Nobel Prize winning economist, Paul Krugman, discusses TPUs and GPUs with Paul Kedrosky. No criticism of Krugman intended, it’s great that computer architecture is getting so much attention. All of a sudden, everyone is talking - and writing - about Google’s TPUs (Tensor Processing Units). The use of TPUs to train Google latest, market leading Gemini 3 model together with Google’s decision to sell TPUs to third parties (apparently including arch-rival Meta) have combined to create a major ‘vibe-shift’ away from Nvidia and towards Google’s hardware. Not just a vibe-shift either as Alphabet’s stock price has been on a tear: The coverage of the latest TPUs (v7) has been so extensive that, rather than add another series of takes, I thought it would more useful for readers to curate a selection of some of the most informative posts and links on TPUv7. Before that, though, I wanted to briefly highlight several points that I can’t recall seeing much discussed elsewhere. A Twelve Year Overnight Success We looked at the origins of Google’s TPU programme in: The origins of the TPU program date Google date all the way back to 2013 - almost a decade before the launch of ChatGPT - when it first became apparent that Google might need to start applying deep learning at scale. The essence of the design dates back even further - into the 1970s. In their 1978 paper Systolic Arrays (for VLSI) H.T Kung and Charles E. Leiserson of Carnegie Mellon University had set out proposals for what they called a ‘systolic system’. A systolic system is a network of processors which rhythmically compute and pass data through the system….In a systolic computer system, the function of a processor is analogous to that of the heart. Every processor