The AI industry is shifting toward "disaggregated inference," splitting processing into two distinct workloads: prefill (compute-heavy prompt ingestion) and decode (latency-sensitive token generation), each requiring different hardware. This shift follows Nvidia's acquisition of LPU IP from Groq and a partnership between AMD and Cerebras to target the complex inference market. The economics changed as AI moved from demo to production traffic, with prefill exploiting massive parallelism while decode is dominated by memory bandwidth and latency. Nvidia invested $20B last December to acquire assets from Groq. SambaNova teamed up with Intel, and Cerebras is joining forces with AMD MI4xx GPUs and Amazon AWS Trainium. Cerebras CEO Andrew Feldman stated, "If you want to play with the big boys, you gotta play with tremendous speed and memory bandwidth." Disaggregation allows operators to avoid wasting compute on the wrong phase and lets cloud providers scale each stage independently, improving throughput, lower tail latency, and serving large models at scale.