AI labs need to start funding historical research

Using LLMs to trace alchemical knowledge and decode 17th century letters

The author tested GPT-6 Sol and Opus 5.5 on two historical problems: decrypting a 1941 Enigma message—where the model independently located supplementary records from the German Federal Archives—and tracing a Latin alchemical passage by Isaac Newton back to a previously unidentified French source. He argues frontier models can now deliver verifiable results on codebreaking, cross-language text tracing, and linking findings across niche subfields, a leap from last year's assistant-level performance. The post does not specify a collaboration framework or funding figures, but points to digitized, falsifiable historical problems as the sweet spot.

Why it matters: The author demonstrates frontier models' real capability in codebreaking and cross-lingual text tracing with two verifiable cases. But the topic is academic history, which limits resonance with AI industry readers, so the score sits right at the featured threshold.