AI in Crypto: After the Meme Frenzy, Is It a Mess or a Transformation?

Written by: W Labs Guatian Laboratory

Introduction

Since ChatGPT emerged at the end of 2022, the AI sector has become a hot topic in the cryptocurrency field. The nomads of WEB3 have already embraced the idea that "any concept can be hyped," not to mention AI, which has unlimited narrative threads and application capabilities in the future. As a result, in the crypto circle, the AI concept initially became popular as a "Meme craze" for a period of time, and then some projects began to explore its practical application value: what new practical applications can cryptocurrency bring to the rapidly advancing AI?

This research article will describe and analyze the evolution of AI in the Web3 field, from the early hype wave to the current rise of application projects, and will 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:

The era of AI memes is already a thing of the past; what should have been harvested and what should have been earned will remain as eternal fragments of memory.

Some foundational WEB3 AI projects have always emphasized the benefits of "decentralization" for AI security, but users are not very convinced; what users care about is whether "tokens can make money" + "how good the product is to use."

If you want 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 could be a wealth hotspot in a longer cycle after AI Meme.

1. The development path differences of AI in Web2 and Web3

• In the Web2 world, AI is mainly driven by tech giants and research institutions, with a relatively stable and centralized development path. Large companies (such as OpenAI and Google) train closed black box models, with algorithms and data not being disclosed, leaving users to only utilize the results without 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 applications, but the decision-making process remains opaque to the public. This lack of transparency is what has led to the emergence of new AI projects like Deepseek in 2025, which seem open-source but are actually "fishing in a barrel."

In addition to the opaque flaws, large AI models in WEB2 also have two other pain points: insufficient user experience across different product forms and inadequate accuracy in specialized niche markets.

For example, if you want to create a PPT, an image, or a video, users will still look for AI new products that have a lower entry barrier and better user experience to use, and they are willing to pay for them. Currently, many AI projects are trying to develop no-code AI products, aiming to lower the entry barrier for users even further.

For many users of WEB3, there should be a sense of helplessness when using ChatGPT or DeepSeek to obtain information about a particular crypto project or token. The data from large models still cannot accurately cover the detailed information of any specific subdivision in this world. Therefore, another development direction for many AI products is to delve into and analyze data in a particular niche industry as deeply and accurately as possible.

• 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 and trustworthiness, hoping to break the monopoly of traditional AI by a few companies in a distributed way. For example, some projects explore validating AI decisions with blockchains (zero-knowledge proofs to ensure that model outputs are credible) or having AI models reviewed by DAOs to reduce bias.

In an ideal scenario, 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 constrained by technological and resource limitations: building decentralized AI infrastructure is extremely challenging (training large models requires massive computational data, and no Web3 project has funding that can match even a fraction of OpenAI's). A few projects that claim to be Web3 AI still rely on centralized models or services, merely incorporating some blockchain elements at the application layer. These Web3 AI projects that are considered reliable at least engage in genuine development applications; while the vast majority of Web3 AI projects are just pure memes, or memes masquerading as real 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 "boom" cycles characterized by dramatic fluctuations in market sentiment: when a concept is hot, funds flock in, driving up token prices and valuations; when it cools down, the project's popularity and funding quickly decline. This cycle makes the development path of Web3 AI more volatile and narrative-driven. For example, an AI concept lacking substantial progress might see its token price skyrocket due to market sentiment; conversely, during a downturn, even technical advancements struggle to gain attention.

We maintain a "low-key and cautious hope" regarding the main narrative of WEB3 AI, "decentralized AI networks". What if it really becomes a reality? After all, there are epoch-making existences like BTC and ETH in WEB3. However, at this stage, everyone still needs to think pragmatically about some scenarios that can be implemented immediately, such as embedding AI agents in 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, 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.