Monika Chauhan

Unlimited token budgets do not produce better AI outcomes. They remove the feedback loops that catch bad assumptions before those assumptions get embedded in products, business decisions, and customer-facing systems. The operational gap between running a local open weight model on a developer workstation and running a governed, auditable, production-grade AI inference cluster is not a gap most organizations have a plan to close.

GPU saturation in centralized cloud regions does not produce linear latency degradation. As utilization climbs, queuing delays grow exponentially and batching decisions that performed acceptably at low load begin adding hundreds of milliseconds to time to first token. Most teams only discover this failure mode after a production workload is already dependent on the architecture they chose.

Agentic AI systems do not behave like chatbots. They loop through multi-turn inference cycles, call external tools and APIs, pull in real-time context, and execute code in sandboxes, all before returning a result. Every hop adds latency, and in a system that loops dozens or hundreds of times per task, that latency compounds into seconds that make production deployment impossible at scale.