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 feasible for the current rapid pace of AI training scaling—approximately 4x per year—to continue through 2030. We investigate four key factors that might constrain scaling: power availability, chip manufacturing capacity, data scarcity, and the “latency wall”, a fundamental speed limit imposed by unavoidable delays in AI training computations.