GPUs are extremely useful where ambiguity is high: human language, semantic interpretation, extraction of meaning from unstructured text, multimodal understanding, contextualization. That is the expensive phase. The probabilistic phase. The phase where the machine helps us translate human expression into structured semantic content.
But once that semantic layer has been stabilized, validated, and converted into formal knowledge, the game changes. At that point, you no longer need a GPU to "understand" the same thing again and again.
You need: explicit structures, ontologies, typed relations, rules, constraints, deterministic checks, symbolic inference. And that is mostly CPU territory.
So the real architecture is not "LLM everywhere forever." It is:
GPU for semantic acquisition. CPU for persistent reasoning and operational inference.
In other words: the model interprets, but the system remembers.
The model translates ambiguity into structure. Once knowledge is formalized, every future inference becomes cheaper, more traceable, more governable, and less dependent on continuous high-cost probabilistic computation.
This is where AI stops being only a generator of answers and starts becoming infrastructure. Operationalized knowledge.