Enterprises are currently overspending on tokens by sending all queries to the most powerful LLMs. A new software category will emerge to intelligently route requests to smaller, cheaper models when possible, creating a critical efficiency and cost-saving layer between companies and foundational model providers.

Compute providers can optimize cost structures and capacity utilization by load-balancing agentic background jobs across global regions based on local sleep cycles and time-of-day pricing. Non-urgent workflows, like asynchronous agents or batch evaluations, can automatically deploy to sleeping markets across continents without developer intervention.

To manage demand in a supply-constrained market, cloud provider Nebius implemented dynamic pricing via auctions and a 15-minute spot market. CRO Mark Boroditsky reveals this strategy uncovered the true market price for GPUs, showing they had been under-pricing B200 clusters by 15% and their pipeline was 20% below what customers would pay.

The advertised per-hour GPU cost is misleading. Because research workloads are spiky and unpredictable, labs over-provision compute. This rampant underutilization means the effective price paid is often 10 times higher than the marketed rate, creating massive deadweight loss.

Because GPUs are the most capital-intensive asset inside a data center, idle compute directly destroys capital. Maximizing inference efficiency relies heavily on managing the key-value (KV) cache across GPU high-bandwidth memory (HBM), host DRAM, and NVMe solid-state storage. Storing and routing precomputed tokens across these tiers prevents redundant matrix multiplications and keeps GPUs saturated at high throughput.

Companies are building intelligent systems that analyze a user's prompt and automatically route it to the most cost-effective model that can handle the task. This avoids using expensive frontier models for simple requests, with some companies like Coinbase successfully keeping costs flat despite exponential usage growth.

A sophisticated gateway that routes queries to different models based on complexity is key to managing AI costs. Simple tasks go to cheap, open-source models, while difficult ones use the frontier. This "expert pattern" allows token usage to rise while keeping costs flat.

Drawing a parallel to AWS's history, AI inference costs are expected to continuously decrease over time. As usage skyrockets, providers will be incentivized to lower prices to capture market share, making fears of escalating costs for startups unlikely to materialize.

Unlike physical commodities like oil, AI compute lacks strong regional price differences. For non-real-time tasks like model training, the physical location of the GPU and its associated latency are negligible. This allows users to source compute globally, driving prices toward a single international benchmark.