theory uplift differentially benefits safety & is underleveraged

[1] We will likely have near-superhuman mathematics AI by Q1 2027. [1] [2] Qualitatively, AI mathematics capabilities are developing significantly faster than automated AI R&D capabilities. [2] [3] Thus, we will likely have a period of time where the rate of our ability to rigorously & usefully verify and understand model behavior and model outputs outpaces the rate of capability development itself. [4] Our ability to take advantage of this period is bottlenecked on the quality of our specification generation infrastructure, elicitation tooling (for proofs & specs etc.), and the institutional capacity for scaling useful outputs with capital. [5] My understanding is that basically no one [3] is working on building infra that can usefully turn >$100M of compute credits into safety-relevant mathematical output. [5.1] The number of theory-driven ASI alignment efforts is also small, but growing. ARC is a much better bet now than it was in 2023. [5.2]. My understanding is also that no one is