We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time (by comparison, Moore’s Law had a 2-year doubling period). Since 2012, this metric has grown by more than 300,000x (a 2-year doubling period would yield only a 7x increase). Improvements in compute have been a key component of AI progress, so as long as this trend continues, it’s worth preparing for the implications of systems far outside today’s capabilities.

We’ve updated our analysis⁠ with data that span 1959 to 2012. Looking at the data as a whole, we clearly see two distinct eras of training AI systems in terms of compute-usage: (a) a first era, from 1959 to 2012, which is defined by results that roughly track Moore’s law, and (b) the modern era, from 2012 to now, of results using computational power that substantially outpaces macro trends. The history of investment in AI broadly is usually told as a story of booms and busts, but we don’t see that reflected in the historical trend of compute used by learning systems. It seems that AI winters and periods of excitement had a small effect on compute used to train models^B over the last half-century.

Starting from the perceptron⁠(opens in a new window) in 1959, we see a ~2-year doubling time for the compute used in these historical results—with a 3.4-month doubling time starting in ~2012. It’s difficult to draw a strong conclusion from this data alone, but we believe that this trend is probably due to a combination of the limits on the amount of compute that was possible to use for those results and the willingness to spend on scaling up experiments. ^C

We followed the same methodology outlined in the original post for this updated analysis. When possible, we programmatically counted the number of FLOPs in the results by implementing the models directly. Since computer architectures varied historically and many papers omitted details of their computational setup, these older data points are more uncertain (our original analysis of post-2012 data aimed to be within a factor of 2–3, but for these pre-2012 data points we aim for an order of magnitude estimate). We’ve also created graphs that provide additional views on the data: one graph lays out compute usage in fundamentals, speech, language, vision, and games over time and another visualizes the error-bar estimates around each data point.

We’re very uncertain about the future of compute usage in AI systems, but it’s difficult to be confident that the recent trend of rapid increase in compute usage will stop, and we see many reasons that the trend could continue⁠. Based on this analysis, we think policymakers should consider increasing funding^D for academic research into AI, as it’s clear that some types of AI research are becoming more computationally intensive and therefore expensive.