The situation is different for organizations that cannot afford high salaries. Let me link to Nate’s explanation from three years ago:
I want to push back a bit against point #1 ("Let's divide problems into 'funding constrained' and 'talent constrained'.) In my experience recruiting for MIRI, these constraints are tightly intertwined. To hire talent, you need money (and to get money, you often need results, which requires talent). I think the "are they funding constrained or talent constrained?" model is incorrect, and potentially harmful. In the case of MIRI, imagine we're trying to hire a world-class researcher for $50k/year, and can't find one. Are we talent constrained, or funding constrained? (Our actual researcher salaries are higher than this, but they weren't last year, and they still aren't anywhere near competitive with industry rates.)
Furthermore, there are all sorts of things I could be doing to loosen the talent bottleneck, but only if I knew the money was going to be there. I could be setting up a researcher stewardship program, having seminars run at Berkeley and Stanford, and hiring dedicated recruiting-focused researchers who know the technical work very well and spend a lot of time practicing getting people excited -- but I can only do this if I know we're going to have the money to sustain that program alongside our core research team, and if I know we're going to have the money to make hires. If we reliably bring in only enough funding to sustain modest growth, I'm going to have a very hard time breaking the talent constraint.
And that's ignoring the opportunity costs of being under-funded, which I think are substantial. For example, at MIRI there are numerous additional programs we could be setting up, such as a visiting professor + postdoc program, or a separate team that is dedicated to working closely with all the major industry leaders, or a dedicated team that's taking a different research approach, or any number of other projects that I'd be able to start if I knew the funding would appear. All those things would lead to new and different job openings, letting us draw from a wider pool of talented people (rather than the hyper-narrow pool we currently draw from), and so this too would loosen the talent constraint -- but again, only if the funding was there. Right now, we have more trouble finding top-notch math talent excited about our approach to technical AI alignment problems than we have raising money, but don't let this fool you -- the talent constraint would be much, much easier to address with more money, and there are many things we aren't doing (for lack of funding) that I think would be high impact.
I don’t think this is quite right. The people working at OpenAI are paid well, but at the same time they are taking huge cuts in salary compared to where they could be working otherwise. (Goodfellow and Sutskever could be making millions anywhere.) And given the distribution of salary, it’s very likely that the majority of both OpenAI and Deepmind researchers are making under $200k—not a crazy amount for Deep Learning talent nowadays.
At least some people at OpenAI are making a ton of money: https://www.nytimes.com/2018/04/19/technology/artificial-intelligence-salaries-openai.html /. Of course not everyone is making that much but I doubt salaries at OpenAI/DeepMind are low. I think the obvious explanation is the best one. These companies want to hire top talent. Top talent is hard to find.
The situation is different for organizations that cannot afford high salaries. Let me link to Nate’s explanation from three years ago:
source: https://forum.effectivealtruism.org/posts/k6bBgWFdHH5hgt9RF/peter-hurford-thinks-that-a-large-proportion-of-people#DvKfX3iN5Z8kuaFs7
I don’t think this is quite right. The people working at OpenAI are paid well, but at the same time they are taking huge cuts in salary compared to where they could be working otherwise. (Goodfellow and Sutskever could be making millions anywhere.) And given the distribution of salary, it’s very likely that the majority of both OpenAI and Deepmind researchers are making under $200k—not a crazy amount for Deep Learning talent nowadays.