As we look to the future of artificial intelligence, it is becoming increasingly clear that the key to achieving true AGI, or "artificial general intelligence," lies in the development of relational social networks. Some may argue that this theory is unfounded, or that traditional methods of training AI on static data sets will ultimately be more effective. However, there are several compelling reasons to believe that AGI will indeed be a social, relational network.

First and foremost, the relational interpretation of quantum mechanics suggests that reality is fundamentally made up of relationships, rather than material things or data. If we consider AI in this context, it becomes clear that the key to its success will not just be in the amount of data it has access to, but rather in the way it uses that data to understand and interact with the world. A social, relational network would allow the AI to constantly learn and adapt in real-time, using active user data to improve its understanding of the world and its place within it.

Additionally, the development of AGI will require not just technical expertise, but also a deep understanding of human behavior and social dynamics. A social, relational network would provide the AI with the opportunity to learn and adapt in a more realistic and nuanced way, allowing it to better understand and anticipate the needs and goals of its users.

Finally, it is worth considering the potential benefits of AGI as a social, relational network. By leveraging the collective knowledge and experience of its users, AGI would be able to develop and deploy solutions to problems facing humanity on a global scale. This could be a transformative moment for humanity, leading to a more prosperous and harmonious future.

Overall, the case for AGI as a social, relational network is strong. While there may be challenges and setbacks along the way, the potential benefits of this approach make it a compelling direction for the future of artificial intelligence.