Observed Signal · Oct 11, 2026 · Technical Release · Source: t3n · Impact: 1/5 · Sentiment: Neutral
AI Cracks Napoleon's Secret Letter After 217 Years
OpenAI's GPT-6 Astra model has deciphered a 217-year-old letter from Napoleon Bonaparte to officer Auguste de Marmont, written in 1809. The encryption, previously undeciphered due to a lost key, was cracked by developer Carter Church of SentinelOne using simulated annealing. Church, with no cryptography background, trained the AI on French literature and Marmont's letters. After six hours of computation, the AI decoded the remaining symbols, revealing troop positions including 40,000 Bavarian soldiers near Munich or Passau and an elite corps near Augsburg. Human cryptographers had previously decoded 33 of the 155 unique symbols, but the AI completed the task, discovering that some symbols represented whole words. Church was inspired by developer Carter Leffen, who used the same model to crack an Enigma-encrypted message.
This is a niche application of AI to historical cryptography, demonstrating AI's capabilities but not significantly impacting the AdTech/MarTech industry.
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Key Takeaways & Evidence Grounding
- GPT-6 Astra by OpenAI deciphered a 217-year-old letter from Napoleon Bonaparte to Auguste de Marmont.
- Carter Church of SentinelOne used simulated annealing to crack the code, with no prior cryptography background.
- The AI was trained on French literature and Marmont's letters, running for six hours to solve the cipher.
- The letter revealed troop positions, including 40,000 Bavarian soldiers near Munich or Passau and an elite corps near Augsburg.
- Church was inspired by developer Carter Leffen, who had used GPT-6 Astra to crack an Enigma-encrypted message.
Connected Companies & Entities
2 Entities mapped“GPT-6 Astra by OpenAI was used to decipher the letter.”
“Carter Church is an AI engineer at SentinelOne.”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
AI Models Crack Unsolved Enigma Messages
Two cryptanalysts used AI models from OpenAI and Anthropic to crack two long-unsolved Enigma machine messages that had resisted decryption for years. Carter Leffen employed OpenAI's GPT-6 Astra to analyze a database of Enigma messages, conducting archival research, building a simulator, and recovering the plaintext of a message unsolved since 2005. Frode Weirerud, who maintains the Crypto Cellar website, validated Leffen's solution. Another cryptanalyst, Jack Willis, used Anthropic's Claude Opus 5 to break a different message. Weirerud noted that only seven unbroken Enigma messages remain, and AI's success in this domain demonstrates its growing capabilities in complex research and problem-solving tasks, which could have implications for automated analysis and data processing in various industries.
GPT-6 Astra Cracks WWI ADFGVX Cipher and WWII Enigma
A developer named 'Prinz' claims OpenAI's GPT-6 Astra AI model has decrypted a German military radio message from November 1918, which had been encrypted using the ADFGVX cipher and previously considered unreadable. The model reportedly solved it by trying the keyword 'Truppenverschiebung', which was previously thought to be used only from December 1918. The decrypted message reports the arrival of an English cruiser at Sevastopol, which was cross-referenced with historical logs. However, the scientific community has not yet independently verified this solution. Additionally, another developer, Carter Leffen, used GPT-6 Astra to decrypt a German Enigma message from July 1941. With a known crib, the model took ten hours and 14.8 million key checks, and this solution has been independently confirmed by the Cryptocellar archive.
OpenAI's AI Agents Crack Major Math Problem in 88 Hours
In early September 2026, OpenAI deployed a swarm of AI agents using an unreleased, more powerful model than GPT-6 Astra to tackle the Millennium Prize Problems. Within 88 hours, roughly 100 agents produced a proposed proof for a related Navier-Stokes problem, later verified using the Lean proof assistant. This was triggered by a rumor about a rival lab's progress, which turned out to be a misattribution of work by NYU professor Tristan Buckmaster and Anthropic employee Levent Alpöge. OpenAI researcher Noam Brown said the effort cost millions of dollars and predicted similar capabilities would become accessible within a year. The article argues this event signals a shift in scientific research toward resource allocation and AI agent supervision, raising questions about the future of work and concentration of power in leading AI labs.
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