Written by: W Labs Guatian Laboratory
Introduction
Since ChatGPT made its debut in late 2022, the AI sector has been a hot topic in the crypto field. WEB3 nomads have always accepted the idea that "any concept can be hyped," let alone AI with infinite narrative contexts and application capabilities. Therefore, in the crypto circle, the AI concept initially became popular in the form of a "MEME wave," and subsequently, some projects began exploring its actual application value: What new practical applications can crypto bring to the rapidly developing AI?
This research article will narrate and analyze the current evolution path of AI in the Web3 domain, from the early hype wave to the emerging application-type projects, and help readers grasp the industry context and future trends through cases and data. Let's throw out an immature conclusion right from the start:
The stage of AI meme is already in the past, let the parts that should be cut and the parts that should be earned remain as eternal memory fragments;
Some basic Web3 AI projects always emphasize the benefits of "decentralization" to AI security, but users are not buying it. Users care about "can the token make money" + "is the product good to use";
If you want to ambush crypto projects related to AI, the focus should shift to pure application-type AI projects or platform-type AI projects (which can centralize many tools or Agents that are easy for C-end users to get started with), which might be the wealth hotspot for a longer cycle after AI MEME
I. 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 concentrated development path. Large companies (such as OpenAI, Google) train closed black-box models with non-public algorithms and data, where users can only use the results, lacking transparency. This centralized control leads to non-auditable AI decisions with bias and unclear responsibilities. Overall, Web2 AI innovation focuses on improving base model performance and commercial application landing, but the decision-making process is opaque to the public. This opacity is precisely what led to the sudden rise of new AI projects like Deepseek in 2025, which seemingly appear open-source but are actually "fishing in troubled waters".
Besides the opacity defect, large AI models in WEB2 also have two other pain points: insufficient experience across different product forms and low precision in professional subdivided tracks.
For instance, when creating a PPT, an image, or a video, users will still seek out new AI products with lower entry barriers and better user experience, and pay for them. Currently, many AI projects are trying to develop no-code AI products to lower user thresholds.
For example, many Web3 users have probably experienced the powerless feeling of using ChatGPT or DeepSeek to obtain information about a crypto project or token, as large models' data still cannot precisely cover detailed information of any subdivided industry in the world. Therefore, another development direction for many AI products is to delve deepest and most precisely into data and analysis in a specific industry.
-AI in the Web3 World
The Web3 world is centered on the crypto industry, a broader concept integrating technology, culture, and community.
Leveraging blockchain's decentralized architecture, Web3 AI projects typically claim to emphasize open-source code, community governance, and transparency, hoping to break the traditional AI monopoly by a few companies in a distributed manner. Some projects explore using blockchain to verify AI decisions (zero-knowledge proof ensures trustworthy model output) or have DAOs audit AI models to reduce bias.
In an ideal state, Web3 AI pursues "public AI," allowing community auditing of model parameters and decision logic, while incentivizing developers and users through token mechanisms. However, in practice, Web3 AI development is still constrained by technical and resource limitations: building decentralized AI infrastructure is extremely difficult (training large models requires massive computing power and data, but no Web3 project has funding close to OpenAI's). Few projects claiming to be Web3 AI actually still depend on centralized models or services, merely integrating some blockchain elements at the application layer. These relatively reliable Web3 AI projects are at least developing real applications; the vast majority are pure MEME or MEME disguised under the banner of real AI.
Moreover, differences in funding and participation models also influence their development paths. Web2 AI is typically driven by research investment and product profitability, with a relatively stable cycle. Web3 AI combines crypto market's speculative attributes, often experiencing "wave" cycles with dramatic rises and falls based on market sentiment: when the concept is hot, funds rush in, driving up token prices and valuations; when cooling, project heat and funds rapidly decline. This cycle makes Web3 AI's development path more volatile and narrative-driven. For example, a concept lacking substantial progress might trigger token price surge due to market sentiment; conversely, even with technological progress, it's hard to gain attention during a low market.
We maintain a "low-key yet cautious expectation" towards the main narrative of WEB3 AI, "decentralized AI network" - what if it actually succeeds? After all, WEB3 has epoch-making entities like BTC and ETH. But at the current stage, everyone needs to practically conceive some immediately implementable scenarios, such as embedding AI Agents in current WEB3 projects to improve project efficiency; or combining AI with other new technologies to generate new ideas applicable to the crypto industry, even if just to attract attention; or AI products serving only the WEB3 industry, providing services that WEB3 organizations or individuals can accept, whether in data precision or better alignment with WEB3 work habits.
