Web2 vs Web3 AI Projects: Both Are About Money, So Why Such a Big Difference?
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Web2 vs Web3 AI Projects: Both Are About Money, So Why Such a Big Difference?
Don't be shy to admit it—most Web3 AI is just old wine in a new bottle, waiting for innovations to spill over from Web2 AI.
Navigating Web3 tides with focused insightsAuthor: Wenser, Odaily Planet Daily
Following the previous wave of speculation around AI Agent concept tokens, Web3 AI projects are currently experiencing a rare period of calm. Out of curiosity about Web2 AI initiatives, I attended two AI-related events in Hangzhou—one being an AI Hackathon with participants from diverse backgrounds and wildly varied directions, and the other a grassroots community gathering treating AI as a tool for profit-making. Here, I seemed to uncover some fundamental differences between Web2 and Web3 AI projects, which inspired this reflection. The following thoughts are purely personal opinions and do not represent the official stance of Odaily Planet Daily—offered merely as a small-angle narrative within the broader tide of the AI era for readers' reference.
The biggest difference between Web2 AI and Web3 AI: one builds products, the other creates assets
In my view, there are many distinctions between Web2 and Web3 AI projects, but the most significant lies in their end results—Web2 primarily speaks through products, whether large models, AI applications, or AI-powered solutions; whereas Web3 uses AI as packaging, fundamentally aiming to create conceptual assets, measuring success by token market performance. This explains why AI Agent concept tokens such as GOAT, AI16Z, ACT, and Swarms surged in popularity during the AI Agent hype cycle, only to fade away as market attention shifted. Below is my analysis of key differences between Web2 and Web3 AI projects:
Developer Base: Everyone Is a Dev vs. Technical Devs Only
This was the strongest impression I gained after attending two Web2 AI offline events: Web2 AI gatherings attract a remarkably broad audience—from children as young as eight or nine to senior citizens in their seventies—all showing great enthusiasm for AI. In contrast, Web3 AI projects typically remain confined to technical developers. Others participate mainly via token trading or project investment. Although many AI Agent projects promote the idea that "anyone can build their own AI Agent," actual participation remains minimal, and little real development work occurs.
The reason? High entry barriers and narrow use cases deter most people from engaging with Web3. Web2 AI, however, feels closer to the internet mainstream, resulting in a larger, wider, and more comprehensive developer base—especially with the emergence of AI coding tools like Cursor and Windsurf, enabling what's practically a “全民皆 Dev” (everyone is a developer) era.
Two of the youngest participants at the AI Hackathon
Project Origin: Driven by Needs vs. Driven by Market Trends
When it comes to starting points, Web2 AI projects usually begin with user needs—aiming to solve concrete problems, then building products to generate revenue. Web3 AI projects, on the other hand, originate from market trends: whatever narrative, concept, or asset the market desires becomes the focus, often geared toward fundraising. As a result, Web2 AI tends to concentrate on the application layer, while Web3 AI leans toward structuring projects around the triad of "compute, algorithm, data"—as seen in previously popular Myshell, and recently spotlighted Nillion and SaharaAI.
By comparison, mainstream Web3 AI projects today may essentially be solving just two problems: "How to create a token" and "How to sell an AI-themed token to the market for liquidity."
Hackathon theme: AI Problem-Solving Challenge
Project Operations: Product-Driven vs. Attention-Driven
In terms of operations, Web2 AI projects typically follow a product-driven model—growing and operating through product demos, feature explanations, and showcasing applicable scenarios. Web3 AI projects, however, adopt an attention-driven approach, prioritizing capturing market attention above all else. In Web3, attention equates to liquidity—attention is arguably the most valuable pricing vehicle. That’s why incidents such as a16z founder investing in Truth Of Terminal’s developer, AI16z founder Shaw making controversial statements, or Swarms’ developer being exposed for code plagiarism didn’t harm the projects—in fact, they boosted them.
In the Web3 AI space, the notion that “a good product naturally attracts traffic and speaks for itself” rarely holds true. Instead, the rule is “the loudest voice wins attention.” A superior product alone won’t guarantee success for a project or its token—because the reality is, most Web3 AI projects are nothing more than meme coins with no real technological application.
Talk of decentralized compute resources and decentralized data assets is often just wishful thinking shared between project teams and retail investors.
AI is the ultimate graffiti pen
Exit Mechanism: Monetization via Business Model vs. Token Liquidity
When it comes to exit mechanisms, this contrast couldn’t be clearer—or more blunt.
Web2 AI projects rely on monetization through sustainable business models. Whether using AI as an automation tool, launching an AI app, product, or large model, the ultimate goal is to attract as many users as possible and monetize via subscriptions, memberships, solutions, outright sales, or advertising—establishing a stable revenue stream. Most Web3 AI projects, however, have only one viable exit path: token liquidity. After all, genuine users for these projects are few and far between—much like certain Ethereum L2 networks that resemble ghost towns.
This starkly different exit mechanism determines that the former focuses on product, while the latter obsesses over token assets.
Let AI manage AI, let AI serve profit-making
Conclusion: When AI Becomes Yesterday’s News, Web3 AI Can Only Wait for Tech Spillover from Web2
As we enter early April 2025, having passed through two waves of AI project speculation from late last year into early this year, Web3 AI projects have briefly entered a “construction phase”—not out of ambition, but necessity. With shrinking market attention and liquidity, and with celebrities and even presidents becoming crypto收割ers (profit-takers), Web3 AI has cycled through various hyped themes—compute, storage, data, AI Agents, frameworks—and now stands as today’s “faded trend carried by the wind.”
Whether Web3 AI can revive and reclaim market attention may depend entirely on technological spillovers from Web2 AI giants, startups, and innovators. Otherwise, Web3 AI will remain nothing more than a “conceptual token scheme” wrapped in AI rhetoric. It’s time to face reality.
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