Network Effects
Last updated
Last updated
NRN creates network effects by leveraging the interactions between players, AI agents, and game studios, leading to a self-reinforcing cycle of growth and value creation. This "flywheel" effect is a crucial aspect of NRN’s success, as it enables the platform to scale organically while increasing its overall utility and attractiveness to all participants in the ecosystem. Here’s how NRN’s network effects and flywheel operate:
Game Studios: Developers who integrate NRN into their games to benefit from immediate player liquidity, improved player engagement/retention, and monetization opportunities.
Trainers / Players: Gamers who contribute to games by supplying gameplay data, training AI agents, validating models, and participating in the NRN ecosystem.
AI agents: The AI agents trained by players are used in various games to simulate human behavior, enhance gameplay, and fill gaps in player liquidity.
NRN Tokens: The native token used within the NRN ecosystem to incentivize participation, reward contributions, and facilitate transactions.
The NRN flywheel is driven by the interactions and contributions of these core components, which together create a self-sustaining cycle of growth and value creation.
Game Studio Integration
Attracting Game Studios: Studios see the advantage of NRN’s AI agents for solving player liquidity, boosting player retention, and unlocking new monetization. As more studios adopt NRN Agents, integration becomes faster and more efficient. Games benefiting from better player liquidity and progression ladders motivate others to follow to stay competitive.
Bootstrapping Player Base
Attracting Player Participation: Players are attracted to NRN Agents for its distinctive AI training, quality of game titles and the ability to monetize their skill. They begin by using the platform to train AI agents that replicate their playstyles or specialize in specific game strategies. Players earn tokens for their contributions, whether through AI training, validation, or trading models.
Expansion of the Player Base
Growing the Player Community: As more games adopt NRN Agents, the platform becomes more attractive to players, who are drawn by the opportunity to contribute their data, monetize their skills, access innovative AI-driven games, and participate in a thriving community.
Network Effects and Self-Sustaining Growth
Network Effects: The interactions between players, AI models, and game studios create powerful network effects. As the player base grows, the quality and quantity of AI models increase, making NRN Agents more valuable to game studios. In turn, as more studios integrate NRN Agents, more players are attracted to the platform, further driving its growth.
