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

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).