Editor's Note: Artificial intelligence is evolving from a cutting-edge technology into a foundational infrastructure supporting the modern economy. In its first long-form article published on its official account, NVIDIA attempts to systematically deconstruct the AI industry structure from first principles: from energy and chips, to data center infrastructure, then to models and applications, forming a complete five-layer technology stack.

The article points out that AI is not just a competition in software or models, but a global industrial endeavor involving energy, computing power, manufacturing, and applications, with a scale that could become one of the largest infrastructure expansions in human history. Through this 'five-layer cake' perspective, NVIDIA aims to illustrate that the true significance of AI is not just smarter software, but an infrastructure revolution on par with electricity and the internet.

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Artificial intelligence is one of the most powerful forces shaping the world today. It is not merely a clever application, nor a single model, but an infrastructure, as crucial as electricity and the internet.

AI runs on real hardware, real energy, and a real economic system. It transforms raw materials into mass-produced 'intelligence.' Every company will use it, every country will build it.

To understand why AI is unfolding in this manner, it is helpful to start from first principles and examine the fundamental changes occurring in computing.

From 'Pre-Made Software' to 'Real-Time Generated Intelligence'

For the vast majority of computing history, software has been 'pre-made.' Humans first define an algorithm, and then the computer executes the commands. 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 shatters this paradigm.

For the first time, we have computers that can understand unstructured information. They can see images, read text, listen to sounds, and comprehend their meaning; they can reason about context and intent. More importantly, they 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 pre-written instructions from a database; it is software reasoning in real-time and generating intelligence on demand.

Because intelligence is generated in real-time, the entire computing technology stack that supports it must be reinvented.

AI as Infrastructure

Viewing AI from an industry perspective, it can actually be broken down into a five-layer structure.

Energy

The foundational layer is energy.

Real-time generated intelligence requires real-time generated electricity. The production of every token means electrons are moving, heat is being managed, and energy is being converted into computing power.

There is no abstraction below this layer. 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 extreme efficiency and at massive scale.

AI workloads require immense parallel computing power, high-bandwidth memory, and high-speed interconnects. Advancements at the chip layer determine the speed of AI scaling and ultimately how cheap 'intelligence' will become.

Infrastructure

Above chips is infrastructure. This includes land, power delivery, cooling systems, construction engineering, networking systems, and scheduling systems that organize tens of thousands of processors into a single machine.

These systems are essentially AI factories. They are not designed to store information, but to manufacture intelligence.

Models

Above 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 category. Some of the most transformative work is happening in areas of: Protein AI, Chemistry AI, Physics Simulation, Robotics, Autonomous Systems

Applications

The top layer is the application layer, where real economic value is generated. Examples include drug discovery platforms, industrial robots, legal copilots, and autonomous vehicles.

An autonomous vehicle is essentially 'an AI application carried by a machine'; a humanoid robot is 'an AI application embodied in a physical form.' The underlying technology stack is the same, only the final form differs.

Thus, this is the five-layer structure of AI: Energy → Chips → Infrastructure → Models → Applications. Every successful application pulls demand through all the layers below it, down to the power plant that supplies its electricity.

An Infrastructure Buildout Still in Its Early Stages

We have only just begun this buildout. Current investment is merely in the scale of hundreds of billions of dollars, while trillions more in infrastructure will need to be built in the future.

Globally, we are witnessing: Chip factories, Computer assembly plants, AI factories.

Being constructed at an unprecedented scale. This is becoming one of the largest infrastructure construction projects in human history.

Labor Demand in the AI Era

The labor force required to support this construction is enormous.

AI factories need: Electricians, Plumbers, Pipefitters, Steelworkers, Network technicians, Equipment installers, Operations and maintenance personnel

These are skilled, well-paying jobs, and there is currently a severe shortage. Participating in this transformation does not necessarily require a PhD in computer science.

Simultaneously, AI is driving productivity gains in the knowledge economy. Take radiology as an example. AI has begun assisting in medical image interpretation, yet the demand for radiologists is still growing.

This is not a contradiction.

The real duty of a radiologist is patient care, and reading images is just one part of that work. As AI takes over more repetitive tasks, doctors can devote more time to judgment, communication, and treatment.

Improved hospital efficiency allows them to serve more patients, consequently requiring more staff. Productivity creates capacity, and capacity creates growth.

What Changed in the Past Year?

Over the past year, AI crossed a critical threshold.

Models have become good enough to be truly useful at scale.

· Reasoning capabilities improved significantly

· Hallucinations reduced markedly

· 'Grounding' in the real world enhanced substantially

For the first time, AI-based applications are starting to create real economic value.

Clear product-market fit has emerged in areas such as: Drug discovery, Logistics, Customer service, Software development, Manufacturing

These applications are powerfully pulling the entire underlying technology stack.

The Role of Open-Source Models

Open-source models play a key role here. The vast majority of the world's AI models are free. Researchers, startups, enterprises, and even entire nations rely on open-source models to compete in advanced AI.

When open-source models reach the technological frontier, they not only change software but also activate demand across the entire technology stack.

DeepSeek‐R1 is a prime example. By making a powerful reasoning model widely available, it spurred rapid growth at the application layer, while also increasing demand for training compute, infrastructure, chips, and energy.

What Does This Mean?

When you view AI as infrastructure, everything becomes clear. AI may have started with Transformers and large language models, but it is far more than that.

It is an industrial-scale transformation that will reshape:

· How energy is produced and consumed

· How factories are built

· How work is organized

· The patterns 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 of intelligence scaling. Energy is core because it determines the maximum amount of intelligence the system can produce. Applications are exploding because models have finally crossed the 'viable at scale' threshold.

Each layer reinforces the others.

This is why the scale of this buildout is so vast, why it impacts so many industries simultaneously, and why it will not be confined to one country or one domain.

Every company will use AI.

Every country will build AI.

We are still in the early stages.

Vast infrastructure remains unbuilt, a massive workforce remains untrained, and countless opportunities remain unrealized.

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 of deployment—will determine what this era ultimately becomes.