Original text: 0xJeff
Compiled by: Yuliya, PANews
As the AI agent field has evolved, the market has undergone a significant transformation from the initial focus on personalized agents. In the early days, people were attracted to agents that could entertain, tell jokes, or "create an atmosphere" on social media. These agents did generate a lot of buzz and attention, but as the market has evolved, one fact has become increasingly clear: practical value is far more important than personalization.
Many agents that focused primarily on personalization generated a lot of attention when they were launched, but ultimately faded from view as they were unable to provide value beyond superficial interactions. This trend highlights a key lesson: in the Web3 domain, substantive value takes precedence over surface-level effects, and utility trumps novelty.
This evolution mirrors the transition in the Web2 AI field. Specialized large language models (LLMs) are continuously being developed to address the specific needs of verticals such as finance, law, and real estate. These models prioritize accuracy and reliability, addressing the limitations of general-purpose AI.
The limitation of general-purpose AI is that it often can only provide "good enough" answers, which is unacceptable in certain scenarios. For example, a popular model may only have a 70% accuracy rate on specific professional questions. This may be sufficient for everyday use, but could have disastrous consequences in high-risk situations like court rulings or major financial decisions. This is why specialized LLMs, capable of achieving 98-99% accuracy, are becoming increasingly important.
So the question arises: Why choose Web3? Why not let Web2 dominate the professional AI domain?
Web3 has several distinct advantages over traditional Web2 AI:
First, there is global liquidity. Web3 allows teams to access capital more efficiently. Through token issuance, AI projects can directly tap into global liquidity, bypassing the time-consuming VC meetings and negotiations. This democratizes funding, enabling developers to acquire the resources they need more quickly.
Second, there is value accrual through token economics. Tokens allow teams to reward early adopters, incentivize holders, and sustain the ecosystem's long-term viability. For example, Virtuals allocates 1% of transaction fees to cover reasoning costs, ensuring its agents remain functional and competitive without relying on external funding.
Third, there is decentralized AI infrastructure. Web3 provides open-source models, decentralized computing resources (such as Hyperbolic and Aethir), and vast open data pipelines (like Cookie DAO and Vana), offering developers a collaborative and cost-effective platform that is difficult to replicate in Web2. More importantly, it fosters a passionate developer community driving innovation.
The Web3 AI Ecosystem
Within the Web3 AI agent ecosystem, we see various ecosystems enhancing their capabilities by integrating new features and unlocking new application scenarios. From Bittensor subnets to Olas, Pond, and Flock, these ecosystems are building more interoperable and functional agents. Meanwhile, user-friendly tools like SendAI's Solana Agent Kit or Coinbase CDP SDK are also emerging.
The following ecosystems are building utility-first AI applications:
ALCHEMIST AI has developed a no-code AI application building platform.
MyShell has created an AI application store focused on image generation, visual novels, and virtual character simulation.
Questflow has launched a Multi-Agent Orchestration Protocol (MAOP) for productivity-enhancing use cases, integrating with Virtuals to create a gamified airdrop and incentive management Santa Claus agent.
Capx AI has launched a utility-first AI application store on Telegram.
Individual Agents Focused on Practical Use Cases
Outside of the ecosystems, individual agents focused on specific domains are also emerging. For example:
- Corporate Audit AI is a financial analysis AI agent dedicated to reviewing reports and identifying market opportunities.
$CPA Agent, developed by Tj Dunham, focuses on computing cryptocurrency taxes and generating reports for users.
This transition from "chatbots that casually chat on social media" to "experts who share professional insights" will continue.
The future of AI agents lies not in casual chatbots, but in domain-specific expert agents that deliver value and insights in an engaging manner. These agents will continue to create thought-sharing and guide users to actual products, whether trading terminals, tax calculators, or productivity tools.
Where Will the Value Concentrate?
The biggest beneficiaries will be agent-centric L1s and coordination layers.
In the agent-centric L1 space, platforms like Virtuals and ai16z are raising industry standards, ensuring their ecosystems prioritize quality. Virtuals remains the top-tier L1 platform in the agent domain, while ai16z's launchpad will soon join the competition. Purely personalized agents are disappearing, replaced by agents that are both practical and appealing.
In the coordination layer, platforms like Theoriq will orchestrate the collaboration of numerous agents, integrating their strengths to provide seamless and powerful solutions for users. Imagine integrating agents like aixbt, gekko, and CPA into a unified workflow to achieve functions like alpha generation, trade execution, and tax processing. Theoriq's task-based discovery framework is moving towards unlocking this collective intelligence.
Final Thoughts
The narrative of utility-first AI applications is just beginning. Web3 has a unique opportunity to carve out a space where AI agents not only entertain, but also solve real problems, automate complex tasks, and create value for users. 2025 will witness the transition from chatbots to collaborative assistants, as specialized LLMs and multi-agent orchestration redefine the perception of AI.
While Web2 and Web3 will gradually converge, Web3's open and collaborative nature will lay the foundation for the most innovative breakthroughs. It is no longer about "having a personality-driven AI agent," but about agents that can provide practical value and create meaningful impact. The ones to watch are agent-centric L1s, coordination layers, and the emerging AI applications. The age of agents is upon us, and this is just the beginning.
