This research article will describe and analyze the current evolutionary path of AI in the Web3 field.

Author: Guatian Laboratory

Introduction

Since the debut of ChatGPT at the end of 2022, the AI sector has been a hot topic in the crypto space. The nomads of Web3 have already embraced the idea that "any concept can be hyped," not to mention AI, which has unlimited narrative contexts and application capabilities for the future. Therefore, in the crypto circle, the AI concept initially gained popularity as a "Meme craze," and then some projects began to explore its actual application value: what new practical applications can crypto bring to the rapidly advancing AI?

This research article will describe and analyze the current evolutionary path of AI in the Web3 field, from the early hype wave to the current rise of application-based projects, and will combine cases and data to help readers grasp the industry context and future trends. Let's throw out some immature conclusions right from the start:

01

The phase of AI memes is already a thing of the past; what should be cut and what should be earned will remain as eternal fragments of memory;

02

Some foundational WEB3 AI projects have consistently emphasized the benefits of "decentralization" for AI security, but users are not very convinced; what users care about is "whether the token is profitable" + "how good the product is";

03

If one wants to invest in AI-related crypto projects, the focus should shift to pure application-based AI projects or platform-based AI projects (which can concentrate many tools or agents that are easy for end users to use); this may be a longer-term wealth hotspot after the AI Meme phase;

Differences in the Development Path 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 (like OpenAI, Google) train closed black-box models, with algorithms and data not disclosed, leaving users to only use the results, lacking transparency. This centralized control leads to AI decisions being un-auditable, with issues of bias and unclear accountability. Overall, Web2 AI innovation focuses on improving the performance of foundational models and commercial application implementation, but the decision-making process is opaque to the public. This opacity has led to the rise of new AI projects like Deepseek in 2025, which appear to be open-source but are actually "fishing in a barrel."

In addition to the transparency flaw, large AI models in WEB2 also face two other pain points: insufficient user experience across different product forms and lack of precision in specialized niches.

For example, if one needs to create a PPT, an image, or a video, users will still seek out new AI products with lower entry barriers and better user experiences, and they are willing to pay for them. Many AI projects are currently trying to create no-code AI products to lower the user threshold even further.

Moreover, many WEB3 users have likely experienced the frustration of using ChatGPT or DeepSeek to obtain information about a specific crypto project or token, as large models' data cannot accurately cover the details of any niche in this world. Therefore, another development direction for many AI products is to achieve the deepest and most precise data and analysis in a specific niche.

AI in the Web3 World

The WEB3 world is centered around the crypto industry, integrating technology, culture, and community into a broader concept. Compared to WEB2, WEB3 attempts to move towards a more open and community-driven approach.

Leveraging the decentralized architecture of blockchain, Web3 AI projects often claim to emphasize open-source code, community governance, and transparency, hoping to break the traditional monopoly of AI by a few companies in a distributed manner. For example, some projects explore using blockchain to verify AI decisions (zero-knowledge proofs ensure the credibility of model outputs) 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 token mechanisms. However, in practice, the development of Web3 AI is still constrained by technical and resource limitations: building decentralized AI infrastructure is extremely challenging (training large models requires massive computational data, and no WEB3 project has the funding to match even a fraction of OpenAI's resources). A few projects claiming to be Web3 AI still rely on centralized models or services, merely integrating some blockchain elements at the application level. These relatively reliable WEB3 AI projects are at least engaged in real development applications; however, the vast majority of WEB3 AI projects are either pure memes or memes masquerading as real AI.

Additionally, 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, 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 hot, funds flood in, driving up token prices and valuations; when cooled, project enthusiasm and funding quickly decline. This cycle makes the development path of Web3 AI more volatile and narrative-driven. For instance, an AI concept lacking substantial progress may see its token price soar due to market sentiment; conversely, even with technical advancements, it may struggle to gain attention during a market downturn.

We maintain a "low-key and cautious expectation" for the main narrative of WEB3 AI, "decentralized AI networks," just in case it actually becomes a reality. After all, there are epoch-making entities like BTC and ETH in WEB3. However, at the current stage, everyone still needs to realistically envision some immediately implementable scenarios, such as embedding AI agents into existing WEB3 projects to enhance their efficiency; or combining AI with other new technologies to generate new ideas applicable to the crypto industry, even if they are just attention-grabbing concepts; or creating AI products specifically for the WEB3 industry, providing services that the WEB3 community can pay for, whether in terms of data accuracy or better alignment with the work habits of WEB3 organizations or individuals.

To be continued, the next article will mainly review and comment on the five waves of enthusiasm for WEB3 AI, along with some products (such as Fetch.AI, TURBO, GOAT, AI16Z, Joinable AI, MyShell, etc.).

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