Why is the difficulty of building web3AI infrastructure greater than expected?

Author: Haotian

1. Currently, most of the active AI projects in web3 have become generally MEME-oriented, boasting a lot of stories that are impossible to realize and implement. The key is that they have attracted most of the attention and liquidity through rapid token issuance to enter the market, as well as the aftermath of the short-term bubble burst (negative EV). This is mainly due to the overly appealing narrative of AI + Crypto, while its actual application challenges are too significant, naturally making it a heavy disaster area for bubble issuance reliant on narrative from the very beginning.

2. The web3AI infrastructure is essentially a reconstruction of the web2 AI infrastructure, and most of the time it is a thankless task. It's similar to how Crypto challenged centralization in the name of decentralization; for a long time, building decentralized network architectures was criticized as redundant and meaningless until subsequent DeFi application scenarios found some value capture points.

The current predicament of web3AI is no different from the initial vision of decentralized Crypto. Most people are still accustomed to casually asking, "What’s the use of web3AI?" But let's not forget that decentralized computing power aggregation, distributed inference, and distributed data labeling networks can all find entry scenarios in terms of training costs, performance, and practicality. It can only be said that the road ahead is long and fraught with obstacles, but its significance is profound.

3. The construction and expansion of web3AI infrastructure has a high cost during the trial and error period, requiring strong rationalist support. For example, it is well known that web3AI requires the establishment of a data layer, but cleaning the massive on-chain and off-chain data requires a significant amount of server maintenance and development costs. At the same time, the costs of integrating mature web3AI APIs, as well as computing power and algorithm tuning, also require investment. If these costs are focused on Agent applications, it is possible to quickly explore business monetization models. However, if the focus is on the infrastructure level, under the current market background where the technological narrative is not very popular, it poses a challenge for many developer teams.

The more troublesome issue is that, unlike traditional web2 infrastructure, web3 AI also needs to address the coordination problem between off-chain data and on-chain verification, the model distribution and update mechanism under a P2P network, and the complex design of replacing traditional business models with Tokenomics incentives, among other challenges. Moreover, the short-sightedness of capital and the speculative atmosphere of market preferences have resulted in hot money flowing into Agent applications that launched hastily just to ride the wave, making it difficult for teams that are truly working at the infrastructure level to gain sufficient support.

4. The illusion problem of web3AI infra-compatible "black box" compatible large models makes its security and trustworthiness in specific scenarios a huge challenge. Seeing the recent output of @SlowMist_Team in terms of MCP security vulnerabilities, I feel that the professional security audit around MCP can already support the positioning of SlowMist as an AI audit company in the future. This is just a concrete case that verifies the unknown security challenges of AI LLMs as a basic data source to access web3 AI infra. However, the problems surrounding web3 AI infra are far more than these, in addition, there are verifiable computing frameworks built through web3 cryptography verification and on-chain consensus mechanisms to ensure that the AI inference process can be traced and verified.

In fact, AI's trusted verification and computing framework is the core area that web3AI infra wants to overcome. When the current large model deals with highly sensitive information such as finance, medical care, and law, the adoption rate in the professional field is greatly limited because it cannot provide the verifiability of the inference process. The maturity of web3 AI infra, such as the underlying zkVM, decentralized Oracle network, decentralized memory solution, etc., can build a set of verifiable and provable computing frameworks for AI, and fundamentally help AI achieve rapid expansion of vertical scenarios.

That's all.

The journey of building infra and applications for web3AI will not be accomplished overnight; it is a long marathon. Those who can truly build infra and application ecosystems that solve real-world problems, those who can balance hype and value in the Go-To-Market process, and those who can find practical business loops while maintaining technological foresight will be the ones who truly laugh last in the industry.