As Google said in a paper in the journal Nature, algorithm’s designs are “comparable or superior” to those created by humans but can be generated much faster. Work that takes months for humans can be accomplished by AI in under six hours. Google has been working on how to use machine learning to create chips for years, but this recent effort seems to be the first time its research has been applied to a commercial product: an upcoming version of Google’s own TPU (tensor processing unit) chips, which are optimized for AI computation. Google’s engineers note that this work has major implications for the chip industry. It should allow companies to explore the possible architecture space more quickly for upcoming designs customize chips for specific workloads more easily. This could help offset the forecasted end of Moore’s Law. AI won’t necessarily solve the physical challenges of squeezing more transistors onto chips, but it could help find other paths to increasing performance at the same rate. The virtuous cycle of AI designing chips for AI looks like it is just getting started. Google itself has explored using AI in other parts of the process such as architecture exploration, and rivals like Nvidia are looking into other methods to speed up the workflow.