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first_img Claude of Anthropic cracked the 25-year puzzle of NSA's elliptic curve

Anthropic's AI model Claude cracked a 25-year-old puzzle in the field of cryptography in less than 30 minutes, tracing a set of numbers provided by the National Security Agency (NSA) to the U.S. government in the late 1990s back to a common random number generator that used timestamps from 1997 and 1998. These numbers have long been suspected of hiding a backdoor.In 2000, the National Institute of Standards and Technology (NIST) adopted a set of elliptic curves provided by the NSA, but the NSA never explained where the partial numbers (seeds) used to generate these curves came from. After Edward Snowden disclosed that the NSA had attempted to undermine cryptographic standards, suspicions about its hidden backdoors intensified. Cryptographers Neal Koblitz and Alfred Menezes discussed this controversy in a 2015 paper, and security researcher Steve Weis spent years searching for answers. In 2023, Filippo Valsorda and others even set up a bounty for this.Claude traced the five NIST curve seeds back to the random number generator lrand48 in an old C software library and reproduced the widely used P-256 curve sequence, finding an exact match with the published seeds among the 1842 candidates. Anthropic stated that this significantly reduces the possibility of hidden weaknesses, and users of NIST curves do not need to make any changes. Bitcoin is unaffected, as its wallets use the simpler secp256k1 curve, which has almost no room for secretly implanted weaknesses. The bounty of $36,864 will be donated to the nonprofit organization Internet Security Research Group behind Let's Encrypt.

Vitalik compared the mining costs of BTC and the inference costs of AI: the former has electricity costs accounting for nearly half, while the latter relies more on capital expenditures for computing power

Ethereum co-founder Vitalik Buterin shared that the cost of Bitcoin mining is roughly half derived from capital expenditures on computing power equipment and the other half from electricity consumption; in contrast, the cost of AI inference is almost entirely dominated by capital expenditures on computing power. This difference reflects the distinct cost structures of the two computing businesses. Bitcoin miners need to continuously pay for electricity and determine their profit margins based on mining machine efficiency, coin prices, and network difficulty changes; AI inference, on the other hand, relies more on investments in expensive hardware like GPUs, as well as equipment utilization and computing efficiency.The industrial boundaries of both are also changing. As the demand for AI computing power grows, some Bitcoin mining companies are beginning to shift their existing electricity resources and data center facilities towards AI and high-performance computing businesses, hoping to transition from pure mining to computing infrastructure services. Some mining companies are leveraging previously allocated electricity capacity for mining to enter the AI data center market. However, transformation does not mean that cost pressures disappear: mining operations are more directly exposed to fluctuations in electricity prices and Bitcoin prices, while AI computing businesses must bear risks related to GPU procurement, depreciation, and underutilization. Both types of businesses compete for electricity and computing resources, but ultimately, profitability depends on how these resources are converted into revenue.
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