The Leaky AI Safety Pipeline

My first AI security paper as an independent researcher (with one other independent collaborator) was just accepted to the Security in Machine Learning Applications workshop at ACNS 2026. This was an 8-month process: I spent 2 weeks convincing myself that what I was trying to do was possible, 4 months building, evaluating, and collecting results, and another 4 months writing the paper (and making strategic mistakes) before submitting.My paper was not radical or paradigm-shifting by any means, but I think getting through peer review and being told by other researchers that the contribution has value is a reasonable bar for becoming a "productive" researcher. If maximizing AI safety research output is our goal, then we ought to maximize the number of productive researchers.How do we actually do that? I quite like this figure, taken directly from Chris_Leong's earlier post on the pipeline toward becoming a productive researcher:I want to focus specifically on the final two stages of this pipeline. Programs like BlueDot and SPAR can get people to understand the frameworks of AI safety, but the conversion rate into productive researchers with peer-reviewed contributions is a major leak in the AI safety research pipeline. There are several fellowships with substantial research output like MATS and other smaller groups, but these are notoriously selective and suffer from a serious shortage of mentors. Once you're past this hurdle and get a paper through peer review, even at a small workshop, the credibility you gain and the networking opportunities you can access at an academic conference can turn AI safety research into a viable career.It is not the case, as I have heard from many of my own friends in the EA community, that getting into a fellowship is the only way to pursue AI safety research. What I propose is that more people should commit to a high-collaboration approach to research, and that we should have scalable online BlueDot-style resources to outline the proces