TradingBase.AI Column | Why Genuine AI Products Are Becoming Scarcer, While "Advanced-Looking Projects" Keep Multiplying

Over the past year, if you have been observing projects that combine AI and Web3, you will notice an increasingly obvious phenomenon: the number of projects is growing, the narratives are becoming more complex, but the products capable of long-term operation are decreasing.

This is not an industry cooling down, but the beginning of structural issues being exposed.

Many projects appear to be technologically advanced, with complete architectures, and even perform impressively during demonstrations. However, once they enter real-world environments, they quickly lose sustainability. This disconnect is not due to a lack of technical ability but because most projects have not established a “product” logic from the start; they have only built a capability showcase system.

The core issue is not AI, but the “system.”

1. Increasing capabilities, but no system formed

Current AI capabilities are sufficient to support complex tasks. Models can analyze data, generate decisions, and execute logic—all no longer bottlenecks. But the problem is that these capabilities are often isolated; they can be called upon but cannot operate continuously.

If a system cannot run stably without human intervention, it cannot be considered a product. Many projects can complete a task once but cannot do it a hundred times; they can succeed in testing environments but cannot sustain output in real environments.

This means they are essentially just a combination of tools, not a system.

2. The long-term misconception of Web3, amplified further by AI

Web3 has always had an implicit problem: storytelling takes precedence over product. Many projects can establish value through consensus and liquidity without mature products. This structure is effective in early stages, but when AI enters, this problem is further magnified.

Because AI can easily “demonstrate capabilities.”

A model interface, an automation process, or a seemingly complex logic can construct a complete story. But a story does not equal a system. A system needs to run continuously, remain stable across different environments, and handle exceptions and risks.

When storytelling replaces the system, projects remain at the “seems to be working” stage and cannot progress into real operation.

3. A true product must meet three conditions

Judging whether a system is a product is not complicated. The key is whether it meets three conditions: can it operate independently, can it produce results continuously, and can it remain effective without human intervention.

These three conditions determine whether a system has the “long-term viability.”

Currently, many so-called AI projects are stuck in the “usable but not sustainable” stage. They can be called upon and showcased but cannot form a closed loop. Such systems cannot accumulate value over time or survive in complex environments.

4. The industry is entering a “filtering phase”

As AI capabilities become more widespread, the focus of competition has shifted. In the past, it was about who could develop more complex functions; now, it’s about who can make systems truly run.

This change indicates that the industry is entering a filtering stage.

Projects relying on storytelling and demonstrations will gradually lose support; those with system capabilities will begin to show advantages. This process will not be quick, but it is inevitable.

5. Why financial scenarios will be the first to determine winners and losers

Among all application scenarios, finance is the closest to “product standards.” Financial systems do not tolerate ambiguity or instability. If a system cannot operate continuously, control risks, or produce stable results, it cannot exist in real capital environments.

This makes finance a natural filter. Only projects with genuine system capabilities can survive here.

TradingBase.AI is essentially building such a system. By integrating data, models, and execution mechanisms, the platform aims to form a long-term trading structure rather than a one-time decision tool.

The value of such a system does not lie in whether a single judgment is correct but in whether it can operate continuously across different market environments and generate stable results over time.

Conclusion

The integration of AI and Web3 is shifting from “capability demonstration” to “system operation.”

When the industry no longer rewards “appearing advanced” but begins to filter for “truly operational systems,” real products will emerge.

Future competition will not belong to those who tell the best stories but to those who can keep their systems running continuously.