How AI Clones Work

Every AI clone is assembled from four ingredients: a corpus of the person's own data, a base foundation model, a personalization technique, and an orchestration layer that decides when each module speaks.

The corpus typically includes written work, transcripts of interviews and meetings, voice recordings of varying quality, and reference photos or video. The richer and more recent the corpus, the more faithful the clone.

Two methods dominate for the text layer. Retrieval-augmented generation (RAG) keeps the person's writings in a searchable index and lets a general model quote from them at inference time. Fine-tuning bakes the person's style directly into a copy of a base model's weights.

RAG is cheaper and easier to update, but the clone's prose only sounds personal where it can find passages to lean on. Fine-tuning produces a more consistent voice but is harder to correct when the person changes their mind. Most production clones combine both.

Voice cloning takes between 30 seconds and 30 minutes of clean audio and produces a model that can speak any text in that voice. Modern systems also model emotion, breath, and laughter, not just timbre.

For video, two approaches coexist. A 'reanimation' model puppets a still image of the person using a driver video. A full neural avatar trains a 3D representation from many minutes of footage and renders new performances from scratch. The first is faster; the second is more expressive.

The least-discussed but most important layer is orchestration: the system that decides when the clone speaks, what it remembers from previous conversations, and where it must escalate to the real person. Good clones know what they do not know.

A persistent memory store turns a stateless model into a continuous agent. It lets the clone remember that a colleague's child was sick last week, or that a long-running project is finally close to launch. Without memory, even a high-fidelity clone feels uncanny because it has no continuity.