In three years, what has AI evolved from a "chat tool" into?

introduction

If you've been following AI for the past three years, you'll notice a significant change: it's no longer just "easy to use," but is beginning to become "irreplaceable." This change didn't happen suddenly; it evolved through a clear three-stage process.

Phase 1: AI is a "new species," but it has not yet entered daily life.

Three years ago, the most popular AI products were very concentrated in one area:

• ChatGPT: Chat and Q&A
• Midjourney: Image Generation
• Character.AI: Virtual Character Dialogue

What they have in common is that they are all "AI native applications," essentially existing to showcase AI capabilities.

The user behavior at that time was also typical:

• Ask a question
• Generate image
• Chatting and entertainment

Essentially, it's about "experiencing AI" rather than "relying on AI." In other words, at this stage, AI is more like a showcase of capabilities than a production tool.

II. Second Phase: AI Begins to Be "Embedded in Every Product"

The real changes have occurred in the last two years.

The main players on the AI ​​application rankings are no longer "pure AI products," but rather mature applications that have been restructured by AI:

• CapCut: 736 million monthly active users, with almost all core functions AI-powered.
• Canva: Restructuring the Design Process Around AI Tools
• Notion: AI functionality penetration rate increased from 20% to 50%+

Even a very crucial signal has emerged:

This means one thing:

AI is no longer a function, but an infrastructure.

Platform differentiation has begun to emerge.

As AI becomes a fundamental capability, the role of large models has also changed:

It has transformed from a "chat tool" into a "user portal".

Two paths gradually became clear:

1) Super Entry Point (Consumer Level)

ChatGPT is doing the following:

• GPTs + App Store
• "Log in with ChatGPT" account system
• Integrate into various aspects of daily life, including shopping, travel, and health.

The goal is clear: to become your starting point for using the internet.

2) Professional work platform (productivity side)

Claude's path was completely different:

• MCP (Model Context Protocol)
• Deeply connect development tools and data systems
• Building complex workflows

It's more like an AI operating system for knowledge workers.

An emerging structure: platform flywheel

When users start integrating AI into their daily systems:

• calendar
• Mail
• CRM
• Workflow

Switching costs will rise rapidly, and platform stickiness will begin to form.

Thus, the classic flywheel came into being:

• More users → More developers
• More developers → More features
• More features mean more user reliance.

This also determines one outcome: this competition will not see one company dominate, but rather two ecosystems coexisting in the long term.

Third stage: AI begins to "do things for you"

The real turning point actually occurred in the last year.

AI is no longer just "generating content for you," but is beginning to "perform tasks for you." From "generating content" to "completing tasks."

Early AI (such as Midjourney and DALL·E) solved the following problems:

• Write content
• Generate image

But what the new generation of products are doing now is:

• Task breakdown
• Automatic execution
• Full delivery

AI Agents are starting to emerge

With OpenClaw as an example, these products have undergone key changes:

• More than just answering questions
• Instead, it involves dismantling the task.
• And execute the entire process automatically.

For example, a complete process:

• Receive target
• Query information
• Analysis and processing
• Output
• Automatic sending

At this point, AI is no longer a tool, but rather: a "software entity capable of action".

Another trend: AI is starting to "help you build products".

Vibe Coding is rapidly emerging, with representative products including:

• Cursor
• Replit
• Lovable

Essentially, they are doing one thing: letting AI directly "make" the product for you. The change brought about by this is not a simple improvement in efficiency, but rather: from "humans writing code" to "humans defining goals and AI completing the construction".

IV. When AI begins to act, why is it moving towards Web3?

As AI moves from "answering questions" to "performing tasks," a very real question arises: how does it complete transactions and settlements? In the traditional internet, these processes rely on platforms and intermediaries, but this system is designed for "humans" and is not suitable for machines to operate independently.

Web3 provides a more suitable underlying architecture for AI:

• 24/7 operation : AI can continuously execute and respond.
• Machine-native interface : Contracts are APIs that can be called directly.
• Programmable assets : Fund transfers can be completed automatically.

This brings about a change: AI can not only "do things", but also automatically complete payments and settlements in the process.

More importantly, blockchain makes transactions immutable and auditable, enabling AI to collaborate without intermediaries. This means that the way trust is built on the internet is changing—from "trust platforms" to "trust rules."

Therefore, the relationship between AI and Web3 is more like a natural division of labor: AI is responsible for action, and Web3 is responsible for settlement. When AI truly begins to participate in transactions and collaborations, this combination is likely to become the foundation of the next generation of the Internet.