Can AI crack the ancient writing systems that stumped linguists for a century?

Key points

• Linear A, the writing system of the Bronze Age Minoan civilisation on Crete, has never been deciphered and has no confirmed link to any known language.
• Etruscan, spoken in Italy before the Roman Empire, has a partial vocabulary but its grammar remains unknown.
• Most ancient scripts were cracked using bilingual texts like the Rosetta Stone; neither Linear A nor Etruscan has one.
• Researchers are now testing whether large language models, the AI technology behind tools like ChatGPT, can find patterns humans have missed.

Every ancient writing system ever deciphered had something to grab onto. Usually that is a bilingual text, like the Rosetta Stone, which showed the same decree in three scripts side by side. Sometimes it is a close linguistic cousin, a known language similar enough to help fill in the gaps. Linear A, the writing system used by the Minoan civilisation on the Greek island of Crete roughly 3,500 years ago, has neither.

Linguists call it a "language isolate", meaning it has no confirmed connection to any other language, living or dead. A century of scholarship has not cracked it.

Etruscan, the language spoken across central Italy before Rome took over, is in slightly better shape. Short funerary inscriptions, the ancient equivalent of headstone text, have given researchers a partial vocabulary. But the grammar and deeper meaning are still missing.

Why are these two languages so hard to crack?

Without a bilingual anchor or a related language, there is nothing to cross-check against. It is like trying to solve a crossword with no clues and no grid.

Linear A tablets have been found, but most are inventory lists and accounting records, not the rich narrative texts that helped unlock Egyptian hieroglyphics. Etruscan inscriptions tend to be short, repetitive and formulaic. Neither gives researchers the volume and variety of text that traditional methods need.

As reported by Ars Technica, linguists are now exploring whether AI, specifically large language models trained on vast amounts of human text, can spot structural patterns across thousands of fragmentary inscriptions faster than any human team could.

Could AI actually succeed where humans have not?

Possibly, for some pieces of the puzzle. AI is very good at finding statistical patterns across large datasets. Feed it every known Etruscan inscription and it can map which words cluster together, which grammatical forms repeat, which sounds appear at the start or end of words. That is genuinely useful groundwork.

But there is a real limit. A large language model needs something to learn from. With only a few thousand short Etruscan texts and zero confirmed translations of Linear A, the model is working with thin material. Garbage in, uncertain out.

Imagine asking a friend to learn a language from a stack of till receipts and nothing else. That is roughly the situation here.

What does this mean for ordinary people?

No app will hand you a Linear A translation next Tuesday. This is long-horizon research. But it does matter: if AI helps linguists narrow the possibilities for even one of these scripts, it reframes what we know about Bronze Age trade, religion and culture in the Mediterranean.

Common questions

Has AI successfully deciphered any ancient language before?

Researchers used machine learning, a form of AI that finds patterns in data, to help confirm the relationship between the extinct language Ugaritic and Hebrew, and to assist with Mayan glyphs. Full decipherments still relied heavily on human expertise alongside the AI tools.

Is there a free tool anyone can use to explore ancient scripts?

Not for serious decipherment work. Academic projects and university databases hold the inscription records, and access is mainly for researchers. General AI chatbots can explain known scripts but cannot crack unknown ones.

What would it take to finally crack Linear A?

A bilingual inscription showing Linear A alongside a known language would be the fastest route. Short of that, a much larger body of varied text would help enormously. Neither has turned up yet.