The Evolution of AI in the Web3 World: From Concept Hype to Practical Applications

Since the emergence of ChatGPT at the end of 2022, AI has been a hot topic in the cryptocurrency field. The Web3 community has always been open to new concepts, not to mention AI, a technology full of infinite possibilities. In the crypto space, the AI concept initially became popular in the form of a "Meme craze," and then some projects began to explore its practical application value: what new practical applications can cryptocurrency technology bring to the rapidly developing AI?

This article will explore the development history of AI in the Web3 field, from the early hype to the current rise of application projects, and will help readers grasp the industry context and future trends through case studies and data. We can draw the following preliminary conclusions:

1. The era of AI memes is over, leaving only eternal fragments of memory;

2. Some basic Web3 AI projects emphasize the benefits of "decentralization" for AI security, but users are not convinced; they are more concerned about whether the "tokens have profit potential" and whether the "products are easy to use".

3. If you want to layout AI-related crypto projects, the focus should shift to pure application-based AI projects or platform-based AI projects that can concentrate multiple easy-to-use tools. This could be a longer-term investment hotspot after AI Memes.

The Differentiation in Development Paths of AI in Web2 and Web3

AI in the Web2 world

AI in the Web2 world is primarily driven by tech 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 use the results, which lack transparency. This centralized control leads to AI decisions being non-auditable, with issues of bias and unclear accountability. Overall, AI innovation in Web2 focuses on improving the performance of foundational models and implementing commercial applications, 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 in reality act as "closed under the guise of open source."

In addition to the opaque flaws, large AI models in Web2 also have two main issues: poor user experience across different product forms and insufficient accuracy in specialized fields.

For example, users tend to prefer low-threshold, good-experience new AI products to generate PPTs, images, or videos, and are willing to pay for them. Currently, many AI projects are trying to develop no-code AI products to lower the usage threshold for users.

Additionally, Web3 users often express frustration when using ChatGPT or DeepSeek to obtain information about specific crypto projects or tokens. The data from large models still cannot accurately cover the detailed information of every niche industry, so another development direction for many AI products is to delve deeper into data and analysis in specific niche industries.

AI in the Web3 world

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

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

Ideally, 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 resource constraints: building decentralized AI infrastructure is extremely challenging, and few projects claiming to be Web3 AI actually rely on centralized models or services, merely integrating some blockchain elements at the application layer. These projects can still be considered relatively reliable, at least in terms of real development applications; while the vast majority of Web3 AI projects remain in the conceptual hype stage or are speculating under the banner of AI.

In addition, the differences in funding and participation models also affect the development paths of the two. Web2 AI is typically driven by research investment and product profitability, resulting in a relatively smooth cycle. In contrast, Web3 AI combines the speculative nature of the crypto market, often experiencing "hype" cycles that fluctuate drastically with market sentiment: when concepts are popular, funds flock in, driving up token prices and valuations; when interest cools, project enthusiasm and funding quickly decline. This cycle makes the development path of Web3 AI more volatile and narrative-driven.

Regarding the main narrative of "decentralized AI networks" in Web3 AI, we currently hold a "cautiously optimistic" attitude. After all, there are epoch-making projects in the Web3 space like Bitcoin and Ethereum. However, at this stage, we need to consider some immediately applicable scenarios in a practical manner, such as:

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. Focus on AI products that serve the Web3 industry, whether improving data accuracy or better aligning with the work habits of Web3 organizations or individuals, providing services that Web3 industry users are willing to pay for.