Planning for the Token Curve While Protecting the Competitive Edge
How will your AI infrastructure perform when token demand reaches billions per day?
This white paper explores how enterprise AI demand, cost, and infrastructure requirements change as organizations move from controlled pilots to full agentic adoption.
What’s Inside
Enterprise AI planning starts with the demand curve
Most organizations evaluate AI infrastructure based on the usage they see today. But pilot-stage demand offers a limited view of the capacity, cost, and control required as AI expands across departments and automated workflows.
This white paper examines how token demand may change at enterprise scale and how those changes affect infrastructure strategy. Using insurance as a worked example, it models the progression from limited AI usage to billions of tokens per day.
See how leaders can compare deployment models, identify potential economic inflection points, and prepare for growing AI demand while protecting proprietary data, model control, compliance, and long-term flexibility.
Plan for full-scale token demand
Current AI usage doesn’t show the complete demand picture. Learn how enterprise token consumption can change a organizations move from pilots to departmental rollouts and full agentic adoption.
Find the economic inflection point
Every deployment model can look affordable at pilot volumes. See how the economics of frontier APIs, hyperscaler-hosted models, and dedicated infrastructure change as token demand grows.
Compare three infrastructure models
Explore the cost, capacity, control, and compliance considerations associated with frontier model APIs, hyperscaler-hosted open models, and dedicated private infrastructure.
Protect your competitive edge
Your AI infrastructure strategy affects more than operating costs. Learn how deployment choices influence data sovereignty, model control, audibility, vendor dependence, and strategic flexibility.
The question isn’t only what it costs to support AI usage today. It’s what token demand will look like at full agentic adoption, and which infrastructure strategy will protect the organization’s cost base and competitive advantage at that scale.
What’s shaping enterprise ai infrastructure
Four considerations for planning at scale
AI infrastructure decisions made during a pilot can shape the economics and flexibility of a much larger deployment. As token demand grows, leaders need a clearer view of future capacity, workload requirements, cost, and control.
Capacity
Infrastructure planning should reflect where enterprise AI usage is going, not only where it stands today. That includes growing demand from automated workflows, agent handoffs, tool calls, retries, and continuously running systems.
Economics
Per-token pricing may remain competitive at lower volumes, but the cost comparison changes as usage grows. Modeling the demand curve helps organizations identify where a different infrastructure approach may become more economical.
Strategy
Frontier APIs, hyperscaler-hosted open models, and dedicated private infrastructure offer different advantages and tradeoffs. The right mix depends on workload complexity, demand, risk, control, and operating requirements.
Control
Proprietary data and the models trained on it can become strategic business assets. Infrastructure choices determine where those assets reside, who controls them, how activity is audited, and how easily the organization can change models or providers.