I’m surprised that Anthropic and GDM have such small numbers of technical safety researchers in your dataset. What are the criteria for inclusion / how did you land on those numbers?
That’s a good question. One approach I took is to look at the research agendas and outputs (e.g. Google DeepMinds AI safety research agenda) and estimate the number of FTEs based on those.
I would say that I’m including teams that are working full-time on advancing technical AI safety or interpretability (e.g. the GDM Mechanistic Interpretability Team).
To the best of my knowledge, there are a few teams like that at Google DeepMind and Anthropic though I could be underestimating given that these organizations have been growing rapidly over the past few years.
A weakness of this approach is that there could be large numbers of staff who sometimes work on AI safety and significantly increase the effective number of AI safety FTEs at the organization.
I’m surprised that Anthropic and GDM have such small numbers of technical safety researchers in your dataset. What are the criteria for inclusion / how did you land on those numbers?
That’s a good question. One approach I took is to look at the research agendas and outputs (e.g. Google DeepMinds AI safety research agenda) and estimate the number of FTEs based on those.
I would say that I’m including teams that are working full-time on advancing technical AI safety or interpretability (e.g. the GDM Mechanistic Interpretability Team).
To the best of my knowledge, there are a few teams like that at Google DeepMind and Anthropic though I could be underestimating given that these organizations have been growing rapidly over the past few years.
A weakness of this approach is that there could be large numbers of staff who sometimes work on AI safety and significantly increase the effective number of AI safety FTEs at the organization.