Huang Renxun personally writes a blog post: AI is the "five-layer cake" infrastructure, requiring trillions of dollars in development!

As tech giants compete to meet the rapidly growing demand in the artificial intelligence (AI) sector, many companies are investing heavily in building AI data centers. It is estimated that the total capital expenditure of just the largest few tech companies has reached $700 billion.

What does $700 billion mean? It’s higher than the GDP of Sweden, Israel, or Argentina. Roughly, it’s equivalent to the combined market value of Disney, Nike, and Target. $700 billion even exceeds the inflation-adjusted total cost of the U.S. Apollo program, which sent humans to the moon twice.

The infrastructure wave has just begun

However, according to NVIDIA CEO Jensen Huang, this astronomical spending is only the beginning of AI infrastructure development.

On Tuesday (the 10th), he unusually published a long blog post titled “AI is a five-layer cake,” stating: “We are just getting started. We have already invested hundreds of billions of dollars. There are trillions of dollars of infrastructure to build.”

Huang wrote: “AI is one of the most powerful forces shaping the world today. It’s not just a smart application or a single model; it’s an essential infrastructure, like electricity and the internet.”

“Globally, we are witnessing chip factories, computer assembly plants, and AI factories rising on an unprecedented scale. This is becoming the largest infrastructure wave in human history,” he added.

Moreover, Huang’s view is not an isolated case. McKinsey estimates that by 2030, global data center investments could reach $6.7 trillion to meet the booming AI demand. This surge in capital expenditure is one of the key drivers of today’s U.S. economic growth.

The “five-layer cake” of AI

Huang vividly compares the AI industry architecture to a “five-layer cake”: Energy → Chips → Infrastructure → Models → Applications.

As early as January this year, Huang explained the “five-layer cake” principle: the bottom layer is energy infrastructure, followed by chips and computing infrastructure, cloud computing, AI models, and at the top, specific industry applications such as finance, healthcare, and manufacturing. He emphasized that each layer requires large-scale construction to ensure the proper functioning of the layer above.

In his latest blog, he further pointed out that “every successful application depends on every layer of architecture below it, extending down to the core power source that supports its operation.”

Huang also noted that AI factories are being built because intelligence can now be generated in real-time. Chips are being redesigned because efficiency determines the speed of AI expansion. Energy is becoming critical because it limits the total amount of AI. Applications are accelerating because their underlying models have surpassed thresholds, enabling large-scale deployment.

“Each layer strengthens the others,” he wrote.

Impact on the workforce

Huang further pointed out that the workforce needed to support this construction is enormous. AI factories require electricians, plumbers, pipefitters, steelworkers, network technicians, installers, and operators.

“These are high-skill, well-paid jobs, and demand far exceeds supply. You don’t need a PhD in computer science to participate in this transformation,” he said.

Meanwhile, Huang also highlighted that AI is driving productivity improvements across the entire knowledge economy. Using radiology as an example: AI can now assist in interpreting scan images, but the demand for radiologists continues to grow. This is not a contradiction.

“Radiologists’ primary role is patient care. Interpreting images is just one part of their work. As AI takes on more routine tasks, radiologists can focus on clinical judgment, communication, and patient care. Hospitals thus improve efficiency, serve more patients, and can hire more staff,” he explained.

Huang summarized: “Productivity creates capacity, and capacity drives growth.”

East Finance Diagram · Key Insights

(Source: Cailian Press)