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 complexities inherent in chip design, which involves numerous interconnected components and intricate design constraints. For over sixty years, automating the chip floorplanning process has posed a challenge for engineers. AlphaChip addresses this by treating chip layout design as a game, akin to how AlphaGo and AlphaZero approached board games. It begins with a blank grid and strategically places circuit components, receiving rewards based on the quality of the final layout. This process is enhanced by an edge-based graph neural network that helps AlphaChip learn the relationships between components, allowing it to improve with each design iteration. Since its inception, AlphaChip has been instrumental in designing layouts for Google’s Tensor Processing Units (TPUs), which are crucial for scaling AI models based on Google’s Transformer architecture. These AI accelerators are foundational to various Google services and are also available to external users through Google Cloud. The pre-training phase of AlphaChip involves practicing on diverse chip blocks from previous TPU generations, which enables it to generate high-quality layouts for current designs. As a result, AlphaChip has consistently produced better layouts with each new TPU generation, significantly accelerating the design cycle and enhancing chip performance. The impact of AlphaChip extends beyond Google, influencing the broader chip design industry and research community. Companies like MediaTek have adopted and adapted AlphaChip to enhance their own chip development processes, demonstrating its versatility and effectiveness. The success of AlphaChip has sparked a surge of interest in applying AI to various stages of chip design, including logic synthesis and macro selection. Looking ahead, the potential of AlphaChip is vast, with aspirations to optimize every aspect of the chip design cycle, from architecture to manufacturing. Future iterations of AlphaChip are in development, with the goal of further revolutionizing chip design for a wide array of applications, including smartphones, medical devices, and agricultural sensors. The ongoing collaboration with the research community aims to create chips that are faster, more cost-effective, and energy-efficient, paving the way for the next generation of technology.