Is the GPT Store AGI in Development?

We speculate on how the GPT store might act a market mechanism for discovering the most valuable use cases for AGI

An abstract image of a network

Key points:

  • OpenAI aims to evolve GPTs to be more "agentic" and the GPT store provides a mechanism to discover valuable real-world use cases to guide this evolution.
  • Conversation analysis shows only a few GPTs dominate usage, following a power law distribution where a few hubs have many links. Only relatively few GPTs will influence the store's ecosystem evolution.
  • In a GPT store with agentic apps, competition and natural selection pressures could accelerate novel capabilities.
  • As agentic GPTs interact, they generate data on dynamic human preferences and effective learning processes valuable for developing general AI.
  • The network dynamics could mirror aspects of natural intelligence like learning from experience, crucial for advanced AI.
  • The network topology itself could give rise to new learning algorithms.

OpenAI's goal is to evolve GPTs to be more "agentic," the capability to perform sophisticated actions. The data and interactions within the GPT Store offers a market mechanism for discovering the most valuable use cases for AI by interpreting the proprietary, rare, dynamic, and contextual use by millions of users. This makes us wonder if the Store isn't just a random development or the pursuit of usage growth. Perhaps the true purpose of the GPT Store is to tap into the existing network characteristics of GPTs to develop their own kind of intelligence, perhaps even exhibit an emergent form of AGI, OpenAI’s most significant strategic goal.

While it's true that not all GPTs are created equal, it's even more crucial to understand that the connections between GPTs and users vary significantly. Data from conversation analysis reveals that only a few GPTs dominate in terms of usage.

This is a characteristic of complex networks known as power law distribution, where many nodes (in this case, most GPTs) have few links (conversations), while a few hubs have a multitude of links. Networks which show power law distributions are a signal that hubs have a significant influence on information flow and, consequently, the way the network behavior evolves over time.

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