AI's New Bottleneck: Context, Not Compute

As AI shifts from simple question-and-answer exchanges to complex agentic systems, the critical bottleneck has moved from GPU compute to context management. Inference workloads now chain hundreds of model calls together, each generating persistent state that must be tracked across sessions. According to Solidigm's AI applied research lead Jeff Harthorn, context has grown faster than both GPU efficiency gains and model architecture improvements. With context windows expanding dramatically and enterprise deployments scaling up, managing persistent state between sessions has become the defining AI infrastructure challenge of 2026.