What happens when you try to leave an AI that already knows you?

In this episode of The People’s AI, presented by the Vana Foundation, we explore a new frontier in data portability: whether you can take your AI memory, context, and chat history with you when you switch models. What begins as a technical question quickly becomes something more personal. As AI tools learn our preferences, workflows, tone, and even our emotional patterns, leaving one model for another can feel less like switching software and more like a breakup.

We look at why this issue suddenly became urgent, how recent user migration between major AI platforms exposed the limits of portability, and what is actually at stake when your chatbot has accumulated months or years of context about you. Along the way, we explore the psychological side of AI attachment, the practical cost of losing workflow memory, and the growing push for model-agnostic tools that let users keep control of their own data. We also examine what can be exported today, what still cannot, and why friction and business incentives still make true portability difficult.

Guests

• Dr. Rachel Wood — Cyberpsychology Expert, Therapist
• Chris Riley — Executive Director, Data Transfer Initiative
• Sankari “Sanks” Nair — Co-founder, Recall
• Jack Spallone — Head of Product, Open Data Labs (Vana Network)

The People’s AI is presented by the Vana Foundation, supporting a new internet rooted in data sovereignty and user ownership, where individuals, not corporations, govern their own data and share the value it creates. Learn more at Vana.org.