What if AI could draft an entire report, not by stringing one word after another, but by predicting a complete paragraph that fits your context—and then the next, and the next?

Imagine a writing assistant that doesn’t just autocomplete your sentences, but understands where you’re going in your narrative, anticipating the structure of your argument like a co-author who sees the bigger picture.

This is the kind of shift enabled by hierarchical token prediction: moving from word-by-word modeling to reasoning across semantic layers like sentences, sections, and themes. Instead of thinking like a typewriter, the model behaves more like a thoughtful editor.

Think of a legal assistant that not only completes legal arguments but proposes how an entire section of a contract could be structured based on the intent behind it.

Or a research copilot that suggests not just citations for a paragraph, but how to lay out an entire paper’s methodology or discussion based on early-stage inputs.

The potential here isn’t about more memory or larger models—it’s about smarter scaffolding. We’re not asking machines to remember more. We’re asking them to understand more.

Hierarchical tokens give models a framework to plan. To reason not just with words, but with purpose. This is how human authors think. And maybe it’s how future AI should too.

It’s a shift in mindset—from autocompletion to co-construction.

— Rogerio Figurelli, Senior IT Architect, CTO @ Trajecta