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One key challenge with AI governance is demonstrating that agreed-upon rules are being applied consistently. Corporations bound by rules want their rivals to be similarly bound. Countries might refuse to place rules on the behavior of their companies unless they can see that their rivals are behaving similarly.
The new scaling paradigm for AI reduces the amount of information a model can learn from per hour of training by a factor of 1,000 to 1,000,000. I explore what this means and its implications for scaling.