The same AI technology amplifying cyber threats can also generate highly secure, formally verified code. This presents a historic opportunity for a society-wide effort to replace vulnerable legacy software in critical infrastructure, leading to a durable reduction in cyber risk. The main challenge is creating the motivation for this massive undertaking.

The purpose of creating a superhuman mathematician is not just to solve proofs, but to establish a system of verifiable reasoning. This formal verification capability will be essential to ensure the safety, reliability, and collaborative potential of all future AI code and superintelligence.

Verifying complex systems is bottlenecked by the human inability to specify all requirements. The future of software development is an interactive process where AI helps propose specifications (e.g., via test generation) and then uses a prover to formally verify them.

While AI can generate code, the stakes on blockchain are too high for bugs, as they lead to direct financial loss. The solution is formal verification, using mathematical proofs to guarantee smart contract correctness. This provides a safety net, enabling users and AI to confidently build and interact with financial applications.

Writing formally verified code, which can be mathematically proven to be secure, has been a niche practice due to its extreme difficulty for humans. Because AI agents don't get bored or frustrated, they could be tasked with writing code in these secure languages, making high-assurance programming practical for the first time.

A major hurdle for formal methods is the effort required to write proofs. Generative AI is becoming capable of producing proofs in formal languages like Lean, which can then be automatically verified by a machine. This could make verified software development scalable for the first time.

Formal verification, the process of mathematically proving software correctness, has been too complex for widespread use. New AI models can now automate this, allowing developers to build systems with mathematical guarantees against certain bugs—a huge step for creating trust in high-stakes financial software.

For critical processes in regulated industries, standard AI model evaluations ("evals") are insufficient. Enterprises like UBS are pushing for research into mathematical proofs to formally verify that AI agents behave correctly across multiple tasks, establishing a much higher standard of trust and safety.

Simply generating a mathematical proof in natural language is useless because it could be thousands of pages long and contain subtle errors. The pivotal innovation was combining AI reasoning with formal verification. This ensures the output is provably correct and usable, solving the critical problems of trust and utility for complex, AI-generated work.