As both crypto and AI are gradually maturing, the industry's central focus has quietly shifted. From the evolution of "Does this theory work?" to "How to keep it stable and support large-scale applications," this is a qualitative leap. The most noteworthy change in 2026 is actually hidden in this phenomenon: performance bottlenecks no longer exist, but the speed of adoption still lags.

Let's first look at crypto. The label of speculative assets is fading, replaced by the identity of infrastructure. Blockchain has achieved speed, low cost, and stability. At this point, true value creation no longer comes from price volatility profits but from improvements in payment and settlement efficiency. Speculation will still happen, but it is no longer the main course; at most, it is a seasoning.

Stablecoins are a perfect example. They do not require any faith backing nor do they hype any novel technology. They succeed purely based on tangible advantages like fast speed, low cost, global reach, and ease of integration into software systems. This is what real crypto adoption looks like.

Once the infrastructure's role is established, the focus shifts to distribution and operation. What will future winners look like? Not the most flashy pioneering projects, but platforms with actual users, complete channels, licensed compliance, and risk control systems—essentially, those who can get things done.

Progress in AI is also quite interesting. Many imagine AI Agents as all-powerful autonomous assistants, but in reality, the ones that truly deliver value are those that understand "how to make humans and tools work more efficiently together." Their strength lies in reducing coordination costs, and through goal-driven approaches, cross-tool collaboration, context management, and conversational iteration, they completely reconstruct the workflow of knowledge work. Of course, all this must be done within a framework supervised and limited by humans.

Regarding the real bottleneck of AI, it is no longer "how smart it is." The key now is trustworthiness—in production environments, hallucinations, logical inconsistencies, and explainability are all zero-tolerance issues. For AI to truly be implemented, it’s not just about how good the models are, but whether the entire system engineering is well-executed. This includes data pipelines, feedback loops, monitoring and alerts, and engineering design for human-machine collaboration.

Finally, in the long term, value will flow to two types of roles: one, the builders who can assemble this system; and two, the platform providers who operate, maintain, and continuously supply scenarios and feedback. Technology itself is just the appetizer; the real dish lies in integration capabilities and commercial implementation.