I’ve written previously about the pitfalls and use cases for AI in augmenting historical research, but things have changed significantly since 2024-25. Occasioned by the dueling releases of GPT-6 Sol and Opus 5.5 this week, I thought I’d share some early results with using these models not just to perform “research assistant” type functions like transcribing documents, but to try to actually solve existing historical problems. The TLDR is that pairing historians working in collaborative groups with the current frontier models would, in my view, produce numerous advances in historical knowledge and interpretation. My guess is that many of these could end up being quite meaningful. This was not the case as recently as last year. I think AI labs, historical researchers, and funding agencies should start actively pursuing these collaborations.

As we’ve seen with the field of mathematics, these models do best when they have a set of problems that LLMs invariably tend to describe as “tractable.” In other words: Have experts in the field already identified a group of problems that need solving? Is the data needed to answer these problems fully digitized and accessible? Do the problems lend themselves to the “spiky” capabilities of frontier AI models — namely multilingual reasoning, advanced math, and/or ability to conduct autonomous research through large datasets or across disciplinary subfields? Are they amenable to solutions that involve writing bespoke code? Most importantly: can a potential solution be clearly proven or disproven? (This last one, it seems to me, is a key part of why reasoning models have run rampant in mathematics but not in humanistic fields).

The above factors mean that the types of historical “open problems” which frontier AI can reasonably be expected to help with are fairly constrained: Anything involving cryptography and codebreaking (For instance, see Astra decrypting a 1941 German army communication and a WWI German radio cipher, or the work that Daniel Bourdeau has been doing here, or my own attempt to use GPT-6 Astra to figure out what is going on with the Elizabethan occultist John Dee’s coded magical book, Liber Loagaeth). Tracing texts across translations and adaptations. As an example of this, I was able to use GPT-6 Astra to determine the identity of a passage that Isaac Newton had freely translated into Latin from a French alchemical text, an identification that seems to have not previously been made. Drawing links between existing findings that are reported only in discrete or niche subfields, or are not yet integrated into scholarship. This last one might end up being the most impactful new method that these tools open up for historical researchers.

For instance, if you read the writeup of Astra breaking a July 10, 1941 Enigma message that had resisted decipherment, it turns out that the key breakthrough was not anything to do with the codebreaking itself, but with noticing the full range of information that was available. Historical cryptological researcher Frode Weierud writes: We are still analysing the GPT–6 Astra logs to see exactly how it executed the break. And we are discovering amazing details. In July 2026, I made the following announcement on the webpage with the 1941 Message List: Note: In July 2026, research in the German Bundesarchiv revealed several collections of radio messages, both enciphered and in cleartext. One of these message collections was from SS-Totenkopf Division’s logistics command, Nachschubführer. Many of these messages were sent to the Ib (Quartiermeister) radio station and are identical to those in this list. Others are new, but most likely related. These new messages are added to the 1941 Message List in bold, with the indicator NF (Nachschubführer) after the message number, indicating that these message numbers belong to the NF numbering. All NF messages are outgoing; hence, the message numbers are in blue. It appears that GPT–6 Astra discovered this note.