Prompt Caching Is Subsidizing Bad AI Architecture
Blog post from Paper Compute Company
The text explores the complexities and inefficiencies in AI workflows, emphasizing the role of prompt caching in reducing costs while potentially obscuring architectural waste. By analyzing nineteen days of Claude Code session data, the author discovered that prompt caching saved 82% of input costs but also masked inefficiencies such as ever-growing prompts, misaligned context blocks, and divergent sub-agent behaviors. Traditional telemetry methods, like logs and billing dashboards, fail to capture the nuanced behaviors of these AI systems, which operate as stateful, branching entities. The author highlights the need for a new form of telemetry focused on session structure and prompt lineage to truly understand and optimize workflow efficiency. This approach can reveal underlying patterns, such as prompt accretion and context dumps, allowing for more informed decisions about maintaining economic and architectural efficiency in AI operations, particularly before potential changes in pricing or system scale disrupt current efficiencies.
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