As I continued to break down Kite, I found an issue that, if not addressed separately, makes all previous analyses seem incomplete: when AI behavior is not just scaled but begins to become highly synchronized, what guarantees that blockchain won't be dragged down by 'rational behavior' itself.
This issue hardly exists in a human-dominated on-chain world.
Human behavior inherently carries noise and uncertainty. Information asymmetry, emotional fluctuations, and judgment errors can keep the market in a state of non-complete synchronization. Even if many people make mistakes, the ways they are wrong often differ, and the system still has some buffer space.
But AI's behavior is not like that.
The advantage of AI lies in standardization, reusability, and rapid convergence. When multiple AIs use similar data sources, strategic frameworks, and optimization objectives, they act with extremely high correlation. This synchronization is efficient on a small scale but can turn into structural pressure on a large scale.
The issue is not whether AI is rational, but whether 'over-rationality' itself loses its stabilizers.
If the chain layer cannot recognize the systemic risks brought by such synchronized behavior, then the outcome often has only two options: either allow efficiency to accumulate infinitely until the system is disrupted by shocks; or use extreme measures to stop losses afterward, but by then the damage has already occurred.
The design of Kite clearly assumes that this scenario will inevitably occur.
It does not rely on human intervention or governance votes for stability, but rather attempts to build a kind of 'self-stabilizing ability' at the chain level. Self-stability does not mean preventing behaviors from occurring, but rather that when behaviors are highly synchronized, the system can automatically converge on risk exposure.
This ability is not a single mechanism, but rather the result of multiple layers of chain structures being superimposed. Identity continuity allows behaviors to be classified and statistically analyzed; budget constraints give the consumption curve a clear upper limit; permission levels prevent behaviors of different risk levels from being infinitely amplified; and audit structures allow the system to recognize long-term deviations rather than only looking at single-point results.
When these structures coexist, the chain finally has the possibility to remain stable in an environment of 'no emotions, no intuition, only algorithms'.
In other words, Kite is not limiting the capabilities of AI, but is providing an operating environment for AI behavior that will not be destroyed by its own efficiency.
This point is crucial because AI will not restrain itself voluntarily. As long as the objective function does not change, it will continue to push in the same direction. If the chain itself does not have self-stabilizing capabilities, then the more 'correct' the AI is, the more likely it is to push the system to extremes.
From this perspective, the underlying logic of Kite is actually very calm. It does not assume that AI will make mistakes, but rather assumes that AI will 'be too right'. Systemic risks often arise from this kind of 'over-correctness'.
This is also why I increasingly believe that the value of Kite does not lie in single-point functionality, but in whether the overall structure holds. Once AI behavior begins to synchronize highly, only chains that can internally generate a stabilization mechanism have the potential for long-term existence.
Kite is clearly designed based on this premise.
