1.2 Special Challenges in the AI Era

When we attempt to fully deploy AI use cases within decentralized networks, the aforementioned problems become particularly acute. Modern AI large language models need to process billions of inference requests daily and demonstrate exceptional accuracy in complex, multi-step tasks. For instance, models like GPT can already coordinate complex workflows spanning thousands of function calls. In practices such as the NOF1.ai First AI Trading Competition, we have seen language models reliably formulate and execute complex multi-step trading plans.

This level of technological maturity signifies that today's autonomous agents can already analyze global market dynamics at the millisecond level and execute investment strategies with precision surpassing human capability. They can coordinate distributed systems, manage high-value portfolios, and make nuanced decisions at speeds and scales unattainable by human teams.

However, we observe that these powerful agents are severely constrained by their underlying infrastructure. An AI agent capable of analyzing global markets in microseconds might still need to wait days for the final settlement of a cross-border payment. An agent entrusted with critical business decision-making often cannot cryptographically prove to the outside world that all its operations are executed within predetermined constraints. End-users face an impossible choice at the operational level: either unconditionally trust the agent's financial operations and bear all the risk, or revert to the traditional model of manually authorizing every transaction, thereby completely destroying the core value of the agent—autonomy.