Original title: The Rise of Web3 AI Apps Original author: Defi0xJeff, Crypto Kol Original translation: zhouzhou, BlockBeats
Editor's note: This article discusses the future of AI agents in Web3, depicting the transition from "entertaining chatbots" to "professional problem solvers." It emphasizes the unique advantages of Web3, such as global liquidity, decentralization, and token economics, making AI agents not only more practical, but also creating profound value for users. The article looks forward to 2025, when dedicated large language models and multi-agent collaboration will redefine the role of AI, and the integration of Web2 and Web3 will drive innovation and usher in an AI era with practicality at its core.
The following is the original content (for easier reading and understanding, the original content has been reorganized):
Agents focused on personality have come a long way since the early days of AI agents. In the beginning, we were attracted to agents that entertained us, made jokes, or simply “trolled” us. These agents captured people’s attention and created a lot of hype, but as the market developed, one thing became increasingly clear: value and utility are more important than personality.
We’ve seen countless personality-driven proxy launches that, despite initial excitement, eventually faded into obscurity because they failed to provide more than surface-level engagement. This trend highlights an important lesson — Web3 values substance over surface, and practicality over novelty.
This evolution echoes a similar shift in AI in Web2. More and more specialized large language models (LLMs) are being developed, optimized for niche use cases, from finance to law, real estate, and more. These models focus on accuracy and reliability, filling the gap in general-purpose AI.
The challenge with general AI is that it often provides answers that are “good enough,” but in many cases that’s not acceptable. For example, a popular model might only be 70% accurate in a particular niche, which might be fine for everyday use, but in high-stakes scenarios — like whether you win a court case or lose millions in a financial decision — it’s terrible. That’s why specialized LLMs, finely tuned to achieve 98%-99% accuracy, are becoming increasingly important.
The key question we are going to explore next is: Why Web3? Why not let Web2 dominate the field of dedicated AI?
Web3 offers several advantages that Web2 cannot match:
Global liquidity
Web3 enables teams to start funding more efficiently. Through token issuance, an AI project can quickly access global liquidity and skip months of venture capital meetings and negotiations. This approach democratizes access to funding and allows developers to get resources for development faster.
Token economics promotes value accumulation
Tokens allow teams to reward early adopters, incentivize holders, and keep the ecosystem going. For example, virtuals io allocates 1% of transaction fees to cover inference costs, ensuring their proxy remains functional and competitive without relying on external funding.
DeAI Infrastructure
Web3 offers open source models, decentralized computing (such as hyperbolic labs and AethirCloud), and access to massive open data pipelines (such as cookiedotfun, withvana), making it possible to build a highly collaborative and cost-effective infrastructure that is difficult to replicate in Web2.
More importantly, Web3 encourages the formation of a passionate developer community to drive innovation together.
Web3 AI Ecosystem
In the Web3 AI agent ecosystem, we are beginning to see the ecosystem improve its capabilities by integrating technologies and unlocking new use cases. From Bittensor subnets to Olas, Pond, and Flock, the ecosystem is building more interoperable and powerful agents. At the same time, more and more easy-to-use tools are emerging to enhance functionality, such as sendaifun's Solana Agent Kit or coinbase's CDP SDK. Ecosystems like this are building AI applications that put practicality first.
alchemistAIapp: A no-code AI application building platform.
myshell ai: An AI app store focused on image generation, visual novels, and "wife simulator".
·questflow: A multi-agent coordination protocol (MAOP) for implementing productivity-enhancing use cases. Questflow’s integration with Virtuals creates Santa agents that gamify airdrops and manage incentives.
0xCapx: A practical AI application store located on Telegram.
In addition to the ecosystem, individual agents are also beginning to emerge in specific areas:
·corpauditai: A financial analysis AI agent that reviews reports and identifies market opportunities.
CPA Agent: Built by RealTjDunham, this agent calculates cryptocurrency taxes and generates reports for users.
This shift from “chatbots chatting randomly on CT” to “professional experts sharing insights in their respective fields” will continue. The future of AI agents is no longer chatbots chatting randomly, but professional experts in their respective fields, providing value and insights in an engaging way. These agents will continue to attract users’ attention and guide them to actual products - whether it is a trading terminal, a tax calculator, or a productivity tool.
Where will the value go?
The biggest beneficiaries will be Agentic L1s and Coordination Layers.
Agentic L1s: Platforms like virtuals io and ai16zdao are raising the bar and ensuring their ecosystems are focused on quality. Virtuals remains the top L1 for agents, and soon, ai16zdao’s launchpad will join the fray. Agents that rely solely on personality are disappearing, replaced by agents that are both useful and engaging.
·Coordination Layers: These layers, like TheoriqAI, will coordinate a group of agents, combining their strengths to provide seamless and powerful results for users. Imagine bringing together agents like aixbt, gekko, and CPA to provide alpha sources, execute trades, and handle taxes — all in one coherent workflow. Theoriq’s task-based discovery framework is a step toward unlocking collective intelligence.
Final Thoughts
The narrative of utility-first AI applications has only just begun. Web3 has a unique opportunity to carve out a space where AI agents are more than just entertainment, but solve real problems, automate complex tasks, and create value for users.
2025 will be the year of the transition from chatbots to co-pilots, where purpose-built large language models (LLMs) and multi-agent coordination will redefine what we think of as AI. Web2 and Web3 will merge, but the open, collaborative nature of Web3 will lay the foundation for some of the most innovative breakthroughs.
It’s no longer about “AI agents with personality”, but agents that provide utility and create meaningful impact. Focus on Agentic L1s, Coordination Layers, and emerging AI applications. The birth of the Agentic era has arrived - and this is just the beginning.
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