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.

The current subsidized AI subscription model is unsustainable. The inevitable shift to pay-per-token pricing will expose the true cost of inference. For tasks like coding, where AI can "hallucinate" and burn tokens in loops, this creates unpredictable and potentially exorbitant costs, akin to gambling.

Howie Lu advises against anchoring AI costs to cheap software subscriptions. Instead, evaluate token costs against the opportunity cost of an equivalent human's time. A $150 agent-written board memo is cheap if it saves days of a CEO's time and produces a superior result.

Ramp's CPO argues companies shouldn't excessively worry about AI token costs. If an AI agent can deliver 10x the output of a human, it's logical and profitable to pay the agent (via tokens) more than the human's salary. This reframes ROI from a cost center to a massive productivity investment.

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.

Heavy use of AI agents and API calls is generating significant costs, with some agents costing $100,000 annually. This creates a new financial reality where companies must budget for 'tokens' per employee, potentially making the AI's cost more than the human's salary.

As AI costs rise, using one powerful frontier model for every task is no longer financially viable. The solution is to create a dedicated "Model Sommelier" role responsible for curating a portfolio of models, continuously testing and selecting the most cost-effective option for each specific business use case.

The current affordability of AI tokens is not sustainable; it's propped up by venture capital funding AI companies operating at a loss. Businesses should treat this as a temporary window for aggressive learning and experimentation before prices inevitably rise to reflect true operational costs.

Goldman's CIO predicts that while unit cost per token will decrease, the explosion in token usage from agentic systems will make total AI compute a major corporate expense. He suggests it should be compared to personnel costs, not traditional IT spending.