Introduction
If you've been following AI over the past three years, you'll have noticed a significant shift: it's no longer just "useful"—it's becoming "indispensable." This change didn't happen overnight; it evolved through three distinct stages.
Phase One: AI Was a "New Species," But Not Yet Part of Daily Life
Three years ago, the hottest AI products were very concentrated:
- ChatGPT: Chat and Q&A
- Midjourney: Image generation
- Character.AI: Virtual character conversations
What they had in common: They were all "AI-native applications," essentially existing to showcase AI capabilities.
User behavior at the time was also typical:
- Asking questions
- Generating images
- Chatting for entertainment
It was essentially about "experiencing AI," not "relying on AI." In other words, AI in this phase was more of a capability showcase than a production tool.
Phase Two: AI Begins to "Embed into Every Product"
The real change happened in the last two years.
The leaders on the AI application charts are no longer "pure AI products" but established applications that have been rebuilt with AI:
- CapCut (Jianying): 736 million monthly active users, with almost all core features AI-powered
- Canva: Redesigned the design workflow around AI tools
- Notion: AI feature adoption grew from 20% → 50%+
An even more critical signal emerged:
AI began contributing close to half of the revenue (ARR)
This signifies one thing:
AI is no longer just a feature; it's infrastructure.
Platform Differentiation Begins
As AI became a foundational capability, the role of large models also changed:
From "chat tool" to "usage entry point."
Two distinct paths became clear:
1) The Super Entry Point (Consumer)
What ChatGPT is doing includes:
- GPTs + App Store
- "Login with ChatGPT" account system
- Integration into life scenarios like shopping, travel, health
The goal is clear: Become the starting point for your internet use
2) The Professional Work Platform (Productivity)
Claude's path is entirely different:
- MCP (Model Context Protocol)
- Deep integration with developer tools, data systems
- Building complex workflows
It's more like: An AI operating system for knowledge workers
An Emerging Structure: The Platform Flywheel
As users began integrating AI into their daily systems:
- Calendars
- CRM
- Workflows
Switching costs rose rapidly, and platform stickiness began to form.
Thus, the classic flywheel effect emerged:
- More users → More developers
- More developers → Richer functionality
- Richer functionality → Users become more dependent
This also leads to one outcome: This competition won't be winner-take-all; it will more likely be two ecosystems coexisting long-term.
Phase Three: AI Begins to "Act on Your Behalf"
The real watershed moment happened in the last year.
AI is no longer just "generating content for you"; it's starting to: Execute tasks for you. From "generating content" to "completing tasks"
Early AI (like Midjourney, DALL·E) solved:
- Writing content
- Generating images
But the new generation of products is now doing:
- Task decomposition
- Automatic execution
- Complete delivery
AI Agents Emerge
Represented by products like OpenClaw, a key change has occurred:
- Not just answering questions
- But decomposing tasks
- And automatically executing the entire process
For example, a complete workflow:
- Receive objective
- Query information
- Analyze and process
- Output result
- Automatically send
At this stage, AI is no longer just a tool; it is: An "actionable software entity"
Another Trend: AI Starts "Building Products for You"
Vibe Coding is rapidly rising, represented by products including:
- Cursor
- Replit
- Lovable
They are essentially doing one thing: Letting AI directly help you "build" the product This change isn't just about efficiency gains; it's a shift from "humans writing code" to "humans defining goals, AI completing the build."
Four: As AI Takes Action, Why is It Moving Towards Web3?
As AI moves from "answering questions" to "executing tasks," a practical question arises: How does it complete transactions and settlements? In the traditional internet, these rely on platforms and intermediaries, but this system was designed for "humans," not for machines to operate independently.
Web3 provides an underlying structure more suited for AI:
- 24/7 Operation: AI can continuously execute and respond
- Machine-Native Interfaces: Contracts as APIs, directly callable
- Programmable Assets: Fund transfers can be automated
The change this brings: AI doesn't just "do things"; it can also automatically handle payment and settlement in the process.
More importantly, blockchain provides immutability and auditability, enabling AIs to collaborate without intermediaries. This signifies a shift in how trust works on the internet—from "trusting the platform" to "trusting the rules."
Therefore, the relationship between AI and Web3 is more like a natural division of labor: AI handles action, Web3 handles settlement. As AI truly begins to participate in transactions and collaboration, this combination will likely become the foundation of the next generation of the internet.
