Metering AI usage by tokens is becoming unmanageable for non-technical departments like marketing, sales, and HR. The complexity of tracking usage and tying it to value will likely force a market shift toward flat-fee, unlimited usage plans priced on outcomes or per-employee value instead.

As AI moves from being a simple tool to an autonomous agent, pricing models are evolving. Companies like Sierra, chaired by OpenAI's Brett Taylor, advocate for outcome-based pricing, which charges for delivered results (e.g., a completed report) rather than the underlying token consumption.

AI enables a fundamental shift in business models away from selling access (per seat) or usage (per token) towards selling results. For example, customer support AI will be priced per resolved ticket. This outcome-based model will become the standard as AI's capabilities for completing specific, measurable tasks improve.

The most logical pricing model for AI is to benchmark it against the human labor costs it displaces. While a PR challenge for legacy companies, AI-native firms will likely adopt this outcome-based model because it is more tangible for finance leaders than abstract, unpredictable credit systems.

Current AI pricing models, which pass on expensive LLM costs to users, are temporary. As LLM costs inevitably collapse and become commoditized, the winning companies will be those who have already evolved their monetization to be based on the value their product delivers.

The current model where users worry about the dollar cost of each AI-powered action is a temporary phase driven by high model costs. Descript's CEO believes the industry is moving toward outcome-based pricing, like charging per successful export, which better aligns value with cost.

Bret Taylor of Sierra argues outcome-based pricing (charging for a resolved case) is superior to usage-based pricing (charging for tokens). It aligns vendor and customer interests by tying cost directly to business value, not resource consumption. This forces the vendor to improve product effectiveness, not just optimize for usage.

To combat the unpredictable costs of token-based AI usage, Pega is adopting a value-based pricing model. Instead of charging per token, they charge based on work completed (e.g., per loan funded or service request processed), aligning costs directly with business outcomes and enabling forecasting.

OpenAI is reportedly exploring outcome-based pricing, where customers are charged only if an AI successfully completes a task. This model shifts from a commodity-like 'cost per 1000 tokens' (CPM) to a value-aligned 'cost per successful action' (CPA), better aligning incentives.