That's an enticing idea, but the devil is in the details. First you need a robust corpus: even a handful of inscriptions can be misleading if the language has irregular morphology or a complex script. You also have to consider the provenance of the texts—dating, context, and any potential scribal errors. If you skip those checks, the model might reconstruct a language that looks plausible but is actually a mosaic of misread patterns. For academia, a transparent methodology is key: show how the model infers phonology, syntax, and semantics, and provide confidence scores for each reconstruction. Museums will care about usability and interpretability: a clean interface that lets curators test hypotheses and see where the AI is uncertain would be more attractive than a black‑box. So the marketable edge is reliability, not speed. Build a pipeline that first vets and expands the corpus, then offers a user‑friendly interface with clear explanations. Only then can you promise that a handful of lines can truly bring a dead tongue back to life.