Suppose labs stumble on a new scaling axis with a multiplicative effect on existing compute, allowing us to go from the equivalent of GPT-6 to GPT-60 ~overnight. This risk also passes through automated AI R&D, but is a bit different from imagining GPT-N training GPT-N+1 using current recipes. It also seems strikingly plausible given the evidence of substantial algorithmic headroom, and fits the pattern of recent speed-ups like the 2-4x efficiency gained from inference-time compute, and perhaps…