AlphaChip has significantly transformed the landscape of computer chip design through the application of advanced AI techniques. Initially introduced in a preprint in 2020, AlphaChip employs a novel reinforcement learning method to optimize chip layouts, which has since been published in Nature and made available as open-source software. This innovative approach has enabled the creation of superhuman chip layouts that are now integral to hardware utilized globally. The development of AlphaChip was motivated by the need to enhance the efficiency of chip design, a process that has historically been labor-intensive and time-consuming. Traditional methods could take weeks or months to produce a chip layout, whereas AlphaChip can generate comparable or superior designs in just hours. This acceleration is particularly evident in the design of Google’s Tensor Processing Units (TPUs), which are crucial for scaling AI models based on Google's Transformer architecture. AlphaChip operates by treating chip floorplanning as a game, akin to how AlphaGo and AlphaZero approached their respective games. It begins with a blank grid and strategically places circuit components, receiving rewards based on the quality of the final layout. A unique edge-based graph neural network allows AlphaChip to learn the intricate relationships between interconnected components, improving its performance with each design iteration. The impact of AlphaChip extends beyond Google’s internal projects; it has influenced the broader chip design industry. Companies like MediaTek have adopted and adapted AlphaChip to enhance their own chip development processes, leading to improvements in power efficiency and performance. The technology has sparked a wave of research into AI applications for various stages of chip design, including logic synthesis and macro selection. Looking ahead, the potential of AlphaChip is vast. It is expected to optimize every phase of the chip design cycle, from architecture to manufacturing, thereby revolutionizing the creation of custom hardware found in everyday devices. Future iterations of AlphaChip are in development, with the aim of producing chips that are faster, cheaper, and more power-efficient, ultimately benefiting a wide range of applications from smartphones to medical devices. The collaborative efforts of a diverse team of researchers have been instrumental in the success of AlphaChip, highlighting the importance of interdisciplinary work in advancing technology. As the field of AI-driven chip design continues to evolve, AlphaChip stands at the forefront, promising to reshape the future of computing.