The AI ​​cycle is here. Should Web3 entrepreneurs switch to AI?

"Have you raised lobsters yet?" This is probably the most common greeting among Web3ers these days.

At the start of 2026, following the phenomenal success of the robots at the Chinese New Year Gala, a new generation of AI agents, represented by OpenClaw, became the new toys among tech enthusiasts. Some used AI for customer service, some used AI to write code, and some even began experimenting with using agents to simulate a whole set of "digital employees." Recently, the concept of "one-person company," which has been frequently mentioned on various internet platforms, refers to a single person using an AI workflow to complete tasks that previously required a small team.

Of course, the Web3 community hasn't been idle either. If you've been following industry media lately, you'll find that many projects are starting to focus on AI agents. Some are researching how agents can directly access on-chain assets or contracts, others are working on payment, identity, or financial infrastructure for agents, some are discussing "agent economic systems" that allow AI to participate in the network like users, and some are even starting to tout the new slogan "Web4.0."

Seeing this, you might actually feel a sense of familiarity.

It's often said that the fashion world is cyclical, but who would have thought the tech world (or more specifically, the crypto world) would follow suit? Remember the bear market that started in 2022? ChatGPT exploded in popularity overnight, and AI suddenly became everyone's talking about. The Web3 community, of course, wasn't idle either, quickly spawning a bunch of new concepts like AI Agents, AI Traders, and automated strategies—it seemed that anything even remotely related to AI could be used to tell a new story. But this frenzy didn't last long. Once the crypto market rebounded, everyone's attention quickly returned to Crypto itself.

However, in the second half of 2025, the crypto market showed signs of a bear market again, so Web3 began looking for new concepts to take over.

However, Portal Labs believes this is precisely where the problem lies. When a narrative becomes popular, many Web3 startups aren't making technical or business judgments, but rather narrative judgments: whichever concept is hot, they jump on . And then they stumble—

Many teams only realize when they actually start working on a project that while concepts can be quickly built, products are difficult to bring to fruition. Where are the users? What are the specific scenarios? How will they generate revenue? Can they attract investment? These questions often only begin to emerge after the project has been running for some time.

Once the hype dies down, what remains in the market is often a bunch of projects that haven't even been successfully implemented. Some products remain stuck in the demo stage, some barely launch but can't find users, and some simply disappear along with the narrative. In the short term, it may seem like a new track has been opened up, but looking back after some time, there's actually not much that has truly survived.

Therefore, the choice between continuing to focus on Crypto and transitioning to AI has become a dilemma. Choosing the former means facing a challenging market with uncertain returns on investment; choosing the latter leaves one uncertain. The technical barriers, talent pool, and competitive environment of AI differ significantly from Web3. Many teams have built their technology stacks, product experience, and community resources over the past few years within the Crypto ecosystem. A complete shift to AI would mean entering a completely unfamiliar arena. From model capabilities and data resources to engineering teams, almost everything would need to be rebuilt.

A more realistic point is that the AI ​​field is already extremely crowded. Large companies, traditional internet enterprises, and numerous startups are all investing heavily in this area. For a startup team originally focused on Web3, entering this market simply because of a shift in narrative can easily lead to finding themselves lacking both technological advantages and industry resources.

In fact, for many Web3 startups, there is another path they can explore. They don't necessarily have to transform into AI companies, but rather continue along their existing Web3 path while considering what capabilities Crypto can add to the AI ​​ecosystem.

If you look closely at the current wave of AI development, you'll find that many key aspects have not yet been fully resolved.

The most typical example is data . Models are becoming increasingly powerful, but the source of training data, its reliability and compliance, and especially how AI agents can achieve 1-on-1 customization, remain unresolved issues without a good mechanism. This is a long-standing fundamental problem for AI that relies on large-scale data training.

Take identity and collaboration, for example. When AI agents begin to participate in task execution, automated transactions, and even operational decision-making, they themselves also need identity, permissions, and collaboration rules. Who can invoke a particular agent? How are tasks divided among agents? How is settlement made after task execution? These questions essentially involve identity and value distribution in open networks.

There's also the issue of payments . Once AI agents begin autonomously calling services, retrieving data, or performing tasks within the network, it means they need a small-amount payment system that can automatically settle payments. However, such a payment structure is difficult to implement in the traditional internet ecosystem.

These all seem to be AI problems, but many solutions already exist within Crypto's technological framework. Whether it's a data-incentivized network, an on-chain identity system, or an open payment network, these are all directions that Web3 has been exploring for the past few years.

If a Web3 startup team really intends to try these directions, there are a few things they must think about first.

The first thing to consider is the team's technical capabilities . Different Web3 projects have vastly different levels of technical expertise. Some teams excel at on-chain protocols, some have long focused on data networks, and others are more application-layer products. If a team has been working on data-related infrastructure for the past few years, such as data collection, data extraction, or data marketplaces, then extending into the data layer surrounding AI will be relatively natural. This could include data contribution networks, verifiable data sources, or providing incentivized data marketplaces for models. If the team is primarily focused on on-chain protocols or infrastructure, they can consider building around the AI ​​Agent's operating environment, such as on-chain agent identity, permission management, task execution protocols, or providing agents with automated settlement and payment capabilities. For teams already developing application-layer products, such as trading tools, content platforms, community products, or consumer applications, AI is more suitable as a capability layer embedded in their existing product system. For example, AI can be used to enhance data analysis capabilities, automate operational processes, or use agents to complete functions that previously required manual processing.

Secondly, it's crucial to examine the existence of real-world business scenarios . Many AI projects disappear quickly not because of technological shortcomings, but because they lacked a clear use case from the outset. Concepts can be incredibly popular, but questions like: Where are the people who actually need this product? Why do they need it? And why are they willing to pay for it? These questions are often left unanswered. Some concepts are widely discussed in the industry, such as "AI + Web3," "Agent economic system," and "AI traders"—they sound grand, but if you delve deeper, the truly stable user base is actually quite small. Conversely, some seemingly less "sexy" needs, such as data processing, automated operations, information filtering, or task execution, are often long-standing and present in real-world business operations. Therefore, when deciding whether to enter a particular AI field, it's more important to examine the scenario itself than to focus on whether the concept is popular: Is this a long-standing business problem? Are people already paying for it? And can AI truly improve efficiency in this process? If these conditions are met, then this direction is more likely to evolve from a narrative into a product.

Further investigation is needed to determine whether Web3 startups have the resources to truly enter these stages .

The issues of data, identity, and payment mentioned earlier are not simply technical problems, but rather problems of network resources.

For example, in data networks, if a team lacks a stable source of data and a user base capable of consistently contributing data, even if the technology is developed, it will be difficult to create a true network effect. Similarly, building an identity system or collaborative network for AI agents requires the participation of real developers, applications, or agents; otherwise, the protocol itself will struggle to form an ecosystem. Payment and settlement systems follow a similar logic. Once AI agents begin calling services, acquiring data, or performing tasks within the network, small payments become very frequent. However, such a payment network is only meaningful when a large number of agents and services exist simultaneously; otherwise, it remains merely a technical module.

Therefore, for many Web3 teams, the real question to assess is not "whether there is technical potential in this direction," but whether they can become part of this network. Whether the team already has data sources, a developer ecosystem, or application scenarios often determines whether a project can truly enter the infrastructure layer of AI, rather than remaining at the conceptual level.