The AI cycle has arrived—should Web3 founders pivot to AI?
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The AI cycle has arrived—should Web3 founders pivot to AI?
When a narrative begins to gain traction, many Web3 startup teams make narrative-driven decisions: they chase whichever concept is trending, only to stumble as a result.
Navigating Web3 tides with focused insightsBy Portal Lab
“Have you raised a lobster yet?” Lately, this may well be the go-to greeting among Web3ers.
At the start of 2026, following the viral sensation of robots at China’s Spring Festival Gala, a new generation of AI Agents—epitomized by OpenClaw—has become the latest tech toy on everyone’s lips. Some use AI for customer service; others for coding; and still others are even experimenting with Agents to simulate full “digital employees.” A recently trending concept across internet platforms—“the one-person company”—refers to an individual running what used to require an entire small team, powered entirely by an AI workflow.
Web3, of course, hasn’t stayed idle either. If you’ve been following industry media lately, you’ll notice numerous projects pivoting toward AI Agents: some exploring how Agents can directly invoke on-chain assets or smart contracts; others building payment, identity, or financial infrastructure for Agents; still others debating an “Agent economy” that enables AI to participate in networks just like human users—and yes, some have even revived the slogan “Web4.0.”
Reading this, you might feel a strong sense of déjà vu.
They say fashion is cyclical—turns out, so is the tech (or crypto) world. Recall the bear market beginning in 2022: ChatGPT exploded overnight, and AI instantly became *the* topic of conversation. The Web3 space was no exception—new concepts flooded in almost immediately: AI Agents, AI traders, automated strategies… it seemed that merely associating with AI was enough to spin a fresh narrative. Yet this buzz didn’t last long. As crypto markets rebounded, attention swiftly refocused back onto crypto itself.
Now, in the second half of 2025, crypto markets are again trending bearish—and Web3 is once more searching for a new narrative to fill the void.
But here lies the crux of the problem—according to Portal Labs. When a narrative gains traction, many Web3 startups aren’t making technical or commercial judgments—they’re making *narrative* judgments: “Which concept is hot? Let’s build that.” And then they stumble.
Many teams only realize, mid-development, that while concepts are easy to assemble, products are hard to ship. Where are the users? What’s the concrete use case? How will revenue sustainably flow? Can they secure investment? These questions often surface only after the project has been underway for some time.
Once hype fades, what remains is often a graveyard of unproven projects: some stuck at the demo stage; others barely launched but unable to attract users; and still others vanishing entirely alongside the narrative. In the short term, it may look like a new sector has opened up—but upon reflection, very little of lasting value remains.
Thus, a dilemma emerges: double down on crypto—or pivot to AI? Choosing the former risks low returns amid a sluggish market; choosing the latter brings uncertainty. AI’s technical barriers, talent requirements, and competitive landscape differ markedly from Web3’s. Many teams’ accumulated expertise—technical stacks, product experience, community resources—has been built entirely within the crypto ecosystem. A full pivot to AI means entering a completely unfamiliar arena, requiring near-total rebuilding: from model capabilities and data resources to engineering teams.
More concretely, the AI space is already intensely crowded. Large-model companies, traditional internet giants, and countless startups have poured massive resources into the field. For a Web3-native startup entering solely due to narrative momentum, it’s easy to find oneself lacking both technical advantage and domain-specific resources.
Yet there exists another viable path for many Web3 startups: not necessarily pivoting *to* AI, but continuing along their Web3 trajectory while asking how crypto can *augment* AI systems—what unique capabilities can crypto bring to the AI stack?
Look closely at this current wave of AI advancement, and you’ll see many critical pieces remain unresolved.
The most obvious is data. Models grow ever more powerful—but where does training data come from? Is it trustworthy and compliant? Especially for AI Agents delivering 1:1 personalized outputs, no robust mechanism yet exists. For AI reliant on massive-scale data, this remains a foundational, persistent challenge.
Then there’s identity and collaboration. As AI Agents begin executing tasks, conducting autonomous trades, or even making operational decisions, they themselves need identities, permissions, and coordination rules. Who can invoke which Agent? How do Agents divide labor? How are outcomes settled post-execution? At their core, these questions revolve around identity and value distribution within open networks.
And then there’s payments. Once AI Agents autonomously call services, fetch data, or execute tasks across networks, they require a micro-payment system capable of automatic settlement—a structure extremely difficult to implement within traditional internet infrastructures.
These appear to be AI problems—but many of their solutions already exist within crypto’s technical toolkit: data incentive networks, on-chain identity systems, and open payment networks—directions Web3 has actively explored over the past several years.
If Web3 startups genuinely intend to explore these avenues, several considerations must be clarified upfront.
First, assess the team’s own technical capabilities. Web3 projects vary widely in technical focus: some specialize in on-chain protocols; others in data networks; still others in application-layer products. A team with years of experience building data infrastructure—data collection, extraction, or marketplaces—can naturally extend into AI’s data layer: e.g., data contribution networks, verifiable data sources, or incentivized data markets for model training. Teams focused on on-chain protocols or infrastructure might instead build Agent runtime environments: on-chain Agent identity, permission management, task execution protocols, or automated settlement and payment capabilities. Meanwhile, application-layer teams—building trading tools, content platforms, community products, or consumer apps—should treat AI as an embedded capability layer: enhancing analytics, automating operations, or delegating manual tasks to Agents.
Second, examine whether a genuine business use case exists. Many AI projects vanish quickly—not due to weak tech, but because they lacked clear, real-world applications from day one. Concepts may generate fervent discussion, but the fundamental questions often go unanswered: Who truly needs this product? Why would they use it? And why would they pay for it? Buzzworthy terms like “AI + Web3,” “Agent economy,” or “AI trader” sound grand—but probing deeper reveals few stable, paying user bases. Conversely, seemingly less “sexy” needs—data processing, automated operations, information filtering, or task execution—are persistent realities in actual businesses. Hence, when evaluating entry into any AI-adjacent direction, prioritize the *use case* over the *concept*: Is this a long-standing business pain point? Are people already paying to solve it? And can AI demonstrably improve efficiency here? Only if all three hold true does the direction stand a real chance of evolving from narrative to product.
Third, assess whether your Web3 startup possesses tangible resources to meaningfully enter these domains.
As noted earlier, data, identity, and payments aren’t purely technical challenges—they’re network-resource challenges.
Take data networks: without reliable data sources or a sustained contributor base, even a technically sound solution struggles to generate real network effects. Similarly, building an AI Agent identity system or collaboration network requires real developers, applications, or Agents actively participating—otherwise, the protocol remains ecology-free. Payment and settlement systems follow the same logic. Frequent micro-payments only matter once large numbers of Agents and services coexist on the network; otherwise, the system remains just another isolated module.
So for many Web3 teams, the real question isn’t “Does this direction offer technical headroom?”—but rather, “Can we become an integral part of this network?” Whether the team already commands data sources, developer ecosystems, or real-world application scenarios often determines whether a project can truly penetrate AI’s infrastructure layer—or remain stuck at the conceptual level.
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