Trillions of dollars in benefits are coming! Jensen Huang makes a major announcement! AI infrastructure development is still in its early stages

Nvidia CEO Jensen Huang Signals Major Positive Developments

On March 10th, local time, Nvidia CEO Jensen Huang unusually published a lengthy blog post titled “AI is a five-layer cake,” systematically explaining the development logic of the artificial intelligence (AI) industry.

Huang believes that the AI industry is undergoing a technological infrastructure buildout comparable to an industrial revolution. Currently, the AI industry is still in its early stages of development. Despite industry investments reaching hundreds of billions of dollars, the true potential of AI has not yet been fully realized. Continued investments of trillions of dollars will be needed to improve the underlying infrastructure.

He predicts that in the coming years, traditional software and app formats may disappear, with a new paradigm—AI Agents—likely to become mainstream.

Previously, McKinsey estimated 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 US economic growth.

Energy is the First Principle of AI Infrastructure

Huang pointed out that AI has become one of the most powerful forces shaping the world today. It is not just a single smart application or model but a critical infrastructure like electricity and the internet, operating on real hardware, energy, and economic foundations. It can absorb raw materials and transform them into scalable intelligence. In the future, every company will use AI, and every country will build AI infrastructure.

In the article, Huang proposed a structural framework for the AI industry: a five-layer technology stack—Energy, Chips, Infrastructure, Models, and Applications. He emphasized that these five layers are strongly interconnected.

The most fundamental layer is energy. Real-time intelligent generation requires real-time electricity. Every generated token is the result of electron movement, heat management, and energy conversion into computing power. There are no abstract layers beneath this. Energy is the first principle of AI infrastructure and a hard constraint on how much intelligence a system can produce.

Huang stated that above energy are chips. These processors are designed to convert energy into computing power on a large scale and efficiently. AI workloads require massive parallel computing, high-bandwidth memory, and fast interconnects. Advances in chip technology determine the speed of AI expansion and the affordability of intelligence.

Above chips is infrastructure, including land, power delivery, cooling systems, construction, networks, and systems that coordinate thousands of processors into a single machine. These systems are “AI factories.” Their purpose is not to store information but to produce intelligence.

Above infrastructure are models. AI models can understand various types of information: language, biology, chemistry, physics, finance, medicine, and the physical world itself. Language models are just one category. Currently, some of the most disruptive work is happening in protein AI, chemical AI, physics simulation, robotics, and autonomous systems.

Huang said that the top layer—the application layer—is the core of AI’s economic value creation, including drug discovery platforms, industrial robots, legal assistants, autonomous vehicles, etc. The same underlying architecture can support different applications, and there is still vast room for innovation in the application layer. He predicts that in the next few years, traditional software and app formats may disappear, with AI Agents potentially becoming the mainstream. Every successful application will drive the layers below—from models, infrastructure, chips—down to power plants, creating a strong industry ripple effect.

Huang also pointed out 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 intelligence expansion. Energy is critical because it limits the total amount of intelligence. Application development accelerates because the underlying models have surpassed thresholds, enabling large-scale deployment.

“Each layer reinforces the others,” he wrote.

AI Infrastructure Development Is Still in Early Stages

Huang wrote, “We have only invested a few trillion dollars so far, but we will need to build infrastructure worth trillions more.”

Globally, chip factories, server assembly plants, and AI data centers are accelerating construction. Huang said this trend could become “one of the largest infrastructure projects in human history.”

Regarding concerns about AI’s impact on jobs, Huang believes AI will not reduce employment but will create many new jobs, especially in infrastructure and skilled trades. The workforce needed for AI infrastructure—electricians, plumbers, steelworkers, network technicians, installers, operators—is large, highly skilled, and well-paid, and currently in short supply. AI is filling significant labor gaps worldwide in truck drivers, nurses, accountants, and more, rather than causing unemployment.

He emphasized, “Participating in this transformation does not necessarily require a computer science PhD.”

Huang also highlighted the role of open-source models in the AI ecosystem. Many AI models are open, and companies, research institutions, and countries rely on these models for AI development. When open-source models reach advanced levels, they drive demand across the entire industry chain. He cited DeepSeek-R1 as a typical example.

After the model was released, it spurred application development and increased demand for training compute power, infrastructure, chips, and energy. In other words, breakthroughs in a single model can pull the entire industry chain downward.

In conclusion, Huang emphasized that AI will not only transform the software industry but also impact energy, manufacturing, labor structures, and economic growth models.

Huang stated, “AI is an industrial-scale transformation that will change how energy is produced, how factories are built, how work is organized, and how economies grow.”

He believes that AI is still in its early stages. Much infrastructure remains to be built, and many talents still need training. “AI is becoming the infrastructure of the modern world.”

(Source: Securities Times)