Contrary to expectations of falling AI costs, the move from simple chatbots to complex, multi-step agentic systems is causing an explosion in token usage. A single user can trigger hundreds of agents, making expensive frontier models economically unsustainable for many application-layer companies.

AI agent spending won't be confined to limited IT budgets. Instead, it will draw from massive line-of-business operating budgets (OpEx), pitched as augmenting core workflows. This shift could realistically double enterprise technology spend.

As AI agents reduce the number of human "seats" required to use software, vendors are accelerating their move from seat-based licenses to usage-based models. The revenue lost from fewer users is expected to be offset by higher consumption, as automated workflows interact with platforms far more intensively than human employees.

Historically, labor costs dwarfed software spending. As AI automates tasks, software budgets will balloon, turning into a primary corporate expense. This forces CFOs to scrutinize software ROI with the same rigor they once applied only to their workforce.

The explosive AI revenue growth stems from corporations re-categorizing the spending. It's no longer a line item in a constrained IT budget but a strategic investment in labor augmentation and replacement. This unlocks a vastly larger pool of capital from operational budgets, fueling hypergrowth.

As AI agents become the primary "users" of sophisticated software, the traditional per-seat licensing model becomes obsolete. Pricing will inevitably shift to a value-based model, tied to outcomes the AI delivers—such as cycle reduction or performance gains—rather than human operators.

A massive budget shift is underway where companies spend exponentially more on AI agents than on foundational software like CRM. One small team spends $500k annually on AI agents versus just $10k on Salesforce, signaling a tectonic shift in software value and spending priorities.

The move away from seat-based licenses to consumption models for AI tools creates a new operational burden. Companies must now build governance models and teams to track usage at an individual employee level—like 'Bob in accounting'—to control unpredictable costs.

The move from pre-agentic to agentic AI workloads consumes massive resources. This has ended the 'AI subsidy era,' forcing companies like Walmart and Uber to implement usage-based models and strict caps on AI spending to control runaway costs and enforce discipline.