Summary: Assume there are several years when AI assistants (“LLMs”) have super-human ability to understand and apply known ideas, but sub-human ability to generate genuinely new ones. In this window, research communication should shift substantially. Rather than writing papers optimized for human readers, we should expect researchers will explain ideas conversationally to an LLM, which produces a written artifact, which audience members then consume through their own LLM. This combines the adaptability of one-on-one communication with the scalability of text. The written artifact probably remains human-readable—for training stability, peer review, and keeping humans in the loop—but its conventions would drift toward an LLM-optimized style: high information density, negligible review of existing knowledge, greater length variability, non-linear organization, exhaustive referencing, and explicit uncertainty markers.
Premise
A cartoon history of communication might use audience size as a key organizing metric:
- One-to-one (e.g., conversation): The oldest format. Even today, this is generally the most efficient way to communicate an idea to a specific person because it is adaptable. The listener can ask the speaker to speed up or slow down, or to explain points they don’t understand. But it doesn’t scale; the process must be repeated for each new listener.
- One-to-few (e.g., lectures, speeches): These formats scale better, but as the audience grows, individual listeners can ask fewer clarifying questions, and speakers can less easily tailor the message to what the audience already knows. Personal tutors are substantially more effective than classroom teachers.
- One-to-many (e.g., written documents, recorded video): These formats scale nearly perfectly because copying information is ~free, but efficiency falls substantially: there is no interactive feedback. (Readers can contact the author, but this reduces to one-on-one communication and doesn’t scale.)
