By 2030, inference on trillion-parameter AI models is expected to cost more than 90% less than in 2025, driven by advances in semiconductors, infrastructure efficiency, and optimized model design.

The economics of AI deployment will hinge on workload routing and model selection rather than token-cost reductions alone, signaling strategic considerations for enterprises.

Agentic AI models consume significantly more tokens—up to 30 times per task—offsetting some of the unit-cost gains from efficiency improvements.

Improved chip utilization, inference-optimized silicon, and edge computing are anticipated to accelerate efficiency gains across AI workloads.

A future value shift is expected toward platforms that intelligently route workloads, using smaller, domain-specific models for routine tasks and reserving frontier models for complex, high-value reasoning.

Large language models could be up to 100 times more cost-efficient than early 2022 versions, though the token-cost metric may not fully translate to lower enterprise costs when using advanced systems.