Written by Anik Bose, General Partner at BGV
Many enterprises are currently enjoying what feels like an affordable GenAI revolution. For a fixed monthly fee per user, employees can experiment freely with AI assistants, generate content, summarize documents, write code, and automate workflows. The economics appear predictable and manageable.
That illusion is about to end.
As organizations move beyond experimentation and begin embedding GenAI into core business processes, the pricing model will increasingly shift from simple seat-based subscriptions to usage-based consumption tied to tokens, model calls, and inference workloads. The result could be a 10-20X increase in AI-related spending for many enterprises.
This should sound familiar.
In the early days of cloud computing, organizations migrated workloads from on-premise infrastructure to seemingly inexpensive cloud services. Initial costs appeared modest, often lower than maintaining physical data centers. But as adoption accelerated, workloads multiplied, storage expanded, and application usage exploded. Enterprises soon faced unexpected cloud bills that dwarfed their original projections. “Cloud shock” became a common experience for CIOs and CFOs alike.
GenAI is following a remarkably similar trajectory.
Today’s fixed-price AI subscriptions encourage broad experimentation. Employees use AI casually, departments run pilots, and costs remain relatively predictable. However, production-scale AI is fundamentally different. Customer service agents generating responses, sales teams running account research, developers using coding copilots, and business applications invoking large language models thousands of times per day all create enormous token consumption.
The challenge is that AI demand is highly elastic. The more useful GenAI becomes, the more employees and applications rely on it. A workflow that generates a few thousand tokens today may consume millions tomorrow when deployed across an enterprise. Advanced reasoning models, agentic workflows, multimodal capabilities, and always-on AI assistants further amplify consumption—and costs.
In fact, the warning signs are already emerging inside the very companies leading the AI revolution.
Over the past year, a new term has entered the enterprise AI lexicon: “tokenmaxxing”—the practice of maximizing AI usage and token consumption, often without corresponding business value. Amazon reportedly shut down an internal AI usage leaderboard after employees began competing to increase token consumption rather than focusing on customer outcomes. Microsoft CEO Satya Nadella has publicly acknowledged the phenomenon, emphasizing that organizations must match the right model to the right task rather than defaulting to the most expensive reasoning models.
The message from AI leaders is becoming clear: more AI usage does not automatically translate into more business value.
This realization is creating an entirely new software category: AI FinOps.
Just as CloudHealth, Apptio, and other FinOps platforms emerged during the cloud era to help enterprises manage runaway infrastructure spending, a new generation of startups is helping organizations gain visibility and control over AI consumption. Companies such as Pay-i provide detailed tracking of token usage, model utilization, cost attribution, and business-unit-level spending across AI applications and workflows. Their platforms help enterprises understand not only how much they are spending on AI, but whether that spending is generating measurable business outcomes.
Many organizations have not yet developed the governance, observability, and financial management disciplines needed to operate AI at scale. They are still budgeting for seats when they should be budgeting for compute consumption. Without visibility into which agents, applications, teams, and models are driving costs, enterprises risk repeating the mistakes of the early cloud era.
The enterprises that thrive in the GenAI era will be the ones that learn from the cloud era. Just as cloud spending required new disciplines around monitoring, optimization, chargebacks, forecasting, and accountability, AI spending will demand the same rigor. Organizations that establish AI governance and cost management early will be able to scale GenAI confidently, while those that ignore consumption economics may find themselves facing the next great enterprise technology bill shock.
The coming GenAI pricing shock is not a question of if—it is a question of when.
