From Text to Video: How AI Understanding Is Evolving Beyond Words
AI is moving beyond text into video and multimodal understanding, revealing the limits of language-only models and opening richer, more grounded ways to learn.
From Text to Video: How AI Understanding Is Evolving Beyond Words
Most AI development has been built on a quiet, rarely examined assumption: language is the primary medium of intelligence. Feed a model enough text, and it will develop something that looks like understanding. GPT-4, Claude, Gemini — these are all, at their core, text-trained systems. They are extraordinarily capable. They are also built on a foundation that has a fundamental ceiling.
That ceiling is starting to become visible.
The Text Assumption
Text is how humans communicate knowledge — but it is not how we acquire it. Yann LeCun has made this argument forcefully: we learn by experiencing the world, not by reading about it. Language is a lossy compression of lived, embodied experience. It is the output of understanding, not its source.
A child does not learn what "falling" means by reading the word. She learns by dropping things, by tumbling herself, by watching objects arc through the air and hit the floor. Text models inherit the distilled residue of millions of such experiences, abstracted into language — but they miss the raw signal underneath.
What Text Cannot Capture
The gaps become clear when you probe for specifics:
- Spatial reasoning — "The cup is to the left of the plate" is trivial to say and genuinely hard to understand without spatial grounding. Text models can repeat the pattern; it's unclear they have the underlying geometry.
- Physical causality — "What happens when you knock the cup off the table" is described in text, but demonstrated in video. The motion, the shatter, the spray of liquid — that is where the physics lives.
- Procedural knowledge — A written description of how to tie a bowline knot is nearly useless compared to a thirty-second video. The hands know something the words don't.
- Emotional nuance — Tone, micro-expression, the slight pause before an answer. These are communicated through voice and face, flattened almost completely in text.
These are not edge cases. They represent entire categories of knowledge that text models handle through pattern matching rather than genuine grounding.
