The market is always full of new concepts. But what is truly scarce is those who can sense risks in advance.

The story of AI has been told for many years, from model capability upgrades to Agent collaboration, and it seems like there are new breakthroughs every week. However, this has also brought about a seriously underestimated issue—when AI begins to make decisions on its own, is the entire system prepared to bear the consequences?

Recently, I have been paying attention to the actual logic of these types of projects and found that there is a very important turning point that is often overlooked.

**AI is crossing a critical threshold**

Most people still see AI as "a smarter tool." But the reality is that, as agentification matures, AI is crossing a seriously underestimated line — from passively executing commands to actively making decisions.

The significance of this transformation is profound. Because once it enters this phase, AI's behavior is no longer just a technical behavior; it begins to exhibit economic attributes. It will consume resources, incur costs, and affect transaction outcomes.

This brings us back to a basic fact: in the real world, all economic activities need to be built on rules. But the problem lies precisely here—this set of rules has not yet been truly established in the field of AI.

**The solution is actually quite straightforward**

Some projects are worth looking at. Their logic is not complicated, and they do not attempt to draw some utopian blueprint, but rather return to a very specific question: how to systematically constrain the behavior of AI.

We have long understood this principle in traditional society—if a system emphasizes efficiency while completely ignoring accountability, it will eventually encounter problems. An effective system needs to find a balance between the two.

What these projects are doing is transferring this set of logic into the world of AI Agents. The focus is on foundational infrastructures such as identity recognition and payment settlement, using a rule framework to constrain the behavioral boundaries of AI.

This may not sound that sexy, but it's precisely the most practical part. Because only when the risks are truly controlled can this system really get going.