The Mathematical AI Safety Institute wants to prove AI is safe the way cryptographers prove codes are unbreakable
Fields Medalist Jacob Tsimerman founds the Mathematical AI Safety Institute (MAISI) to pursue provable AI safety guarantees, launching January 2027.
Canadian mathematician and new Fields Medal recipient Jacob Tsimerman announced the Mathematical A.I. Safety Institute (MAISI), an independent Bay Area institute that will begin work in January 2027 with 10-30 mathematicians. Tsimerman is also joining OpenAI's safety team and argues AI needs a much higher safety standard. MAISI aims to formalize what safety means, prove multi-agent systems avoid unwanted outcomes, and explore tools like zero-knowledge proofs that let labs demonstrate correctness without exposing trade secrets.
- MAISI will employ 10-30 mathematicians in the San Francisco Bay Area starting January 2027
- Founder Jacob Tsimerman won a Fields Medal and is joining OpenAI's safety team
- Goals include rigorous definitions of safety and guarantees for multi-agent AI systems
- Zero-knowledge proofs cited as a potential tool for verifiable AI behavior
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Canadian mathematician Jacob Tsimerman, a fresh Fields Medal recipient, has announced the founding of the Mathematical A.I. Safety Institute (MAISI). The independent research institute in the San Francisco Bay Area plans to start work in January 2027 with ten to thirty mathematicians tackling AI safety problems, the New York Times reports. Tsimerman, who is also joining OpenAI's safety team, says the field needs "a much, much higher level of safety standard than we’re currently getting."
With an encryption scheme, you can prove it's unbreakable without trying every possible attack. AI has no such shortcut. Safety only shows up in practice, and according to MAISI, there isn't even a clear definition of what "safe" means, not even in theory.
That's the kind of proof MAISI wants to make possible. The goal is to show that a system acts responsibly and produces correct results, that multiple AI agents working together don't trigger unwanted outcomes, and that systems can withstand vulnerabilities nobody has found yet. One tool could be zero-knowledge proofs, which let a system demonstrate it isn't cheating without exposing the trade secrets of AI labs.
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