2 "Essays and Perspectives" Posts

Hash Collisions: Why Your 'Unique' Fingerprints Aren't (And Why That's Usually OK)

In 2017, Google researchers generated two different PDF files with identical SHA-1 hashes, finally proving what cryptographers had warned about for years: hash functions don’t create truly unique fingerprints ( Stevens et al., 2017 ). This “SHAttered” attack required 9 quintillion SHA-1 computations, which is the equivalent to 6,500 years of single-CPU computation. The attack cost approximately $45,000 in cloud computing resources, making it accessible to well-funded adversaries but not casual attackers.

Yet despite this proof, we still trust hash functions for everything from Git commits to blockchain transactions to password storage. The reason is simple: while collisions are mathematically inevitable, meaningful collisions remain virtually impossible. The full story of hash collisions is more nuanced than “unique” versus “not unique.”


“In cryptography, ‘secure’ has always meant ‘secure for now’.”


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From Solow to ChatGPT: Why Total Factor Productivity Can't Keep Up With Generative AI

If ChatGPT can write code, summarize legal briefs, and help draft business strategies in seconds, why doesn’t that show up in our productivity statistics?

Economists have long relied on a metric called Total Factor Productivity (TFP) to measure technological progress. But in an era of free digital tools and generative AI, TFP looks more like a rearview mirror than a windshield. It tells us a lot about the past, but almost nothing about where the economy is headed.


You can see the computer age generative AI everywhere but in the productivity statistics.

(Adapted from Robert Solow, 1987)


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