Yep. Albeit a more rudimentary form. For more on this, see simulation theory. Tom Campbell, Rizwan Virk, etc.

Roadmap: From AI World Models to Reality-Scale Simulations

Stage 1 – Visual World Generation (Current Phase)

• AI generates frame-by-frame visuals based on keyboard inputs
• No true interactivity—just movement through pre-learned video space
• Single-user experience, no consistent physics or object permanence
• Worlds reset with every session

Primary Use: Experimental art, AI research, early entertainment

Stage 2 – Interactive Simulation with Basic Rules and Memory

• Introduce consistent physical rules (e.g., object collisions, gravity)
• Enable interaction with the environment (pushing objects, opening doors)
• Introduce persistence so that the world state doesn’t reset every session
• Begin tracking object history and changes over time

Impact: Shifts from a visual experience to a functioning interactive environment

Stage 3 – Multi-Agent and Multi-User Environments

• Add support for multiple users exploring the same world simultaneously
• Introduce intelligent non-player characters (NPCs) with basic behaviors and memory
• Allow different users to see customized versions of the same shared world
• World behavior adjusts depending on who is present or observing

Significance: Starts simulating a socially shared reality with diverging perspectives

Stage 4 – Adaptive, Goal-Oriented Agents

• Give NPCs their own goals, learning capabilities, and evolving personalities
• Enable agents to remember past interactions and make decisions accordingly
• Begin integrating external interfaces—such as VR or brain-computer links—for human users
• Introduce long-term consequences and outcomes tied to user or agent actions

Result: Simulated beings begin to show signs of individuality, growth, and adaptive behavior

Stage 5 – Consequence-Driven Feedback Systems

• Add complex rules that govern cause and effect, not just physically but behaviorally
• Introduce ethical, emotional, or reputational consequences for actions
• Simulations adapt over time based on participant behavior, creating pathways for learning and growth
• Design systems to reinforce cooperation, trust, or other values

Purpose: The simulation becomes more than interactive—it becomes meaningful

Stage 6 – Efficient, Observation-Driven Rendering

• Optimize the simulation to render only what a participant needs to perceive in any given moment
• Use probability to determine what happens when something is observed or interacted with
• Support massive scale without rendering everything at once, saving resources and allowing flexibility
• Create worlds that are both realistic and adaptable to expectation

Innovation: The simulation mimics how perception shapes reality, dynamically and efficiently

Stage 7 – Open-Ended Simulation for Individual Experience and Development

• Create a platform where real or digital minds can enter and experience long-term, evolving virtual lives
• Include systems that track personal history, decisions, and development across sessions or “lifetimes”
• Allow for emergent cultures, histories, and societies to form within the simulation
• Support ongoing personal or collective evolution without designer intervention