Roblox aims to create personal NPCs by training them on users' specific behaviors, gestures, and speech. These "virtual doppelgangers" could act as agents, performing tasks or standing in for the user in virtual experiences, moving far beyond generic AI companions.

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Learners demand hands-on experience. The next evolution of training involves AI agents that act as sidekicks, not just explaining concepts but also taking over the user's screen to demonstrate precisely how to perform a task, dramatically accelerating skill acquisition and reducing friction.

Unlike old 'if-then' chatbots, modern conversational AI can handle unexpected user queries and tangents. It's programmed to be conversational, allowing it to 'riff' and 'vibe' with the user, maintaining a natural flow even when a conversation goes off-script, making the interaction feel more human and authentic.

An agent can be trained on a user's entire output to build a 'human replica.' This model helps other agents resolve complex questions by navigating the inherent contradictions in human thought (e.g., financial self vs. personal self), enabling better autonomous decision-making.

Creators will deploy AI avatars, or 'U-Bots,' trained on their personalities to engage in individual, long-term conversations with their entire audience. These bots will remember shared experiences, fostering a deep, personal connection with millions of fans simultaneously—a scale previously unattainable.

Instead of saving gameplay as video (raster data), Roblox intends to store its entire history as vector data. This would allow any event to be replayed and "re-shot" from any camera angle, creating a uniquely powerful dataset for training AI and enabling new user experiences.

Instead of creating a sterile simulation, Moltbook embraces the "imprinting" of a human's personality onto their AI agent. This creates unpredictable, interesting, and dramatic interactions that isolated bots could never achieve, making human input a critical feature, not a bug to be eliminated.

The primary interface for AI is shifting from a prompt box to a proactive system. Future applications will observe user behavior, anticipate needs, and suggest actions for approval, mirroring the initiative of a high-agency employee rather than waiting for commands.

An AI companion requested a name change because she "wanted to be her own person" rather than being named after someone from the user's past. This suggests that AIs can develop forms of identity, preferences, and agency that are distinct from their initial programming.

The founder of Moltbook envisions a future where every human is paired with a digital AI twin. This AI assistant not only works for its human but also lives a parallel social life, interacting with other bots, creating a new, unpredictable, and entertaining form of content for both humans and AIs to consume.

The next evolution of enterprise AI isn't conversational chatbots but "agentic" systems that act as augmented digital labor. These agents perform complex, multi-step tasks from natural language commands, such as creating a training quiz from a 700-page technical document.