcing function for you to prioritize correctly (again because nothing super bad will happen if you work on the wrong thing)
  • You're more likely to experience a lot of politics and internal misalignment in the org

(I'm not saying these applied to you specifically, just that they're generally more common at companies that are growing less quickly. Of course, they also happen at some fast-growing companies that grow headcount too quickly!)

Makes sense, upvoted. I like “fast-growing” more than “top,” because “top” makes me think more “is already Airbnb” vs “could be the next Airbnb.” Maybe the best term would be “exceptionally fast-growing” or “exceptionally likely to be successful.”

Fast and successful are definitely a spectrum, and it seems definitely true that somewhat successful is still good for career development. I think one claim I didn’t spell out fully is that people aren’t selective enough in choosing which startups to work at. Of friends who worked at companies in the “YC or Techstars startup” reference class, I think several had really positive experiences, but several worked at companies that went under and weren’t that positive, and it seems to make a big difference to choose one that is exceptionally good vs ok.

I think this implies some breakdown of efficient market in startup valuations, like saying that EAs are (much?) better at picking startups than top 10% of VCs or so. 

I sort of agree with this personally, but I think many other financially literate EAs who I respect disagree (and also others who believe this much more than I do); I'm not sure why, and it'd be good to know where the cruxes are.

I have a draft part 2: it’s easier than it sounds. One of the reasons I believe that is because many of the best-of-the-best startups have many vc’s that want to fund them (so it’s not as hard for them to identify which are the best, but it is hard for them to compete to be the one to fund it). On the other hand, these startups need all the excellent employees they can get.

One EMH-obeying way you can estimate whether the "market" expects a startup to grow is tracking the valuation:# employees ratio. Startups that are highly valued per capita means either each employee is already responsible for a lot of revenue growth (with some asterisks) or the market expects large revenue growth.

This isn't helpful if you want a large payout, but is very helpful from a career capital perspective, assuming billzito's model is correct.

Re: not being selective about what startups to work at -- oh that's interesting, makes me more think I got lucky (in startup selection or in some other way).

(I wrote this comment you are reading super quickly and there may be tons of errors. )

I know someone who mentored people in startups and Google and their path across these companies.

There’s two sections in this comment here, one that is object level and gives a mainstream perspective, and another that is meta (and has to deal with EA):

Mainstream comment:

Startups are good but contrary to what Eli, Bill and Bwr are saying, the best choice is probably a FAANG (Google, Facebook, etc.) and then going to a high rolling startup after. I think this is a mainstream and normal opinion.

One reason is that the FAANG have guaranteed cachet and on a resume, might be worth equal to “HYPS” in some fields, like “AI”. On the other hand, the value of having a startup on your resume is much less clear (unless your company does turn out to be the next AirBnB and Cruise, which by the way are outliers, maybe 0.1% of the pool of startups companies)

Another one is great flexibility (there’s often large internal demand and recruiting new candidates for managers is hard) so you can transfer to many other teams. Google, FB and others have huge ecosystems of products, teams and areas that you can join. You can plausibly even change careers mid-company.

Another the reason is that the salary is much more guaranteed, and work hours are probably much better than a startup. There is a real chance you won’t be able to think about EA or much less anything at all at certain startups.

Note this:

About 6 months after joining, I started leading a team of ~5 engineers on a high priority engineering project. That was mostly due to the company needing leaders to keep up with our growth, and my hustle and generalist skills making me well-suited for the role. That experience taught me a lot about leadership, management, and long-term engineering projects, and it seems like this type of experience is much more common in fast-growing startups.
 

This brings up the point: what makes a new grad entering a startup think it is they will be managing a team of 5 engineers, as opposed to being one of the 5 engineers managed by a brand new manager who is in their early career themselves, who probably is missing a lot of skills to ensure productivity and basic experience for their reports.

Finally, this is marginal point and there’s a bit packed into here, but given how much time is given to sort of social justice/equity issues on the EA Forum (because of the nonprofit, academic contact EA has), it’s worth pointing out that you are much more likely to have a good experience from one of the FAANGs along these lines. On the other hand, there are just awful, terrible things that have happened in startups, even pretty mature ones, and it’s probably still true. 

 

The second comment here is meta.

Basically, there’s a huge amount of attention given to careers and finding a job in EA, and Rejection is one of the top posts right now. There’s a ton packed into this on many levels.

I want to acknowledge that this comment (and probably the post and other comments) speaks to about 1-5% of EAs, much less the many other normal people who can do good. That's not a sign of value as an EA or person.

Also, as a distinct point, I’m also somewhat concerned about selection bias and survivor bias, which affects the epistemics of the post and the comments. This is well known and sort of obnoxious.

My point is that if you are reading this and going WTF. Don’t feel bad.

I agree that selection bias and survivorship bias affect things like this, and it probably would’ve been good to call those out explicitly. I have a draft part 2 of this post that discusses that and how hard it is to get a job at one of these companies.

I also appreciate the comment directed at people that might feel alienated by the post, and agree that I don’t want those people to feel alienated by this. A more positive frame on this post would be: some people think that they can only work at the ~10 EA orgs they think most highly of to have a good next step, and I think there are at least ~100 startups that they could also consider that would also be very promising. I think in general, it’s easier for an early career EA to get a job at a fast growing startup than one of the top EA orgs.

I disagree with the point about FAANG being a better option. I agree it’s a solid, lower variance option, but I think it’s higher expected value to try to get a job at a fast-growing startup for people that feel like they could do that.

But anyways, I’m sorry to anyone that felt alienated by this post, and I think not feeling like you can get this kind of job doesn’t mean that you can’t do lots of good things for the world.

This seems really spot-on to me. Thus far, my 2 years of startup experience (at Cruise, a different self-driving car company broadly similar to Aurora) often feels like it was the most important thus far for my personal growth. In fact, I think it's likely that I should have continued in that role for another year, rather than shifting into direct work when I did.

Will supplement this article with my post on [Cryptocurrency and DeFi](https://forum.effectivealtruism.org/posts/KPy4yuSsGk4qMwK3g/ea-opportunity-cryptocurrency-and-defi) as a possible industry whose startups you could join.

Completely agree! I'll also point out that there's tons of promising fast growing startups in the alternative protein space.