arXiv:2606.12647v1 Announce Type: cross Abstract: AI-augmented computing delegates natural language queries, code generation requests, and other open-ended tasks to a cluster of AI models that processes queries and generates responses. This paradigm introduces a resource dimension that neither classical time nor space complexity captures: the cost of sending queries to and receiving responses from such a cluster. We introduce token complexity, a formal resource measure defined as the minimum expected token cost to achieve a specified level of output quality on a task, and develop a taxonomy cl
The proliferation of AI-augmented computing necessitates a new framework for understanding resource consumption beyond traditional computational complexity measures.
A formal framework for 'token complexity' provides a critical metric for evaluating AI system efficiency, cost, and ultimately, economic viability and scalability.
The introduction of token complexity shifts the focus from purely time/space efficiency to the cost of interaction with AI models, impacting design, deployment, and financial models for AI-driven services.
- · AI model developers (for optimization)
- · Cloud providers (offering cost-efficient token services)
- · Enterprises leveraging AI (improved cost predictability)
- · AI researchers (new theoretical foundations)
- · Inefficient AI service providers
- · Developers ignoring token costs
- · Early adopters with opaque AI billing models
This establishes a foundational metric for comparing and optimizing AI services based on their token efficiency.
It will drive innovation in more efficient token generation and consumption, leading to lower operating costs for AI applications.
The concept of token complexity could become a standard unit of economic value calculation in AI-driven economies, influencing investment and market structures.
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Read at arXiv cs.AI