ChatGPT is NOT a LLM – GPT is

Summary

The AI industry has a terminology problem that’s creating fundamental misunderstandings about what we’re actually building and using. When people say “ChatGPT” and “Large Language Model” interchangeably, they’re conflating two fundamentally different concepts - and this confusion is shaping everything from product development to user expectations to investment decisions. This isn’t just semantic pedantry. The distinction between LLMs and AI agents represents one of the most significant architectural shifts in computing since the transition from procedural to object-oriented programming. Understanding this difference is crucial for anyone building, investing in, or simply trying to make sense of the current AI landscape. The Genesis: From Interface to Intelligence Originally, ChatGPT started off as exactly what its name suggested - a chat interface to GPT. It was essentially a thin wrapper around OpenAI’s language model, providing an easier way to send inputs to the GPT LLM and receive responses. Think of it as a more user-friendly version of the OpenAI API playground. But over the years, ChatGPT has evolved into something fundamentally different. What began as a simple interface has transformed into a sophisticated agent system that happens to use LLMs as one component of its cognitive architecture. This evolution mirrors a broader industry trend toward agentic AI systems. The Fundamental Nature of LLMs: Beautiful Limitations To understand why this distinction matters, we need to grasp what LLMs actually are at their core. Large Language Models are essentially very sophisticated pattern matching systems trained on vast amounts of text data. They’re like incredibly well-read librarians with perfect recall but no ability to learn new information after their training. An LLM’s knowledge is frozen at its training cutoff. It doesn’t learn, evolve, or update its understanding based on new interactions. LLMs are fundamentally stateless functions - they receive context and ...