Author: Jensen Huang, CEO of Nvidia; Translator: Peggy, BlockBeats
Editor's Note: Artificial intelligence is gradually evolving from a cutting-edge technology into the infrastructure supporting the modern economy. In its first long article published on its official account, Nvidia attempts to systematically analyze the industrial structure of AI from first principles: from energy and chips to data center infrastructure, and then to models and applications, forming a complete five-layer technology stack.
The article points out that AI is not just a competition of software or models, but a global industrial construction involving energy, computing power, manufacturing, and applications, the scale of which may become one of the largest infrastructure expansions in human history. Through this "five-layer cake" perspective, Nvidia attempts to illustrate that the true significance of AI is not just smarter software, but an infrastructure revolution comparable in scale to electricity and the internet.
The following is the original text: Artificial intelligence is one of the most powerful forces shaping the world today. It is not a smart application, nor a single model, but an infrastructure as vital as electricity and the internet. AI runs on real hardware, real energy, and real economic systems. It transforms raw materials into "intelligence" produced on a large scale. Every company uses it, and every country builds it. To understand why AI unfolds in this way, it is helpful to start with first principles and look at the fundamental changes that have occurred in the field of computing. From "Pre-built Software" to "Real-Time Generated Intelligence" For most of the history of computer development, software has been "pre-built." Humans first describe an algorithm, and then the computer executes the instructions. Data must be meticulously structured, stored in tables, and retrieved through precise queries. SQL is indispensable because it enables this entire system to function. AI, however, disrupts this model. For the first time, we have a computer capable of understanding unstructured information. It can see images, read text, hear sounds, and understand their meaning; it can reason about context and intent. More importantly, it can generate intelligence in real time. Every response is a new generation. Every answer depends on the context you provide. This is no longer software retrieving existing instructions from a database, but software reasoning in real time and generating intelligence on demand. Because intelligence is generated in real time, the entire computing technology stack supporting it must be reinvented. AI as Infrastructure From an industry perspective, AI can actually be broken down into a five-layer structure. Energy At the very bottom is energy. Real-time generated intelligence requires real-time generated electricity. The generation of each token means that electrons are moving, heat is being managed, and energy is being converted into computing power. Below this layer, there is no abstraction. Energy is the first principle of AI infrastructure and the fundamental constraint determining how much intelligence the system can produce. Chips Above energy are chips. These processors are designed to convert energy into computing power with extremely high efficiency and on a large scale. AI workloads require massive parallel computing power, high-bandwidth memory, and high-speed interconnects. Advances at the chip level determine the speed of AI expansion and how cheap "intelligence" will ultimately become. Infrastructure: Above the chips lies infrastructure. This includes land, power transmission, cooling systems, construction engineering, network systems, and the scheduling system that organizes tens of thousands of processors into a single machine. These systems are essentially AI factories. They are not designed to store information, but to create intelligence. Models: Above the infrastructure are models. AI models can understand various types of information: language, biology, chemistry, physics, finance, medicine, and the real world itself. Language models are just one type. One of the most transformative works is happening in the following areas: protein AI, chemical AI, physical simulation, robotics, and autonomous systems. At the top is the application layer, where real economic value is generated. Examples include drug discovery platforms, industrial robots, legal copilots, and self-driving cars. A self-driving car is essentially an "AI application carried by a machine"; a humanoid robot is an "AI application carried by a body." The underlying technology stack is the same, only the final form differs. Therefore, this is the five-layer structure of AI: energy → chip → infrastructure → model → application. Every successful application will affect all layers downwards, down to the power plant that supplies it at the very bottom. This is an infrastructure construction project still in its early stages. We've only just begun. Current investment amounts to a few hundred billion dollars, but trillions more will be needed in infrastructure in the future. Globally, we are seeing chip factories, computer assembly plants, and AI factories being built on an unprecedented scale. This is becoming one of the largest infrastructure projects in human history. The labor demand of the AI era requires a massive workforce to support this construction. AI factories need electricians, plumbers, pipe installers, steel structure workers, network technicians, equipment installers, and maintenance personnel. These are highly skilled, well-paid jobs, and are currently in extremely short supply. Participating in this transformation doesn't necessarily require a PhD in computer science. Meanwhile, AI is driving productivity gains in the knowledge economy. Take radiology as an example. AI has begun to assist in interpreting medical images, yet the demand for radiologists continues to grow. This isn't contradictory. Radiologists' true responsibility is caring for patients, and interpreting images is just one part of that. As AI takes over more and more repetitive tasks, doctors can dedicate more time to diagnosis, communication, and treatment. Increased hospital efficiency allows them to serve more patients, thus requiring more staff. Productivity creates capacity, and capacity creates growth. What changes have occurred in the past year? In the past year, AI has crossed a key threshold. The model is good enough to truly function in large-scale scenarios. Reasoning ability is significantly improved. Hallucinations are significantly reduced. Grounding with the real world is greatly enhanced. For the first time, AI-based applications are beginning to create real economic value. Significant product-market fit has emerged in the following areas: drug development, logistics, customer service, software development, and manufacturing. These applications are strongly driving the entire underlying technology stack. The Role of Open Source Models Open source models play a crucial role. The vast majority of AI models in the world are free. Researchers, startups, enterprises, and even entire countries rely on open source models to compete in advanced AI. When open source models reach the forefront of technology, they not only change the software but also activate demand across the entire technology stack. DeepSeek-R1 is a prime example. By making a powerful inference model widely available, it has driven rapid growth at the application layer, while also increasing the demand for training computing power, infrastructure, chips, and energy. What does this mean? Everything becomes clear when you think of AI as infrastructure. AI may have started with Transformers and large language models, but it is much more than that. It's an industrial revolution that will reshape: How energy is produced and consumed; how factories are built; how work is organized; and the model of economic growth. AI factories are being built because intelligence can now be generated in real time. Chips are being redesigned because efficiency determines the speed at which intelligence expands. Energy is central because it determines the maximum amount of intelligence a system can produce. Applications are exploding because models have finally crossed the threshold of "scalable availability." Each layer reinforces the others. This is why this construction is so massive, why it will impact so many industries simultaneously, and why it won't be confined to a single country or sector. Every company will use AI. Every country will build AI. We are still in the early stages. Vast amounts of infrastructure are yet to be built, a large workforce is yet to be trained, and numerous opportunities are yet to be realized. But the direction is very clear. Artificial intelligence is becoming the foundational infrastructure of the modern world. And the choices we make today—the speed of construction, the breadth of participation, and the responsibility for deployment—will determine what this era will ultimately look like.
Alex
Miyuki
Weiliang