Denizens of Silicon Valley have called Moore's Law "the most important graph in human history," and economists have found that Moore's Law-powered I.T. revolution has been one of the most important sources of national productivity growth. But data substantiating these claims tend to either be abstracted - for example by examining spending on I.T., rather than I.T. itself - or anecdotal. In this project, we assemble direct quantitative evidence of the impact that computing power has had on five domains: two computing bellwethers (Chess and Go), and three economically important applications (weather prediction, protein folding, and oil exploration). Computing power explains 49%-94% of the performance improvements in these domains. But whereas economic theory typically assumes a power-law relationship between inputs and outputs, we find that an exponential increase in computing power is needed to get linear improvements in these outcomes. This helps clarify why the exponential growth of computing power from Moore's Law has been so important for progress, and why performance improvements across many domains are becoming economically tenuous as Moore's Law breaks down.
Neil Thompson, Shuning Ge, and Gabriel Manso
| open | |||||
|---|---|---|---|---|---|
| Seok Lab | 2020 | 54.63 | 151 MFLOPS | ||
| DeepMind (Alphafold2) | 2020 | 92.40 | 12.2 TFLOPS | ||
| DeepMind (Alphafold1) | 2018 | 68.50 | 13.2 GFLOPS | ||
| FeigLab | 2018 | 62.98 | 4.9 GFLOPS | ||
| Baker's Lab | 2015 | 65.26 | 421 MFLOPS |
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