Original Title: “AI in Crypto: After the Meme Frenzy, Is It a Mess or a Rebirth? (Part One) By Wlabs”

Original source: Guotian Laboratory Wlabs

Introduction

Since the emergence of ChatGPT at the end of 2022, the AI segment has become a hot topic in the crypto field. The nomads of WEB3 have already embraced the idea that “any concept can be hyped,” let alone AI, which has unlimited narrative threads and application capabilities for the future. Therefore, in the crypto circle, the AI concept initially gained popularity as a “Meme craze” for a while, 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 evolutionary path of AI in the Web3 field, from the early hype wave to the current rise of application-based projects, and combine cases and data to help readers grasp the industry context and future trends. Here, let’s throw out an immature conclusion right from the start:

1. The era of AI memes is already in the past; what should be cut and what should be earned are left as eternal fragments of memory.

2. Some fundamental 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 “tokens make money” + “how good the product is to use”.

3. If you want to invest in AI-related cryptocurrency 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 could be a wealth hotspot in the longer term after the AI Meme.

The Differences in the Development Path of AI in Web2 and Web3

AI in the Web2 World

In the Web2 world, AI is primarily driven by tech giants and research institutions, with a relatively stable and centralized development path. Large companies (such as OpenAI, Google) train closed black-box models, with algorithms and data not made public, leaving users with only the results and lacking transparency. This centralized control leads to AI decision-making being non-auditable, with issues of bias and unclear accountability. Overall, Web2’s AI innovation focuses on enhancing the performance of foundational models and commercial application implementation, but the decision-making process is opaque to the general public. It is this pain point of opacity that has led to the emergence 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 opaque flaws, large AI models of WEB2 also have two other pain points: insufficient user experience across different product forms and inadequate accuracy in specialized sub-sectors. For example, if users want to generate a PPT, an image, or a video, they will still look for new AI products with lower entry barriers and better user experiences to use and are willing to pay for them. Currently, many AI projects are trying to create no-code AI products to lower the entry barriers for users even further.

For many users of WEB3, there has likely been a feeling of frustration when using ChatGPT or DeepSeek to obtain information about a specific crypto project or token. The data from large models still cannot accurately cover the detailed information of any specific sub-industry in this world. Therefore, another development direction for many AI products is to achieve the deepest and most precise data and analysis within a specific sub-industry.

AI in the Web3 World

The WEB3 world is a broader concept centered around the cryptocurrency industry, integrating technology, culture, and community. Compared to WEB2, WEB3 attempts to move towards a more open and community-driven approach. With the decentralized architecture of blockchain, Web3 AI projects often claim to emphasize open-source code, community governance, and transparency, aiming to break the traditional monopoly of AI held by a few companies in a distributed manner. For example, some projects explore using blockchain to validate 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”, so that model parameters and decision-making logic can be audited by the community, and at the same time, developers and users are incentivized to participate through the token mechanism. However, in practice, the development of AI in Web3 is still limited by technology and resources: it is extremely difficult to build a decentralized AI infrastructure (training a large model requires a large amount of computing data, but no WEB3 project can have a fraction of OpenAI’s amount of funds), and a small number of projects that claim to be Web3 AI still rely on centralized models or services, but only some blockchain elements are connected to the application layer. At least the application is still being developed in real life; However, the vast majority of WEB3 AI projects are still pure memes, or memes under the banner of real AI.

In addition, the difference between funding and participation models also affects the development paths of the two. Web2 AI is usually driven by research investment and product monetization, and the cycle is relatively flat. Web3 AI, combined with the speculative nature of the crypto market, often has a “boom” cycle that fluctuates with market sentiment: when the concept is hot, funds rush in to push up the price and valuation of the token, and when it cools, the project’s heat and funds quickly decline. This cycle makes the path of Web3 AI more volatile and narrative-driven. For example, an AI concept that lacks substantial progress can also trigger a spike in the price of a token due to market sentiment; On the contrary, when the market is in a downturn, even if there is technical progress, it is difficult to get attention.

We maintain a “low-key and cautious hope” for the main narrative of WEB3 AI, which is the “decentralized AI network”. What if it really becomes a reality? After all, there are epoch-making entities like BTC and ETH in WEB3. However, at this stage, everyone still needs to think practically about some scenarios that can be implemented immediately, such as embedding AI Agents into current WEB3 projects to improve the efficiency of the projects themselves; or combining AI with some other new technologies to generate new ideas applicable to the crypto industry, even if it’s just a concept that can attract attention; or simply creating AI products that serve the WEB3 industry, whether in terms of data accuracy or aligning more closely with the work habits of WEB3 organizations or individuals, to provide services that the community in the WEB3 industry can pay for.

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

Reference article:

[ Web3 AI vs. Web2 AI: Why Open-Source and Transparency Will Win ](

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