The Evolution of AI in the Web3 Field: From Hype to Practical Application

Since the launch of ChatGPT at the end of 2022, AI has been a hot topic in the cryptocurrency sector. The Web3 community has always been open to various new concepts, especially when it comes to the boundless potential of AI technology. As a result, the AI concept initially erupted in the crypto space in the form of a "hype wave," and then some projects began to explore its actual application value: what innovative applications can cryptocurrency technology bring to the burgeoning AI?

This article will analyze the development history of AI in the Web3 field, from the early hype wave to the rise of current application-based projects, and will help readers grasp the industry context and future trends by combining case studies and data. Our preliminary conclusion is:

1. The hype phase of AI memes has passed, leaving behind some unforgettable memories;
2. Some infrastructure-based Web3 AI projects emphasize the benefits of "decentralization" for AI security, but users are more concerned about "token yields" and "product experience";
3. Future AI-related crypto projects may shift their focus towards purely application-based AI projects or platform-based AI projects that integrate various user-friendly tools, which could represent longer-term investment opportunities after the AI hype.

Differences in the Development Path of AI in Web2 and Web3

AI in the Web2 World

In the Web2 world, AI is mainly driven by technology giants and research institutions, with a relatively stable and centralized development path. Large companies train closed black-box models, with algorithms and data not disclosed, leaving users only able to utilize the results, resulting in a lack of transparency. This centralized control leads to AI decision-making being non-auditable, with issues of bias and unclear accountability. Overall, AI innovation in Web2 focuses on enhancing the performance of foundational models and commercial application implementation, but the decision-making process is opaque to the public. This pain point has led to the emergence of new AI projects like Deepseek in 2025, which appear to be open-source but are essentially "black boxes."

In addition to being opaque, large AI models in Web2 also face two issues: poor experience across different product forms and insufficient accuracy in specialized fields.

For example, when generating PPTs, images, or videos, users tend to prefer professional AI tools that have a low barrier to entry and a good user experience, and they are willing to pay for them. Currently, many AI projects are trying to create no-code products to lower the user barrier.

For example, Web3 users often feel powerless when using ChatGPT or DeepSeek to query information about a certain crypto project or token, as the data from large models does not yet cover all the details of niche industries accurately. Therefore, another direction for the development of AI products is to delve deeper and refine within specific fields.

AI in the Web3 world

Web3 is a broader concept centered around the cryptocurrency industry, integrating technology, culture, and community. Compared to Web2, Web3 leans more towards an open and community-driven approach.

With the decentralized architecture of blockchain, Web3 AI projects often emphasize open source code, community governance, and transparency, attempting to disrupt the traditional AI monopoly held by a few companies in a distributed manner. For example, some projects explore using blockchain to verify AI decisions or have DAOs review AI models to reduce bias.

In an ideal state, Web3 AI pursues "open AI," allowing model parameters and decision logic to be audited by the community, while incentivizing developers and users to participate through a token mechanism. However, in practice, the development of AI in Web3 is still limited by technology and resources: building decentralized AI infrastructure is extremely challenging, and a few Web3 AI projects still rely on centralized models or services, only integrating some blockchain elements at the application layer. These projects can still be considered excellent, at least in terms of real development applications; however, most Web3 AI projects are still purely speculative or hype under the banner of AI.

In addition, the differences in funding and participation models also affect the development paths of both. Web2 AI is generally driven by research investment and product profitability, with relatively smooth cycles. In contrast, Web3 AI combines the speculative nature of the crypto market, often experiencing "boom" cycles that fluctuate dramatically with market sentiment: when concepts are popular, funds rush in to drive up token prices and valuations, and when the excitement cools, the project's popularity and funding quickly decline. This cycle makes the development path of Web3 AI more volatile and narrative-driven.

For the main narrative of Web3 AI "decentralized AI networks", we currently hold a "low-key and cautious expectation" attitude. After all, there are epoch-making entities like BTC and ETH in the Web3 field. However, at this stage, we need to realistically envision some scenarios that can be immediately implemented, for example:

1. Embed AI agents in existing Web3 projects to improve project efficiency.
2. Combine AI with other new technologies to generate new ideas applicable to the cryptocurrency industry;
3. AI products specifically designed for the Web3 industry, providing services that professionals in Web3 are willing to pay for in terms of data accuracy or alignment with work habits.