The Construction of the Computing Power Network: Not About “Building Roads,” but About “Regulating Traffic” – A Deep Transformation Concerning Efficiency and the Market
Hello everyone, I’m your financial journalist and economist. Today, we’re going to discuss a very important topic from the State Council’s executive meeting: the Computing Power Network.
Many people, upon hearing terms like “computing power network” or “coordination of computing and power,” might immediately think, “Another large-scale infrastructure project from the government, just more construction?”
That’s completely wrong.
If we view it merely as building roads or bridges, we’re misinterpreting the core spirit of this meeting. The strongest message from this meeting is that the construction of the computing power network is not primarily about building; rather, it’s about matching supply with demand. It’s more like a massive, dynamic “traffic management system” rather than a static collection of hardware.
To help you fully understand the logic behind this, I’ve broken down this news into five key points and explained them in plain language.
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Point 1: The Major Shift in Thinking – From “Spreading Out” to “Matching”
In one sentence: The government is no longer simply focusing on the quantity of computing hardware (such as GPUs and servers) but emphasizes the deep integration of computing power with electricity and the leading role of market mechanisms.
In the past, when we built infrastructure, we focused on forward planning—for example, building high-speed railways and bridges that would last for decades with high certainty. However, artificial intelligence (AI) is different; its demand fluctuates rapidly and unpredictably.
This meeting made it clear that the computing power network is the foundation of AI, but we can’t apply the traditional infrastructure approach of government-driven, centralized construction. The AI sector is full of uncertainties, and if we only focus on building without considering usage, we might end up with a lot of idle servers.
Change in core logic:
- Old approach: How many GPUs do I need? How many data centers do I need to build? How much electricity do I need to generate?
- New approach: How much computing power does the market need now? How can we deliver electricity in the most cost-effective and environmentally friendly way? How can we match supply and demand in real time?
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Point 2: Why is “Electricity” the Most Certain “Ace” in the Computing Power Network?
The news mentions, “The power system is clearly the most certain and well-prepared area in China’s computing power network construction.”
In simple terms: Imagine the computing power network as a supercar; GPUs are the engine, and fiber optics are the transmission shafts, while electricity is the fuel.
Global competition in AI means everyone can buy or is developing chips (engines), but China has a significant advantage in electricity:
1. We have plenty of electricity: China is the world’s largest electricity producer with a wide power grid coverage.
2. Our electricity is green: Wind and solar power are rapidly growing.
3. Our infrastructure is strong: We lead the world in ultra-high-voltage power transmission technology.
Therefore, the government’s emphasis on “coordination of computing and power” means using our electricity advantages to turn them into computing power advantages.
For example, we can build data centers in areas with cheap electricity and abundant wind power, such as Inner Mongolia or the northwest, and use ultra-high-voltage transmission to deliver electricity there, or even build data centers next to power plants (directly connected to green energy sources), significantly reducing computing costs. This is about using our strength in electricity’s reliability to support the uncertainty of the AI sector.
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Point 3: Why Can’t We Build the Computing Power Network Like We Build High-Speed Railways?
The news highlights a profound point: “We must avoid making the construction of new infrastructure like the computing power network rely on traditional methods.”
In simple terms: Building high-speed railways is a “certainty project” – it’s useful today and will be in the future. But the computing power network is a “possibility project.”
Imagine a situation where big models are very popular today, and everyone buys computing power; tomorrow, technology might change, or new use cases might emerge, altering demand. If we build all the data centers ten years in advance, and then technology evolves or demand doesn’t keep up, those centers could become useless, resulting in huge wasted investments.
The AI industry is about learning by doing:**
- Technology is constantly evolving.
- New use cases are still being explored.
- Demand is dynamic.
Therefore, the construction of the computing power network must be flexible. We can’t adopt a “Great Leap Forward”-style approach but need to adjust our plans like nurturing a plant, adjusting the layout based on market demand (electricity availability).
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Point 4: The Real Challenge Isn’t “Building” Itself, but How to Connect “Electricity” and “Computing”
The news discusses a key issue: “How to improve the coordination of systems and promote market-oriented reforms in the electricity sales and power grid sectors.”
In simple terms: The current electricity market still has remnants of a “planned economy” – power plants generate electricity, grid companies dispatch it, and users consume it, with relatively fixed prices and slow response times.
Computing power demand for AI training is highly variable and requires large amounts of electricity at times, and the demand for green energy (such as wind and solar) is desirable. The challenge is:
1. Mismatch in timing: Wind and solar power production is dependent on the weather, with more electricity available at noon and less at night, while AI training runs 24/7.
2. Price insensitivity: Current pricing mechanisms make it difficult for computing centers to adjust their usage based on price fluctuations.
Solutions: The electricity market needs to be more market-oriented:
- Let prices reflect supply and demand in real time.
- Adjust computing power usage according to prices: If green energy is cheap at a certain time and place, allocate computing resources there.
- Break barriers: Grid companies should act as “dispatchers” rather than just transporters, facilitating direct connections between computing centers and power sources.
This is about “breaking old patterns and establishing new ones,” bringing electricity and computing power under the same market mechanism to work together efficiently.
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Point 5: What Should the Government Do? From “Taking Charge” to “Creating a Framework”
The news concludes, “The government needs to focus on creating a better trading environment to reduce matching costs and improve allocation efficiency.”
In simple terms: In the past, the government was both the designer and builder of infrastructure, deciding everything. But in the era of the computing power network, the government’s role must change from active construction to providing a framework:
- Reduce transaction costs: Simplify and make contract signing, settlement, and dispatching processes between power and computing companies more transparent.
- Provide clear information: Make market participants aware of where electricity is cheap, where computing resources are available, and where demand is high.
- Encourage experimentation: Allow companies to try different models and reduce their risk of failure.
Why? Because the market knows best:
- The government doesn’t know which AI models will succeed.
- The government doesn’t know where green energy is cheapest.
- Companies, being on the front lines, are more informed.
The government’s role is to make the market clear and predictable, allowing electricity and computing power to flow to where they’re most needed efficiently.
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Conclusion: A Structural Revolution That Adapts to Change
To conclude, let’s use a line from the news: “As the old years pass, new ones arrive, bringing even more beautiful blossoms.”
The construction of the computing power network is not a one-time project but a continuous, dynamic, and structural reform.
- For ordinary people: This means future AI services will likely be cheaper and more stable due to improved efficiency in power and computing resource allocation.
- For businesses: It presents a huge opportunity; those who can effectively utilize the coordination of computing and power will have an edge in the AI cost competition.
- For the country: It’s a crucial step in transforming our electricity advantages into AI advantages.
Remember this key point: The computing power network is not about building more “boxes” but about creating a “smart brain” that can real-time perceive changes in supply and demand, ensuring every unit of electricity and every computing resource is used efficiently.
This is a new path China is taking in infrastructure development, one that is more market-driven and dynamic than before.