Most people use language model–based tools in a simple, one-way pattern: you open a chat, ask a question, get an answer, maybe copy a bit into a document, and move on. Knowledge flows in only one direction: from the system to the individual user.

From a distance, that is a big problem. Very few users publish or send knowledge back. Almost nobody takes what they learn and makes it available to others through the same system. The result is that knowledge does not really flow in an organization. It sits in private chats and documents, instead of being shared and reused.

If we care about knowledge, this is upside down. Knowledge should flow in all directions. It should move from system to user, from user back to the system, and between users. When that happens, the value of each answer grows, because it can be reused and improved by others instead of being consumed once and forgotten.

Today, most language model systems are built for consumption, not contribution. The tools make it easy to ask and receive, but hard to share and scale. There is usually no simple way to turn a good answer into shared knowledge, no smooth way to capture corrections or organization-specific details, and no clear path to let others benefit from what one person has already figured out.

To change this, we need systems that make it easy and natural to contribute. Adding knowledge must be almost as easy as asking for it. For example, it should be possible to save a good answer as shared knowledge with a single action, and to quickly add just enough context so others can understand and reuse it. Reusing what already exists must be simpler than starting from scratch, with search that shows both model-generated answers and user-contributed content in the same place.

When that works, every interaction can become more than a one-off answer. A conversation can turn into a reusable explanation, an internal guideline, or a small FAQ entry. Over time, this creates a layer of shared knowledge that reflects how the organization actually works, not just what the base model knows.

The goal is to share and scale knowledge, not only to consume it. We want systems that learn from their users and help knowledge circulate: in, out, and across. Instead of a one-way flow of information from model to user, we can build a two-way and many-to-many flow where each good answer has the potential to help many others.

The next time you get a useful answer from a language model, do not stop at copying it into your own document. Ask yourself: who else could use this, and how can I make it easy for them to find it? That small step is what turns a one-way knowledge system into something that truly shares and scales knowledge.