Hacker News Reader

Comment by telotortium | Hacker News Reader

If you go back to the old 80,000 hours blog posts, they stress not getting into the most oversubscribed fields, given that the marginal contribution of a incoming do-gooder is likely to be minimal due to the great interest in those fields already attracting a lot of intelligent people. Before COVID, I might agree that EAs should get into pandemic prevention - Bill Gates was certainly concerned. After COVID, pandemic prevention is probably only second to climate change in the level of oversubscription. What is your alpha in pandemic prevention?

View on HN

I might agree with you if pandemic prevention actually seemed oversubscribed. Instead, when I went looking for what needed doing I found lots of valuable potential projects that didn't have anyone working on them, and others that had been started but limited by hiring.

One good list of projects: https://forum.effectivealtruism.org/posts/u5JesqQ3jdLENXBtB/...

In any case, my 'alpha' was something like, I'm a software engineer and engineering manager willing to work in an academic environment well below market wages, in a situation where the organization needed someone with my skills.

Reading this I'm dumbstuck by how much EA is just "the Chicago school, but for 'engineering' instead of 'economics'".

I'm not really sure what you mean by this? Which era of the Chicago School are you talking about?

Please explain how AI risk is not oversubscribed.

What fraction of people working on AI in general do you think it would be appropriate to have work on AI risk?

So, first, I went into pandemic prevention / mitigation and not AI risk, and I haven't been claiming here that AI risk needs more people. Compared to the median EA, I am less convinced that AI risk is where we should be putting the marginal person, though I do think it is still important and a good place for people who are well suited for it.

The main problem I see with AI risk is not that it is a small problem, or that there are too many people working on it, but that we don't really know how to make progress and historical attempts maybe have ended up making things worse.

Still, I think gradys' point above is a good one: the fraction of people working on making AI more capable is far far larger than the fraction working on making sure it reliably does what we want it to do even in unusual situations.

Reply on news.ycombinator.com