Introduction
Since ChatGPT made its debut at the end of 2022, the AI sector has been a hot topic in the cryptocurrency field. WEB3 nomads have already embraced the idea that 'any concept can be hyped,' not to mention the limitless narrative threads and application capabilities of AI in the future. Thus, in the crypto circle, the AI concept initially surged to popularity as a 'meme craze,' and later some projects began exploring its practical application value: what new practical applications can cryptocurrency bring to AI, which is advancing rapidly?
This research article will narrate and evaluate the current evolution path of AI in the Web3 field, from the early hype waves to the current rise of application-based projects, and use cases and data to help readers grasp the industry context and future trends. Let's throw out an immature conclusion right at the start:
01. The phase of AI memes is already a thing of the past; what should have been cut and what should have been earned remains as eternal fragments of memory.
02. Some foundational WEB3 AI projects have always emphasized that 'decentralization' can bring benefits to AI security, which users are not very willing to pay for; what users care about is 'whether the token is profitable' + 'how good the product is.'
03. If one wants to invest in AI-related cryptocurrency projects, the focus should shift to pure application-based AI projects or platform-based AI projects (which can aggregate many tools or agents that are easy for end users to use); this may be the longer-term wealth hotspot after the AI meme.
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 concentrated development path. Large companies (like OpenAI, Google) train closed black-box models, with algorithms and data not made public, leaving users only able to utilize their results, lacking transparency. This centralized control leads to AI decisions being un-auditable, with issues of bias and unclear accountability. Overall, AI innovation in Web2 focuses on enhancing the performance of foundational models and implementing commercial applications, but the decision-making process remains opaque to the public. This opacity has led to the rise of new AI projects like Deepseek in 2025, which appear open-source but are essentially 'fishing in a barrel.'
In addition to the opacity issue, large AI models in WEB2 also face two other pain points: insufficient user experience across different product forms and insufficient accuracy in specialized sub-fields.
For example, if one wants to create a PPT, an image, or a video, users will still seek out AI products with lower entry barriers and better user experiences, and will pay for them. Currently, many AI projects are attempting to create no-code AI products, aiming to lower the user entry threshold even further.
For many WEB3 users, there has likely been a sense of powerlessness when using ChatGPT or DeepSeek to obtain information about a particular cryptocurrency project or token; large model data still cannot accurately cover the detailed information of any specific sub-industry in this world, so another development direction for many AI products is to achieve the deepest and most accurate 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 path.
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 a few companies over AI in a distributed manner. For example, some projects explore using blockchain to verify AI decisions (zero-knowledge proofs ensure model outputs are credible) 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 motivating developers and users to participate through a token mechanism. However, in practice, the development of AI in Web3 is still limited by technology and resources: building decentralized AI infrastructure is extremely challenging (training large models requires massive computational data, yet no Web3 project has funding that can match even a fraction of OpenAI's). A few projects claiming to be Web3 AI still rely on centralized models or services, only integrating some blockchain elements at the application level; these relatively reliable Web3 AI projects are at least engaged in real development applications; while the vast majority of Web3 AI projects are either 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 usually driven by research investment and product profitability, with a relatively smooth cycle. In contrast, Web3 AI combines the speculative nature of the cryptocurrency market, often experiencing 'hype' cycles that fluctuate wildly with market sentiment: when concepts are hot, funds rush in to drive up token prices and valuations; when cooled, project interest and funding rapidly decline. This cycle makes the development path of Web3 AI more volatile and narrative-driven. For example, an AI concept lacking substantial progress may trigger a token price surge due to market sentiment; conversely, even with technological advancements, it is hard to gain attention during a downturn.
We maintain a 'low-key and cautious expectation' for the main narrative of WEB3 AI, 'decentralized AI networks'; what if it actually becomes a reality? After all, WEB3 still has epoch-making existences like BTC and ETH. However, at the current stage, everyone still needs to concretely envision some immediately applicable scenarios, such as embedding AI Agents into current WEB3 projects to improve their efficiency; or combining AI with other new technologies to generate new ideas applicable to the cryptocurrency industry, even if they are just concepts that attract attention; or creating AI products solely for the WEB3 industry, whether in terms of data accuracy or better alignment with the working habits of WEB3 organizations or individuals, to provide services that the WEB3 demographic can pay for.
To be continued, the next article will mainly review and comment on the five waves of WEB3 AI hype and some of the products (such as Fetch.AI, TURBO, GOAT, AI16Z, Joinable AI, MyShell, etc.).
References:
[ 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/)
