We interviewed five AI researchers from leading AI companies about a scenario where AI systems fully automate AI capabilities research. To ground the setting, we stipulated that each employee is replaced by 30 digital copies with the same skill set and the ability to think 30 times faster than a human. This represents a 900-fold increase in the cognitive labor that AI companies direct towards advancing AI capabilities.

Our key takeaways are:

1. Compute for experiments will likely be a bottleneck. Finding better algorithms often requires running computationally expensive ML experiments. Even with abundant cognitive labor, AI progress would be constrained by the time needed for these experiments to run.
2. AI cognitive labor could probably extract significantly more research insights out of limited compute. AI could increase efficiency by improving experimental design, high-level research directions, and project prioritization. Other strategies include running experiments at smaller [...]

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