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Can AI Scaling Continue Through 2030? – Epoch AI

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We investigate the scalability of AI training runs. We identify electric power, chip manufacturing, data and latency as constraints. We conclude that 2e29 FLOP training runs will likely be feasible by 2030. In recent years, the capabilities of AI models have significantly improved. Our research suggests that this growth in computational resources accounts for a significant portion of AI performance improvements.1 The consistent and predictable improvements from scaling have led AI labs to aggressively expand the scale of training, with training compute expanding at a rate of approximately 4x per year. To put this 4x annual growth in AI training compute into perspective, it outpaces even some of the fastest technological expansions in recent history. It surpasses the peak growth rates of mobile phone adoption (2x/year, 1980-1987), solar energy capacity installation (1.5x/year, 2001-2010), and human genome sequencing (3.3x/year, 2008-2015). Here, we examine whether it is technically feas

Can AI scaling continue through 2030? | Epoch AI Introduction In recent years, the capabilities of AI models have significantly improved. Our research suggests that this growth in computational resources accounts for a significant portion of AI performance improvements . 1 The consistent and predictable improvements from scaling have led AI labs to aggressively expand the scale of training , with training compute expanding at a rate of approximately 4x per year. To put this 4x annual growth in AI training compute into perspective, it outpaces even some of the fastest technological expansions i

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