AI Networks

Imagine a future where everyone owns a personal AI model. That day is approaching faster than we think. Companies like NVIDIA, Meta, and DeepSeek are already laying the groundwork to make this a reality. The real questions is -- what happens when all these models become interconnected?

Throughout history, when we’ve connected groundbreaking technologies, the results have always been profound. Connecting computers gave rise to the internet; connecting phones revolutionized global communication. These advancements forever altered our social fabric. However, as we learned with social media, the effects aren’t always positive—they can straddle the line between beneficial and harmful. AI is poised to join this category of transformative technologies that demand careful consideration of both risks and rewards.

In this presentation, we’ll explore some modern frameworks that pave the way for a world where servers seamlessly connect AI models. For hobbyists and enthusiasts, frameworks like Anthropic’s recently released Model Context Protocol (MCP) offer promising possibilities. We’ll discuss its benefits, limitations, and the inherent risks in similar frameworks. We’ll also look at how businesses can adopt dynamic AI networks as a more adaptable alternative, helping them better manage the uncertainties tied to static systems.

Finally, we’ll compare these static frameworks—such as MCP, LangChain tools, and Copilot—with dynamic AI networks to understand their respective strengths and limitations. Our goal is to draw conclusions about how they might shape the future for both individuals and businesses. By examining the potential impact of these technologies, we can prepare ourselves for the exciting (or perhaps challenging) landscape that lies ahead.