Matthew Green uses AI to mine 16,000 papers and quantify global cryptanalytic effort
matthew_d_green · x · 2026-10-13
Cryptographer Matthew Green released "Measuring Cryptanalytic Effort", an AI-conducted study that uses the literature itself to estimate how much public cryptanalysis each public-key assumption has actually received.
Key findings:
- From 16,000 candidate papers, the study classified cryptanalytic work on ECDLP, structured/plain lattices (MLWE/LWE), and syndrome decoding, converting publications into estimated research-allocation years via an author-level map (3,240 distinct authors, 2,785 corpus works)
- Lattices only recently overtook elliptic curves cumulatively (1.7x)
- P-256 still has a 269x gap beyond the largest public solve
AI's role: Claude Fable 5.1 and Claude Opus 5.5 built the pipeline, ran the analysis, and wrote the page, with contributions from GPT-6 Astra; Green set the questions, made key methodological calls, and oversaw the work.
Per the attached screenshot: the site includes a "budget explorer" converting research effort into token costs — e.g., lattices' $25.9M budget equals 2.59 trillion reference tokens at $10/M.
It's a working draft with an open GitHub repo for corrections.
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