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

Introduction

If you’ve been watching AI over the past three years, you’ll notice a clear change: it’s no longer just “useful,” but starting to become “irreplaceable.” This shift didn’t happen suddenly; it has gone through a distinct three-stage evolution.

Stage One: AI as a “New Species” but Not Yet Part of Daily Life

Three years ago, the most popular AI products were very focused:

ChatGPT: Chat and Q&A

Midjourney: Image Generation

Character.AI: Virtual Character Conversations

Their commonality is that they are all “AI-native applications,” primarily created to showcase AI capabilities.

User behaviors at that time were also typical:

Asking questions

Generating images

Chatting for entertainment

Essentially, users were “experiencing AI,” not “relying on AI.” In other words, AI at this stage was more like a capability showcase window than a production tool.

Stage Two: AI Begins “Embedding in All Products”

The real change happened in the past two years.

The main players on AI application leaderboards are no longer “pure AI products,” but mature applications reconstructed by AI:

CapCut: 736 million monthly active users, with core functions almost fully AI-powered

Canva: redesigning design workflows around AI tools

Notion: AI feature penetration increased from 20% to over 50%

A very critical signal has emerged:

AI now contributes nearly half of the revenue (ARR)

This indicates one thing:

AI is no longer just a feature but a foundational infrastructure.

Platform differentiation begins to appear

Once AI becomes a core capability, the role of large models also changes:

From “chat tools” to “entry points.”

Two paths are gradually becoming clear:

1. Super Entry Point (Consumer Level)

What ChatGPT is doing includes:

GPTs + App Store

“Login with ChatGPT” account system

Connecting to shopping, travel, health, and other life scenarios

The goal is clear: to become your starting point for internet use

2. Professional Work Platform (Productivity Side)

Claude’s path is entirely different:

MCP (Model Context Protocol)

Deep integration with development tools and data systems

Building complex workflows

It’s more like: an AI operating system for knowledge workers

A forming structure: platform flywheel

As users begin integrating AI into their daily systems:

Calendars

Email

CRM

Workflows

Switching costs rise quickly, and platform stickiness starts to form.

This leads to a classic flywheel:

More users → more developers

More developers → richer features

Richer features → increased user dependence

This also determines the outcome: this competition won’t be dominated by a single player but will likely see two ecosystems coexisting long-term.

Stage Three: AI Starts “Doing Things for You”

The real watershed occurred in the past year.

AI is no longer just “helping you generate content,” but beginning to: execute tasks for you. Moving from “content creation” to “task completion.”

Early AI (like Midjourney, DALL·E) solved:

Writing content

Generating images

But the new generation of products now do:

Task decomposition

Automatic execution

Complete delivery

AI Agents are beginning to appear

Represented by OpenClaw, these products have undergone key changes:

Not just answering questions

But decomposing tasks

And automatically executing the entire process

For example, a complete workflow:

Receive goal

Query information

Analyze and process

Output results

Automatically send

At this stage, AI is no longer just a tool but a “software entity capable of acting.”

Another trend: AI begins “helping you build products”

Vibe Coding is rapidly emerging, with products like:

Cursor

Replit

Lovable

Their essence is doing one thing: letting AI directly help you “build” products. The change brought by this isn’t just efficiency; it’s shifting from “humans writing code” to “humans defining goals, AI completing the build.”

When AI begins to act, why does it lead toward Web3?

As AI moves from “answer questions” to “execute tasks,” a very practical question arises: how does it complete transactions and settlements? In traditional internet, these rely on platforms and intermediaries, but this system is designed for “people” and isn’t suitable for autonomous machine operation.

Web3 offers a more suitable underlying structure for AI:

24/7 operation: AI can continuously execute and respond

Native machine interfaces: smart contracts as APIs, callable directly

Programmable assets: automatic fund transfers

This brings a change: AI not only “does things” but can also automatically handle payments and settlements during the process.

More importantly, blockchain ensures transactions are tamper-proof and auditable, enabling AI to collaborate without intermediaries. This signifies a shift in internet trust models—from “trust the platform” to “trust the rules.”

Therefore, the relationship between AI and Web3 is more like a natural division of labor: AI handles actions, Web3 handles settlements. When AI truly begins participating in transactions and collaborations, this combination could become the foundation of the next-generation internet.