Vertical proxy: Application scenarios and interpretation of disruptive potential of encryption native proxy

Artificial intelligence agents (AI Agents) are rapidly integrating into daily operations of enterprises, from large companies to small businesses, almost all areas have begun to be used, including sales, marketing, finance, law, IT, project management, logistics, customer service and workflow automation. We are moving from an era of manual processing of data, performing repetitive tasks, and using Excel tables to an era of autonomous operation by AI agents around the clock, which not only improves efficiency but also significantly reduces costs.

1. Application case of AI agents in Web2: Y Combinator's perspective

• Apten:
A sales and marketing optimization tool combining AI and SMS technology.

Bild AI:

An AI tool that can read architectural blueprints, extract materials and specification information and perform cost estimates.

Casixty:

A marketing AI agent that can identify Reddit hot topics and automatically respond to them to improve brand interaction. Imagine the potential to apply it to crypto community (CT)!

These examples show that AI agents are revolutionizing traditional industries, automating tasks and optimizing processes. While Web2 companies have quickly adopted AI proxy, the Web3 sector has also begun to embrace the technology, but there are key differences between the two.

2. Unlike Web2 that only focuses on operational efficiency, Web3's AI agent and blockchain technology have deeply integrated, opening up a new application scenario.

Web3 AI Agent: Beyond Simple Chatbots

Web3 Agents were originally mostly chatbots on Twitter, but now they have grown rapidly, integrating with a variety of tools and plug-ins to perform more complex operations. For example:

• @sendaifun: Solana AI proxy toolkit that supports from basic token management to complex DeFi operations.
• @ai16zdao: Integrates more than 100 plug-ins, covering social media interactions, automated transactions and DeFi operations.
• @Cod3xOrg, @Almanak__: Provides a codeless infrastructure that allows users to create autonomous transaction agents.
• @gizatechxyz: An independent DeFi assistant designed for investors.

DeFi (decentralized finance), as the largest sector in the crypto field (locked positions value exceeding US$100 billion), has become the field with the most concentrated application of crypto-native AI agents, namely DeFAI (decentralized finance artificial intelligence). AI agents in DeFi not only simplify complex operations through natural language processing (NLP), but also use on-chain data to create new opportunities. Blockchain provides rich structured data (vouchers, transaction history, profit and loss records, governance activities and lending models) that AI can process and analyze, automate processes and optimize decisions.

3. Web2 vertical domain proxy based on encryption infrastructure

We also see trends in Web2 vertical domain proxy integrating encryption native models, such as Virtuals.io on Solana:

• Perspective AI: An AI-driven fact-checking tool based on continuous optimization of community feedback.

• R6D9: A personal assistant tool that allows booking air tickets, taxis, ordering groceries and scheduling meetings.

• HeyTracyAI: An AI-driven sports commentary and analysis platform that focused on NBA events in the early stage.

Unlike traditional SaaS models, these agents usually use token gating mechanisms, where users need to stake or hold a specific number of tokens to access advanced features, but basic services are usually free. The revenue mainly comes from token transaction fees and API usage fees.

4. Can Web3 AI agent compete with Web2 startups?

In the short term, the Web3 team faces challenges in finding product market fit (PMF) and user adoption, requiring at least $1 million to $2 million in annual recurring revenue (ARR) to compete effectively. But in the long run, the Web3 model has advantages:

• Community-driven growth: Promote community spontaneous growth through token incentives and consistency of interests.
• Global Liquidity and Accessibility: Decentralized and unmanaged platforms eliminate barriers to adoption and enable seamless global access.

The rise of DeepSeek and the interest of Web2 AI talents in open source AI are accelerating the integration of the field of encryption and artificial intelligence.

5. Core application scenarios of encrypting native AI agents

• DeFAI: Provides front-end support for DeFi infrastructure to improve efficiency and user experience.
• Research and Inference Agent: AI-driven research assistant that analyzes data, filters noise and generates actionable insights such as @soleng_agent and @CertiK_Agent.
• Data-driven AI Agent: Utilize on-chain data and social media data to empower independent decision-making and execution.

These three fields represent the most promising directions for crypto-native AI agents.

6. Summary of the status quo and future prospects

The market has been consolidating recently, and tokens related to altcoins and AI agents have experienced a pullback. But the fundamentals of tokens are gradually becoming clear. AI agents in the Web2 vertical field have proven their value, while AI agents in the Web3 vertical field, although in their early stages, have great potential. By combining token incentives, decentralized access and deep integration with blockchain data, Web3 AI agents have the opportunity to surpass their Web2 counterparts.

The core question is still: Can AI agents in the vertical field of Web3 achieve adoption rates comparable to Web2, or can they completely reshape this field by leveraging the native advantages of blockchain? With the continued development of Web2 and Web3 vertical AI agents, the boundaries between the two may be blurred. Teams that successfully integrate the advantages of both will shape the future of next-generation automation and intelligence in the digital economy.

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