The newly launched platform attempts to estimate the total number of cryptanalyst-hours spent by the global research community in analyzing various public-key cryptographic algorithms. By examining roughly sixteen thousand candidate papers, the project classifies and measures cryptanalytic work focusing on the Elliptic Curve Discrete Logarithm Problem (ECDLP), structured and plain lattices, syndrome decoding, and their various comparators. It then converts this massive body of published research into estimated research-allocation years utilizing a detailed, person-level author map.
The underlying methodology, detailed in the project’s abstract, relies heavily on the academic literature itself as the primary measurement instrument. However, the creators of the study are quick to note that these publication-based estimates come with significant caveats and may substantially overstate the actual time spent on pure cryptanalysis, given that academic papers encompass a wide range of activities beyond direct attacks, including writing, administrative duties, and pedagogical efforts. Nevertheless, the accumulated data reveals intriguing shifts in the cryptographic landscape. For instance, the data indicates that research into lattices has only recently overtaken elliptic curves in this cumulative measure, a historical trajectory that the project expresses as inference budgets calculated under stated productivity assumptions.
What makes the technical execution of this study particularly noteworthy is its heavy reliance on advanced artificial intelligence models. The entire research pipeline—from conception and data processing to analysis and the final generation of the web page—was built, executed, and written by AI models, specifically Claude Fable 5.1 and Claude Opus 5.5, with supplementary contributions from GPT-6 Astra. Meanwhile, Matthew Green directed the overarching project by setting the core research questions, making the critical methodological decisions, and overseeing the entire workflow from start to finish.
Despite its innovative approach, the project inherently faces certain blind spots that are vital to understanding the full scope of cryptographic research. Because the metric relies exclusively on published academic work, anything that remains unpublished is entirely excluded from the tally. This omission creates two notable blind spots in the data. First, it leaves out academic cryptanalytic work that did not successfully result in a published paper, whether due to dead ends, incomplete findings, or the rigorous and often arbitrary nature of peer review. Second, and perhaps more importantly, it completely misses the vast universe of cryptanalysis conducted in strict secrecy by national intelligence agencies, military organizations, and advanced commercial entities that have little incentive to share their breakthroughs or methodology with the open academic community.
Even with these recognized limitations, security experts view the project as a valuable first stab at establishing a standardized metric for measuring the human capital and intellectual investment poured into evaluating public-key security assumptions. As the cryptographic community prepares for the long-term transition to post-quantum cryptography—where lattice-based algorithms and syndrome decoding are expected to play central roles—having a better understanding of how much scrutiny these mathematical assumptions have actually received in the open literature provides crucial context for assessing their long-term viability and resilience against determined attackers.
The project is now live on the web, contributing to ongoing discussions within the academic and security communities regarding research metrics, the historical evolution of public-key cryptography, and the growing role of automated AI tools in conducting complex meta-analyses of scientific literature.
Cyber Security & Cryptography
Cyber Security & Cryptography
Cyber Security & Cryptography
