How trustworthy can AI be? This question is no longer an abstract ethical debate, but is rapidly transforming into a highly practical financial risk issue. In a future where autonomous AI agents handle asset management and payments, where should we place the responsibility for the decision to "entrust" these tasks to AI?
One concrete solution has been proposed to this question: the ARS (Agentic Risk Standard), a new framework for managing the financial risks of autonomous AI. Essentially, it's the idea of taking on AI risk through "finance." The essence of the ARS proposed here lies in its attempt to guarantee AI reliability through "finance" rather than "technology."
Traditionally, AI safety has been discussed in terms of technical approaches such as model accuracy and bias reduction. However, when autonomous agents handle funds, the problem shifts from "will it work correctly?" to "who will bear the losses when it fails?"
ARS simplifies this problem by classifying AI tasks into two categories and applying different risk management methods to each. Simple tasks are protected by escrow (payment intermediation), while complex tasks involving fund manipulation are guaranteed by underwriting (insurance).
In other words, the system is designed with the assumption that "risk will occur," and the losses are financially assumed in advance. This concept overlaps with the evolution of DeFi and stablecoins. Finance can always be described as a history of "how to design trust." And AI is also beginning to be incorporated into this framework.
