If frontier AI progress shifts from pre-training compute to inference-time compute, AI governance must be rebuilt around deployment-time capabilities and transparency, with different implications depending on whether that inference happens during deployment or inside the training process.
For example, if you scale up training compute by 1 OOM, that means 0.5 OOMs more parameters and 0.5 OOMs more data
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Inference Scaling Reshapes AI Governance
If frontier AI progress shifts from pre-training compute to inference-time compute, AI governance must be rebuilt around deployment-time capabilities and transparency, with different implications depending on whether that inference happens during deployment or inside the training process.