But AI policy researcher Jai Vipra told DFD there are several reasons why computing power is emerging as a top early concern for AI competition. For one, it’s easier for regulators to understand than data, which is abstract and opaque, making it difficult to know how AI firms are using it.
“Right now, compute seems like a more concrete input to regulate,” said Vipra, who co-authored a “Computational Power and AI” report released last week by the AI Now Institute, a New York-based think tank focused on the technology’s social implications.
And — due largely to a shortage of the advanced chips needed to train and run the most advanced AIs — computational power has become the most important bottleneck to AI adoption, lending it a sense of urgency. ”It’s the constraint right now,” Vipra said.
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