Large Language Models (LLMs) are often equated with Artificial Intelligence (AI) - but they are not the same thing.
LLMs are statistical models. They generate text by predicting the most likely next word based on patterns learned from data. That’s powerful, but it’s not “intelligence” in the human or even deterministic sense.
Some people compare LLMs to the evolution of programming abstraction layers:
- machine language became easier with assembler
- assembler became easier with C/C++
- C/C++ became easier with higher-level languages like Java or C#
Following that logic, it might seem natural to think of LLMs as the next abstraction layer for programming or thought.
But that analogy doesn’t quite hold.
The key difference is determinism.
When you move from machine code to C to Java, each layer adds abstraction - but the results remain deterministic. The same input will always produce the same output.
LLMs, on the other hand, are probabilistic. Their responses are guided by chance within the boundaries of their training. The same prompt can lead to different outputs, even under the same conditions.
That unpredictability makes them fundamentally different. Not a higher layer of abstraction - just a new kind of tool.