What Prediction Gives
A Transformer can learn rich regularities in token sequences and use attention to condition each next output on a large context. That makes it powerful for drafting, translation, code assistance, summarization, retrieval interfaces, and model-building work.
Those abilities are real. They should be described as abilities of the trained system, not as proof that the generated pattern already carries grounded meaning by itself.
Where Meaning Enters
Meaning depends on use, reference, responsibility, feedback, and correction. A generated statement becomes useful when someone can connect it to a model, a measurement, a proof, a circuit, a file, a user need, or another external constraint.
Without that connection, fluent text can look more complete than it is. The right use is to keep the mechanism attached to the result: what data shaped it, what claim it is making, and how the claim can be checked.
The Useful Role
Transformers can improve mathematical work when they help search representations, compare formulations, draft explanations, or expose missing structure. They are strongest when paired with explicit models and verification, not when treated as a substitute for grounding.