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Everyone assumes ChatGPT’s memory is some sophisticated RAG system with vector databases and semantic search. Manthan reverse engineered it. The actual architecture is almost disappointingly simple: session metadata that expires, explicit facts stored as text, lightweight chat summaries, and a sliding window. No embeddings. No similarity search. No retrieval at scale. The interesting part? This explains why it feels so fast. Traditional RAG systems embed every message, run similarity searches on each query, pull full contexts. ChatGPT just injects pre-computed summaries directly. They’re trading detailed historical context for latency. This is the same lesson that keeps emerging across AI infrastructure: when you control the entire stack, curated simplicity often outperforms sophisticated complexity. OpenAI doesn’t need to build a general retrieval system. They just need one that works for ChatGPT. The four-layer architecture (session metadata → stored facts → conversation summaries → sliding window) is basically a handcrafted memory hierarchy. Each layer has different persistence and different purposes. Session metadata adapts in real-time. Facts persist forever. Summaries provide continuity. The window maintains coherence. Anthropic’s memory system uses a similar pattern. The models that feel most personal aren’t the ones with the most sophisticated retrieval. They’re the ones that store the right things and inject them at the right time.