Jensen Huang's rare statement: AI is an important force reshaping the world, a fundamental infrastructure like electricity and the internet.
On March 10, NVIDIA CEO Jensen Huang rarely elaborated in a personal article about the developmental logic of the AI industry.
He pointed out that AI should not be understood as a single model or application, but as an emerging infrastructure system.
Artificial Intelligence (AI) is one of the most powerful forces shaping the world today. It is not just a smart application or a single model; it is indispensable infrastructure, like electricity and the internet.
In his view, the AI industry is undergoing an industrial-revolution-level construction of technological infrastructure. Although thousands of billions of dollars have already been invested globally, the overall construction is still at an early stage.
Jensen Huang stated, AI is a "five-layer cake" infrastructure—energy, chips, infrastructure, models, applications, which still require trillions of dollars more for construction.
AI is transforming from "software" to real-time generated intelligence
Jensen Huang first explained the fundamental differences between AI and traditional software.
For decades, software has essentially been "pre-written programs." Developers write algorithms and computers execute them according to rules. Data must be structured and accessed via database queries. AI changes this paradigm.
Jensen Huang wrote: "This is the first time in computer history that machines can understand unstructured information—images, text, sound—and grasp their meaning."
More importantly, AI does not retrieve answers from a database, but generates intelligence in real time.
He explained: "Each answer is newly generated, each output depends on context. Computers are no longer just executing instructions; they are reasoning."
Because intelligence is generated in real time, this forces the entire computing architecture to be redesigned.
The "five-layer structure" of the AI industry
In the article, Jensen Huang proposed a structural framework for the AI industry: a five-layer technology stack—energy, chips, infrastructure, models, applications. He emphasized strong coupling between these layers.
Energy The foundational layer is energy. Real-time generated intelligence requires real-time generated electricity. Each token generated is the result of moving electrons, heat management, and converting energy into computing power. Below this layer, there are no abstraction layers. Energy is the first principle of AI infrastructure and is the hard constraint that determines how much intelligence a system can produce.
Chips 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 capability, high-bandwidth memory, and fast interconnect technology. Advances in the chip layer determine the expansion speed of AI and the affordability of intelligence.
Infrastructure Above the chip layer is infrastructure. This includes land, power delivery, cooling systems, construction, networking, and systems that coordinate thousands of processors to form a machine. These systems are "AI factories." Their purpose is not just to store information, but to manufacture intelligence.
Models Above infrastructure are models. AI models can understand multiple types of information: language, biology, chemistry, physics, finance, medicine, and the physical world itself. Language models are just one category. Some of the most disruptive work is now happening in protein AI, chemical AI, physical simulation, robotics, and autonomous systems.
Applications The top layer is applications, where economic value is created. Drug discovery platforms, industrial robots, legal assistants, and self-driving cars belong to this category. Autonomous vehicles are AI applications embodied in machines; humanoid robots are AI applications embodied in flesh. They use the same technology stack but achieve different outcomes.
AI infrastructure construction is still at an early stage
In terms of industry scale, Jensen Huang provided a clear judgment.
He said: "We have only invested a few hundred billion dollars so far, but will need trillions of dollars in infrastructure construction in the future."
Globally, chip factories, server assembly plants, and AI data centers are accelerating construction. Jensen Huang said this trend may become "one of the largest infrastructure developments in human history".
At the same time, this brings new labor demand. AI data center construction requires a large number of technical workers, including electricians, plumbers, network engineers, and equipment installers.
He emphasized: "Participating in this transformation does not require a computer PhD."
Open source models drive AI industry expansion
Jensen Huang also specifically mentioned the role of open source models in the AI ecosystem.
He pointed out that a large number of AI models worldwide are open, and enterprises, research institutions, and countries all rely on these models to participate in AI development. When open source models reach advanced levels, they drive demand throughout the industry chain.
He gave an example: "DeepSeek-R1 is a typical case."
After this model was publicized, it stimulated application development, and also increased demand for training compute power, infrastructure, chips, and energy. In other words, a breakthrough in one model drives the entire industry chain downward.
AI's impact goes beyond the software industry
At the end of the article, Jensen Huang emphasized that AI not only changes the software industry, but will also affect energy, manufacturing, labor structure, and modes of economic growth.
He said: "AI is an industrial-scale transformation; it will change how energy is produced, how factories are built, how work is organized, and how the economy grows."
He believes that AI is still in its early stages. Much infrastructure remains unbuilt, and lots of talent remains untrained.
But the trend is very clear: "AI is becoming the infrastructure of the modern world."
"AI is a 'Five-layer Cake' Infrastructure"
March 10, 2026. Speaker: Jensen Huang
Artificial Intelligence (AI) is one of the most powerful forces shaping the world today. It is not just a smart application or a single model; it is indispensable infrastructure, like electricity and the internet.
AI runs on real hardware, real energy, and a real economic base. It acquires raw materials and transforms them into intelligence on a large scale. Every company will use it, every nation will build it.
To understand why AI is developing in this way, we need to reason from first principles and examine the fundamental changes occurring in the computing field.
From prerecorded software to real-time intelligence For most of computing history, software was "prerecorded." Humans wrote algorithms; computers executed them. Data had to be carefully structured, stored in tables, and retrieved with precise queries. SQL became indispensable because it kept that world running.
However, AI disrupted this model.
This is the first time ever that computers can understand unstructured information. It can see images, read text, listen to sounds, and understand their meaning. It can reason about context and intent. Most importantly, it generates intelligence in real time.
Every response is newly created. Every answer depends on the context you provide. This is no longer software that retrieves stored instructions, but software capable of reasoning and generating intelligence on demand.
Precisely because intelligence is generated in real time, the entire underpinning technology stack must be reinvented.
AI as Infrastructure When you look at AI from an industrial perspective, it can be broken down into a five-layer technology stack.
Energy The foundational layer is energy. Real-time generated intelligence requires real-time generated electricity. Each token generated is the result of moving electrons, heat management, and converting energy into computing power. Below this layer, there are no abstraction layers. Energy is the first principle of AI infrastructure and is the hard constraint that determines how much intelligence a system can produce.
Chips 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 capability, high-bandwidth memory, and fast interconnect technology. Advances in the chip layer determine the expansion speed of AI and the affordability of intelligence.
Infrastructure Above the chip layer is infrastructure. This includes land, power delivery, cooling systems, construction, networking, and systems that coordinate thousands of processors to form a machine. These systems are "AI factories." Their purpose is not just to store information, but to manufacture intelligence.
Models Above infrastructure are models. AI models can understand multiple types of information: language, biology, chemistry, physics, finance, medicine, and the physical world itself. Language models are just one category. Some of the most disruptive work is now happening in protein AI, chemical AI, physical simulation, robotics, and autonomous systems.
Applications The top layer is applications, where economic value is created. Drug discovery platforms, industrial robots, legal assistants, and self-driving cars belong to this category. Autonomous vehicles are AI applications embodied in machines; humanoid robots are AI applications embodied in flesh. They use the same technology stack but achieve different outcomes.
This is AI’s "five-layer cake": Energy → Chips → Infrastructure → Models → Applications.
Every successful application strongly drives each underlying layer, all the way down to the power plant that keeps it running.
We are only just beginning this construction process. We have invested hundreds of billions of dollars so far, but trillions of dollars in infrastructure remain to be built.
Globally, we see chip plants, computer assembly plants, and AI factories rising at unprecedented scale. This is becoming one of the largest infrastructure builds in human history.
The workforce supporting this build is enormous. AI factories need electricians, plumbers, pipe fitters, steel workers, network technicians, installers, and operators. These are high-skilled, high-paid jobs, and there is currently a shortage. You don’t need a computer science PhD to participate in this transformation.
Meanwhile, AI is driving productivity improvements across the knowledge economy. Take radiology: now AI can help read scan images, yet demand for radiologists continues to grow—which is not a paradox.
The core responsibility of radiologists is patient care, and reading scans is only part of the process. When AI takes on more routine repetitive tasks, radiologists can focus more on diagnosis, communication, and patient care. As a result, hospitals operate more efficiently, can serve more patients, and hire more staff.
Productivity creates capacity; capacity drives growth.
What changed in the past year? Over the past year, AI crossed a key threshold. Models became good enough to deliver practical value at scale. Reasoning improved, hallucinations decreased, grounding in facts improved significantly. For the first time, applications based on AI are now generating real economic value.
Applications in drug discovery, logistics, customer service, software development, and manufacturing have demonstrated strong product-market fit. These applications powerfully drive every layer underneath.
Open source models are critical here. Most models in the world are free. Researchers, startups, large companies, and entire nations rely on open source models to participate in AI innovation. When open source models reach the cutting edge, they change more than just software—they activate demand across the entire technology stack.
DeepSeek-R1 is a strong example. By making a powerful reasoning model widely available, it accelerates application adoption at the top layer, while also increasing demand for training, infrastructure, chips, and energy at the underlying layers.
What does this mean? When you see AI as indispensable infrastructure, its far-reaching impact becomes clear.
AI started with large language models built on the Transformer architecture. But it is much more than that. It is an industrial transformation that reshapes how energy is produced and consumed, how factories are built, how work is organized, and how economies grow.
We build AI factories because intelligence is generated in real time; we redesign chips because efficiency determines the speed of intelligent expansion; energy is core because it sets the absolute ceiling for intelligent output; applications are landing rapidly because their underlying models have crossed the threshold, enabling practical value at scale.
Every layer mutually reinforces the others.
That’s why this infrastructure build is so vast. That’s why it touches so many industries. That’s why it isn’t limited to a single country or field. Every company will use AI; every country will build it.
We are still at an early stage. Most infrastructure has not yet been built; most of the workforce is still untrained; most opportunities remain unexploited.
But the direction is clear.
AI is becoming the foundational infrastructure of the modern world. The choices we make now—how fast we build, how wide we participate, how responsibly we deploy AI—will ultimately shape the future of this era.
Original article link: https://blogs.nvidia.com/blog/ai-5-layer-cake/
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