Persistent AI memory should follow you across tools, not lock you into a single chat product. The practical approach is a separate memory layer that stores what matters and delivers it into Claude, ChatGPT, Cursor, and agents through standard interfaces such as MCP.
People do not use one AI. They move between an IDE assistant, a long-form chat, a browser agent, and whatever shipped inside their company last quarter. Context should not reset at each doorway.
The portability problem
Product-native memory is convenient until you switch tools. Then your preferences, project state, and decision history are trapped behind someone else’s retention policy and UI. That is a fragile foundation for serious work.
A better shape
Keep memory as its own layer. Compact what matters. Reason over it locally where you need control. Deliver it into the hosts you already use. When a new client appears, you connect the layer. You do not rebuild your autobiography.
That is the Archilas bet in one sentence: a persistent memory layer for AI that reasons over your history, stays grounded, and meets you in the tools you already chose.