AI isn't just a tool upgrade — it's a new computing platform revolution.
Part 1: The Cracks Are Already Showing
I've been job hunting recently, and I noticed something interesting: genuine "LLM integration developer" roles are still surprisingly rare. What's more interesting is that even when companies do post them, most require:
- AI Agent experience
- LLM project experience
- RAG experience
- AI Workflow experience
Here's the problem: LLM development has only exploded in the past few years. How many engineers actually have complete AI development experience? Many engineers only started transitioning into LLM development a few months ago.
If you keep the bar this rigid and can't hire anyone, those people will get picked up by other companies. In another year or two, you might not be able to hire them at all, even if you want to.
(So if I'm job hunting right now — you could hire me today. Just don't make me do LeetCode.)
But the really interesting part isn't the hiring market. It's that most companies, even now, have no idea how to make money with AI. The people who are actually using LLMs to build things are indie developers, small teams, hackers, and solo founders. They don't even know if it will be profitable — but they're running experiments anyway, because "this thing is just too cool."
That hacker intuition is hard to explain with traditional business logic. Most great tech revolutions didn't start with a clear business model. They started because a group of people thought something was fascinating.
That's how the internet started. Personal computers. Smartphones. And now AI.
The real danger is that many large companies are still sitting comfortably in their existing lanes, asking:
- Can AI make money?
- How do we calculate AI ROI?
- Will AI disrupt our current business?
But the question they should actually be asking is:
"Will our company still exist in ten years?"
Because history has already answered this. Kodak didn't die because its technology was weak. Nokia didn't die because its engineers weren't good enough. They died because when a new computing platform arrived, they were still living in the old era.
And right now, the cracks are already showing.
The way I see it, a Niagara Falls is being held back by a thin mud wall — and that wall has started to crack.
Today, 90% of internet companies are already standing at the edge of a cliff. They just haven't realized it yet. Don't believe me? Let's run a social experiment starting now:
- Build an AI Skill for Jira
- Build an AI Skill for productivity tools
- Build AI-native versions of various Web2.0 apps
Watch what happens.
Part 2: The Web4.0 Architecture
"Web3.0" is a term that's been talked to death. Why? Because it never produced a computing paradigm genuinely capable of restructuring Web2.0.
But AI is different.
I'm calling this wave Web4.0, because AI is starting to deeply penetrate software itself. It's no longer just a search bar, a chatbot, or an assistant tool — it's gradually becoming part of the operating logic of software.
I'd even argue this will be the fourth industrial revolution, because for the first time, machines are beginning to participate in producing software themselves.
1. The Software Interface
The software interface of Web4.0 will look very different from today's — but not completely unfamiliar.
Future software will most likely split into: software on the left, AI on the right.
The left side will still be traditional GUI:
- Task lists
- Tables
- Charts
- Dashboards
- Status bars
Humans still need to see state, so GUI isn't going away.
But the right side will become an AI operation layer. Users won't primarily interact through buttons anymore — they'll accomplish most tasks through natural language, conversation, and intent.
For example:
"Move this issue to next week and notify the relevant team members."
AI will:
- Update the issue
- Change the status
- Send notifications
- Adjust the timeline
The left-side GUI's role shifts to: showing the current state of the system. Users can even watch AI operate within the system and step in manually when needed.
Software will shift from:
"Humans operate software"
to:
"AI operates software. Humans supervise AI."
2. System Architecture
The core shift in Web4.0 is that every frontend will eventually connect to an AI engine.
Whether it's:
- App
- Web
- Desktop
- Skill
- Agent
Everything will plug into:
SLM + RAG
Many people assume the future will be dominated by ever-larger models, but I don't think so. LLMs are too expensive, enterprise-sensitive data can't leave the building, and no serious company wants its core technology dependent on someone else's API. A truly mature company will never build its core business permanently on external infrastructure.
So Web4.0 will inevitably move toward:
Each company's own SLM (Small Language Model) + proprietary RAG.
LLMs will be more like early exploration tools, general reasoning engines, and product validation platforms. Mature products will eventually own their own:
- AI Engine
- Memory
- Knowledge Base
- Workflow System
The competitive moat for companies will gradually shift away from:
- Frontend pages
- CRUD systems
- Database design
And toward:
- RAG architecture
- Workflow orchestration
- Enterprise knowledge organization
- Agent collaboration systems
3. The Product Lifecycle
The lifecycle of Web4.0 products will also change.
In the early stage, most teams will go straight to:
- OpenAI
- Claude
- Gemini
Combined with:
- MCP
- RAG
- Workflow
To ship fast — because the cost of experimentation is low, and the product can "come alive" from day one.
This is completely different from before. Products used to require massive amounts of custom logic before they were usable. Now AI already ships with enormous general-purpose capability.
But at the mature stage, companies will gradually migrate to:
SLM + proprietary RAG
The reasons are practical:
- Reduce costs
- Control data
- Reduce API dependency
- Ensure stability
- Establish technical sovereignty
So the typical Web4.0 product evolution path will likely look like:
LLM API
↓
RAG
↓
Workflow
↓
SLM
↓
Enterprise AI Engine
4. Customer Support
Customer service may be one of the first industries to be fully restructured.
But this time, it's real AI support — not the "fake AI that makes everyone want to throw their phone" from before.
Old AI customer service:
- Couldn't follow context
- Couldn't hold a continuous conversation
- Couldn't read emotions
- Only matched keywords
So users always ended up demanding a human.
Web4.0 AI support is different. It will genuinely understand:
- Context
- Conversation history
- User sentiment
- User behavior
It can even detect:
"This user is getting frustrated."
And proactively say:
"Let me connect you with a human agent."
Most companies' support operations will become fully AI-manageable. The scenarios that still require humans will shrink to:
- High-stakes decisions
- Emotional de-escalation
- Edge case handling
Another industry, restructured.
5. Version Iteration
This is a more radical idea, but I think it's cool — and the kind of thing that could go viral.
It's this:
"What goes into the next version is decided by user vote."
AI will:
- Analyze user behavior
- Summarize user needs
- Auto-generate candidate features
- Let users vote
And eventually, AI will auto-implement some of those features too.
The old software development flow:
Product Manager
↓
Requirements
↓
Engineering
In the Web4.0 era, it may gradually become:
Users
↓
AI Analysis
↓
AI Implementation
↓
User Feedback
Software will enter:
"The era of high-velocity self-evolution."
Part 3: Web4.0 Is Not an Upgrade — It's a Replacement
Many companies still think of AI as a plugin, a feature, a chat window, a productivity tool.
But what AI is actually changing is the entire software architecture.
Web4.0 is not "Web2.0 + AI." It's a new computing platform — just like:
- PCs replaced mainframes
- Smartphones replaced parts of the PC
- Cloud computing restructured enterprise systems
AI will redefine:
- Software
- Workflows
- Organizational structures
- Development models
- User interaction
- Enterprise architecture
Most companies think they're just waiting for AI to mature.
But actually:
AI is waiting to replace them.
We may be standing at the single biggest technological inflection point since the invention of the computer. And many companies are already at the edge of the cliff — they just haven't looked down yet.
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