The AI cycle has arrived—should Web3 entrepreneurs switch to AI?

“Have you raised lobsters?” Recently, among Web3ers, this is probably the most common greeting.

At the start of 2026, after the Chinese New Year Gala robot stole the show, a new generation of AI Agents represented by OpenClaw has become the new toy among tech enthusiasts. Some use AI for customer service, others for coding, and some are even experimenting with Agents to simulate entire “digital employees.” The concept of a “one-person company,” which has been frequently discussed across various internet platforms lately, refers to an individual running a work pipeline that previously required a small team.

Of course, Web3 hasn’t been idle either. Lately, if you pay attention to industry media, you’ll notice many projects starting to focus on AI Agents. Some are researching how Agents can directly interact with on-chain assets or smart contracts, others are developing payment, identity, or financial infrastructure for Agents, and some are discussing an “Agent economy” that allows AI to participate in networks like users do. There are even new calls for a “Web4.0.”

Seeing this, a familiar feeling arises.

They say fashion is cyclical, and it turns out the tech (or crypto) world is the same. Remember the bear market starting in 2022? ChatGPT exploded overnight, and AI suddenly became everyone’s hot topic. The Web3 community didn’t lag behind; quickly, a bunch of new concepts emerged—AI Agents, AI traders, automation strategies—anything related to AI could be spun into a new story. But this excitement didn’t last long. As the crypto market rebounded, everyone’s attention quickly shifted back to crypto itself.

Now, in the second half of 2025, with the crypto market showing signs of a bear trend again, Web3 is searching for new concepts to latch onto.

However, from Portal Labs’ perspective, the problem lies precisely here. When a narrative becomes popular, many Web3 startups aren’t actually making technical or business judgments—they’re just following the hype: whichever concept is hot, they jump on it. And then they often stumble—

Many teams only realize after pushing their projects forward that while concepts can be quickly assembled, products are hard to implement. Where are the users? What are the specific use cases? How to sustain revenue? Can they attract investment? These questions often only surface after some time.

When the hype fades, what remains are often unfinished projects. Some products stay stuck in the demo stage, some barely launch without finding users, and others simply disappear along with the narrative. It may seem like a new track has opened up in the short term, but looking back after a while, few projects truly stand out.

This leads to a dilemma: continue deepening in crypto or switch to AI? Choosing the former means facing a tough market with uncertain returns; choosing the latter means facing an entirely new terrain. AI’s technical barriers, talent structure, and competitive environment differ significantly from Web3. Many teams’ accumulated tech stacks, product experiences, and community resources over the past few years are built within the crypto ecosystem. Switching entirely to AI is like entering a completely unfamiliar track—requiring rebuilding from models and data resources to engineering teams.

A more pragmatic point is that the AI field itself is already highly crowded. Major model companies, traditional internet giants, and countless startups are pouring enormous resources into this space. For a Web3 startup, simply pivoting into AI because of hype often reveals a lack of technical advantage and industry resources.

In fact, many Web3 teams still have a viable path. They don’t necessarily need to pivot into AI; instead, they can continue on their Web3 journey while exploring how crypto can complement AI systems.

Looking closely at current AI development, many key issues remain unresolved.

The most obvious is data. Models are becoming more powerful, but where does training data come from? Is the data trustworthy and compliant? How can AI Agents achieve 1v1 customization? These questions lack effective mechanisms. For AI relying on large-scale data training, this is a fundamental, long-standing issue.

Another is identity and collaboration. When AI Agents start participating in task execution, automated trading, or operational decision-making, they also need identities, permissions, and collaboration rules. Who can call an Agent? How do Agents coordinate? How are tasks settled after completion? These questions fundamentally involve identity and value distribution in open networks.

Payment is also a concern. Once AI Agents begin autonomously calling services, fetching data, or executing tasks online, they require a small, automated settlement system. In traditional internet systems, such payment structures are difficult to implement.

While these seem like AI problems, many solutions already exist within the crypto ecosystem. Data incentive networks, on-chain identity systems, and open payment networks have been explored by Web3 over the past few years.

If Web3 startups truly want to explore these directions, several things must be considered first.

First, assess the team’s technical capabilities. Different Web3 projects have varying levels of technical accumulation. Some excel at on-chain protocols, some focus on data networks, others on application-layer products. If a team has been working on data infrastructure—data collection, extraction, or markets—then extending into AI data layers, such as data contribution networks, verifiable data sources, or incentivized data markets, is a natural step. If the team is more focused on protocols or infrastructure, they might consider building on AI Agent environments—on-chain identities, permission management, task execution protocols, or automated settlement and payment systems. For application teams—like trading tools, content platforms, or community apps—AI can be embedded as a capability layer, enhancing data analysis, automating operations, or handling functions previously done manually.

Second, evaluate whether there are real business scenarios. Many AI projects fade quickly not because of technical issues but because they lack clear use cases from the start. Concepts like “AI + Web3,” “Agent economy,” or “AI traders” sound grand, but upon closer inspection, the actual user base is limited. Conversely, more mundane needs—data processing, automation, information filtering, task execution—are often long-standing in real business. When considering entering an AI direction, it’s better to focus on whether the scenario addresses a persistent business problem, whether there are paying users, and whether AI can genuinely improve efficiency in that context. If these conditions are met, the project is more likely to turn from hype into a real product.

Third, consider whether the team has the resources to penetrate these areas.

Data, identity, and payment are fundamentally network resource issues, not just technical ones.

For example, a data network requires stable data sources and a community willing to contribute data; without these, even the best technology can’t generate network effects. Similarly, building an identity or collaboration network for AI Agents needs active developers, applications, or Agents involved; otherwise, the protocol can’t form an ecosystem. Payment and settlement systems also depend on a large number of Agents and services operating simultaneously; otherwise, the small payment flows are insignificant.

Therefore, for many Web3 teams, the real question isn’t whether there’s technical space in this direction, but whether they can become part of that network. Do they already have data sources, developer ecosystems, or application scenarios? These factors often determine whether a project can truly enter the AI infrastructure layer, rather than just remaining at the conceptual level.