We are in age of AI, with rapidly changing algorithms and a somewhat synergistic change in hardware. MLPerf is a recent benchmark suite that serves as a way to compare and evaluate hardware. However it has several drawbacks - it is dominated by CNNs and does a poor job of capturing the diversity of AI use cases, and only represents a sliver of production AI use cases. This paper performs a longitudinal study of state-of-art AI applications spanning vision, physical simulation, vision synthesis, language and speech processing, and tabular data processing, across three generations of hardware to understand how the AI revolution has panned out. We call this collection of applications and execution scaffolding the CaSiO suite. The paper reports on data gathered at the framework level, device API level, and hardware and microarchitecture level. The paper provides insights on the hardware-software revolution with pointers to future trends.