Transferability of Token Usage Rights: A Design Space Analysis of Generative AI Services

TL;DR

This paper analyzes how token usage rights in generative AI services can be designed for transferability, enhancing user autonomy by applying a structured design space framework to existing billing policies.

Contribution

It introduces a formal framework with five design axes and five transferability types, providing a systematic analysis of token rights across major AI services.

Findings

Identified five key design axes influencing token transferability.

Classified five concrete transferability types in existing AI services.

Reframed tokens as a user-centered design element rather than just economic units.

Abstract

With the rapid spread of generative AI services, the token has gained value not only as a technical unit of language processing but also as an economic currency for accessing AI services. Major AI model providers have adopted token-based billing as their default service model, requiring users to purchase platform-bound, fixed token usage rights. However, the fixedness of these usage rights is grounded in the billing-policy decisions of service providers rather than in any technical necessity. This study defines the Transferability of token usage rights as a design property that allows users to flexibly reallocate purchased data resources free from the constraints of time, account, and service. Drawing on the Design Space Analysis framework of MacLean et al. (1991), we identify five design axes (Target, Direction, Unit, Control, Reversibility) and five concrete Transferability types (carry-over, co-management, transfer, conversion, and trade) by analyzing the billing policies and terms of service of four major LLM services (ChatGPT, Claude, Gemini, Grok). Our analysis reframes the token from a purely economic-technical primitive into a core element of user-centered system design that expands user choice and autonomy.