  Barroso's seminal contributions in energy-proportional warehouse-scale
computing launched an era where modern datacenters have become more energy
efficient and cost effective than ever before. At the same time, modern AI
applications have driven ever-increasing demands in computing, highlighting the
importance of optimizing efficiency across the entire deep learning model
development cycle. This paper characterizes the carbon impact of AI, including
both operational carbon emissions from training and inference as well as
embodied carbon emissions from datacenter construction and hardware
manufacturing. We highlight key efficiency optimization opportunities for
cutting-edge AI technologies, from deep learning recommendation models to
multi-modal generative AI tasks. To scale AI sustainably, we must also go
beyond efficiency and optimize across the life cycle of computing
infrastructures, from hardware manufacturing to datacenter operations and
end-of-life processing for the hardware.
