The Hidden Taxes of Prompt-Only AI

In the eighth installment of his series on building an AI memory stack, Ken Walger explores the architectural costs associated with relying solely on large prompts for AI context. Walger argues that treating context windows as infinite memory leads to 'architectural taxes'—predictable costs in latency, compute, and attention. He identifies five key taxes: Prose, Context, Retrieval, Observer, and Ingestion. Each represents a trade-off in how information is stored, moved, and validated. Instead of simply expanding prompts, the author advocates for a more disciplined approach to memory infrastructure, emphasizing that storage and communication are distinct problems. By optimizing for structured, verified data rather than raw prompt volume, developers can reduce unnecessary overhead. Ultimately, the goal is not to eliminate all costs, but to ensure that resources are spent on the right tasks, such as integrity and explainability, rather than inefficiently managing noise within the context window.