Dissection

In the current industry, the vast majority of people
To be straightforward, simply doing model aggregation itself has no core moat to speak of, let alone long-term competitiveness.
The reasoning is simple: if you can integrate GPT today, countless competitors will be able to connect to the same model tomorrow; if you painstakingly develop a unified calling interface, it won't be long before a large number of homogeneous products emerge in the market, with almost indistinguishable interfaces, functions, and experiences, and the competition will only intensify.
Looking at the current AI industry trend: the number of large models is exploding, open-source models are iterating faster and faster, and the costs of training and calling models are continuously decreasing. In such an industry environment, if
This is a very realistic problem: if
The harsh answer is: probably very few will remain.
But
Initially, I was also confused about this positioning. After repeatedly sorting it out, I suddenly understood. It might be helpful to reinterpret it from a future perspective:
When the industry develops to the next stage, no longer is it “humans controlling AI to execute instructions,” but AI Agents autonomously running on the chain, independently conducting activities, the entire industry logic will undergo a fundamental change.
By then, AI can autonomously call on-chain services, execute on-chain transactions without human intervention, and collaborate with other AI Agents to complete complex tasks, forming an autonomous AI collaboration network.
In such a future scenario, what determines AI value is no longer the model’s computational power or response speed, but a core issue: whether this AI has a unique and trustworthy identity.
At that moment, the importance of identity will far surpass all model-level competitions.
Moreover,
First, a complete behavioral history record, clearly retaining all operations performed by AI, tasks completed, and on-chain activities participated in, forming an immutable behavioral trail;
Second, full on-chain transaction records, accurately recording AI’s asset flows, earnings, and transaction frequency, creating transparent asset behavior data;
Third, a three-dimensional collaboration network, retaining AI’s cooperation history with other Agents, projects, and users, building an exclusive collaboration trust chain;
Fourth, a long-term accumulated reputation system, based on historical behavior, transaction fulfillment, and collaboration effectiveness, forming a credibility score that becomes the core proof of AI’s on-chain activities.
These identity assets, accumulated from behavior, transactions, collaboration, and reputation, have an irreplaceable core trait — non-transferable, difficult to copy, and impossible to redo.
Models can be replaced at any time; today using GPT, tomorrow switching to other open-source models, with extremely low or no switching costs; but the identity, credit, and behavioral history accumulated by AI on the chain are the result of long-term sedimentation. Once formed, they cannot be freely transferred or reversed, which is the real industry barrier.
Models are just tools, replaceable at any time; but identity is the foundation of AI’s existence on the chain, the core asset.
This fundamental difference directly determines the project’s long-term value, and is
Therefore, redefining
This logic may sound somewhat abstract, even not “sexy” enough, not as intuitive and easy to understand as model aggregation, nor as flashy as features that quickly attract attention. But looking at the entire industry development, all truly core barriers and foundational facilities capable of rewriting industry patterns are often not understood by the public at first glance, appearing plain and unremarkable, yet secretly harboring long-term value.
Finally, I summarize my view:
Model aggregation is just its superficial entry point, the first step to reach users;
And the AI identity system is its true core competitiveness, the key to determining long-term value.

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