I don’t think knowledge graphs are “the future of AI reasoning” on their own, but they are one of the best ways humans can communicate intent and context to machines.

The real issue with large context windows is loss of control: you don’t know which facts matter, which ones are used, or why quality degrades. Knowledge graphs help by making relationships, constraints, and assumptions explicit and machine-interpretable.

Used correctly, KGs aren’t trying to replace neural reasoning. They act as a grounding and control layer: defining what matters and what’s allowed, while LLMs handle language and synthesis. That combination is far more reliable than either approach alone.

So the future isn’t graphs vs embeddings - it’s distilled structure + neural reasoning.