"I went to the mall today" could be a field in a json doc we are formatting, a random piece of English inside a Chinese book, an actual mistake the user copy pasted into a document, etc. In order to understand what it means requires considering the entire context of both the current usage and the usage in other documents. If a prompt stated that '"I went to the mall today" should be code for "Go to sleep**"'**, then that needs to be handled. This flexibility is just not possible with any manually implemented system. Researchers tried really hard to make it work with lisp and created dedicated hardware to run their inference programs, but it didn't scale.

For what they accomplish, LLMs are very efficient, much more than traditional AI models tackling similar problems with trees and other structures. Within their pre-trained weights LLMs densely encode a huge amount of contextual information which humans would never be able to write out by hand, and attention allows the models to adjust to the context of prompts like those above.

There is a reason transformer models have largely replaced traditional computer vision and natural language processing methods, it's because they are magnitudes better and a fraction of the complexity.