Entering 2026, the rules of AI are quietly changing.

Last year's rapid evolution of reasoning models clearly indicated that the industry's focus is shifting from simply pursuing model capabilities to three more practical issues: Can AI really get things done? Can it be trusted? How can fair valuation be achieved?

Researchers have provided their judgments from several perspectives. Harvard professor Scott Kominers said that by November, he was able to converse with models using instructions and dialogues close to those given to PhD students, and the models could still produce genuinely novel answers. In other words, AI has upgraded from a helper to a true research partner.

Another key issue is the identity verification of agents. Sean Neville, co-founder of Circle, proposed that for AI agents to become independent trading entities, they must have a traceable, authorized, and verifiable identity system—he calls this KYA (Know Your Agent). This system is somewhat like giving AI agents an ID card.

But the most interesting aspect is the imbalance in economic models. Industry insiders point out that current AI models extensively scrape content from the internet to enrich themselves, yet do not contribute any traffic value to the web, leading to a serious misalignment of interests between the information layer and the execution layer. His suggestion is that as long as the agent completes the task, the rewards should be distributed to all participants providing information, data, or content. With blockchain-supported nano-payment technology, this mechanism becomes truly feasible.

In simple terms, the AI competition in 2026 boils down to these three points: being more capable, having an identity, and fair distribution.