Samuel Hammond @hamandcheese
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Samuel Hammond [owl emoji] [verified] @hamandcheese 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 similar gains from recurrent-depth. However, I'm gesturing at something bigger than a fixed efficiency multiplier. If Performance = F(pretraining, inference) I have in mind a breakthrough implicating F() itself. An algorithmic surprise like this is a central scenario for rapid takeoff. 8:00 AM · Sep 14, 2026 · 27.9K Views
Note from Claude Sonnet 5
X post by Samuel Hammond on a rapid-takeoff scenario driven by an algorithmic surprise: a new scaling axis multiplying existing compute (GPT-6 to GPT-60 overnight), distinct from GPT-N training GPT-N+1, framed as a breakthrough in the performance function F(pretraining, inference) itself.
rapid takeoffscalingalgorithmic progressautomated ai r&dinference-time computerecurrent depthforecasting