Technology firms investing heavily in artificial intelligence are facing significant challenges in determining how to price their AI-based services to customers. The core issue stems from the unpredictable nature of token consumption, the fundamental computational units that power large language models and AI agents. While the cost per individual token has declined substantially, overall usage has increased dramatically, creating uncertainty around total expenses.
Token consumption patterns are inherently difficult to forecast because subtle variations in user prompts can produce different outputs, and the use of multiple AI agents working together further complicates cost calculations. Goldman Sachs projects that external token consumption will increase 24 times from 2026 to 2030, reaching 120 quadrillion tokens monthly as organizations transition to agentic AI systems. Many companies lack clear visibility into their token usage until bills arrive or budgets are exhausted, leading to unexpected costs that can accumulate rapidly across large organizations.
Companies like Microsoft and Uber have experienced significant token expenditure surprises, prompting efforts to manage consumption more carefully. However, the non-deterministic nature of AI outputs creates a fundamental pricing dilemma: companies cannot easily predict the value they will receive from token expenditure, making it difficult to establish fixed pricing models with customers or commit to multi-year agreements.
Industry executives are exploring various pricing approaches, including flat fees, per-result billing, and bundled incident charges. However, any chosen pricing structure faces the risk of obsolescence if AI providers alter their own token pricing. Additionally, scaling AI services to thousands of end users introduces further complexity, as costs can unexpectedly escalate when companies add features like security testing or safeguards. Without settled pricing models, companies remain in uncertain territory as they attempt to commercialize AI-based offerings.
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