📝 Hello everyone, I am 𝟏𝟎. While everyone is discussing whether you should raise lobsters, the true builders are quietly catching up.

At the beginning of 2026, AI, especially AI Agents, became a hot topic. With the robot performance during the Chinese New Year Gala sparking widespread discussion online, AI began to rapidly penetrate various industries, changing people's perceptions of work from AI customer service to digital employees.

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1. The Intersection of AI and Web3: Trends and Thoughts

Under this trend, Web3 entrepreneurs are also beginning to attempt to combine AI with blockchain, exploring new AI economic models.

However, as a long-term observer of Web3, I am somewhat worried about this trend. Whenever the market is sluggish, the tech circle is always keen to find new lifelines. Just like when ChatGPT was booming in 2022, Web3 also quickly followed suit, but after the market rebounded, everyone swiftly returned to cryptocurrency itself.

Now, with the crypto market once again sluggish, AI has become the new focus. So, do Web3 entrepreneurs really need to put down their current projects and turn to AI? This question is worth pondering.

2. Narrative-driven vs. value-driven.

One of the biggest mistakes many Web3 teams make is not choosing the wrong track, but making narrative judgments instead of business judgments.

This mindset typically manifests as: doing whatever concept is trending. Especially in a bull market, this chasing trends entrepreneurial logic might sustain itself for a while with financing stories, but once entering a bear market, the real commercial landing will expose the issues. For instance, when everyone is following the trend of AI+Web3, few ask the most critical questions: Where are our users? What specific pain points are we addressing? What is our profit model?

The result is often that concepts are built quickly, but products remain at the demo stage, and user growth curves are often dismal. By the time the AI craze has passed, all that’s left in the market are fragments scattered everywhere.

The reality is that the AI track is already a red ocean. Large companies, internet giants, and thousands of top AI startups are investing resources and talent far beyond yours in this field. If a Web3 team enters this unfamiliar domain just to chase the narrative trend, they may find themselves without technical barriers, without data resources, and even needing to start from scratch to build basic industry connections. It's like a successful fisherman suddenly switching to lobster farming; while it might succeed, the cost is abandoning all the experience accumulated in the past.

3. The combination of Web3 and AI: you don't have to choose one or the other.

That said, can Web3 entrepreneurs really only choose one? Either stick to Crypto or completely pivot to AI? In fact, there is a third path, and it may be broader than both sides.

If you closely observe the bottlenecks in this wave of AI development, you'll find several key issues that are precisely the areas where Web3 technology excels.

Data credibility and incentive issues: AI requires a large amount of data, but is the data credible? Is the labeling accurate? Web3's decentralized ledger and token incentives can build a closed loop for data contribution, verification, and circulation, solving these pain points.

The identity and payment of AI Agents: When AI Agents begin to execute on-chain transactions or manage assets, identity authentication and payment systems are needed. Web3's on-chain identity protocols and micropayment networks can just solve these problems.

Rebalancing safety and trust: AI Agents may be manipulated or rely on tampered data, bringing enormous risks. The transparency, verifiability, and decentralized consensus mechanisms of Web3 can provide a more reliable trust guarantee for AI decision-making.

Therefore, Web3 can not only integrate with AI but can also provide critical infrastructure for AI. Rather than competing with others in the AI field, it is better to leverage Web3's advantages to address the core issues of AI.

4. Find your position: What is your team suited to do?

Of course, everyone understands this principle; the key is how to implement it. If you want to follow this path, there are a few questions that must be clearly thought out:

First, recognize the team's DNA. What are you good at?

If you are good at data networks (data collection, indexing, markets, etc.), you can try to build a data incentive layer for AI, creating a network that encourages users to provide high-quality data, and using blockchain to ensure data traceability and immutability.

If you are good at on-chain protocols and infrastructure, you might consider identity and payment protocols for AI Agents. For example, developing a machine payment protocol similar to x402, or an on-chain identity system based on the ERC-8004 standard, allowing each Agent to operate on-chain like an independent economic entity.

If you are good at application layer products (trading tools or content platforms, etc.), you can embed AI into existing products, such as smart customer service, automated operations, on-chain data analysis assistants, etc., to enhance product experience rather than starting from scratch.

Secondly, scenarios are more important than concepts. Whether a direction is feasible should not be judged by how popular it is on Twitter, but by whether there is actual business demand. Concepts like the Agent economic system and AI+Web3 sound grand, but if you ask who is paying for what scenarios, many people can't answer. In contrast, some seemingly less attractive scenarios, such as data processing, automated risk control, information filtering, and automated trading execution, often have stable payment demand.

Finally, resources determine how far you can go. If you want to build a data network, do you have a stable data source? If you want to create an Agent identity protocol, are there developers willing to build an ecosystem based on your standards? If you want to do payment settlement, do you have enough Agents and services running on the network to create an effect? These questions are essentially resource issues, not technical issues.

5. How Web3 entrepreneurs can respond to the AI wave.

For Web3 entrepreneurs, the current wave of AI is neither a trend that should be blindly followed nor a bubble that should be ignored. It is a rare opportunity window, a chance for Web3 technology to move from self-entertainment to empowering the real economy.

Rather than squeezing into the AI track to farm lobsters, think about how to be the one providing water, electricity, and coal for the entire lobster industry chain. AI takes care of being smart, while Web3 takes care of being trustworthy. When AI Agents need identity, payments, collaboration, and credible data, they will find that the only place on this planet that can provide these open infrastructures is the Web3 world we are in.

The future winners will not be those who are best at chasing trends, but those who can settle down and use their strengths to complement others' weaknesses. The AI wave has arrived; Web3 entrepreneurs need not rush to change careers, but can instead ask themselves: In this technological revolution, which nail is my hammer best suited to strike?