Why we're rethinking cache for the AI era

This post introduces the challenge of AI traffic impacting CDN cache performance due to high unique URL ratios, content diversity, and inefficient crawling patterns. It details how AI crawlers, particularly for training, lead to increased cache miss rates and churn of popular content. The post proposes directions for adapting CDN cache architectures to accommodate AI traffic, suggesting tiered caching strategies based on AI workload latency tolerance (e.g., higher latency for training data collection, lower latency for RAG). It highlights the limitations of traditional LRU cache eviction and pre-fetching strategies in the face of AI crawler behavior.