The digital economy is being redefined by AI
Recently, while following discussions around the Digital Economy conference in Beijing, I had a very strong impression that although the theme is still labeled as “digital economy,” the real focus has quietly shifted toward artificial intelligence.
From what I have seen across different public information, whether it is industrial direction, enterprise digital transformation, or talent development systems, AI is being mentioned more and more frequently. The term “digital economy” still exists, but it no longer feels like a simple extension of the internet era. Instead, it feels more like a broader framework that is now carrying AI, data systems, and industrial restructuring together.
This made me start thinking about a simple question: is the digital economy already a stable concept, or is it still being redefined as we speak?
And I don’t approach this from a policy perspective. I feel it through my daily work.
Real changes inside cross-border e-commerce
As someone working in cross-border e-commerce, my daily work is actually very concrete.
Most of the time is spent dealing with products, customers, orders, logistics, and payments. A normal day is usually just checking orders, responding to customers, and doing reconciliation work.
In this rhythm, “macro changes” are not something you immediately notice. But they slowly enter your system over time.
For example, I started asking myself questions like:
Why are cross-border payment fees always so high? Why do different platforms have such different settlement speeds? Why do exchange rates change every day, and yet feel so unpredictable?
These may sound like small operational questions, but if you keep following them, they lead you to a much bigger one:
How does money actually move in this system we are all part of?
It was from these very practical questions that I first started paying serious attention to AI and crypto.
AI entering my daily workflow
When I first started using AI, it was really just experimentation.
Writing product descriptions, organizing information, or generating rough drafts for content (related: AI in daily workflow ). Nothing structured, just trying things out.
But slowly, it became a habit.
Now, before I start working on something, I often talk with AI first — almost like organizing my thoughts before taking action .
Sometimes it is at night while handling orders, and I ask AI to help me structure data. Sometimes I am looking at scattered information and need it to reorganize everything into something clearer. Sometimes there is no clear question at all, but I still use it just to think through things.
Gradually, I realized something had changed:
Many tasks that used to be done step by step on my own are now done together with AI.
And this did not happen suddenly. It happened in a very gradual, almost invisible way.
AI is not just a tool, but a kind of competitive environment
At some point, I developed a very simple understanding of AI.
At its core, AI is still just a tool. It can improve efficiency and reduce repetitive work, but it does not make decisions for you, and it does not think for you.
What matters more is not AI itself, but the relationship between humans and AI.
In my daily interaction with it, I gradually became more aware of one thing:
AI is not a “monster,” nor is it something that needs to be over-glorified.
It feels more like an amplified environment that constantly pushes people to learn new capabilities.
It can be a tool, or it can feel like a competitor in some way, but what it becomes depends entirely on how you use it and how you define its role in your life.
If I had to describe it, I would say I see it as a “never-stopping competitive companion.”
It doesn’t replace you, but it constantly reminds you whether you are still improving.
From digital economy to AI economy
If I place these personal experiences into a larger structure, some interesting patterns start to appear.
In the Beijing Digital Economy conference, AI is no longer just a technical topic. It is now part of discussions about industrial structure, education systems, and even talent development.
One detail that stood out to me was that in Singapore, schools are already starting to teach students how to use AI tools like ChatGPT.
That detail stayed with me for a long time.
Because it signals something important:
AI is no longer just a workplace tool. It is becoming a basic capability.
It is even possible that in the future, people will no longer ask whether they should use AI, but simply assume they need to know how to use it.
When I put everything together, one trend becomes clearer:
The digital economy itself is being redefined by AI.
What used to be digital economy was built around e-commerce, platforms, data, and the internet. None of these have disappeared, but they are being reorganized, and AI is becoming the underlying structure that connects them.
In other words, the question may no longer be whether something is “digital,” but whether it is “AI-enabled.”
Global synchronized changes
Earlier this year, AI agent tools started to be widely discussed, including products like OpenClaw.
In practice, these tools are not cheap due to token costs, and for individuals they can feel expensive. But in medium and large companies, they are already starting to have real impact.
In areas like content generation, information processing, basic analysis, and workflow automation, AI is already taking over parts of work that used to require human effort.
It is not fully replacing people, but it is changing the boundary of what one person can realistically do.
And that shift is more important than the tools themselves.
Because it shows that AI is no longer a concept. It is already entering real production systems.
Larger systems changing at the same time
If we zoom out further, this is not an isolated change.
AI is changing how work is done. Remote work is changing geographical boundaries. Global payment systems are being reshaped. Cross-border settlement, digital currencies, and on-chain systems are becoming more common in mainstream discussions.
At the same time, demographic structures are also shifting. Aging populations are affecting long-term policy in many countries, and the sustainability of pension systems is being discussed more often.
These topics belong to completely different fields, but they seem to point in the same direction:
Many systems we once considered stable are changing at the same time.
And new systems have not fully formed yet.
How individuals respond to uncertainty
Perhaps this is why more and more people are starting to learn things they would not have touched before.
Some are studying AI because they want to know whether their jobs will be affected. Some are studying crypto because they want to understand new forms of assets. Some are exploring cross-border living because they are thinking about future uncertainty. Some are trying to build multiple income streams because they no longer fully trust a single structure of stability.
On the surface, these choices look scattered. But at a deeper level, they are pointing toward the same thing:
In a world that is changing faster and faster, people are trying to keep more options open for themselves.
The meaning of Flowanriver
Perhaps this is also why I started writing Flowanriver .
Not because I have found answers, but because I still have many unanswered questions.
How AI will continue to change work. How the digital economy will evolve into a new system. How global payments and asset structures will shift. And how individuals should adjust themselves within these changes.
I am still learning through these questions, and still thinking through them.
For me, Flowanriver is simply a place to record this process.
It is not about conclusions, but about trying to understand what is happening in a rapidly changing world.
And maybe one day, when we look back, we will realize that “digital economy” was only a transitional term.
What it really pointed to was a system being reorganized by AI.
And all of us are already inside it.