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.

翻译：随着生成式AI服务的迅速普及，token不仅作为语言处理的技术单元，更作为访问AI服务的经济货币获得了价值。主流AI模型提供商已将基于token的计费作为默认服务模式，要求用户购买平台绑定的固定token使用权。然而，这些使用权的固定性源于服务提供商的计费政策决策，而非任何技术必要性。本研究将token使用权的可转移性定义为一个设计属性，允许用户灵活地重新分配已购买的数据资源，不受时间、账户和服务的约束。借鉴MacLean等人（1991年）的设计空间分析框架，我们通过分析四种主流大语言模型服务（ChatGPT、Claude、Gemini、Grok）的计费政策和服务条款，确定了五个设计轴（目标、方向、单位、控制、可逆性）和五种具体的可转移性类型（结转、共管、转移、转换和交易）。我们的分析将token从纯粹的经济-技术原语重新定义为以用户为中心的系统设计的核心元素，从而扩展用户的选择和自主权。