LLMs work because they speak human.
That's the real innovation. Not the size of the models or the training data, it's that they understand natural language the way we actually use it. Messy, contextual, full of exceptions.
You can tell ChatGPT "make it more professional but keep the casual tone" and it gets it. Try programming that instruction into traditional software. You'd spend months defining what "professional" means, building taxonomies for "casual," creating exception handlers for edge cases.
But here's the problem: that natural flexibility crashes into digital reality.
Digital systems are fundamentally rigid. Databases need structured fields. APIs expect specific formats. Workflows demand binary decisions. Yes or no, approved or rejected, category A or category B.
The world isn't binary. It's fluid.
When you tell an LLM "this customer is frustrated but loyal," it understands the nuance. When you try to put that same customer into your CRM, you're forced to pick: frustrated OR loyal. The system can't handle both. It can't capture the contradiction that makes the insight valuable.
This creates a translation problem that didn't exist before.
Pre-LLM, we accepted that software was clunky. We learned its language. Dropdown menus, mandatory fields, rigid categories. We bent our thinking to fit the system.
Now LLMs show us what natural interaction feels like. We can think out loud, change our minds mid-sentence, reference context from three conversations ago. The AI gets it.
But then we hit the wall. The LLM understands perfectly, but it still has to cram that understanding into the same rigid systems we've always had.
The innovation of natural language interface is real. But it exposes just how fundamentally broken our digital infrastructure is for handling the way humans actually think and work.
We're not just building better AI. We're discovering that everything else needs to be rebuilt too.