Jacob Tsimerman isn’t waiting for regulators to define safety. The Canadian mathematician and fresh Fields Medal recipient is taking matters into his own hands by founding the Mathematical A.I. Safety Institute (MAISI). Based in the San Francisco Bay Area, this independent research hub plans to open its doors in January 2027. It won’t be a massive operation initially. Tsimerman intends to staff the institute with just ten to thirty elite mathematicians. Their mission? To bring the rigor of cryptography to the chaotic world of artificial intelligence.
Current AI safety protocols rely heavily on empirical testing. We throw attacks at models and see if they break. It’s reactive and incomplete. Tsimerman argues this approach is fundamentally flawed because there isn’t even a clear theoretical definition of what “safe” means in AI. Cryptography offers a better blueprint. You can prove an encryption scheme is unbreakable without testing every possible key. MAISI wants to create similar mathematical guarantees for AI systems. They aim to prove that agents act responsibly, produce correct results, and don’t trigger catastrophic failures when working together.
One promising avenue involves zero-knowledge proofs. This cryptographic technique allows a system to demonstrate it followed specific rules without revealing its underlying code or trade secrets. For AI labs protective of their proprietary models, this could be a breakthrough. It offers a way to verify safety without exposing intellectual property. Tsimerman, who is also joining OpenAI’s safety team, emphasizes that the industry needs “a much, much higher level of safety standard than we’re currently getting.” He sees the current patchwork of benchmarks as insufficient for the risks involved.
This shift toward formal verification marks a significant maturation in AI alignment research. While many organizations focus on behavioral guardrails, MAISI targets the foundational logic. If successful, it could move safety from a probabilistic guess to a mathematical certainty. However, the timeline is ambitious. Starting work in 2027 leaves a gap where rapid AI development continues unchecked by these new standards. Whether mathematics can tame neural networks remains an open question, but Tsimerman’s credentials suggest he’s not asking lightly.
