Original Article Title: "AI in Crypto: Is It Chaos or a Rebirth after the Meme Frenzy? (Part One) By Wlabs"

Original Source: Guatian Laboratory Wlabs

Introduction

Since the debut of ChatGPT at the end of 2022, the AI sector has been a hot topic in the crypto space, with the WEB3 nomads embracing the idea that "any concept can be hyped," not to mention the limitless narrative and application capabilities of AI in the future. Therefore, in the crypto community, the AI concept initially skyrocketed in the form of a "Meme Craze" for a period of time, after which some projects began to explore its practical application value: What new practical applications can encryption bring to the thriving AI?

This research article will describe and evaluate the current evolutionary path of AI in the Web3 field, from the early hype wave to the current rise of application-oriented projects. By combining case studies and data, it will help readers grasp the industry context and future trends. Let's start by throwing out some immature conclusions:

1. The AI meme phase is already a thing of the past, and what needed to be cut should be left as eternal fragments of memory;

2. Some foundational WEB3 AI projects have always emphasized the benefits of "decentralization" for AI security, which users are not very receptive to. Users care about "whether the token is profitable" + "whether the product is user-friendly";

3. If you want to ambush AI-related crypto projects, the focus should shift to pure application-oriented AI projects or platform-oriented AI projects (which can integrate many user-friendly tools or agents), as this may be the longer-term wealth hot spot after the AI Meme frenzy;

Divergent Development Paths of AI in Web2 and Web3

AI in the Web2 World

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, Google) train closed black-box models, and the algorithms and data are not public, with users only able to use their outputs, lacking transparency. This centralized control leads to unauditable AI decisions, biases, and unclear responsibilities. Overall, AI innovation in Web2 focuses on improving the performance of base models and landing business applications, but the decision-making process is opaque to the public. It is this opacity that has led to the rise of new AI projects like Deepseek in 2025, which appear to be open-source but are actually "phishing expeditions."

In addition to the opacity issue, large-scale AI models in WEB2 also face two other pain points: insufficient user experience in different product forms and lack of precision in professional niche tracks. For example, when generating a PPT, an image, or a video, users still tend to seek out AI products with lower entry barriers and better user experience to use, and are willing to pay for them. Currently, many AI projects are trying out no-code AI products to lower the user entry barrier.

For many WEB3 users, there have been instances where using ChatGPT or DeepSeek to obtain information about a cryptocurrency project or token resulted in a feeling of powerlessness, as large model data is still unable to accurately cover details of any niche industry in this world. Therefore, another development direction for many AI products is to deeply and accurately integrate data and analysis into a specific niche industry.

AI in the Web3 World

The WEB3 world is centered around the cryptocurrency industry and is a broader concept that combines technology, culture, and community. Compared to WEB2, WEB3 is more inclined towards an open and community-driven approach. Leveraging a decentralized blockchain architecture, Web3 AI projects usually emphasize open-source code, community governance, and transparency, aiming to break the traditional AI monopoly held by a few companies in a distributed manner. For example, some projects explore using blockchain to validate AI decisions (zero-knowledge proofs ensure model output trustworthiness) or have AI models audited by DAOs to reduce bias.

Ideally, Web3 AI strives for "open AI," allowing model parameters and decision logic to be audited by the community, while incentivizing developers and users through token mechanisms. However, in practice, the development of Web3 AI is still constrained by technical and resource limitations: building a decentralized AI infrastructure is extremely challenging (training large models requires massive computational power and data, yet none of the WEB3 projects have the funding comparable to OpenAI), and a few projects claiming to be Web3 AI still rely on centralized models or services, only integrating some blockchain elements at the application layer. These Web3 AI projects with some credibility are still developing real-world applications; the vast majority of WEB3 AI projects are pure memes or memes disguised as real AI.

Furthermore, differences in funding and participation models also impact the development paths of both. Web2 AI usually follows a relatively stable cycle driven by research investment and product profitability. In contrast, Web3 AI combines the speculative nature of the crypto market and often experiences "hot and cold" cycles driven by market sentiment: when the concept is hot, funds pour in, driving up token prices and valuations, but when things cool down, project hype and funds rapidly decline. This cycle makes the development path of Web3 AI more volatile and narrative-driven. For example, a conceptually lacking AI project may experience a surge in token prices due to market sentiment even with no substantial progress; conversely, even with technological advances, it may be difficult to attract attention during a bear market.

When it comes to the primary narrative of WEB3 AI, the concept of a "decentralized AI network" is currently still maintaining a kind of "subdued and cautious anticipation." What if it actually comes true? After all, within WEB3, there are groundbreaking entities like BTC and ETH. However, at this current stage, everyone still needs to pragmatically conceive some scenarios that can be immediately implemented. For example, embedding some AI Agents in current WEB3 projects to enhance the projects' own efficiency, or combining AI with 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 developing AI products to serve the WEB3 industry, whether it be by providing services that the WEB3 community is willing to pay for 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 focus on the five waves of hype about WEB3 AI and review and critique some products within it (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 ](https://www.linkedin.com/pulse/web3-ai-vs-web2-why-open-source-transparency-win-ocada-ai-8iuaf/)

Original Article Link

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