AI licensing has shifted from upfront training payments to dynamic grounding or RAG arrangements that price access by usage. Terminology varies across vendors and stakeholders, with grounding, content inference compute, and retrieval-augmented generation often referring to similar technical approaches. Grounding/RAG deals charge based on real-time fetching of publisher content, enabling models to answer queries about recent events beyond training cutoffs. One-time lump-sum payments have largely been replaced by recurring, per-usage fees. Training windows for many models can be months out of date, so live retrieval has become essential for up-to-date responses.
In fast-moving digital areas like AI, the terminology tends to splinter quickly. Vendors, publishers, platforms and analysts coin their own terms: for instance, "grounding," "content inference compute", and "retrieval augmented generation" (RAG) are all intertwined and refer more or less to the same thing. Those who can't be bothered with jargon of any sort simply call grounding and RAG "web search."
In a nutshell, payment terms of grounding or "RAG" deals are based on how AI systems fetch live content from publishers in real time. If a person searches for an update on some recent news like, "Show me an update on the meeting between Trump and Zelensky," which happened over the last week, AI engines won't have that stored in their training.
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