The least verifiable part of AI R&D (and the thing which may bottleneck the automated researchers) is making calls on large experiments and big training runs. Interestingly, this may also partly explain why model parameter scaling has been slow for the last few years. Even for human AI researchers, it's better to run more experiments and do training runs at small(er) scale, and make up for the benefits of scaling with doing research faster and thus finding more compute efficiency gains.