When language becomes the primary operational interface,
human value no longer resides in parameter tuning,
but in narration, selection, and endorsement.
This observation predates large language models.
I first arrived at it around 2017,
at a time when LLMs did not yet exist in their current form.
What was already evident, however, was the rise of systems such as AlphaGo
and Generative Adversarial Networks (GANs).
These systems demonstrated that structure, evaluation, and meaning
could be negotiated without explicit symbolic programming.
In this sense, images were already functioning as a form of language—
not descriptive language, but operational language:
a medium through which intent, constraints, and outcomes
could be expressed, tested, and refined.
What changed with LLMs was not the emergence of this dynamic,
but its generalization and accessibility.
Language became the dominant interface not because it is more precise than parameters,
but because it allows humans to operate at the level of
intent articulation, value judgment, and responsibility assignment.
Seen this way, the human role in AI systems
was already shifting before language models made it visible.
LLMs merely made the shift legible.