a16z crypto brought together experts including Stanford professor Dan Boneh and a16z crypto partner Eddy Lazzarin to discuss whether fast-moving AI research poses a direct threat to modern cryptography. The view presented in the discussion was cautious rather than alarmist: recent progress in mathematics-related AI work has not produced cryptographic breakthroughs, nor has it shown that the security of any specific cryptographic assumption has deteriorated.
The experts said modern cryptography rests on assumptions that certain mathematical problems do not have efficient algorithms. If that assumption holds, stronger AI alone would not be able to discover an algorithm that does not exist. They linked that idea to the still-unsolved P versus NP problem in computer science and said current AI progress remains far from reaching that frontier.
At the same time, the panel argued AI could materially shorten cryptographic review cycles. Work that once might have taken a decade to pressure-test could now be challenged by running models for a month in an attempt to break an assumption. They also noted that migration efforts driven by quantum risk have already prepared the ground for part of the AI-related threat model, while cryptographic agility gives systems a way to switch methods if weaknesses emerge. For developers, the most immediate use case is using AI to find more bugs before software goes live.
According to ChainCatcher, a16z crypto invited experts including Stanford University professor Dan Boneh and a16z crypto partner Eddy Lazzarin to discuss the threat AI may pose to cryptography.
The experts said recent advances in AI tied to mathematics have not included cryptographic breakthroughs. They also said those advances have not shown any decline in the security of specific cryptographic assumptions.
In the discussion, the panel said modern cryptography depends on the assumption that efficient algorithms do not exist for certain mathematical problems. If that assumption remains true, then even much stronger AI would not be able to find an algorithm that does not exist. They added that this assumption is related to the unresolved P versus NP problem in computer science, and that current AI progress is still far from that boundary.
The experts also said AI could sharply reduce the time needed for cryptographic review. An assumption that may previously have needed 10 years of validation could now be pressure-tested by running models for 1 month in an attempt to break it.
The discussion also noted that migration work already underway because of the quantum threat has prepared systems for part of the AI risk. Cryptographic agility, they said, can help systems switch methods if a weakness is found.
For developers, the most direct near-term opportunity is to use AI to identify more vulnerabilities before software is deployed.
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