In 2018, James Kobielus wrote an article on the AI market’s shift to workload-optimized hardware platforms, in which he proposed:
Workload-optimized hardware/software platforms will find a clear niche for on-premises deployment in enterprises’ AI development shops. Before long, no enterprise data lake will be complete without pre-optimized platforms for one or more of the core AI workloads: data ingest and preparation, data modeling and training, and data deployment and operationalization.
We are seeing Kobielus’ words come true. In the past year, nearly 100 companies have announced some sort of AI-optimized IP, chip, or system optimized, primarily for inferencing workloads but also for training. Hyperscalers like Facebook, Amazon, and Google are increasingly talking publicly about "full-stack" optimization of AI, from silicon, through algorithms, up to the application layer.