Written by: Jensen Huang

Translated by: Peggy, BlockBeats

Artificial intelligence is one of the most powerful forces shaping the world today. It is not a smart application or a single model, but an infrastructure as crucial as electricity and the internet.

AI operates on real hardware, real energy, and real economic systems. It transforms raw materials into "intelligence" for large-scale production. Every company will use it, and every country will build it.

To understand why AI unfolds in this way, it is helpful to start from first principles and examine the fundamental changes that have occurred in the field of computing.

From "Pre-fabricated Software" to "Real-time Generated Intelligence"

For most of computing history, software has been "pre-fabricated." Humans first describe an algorithm, and then the computer executes it according to instructions. Data must be carefully structured, stored in tables, and retrieved through precise queries. SQL is indispensable because it enables the entire system to function.

And AI breaks this model.

For the first time, we have a computer that can understand unstructured information. It can see images, read text, listen to sounds, and comprehend their meanings; it can reason context and intent. More importantly, it can generate intelligence in real time.

Every response is a new generation. Each answer depends on the context you provide. It 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 that supports it must also be reinvented.

AI as Infrastructure

From an industrial perspective, AI can actually be broken down into a five-layer structure.

Energy

The bottom layer is energy.

Real-time generated intelligence requires real-time generated power. The production of each token implies 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 is the fundamental constraint on how much intelligence the system can produce.

Chips

Above the energy layer are chips. The design goal of these processors is to convert energy into computing power with extreme efficiency under large-scale conditions.

AI workloads demand enormous parallel computing power, high bandwidth memory, and high-speed interconnections. Advances at the chip layer determine the speed of AI scaling and how cheap "intelligence" will ultimately become.

Infrastructure

Above the chip layer is infrastructure. This includes land, power transmission, cooling systems, construction engineering, network systems, and scheduling systems that organize thousands of processors into a single machine.

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

Models

Above the infrastructure layer are models. AI models can understand various types of information: language, biology, chemistry, physics, finance, medicine, and the reality of the world itself.

Language models are just one category. One of the most transformative works is occurring in the following fields: protein AI, chemical AI, physical simulation, robotics, and autonomous systems.

Applications

At the top layer are applications, where real economic value is generated. For instance, drug discovery platforms, industrial robots, legal copilots, and self-driving cars.

A self-driving car is essentially an "AI application carried by machines"; a humanoid robot is an "AI application carried by a body." The underlying technology stack is the same, only the final presentation differs.

Thus, this is the five-layer structure of AI: energy → chips → infrastructure → models → applications. Every successful application pulls down all levels, all the way to the power plants that supply it.

An Early Infrastructure Buildout

We have just begun this buildout. The current scale of investment is only a few hundred billion dollars, while future infrastructure needs to be built at a scale of trillions of dollars.

Globally, we are seeing: chip factories, computer assembly plants, AI factories.

An unprecedented scale is being built. This is becoming one of the largest infrastructure builds in human history.

Labor Demand in the AI Era

The scale of labor needed to support this buildout is enormous.

AI factories require: electricians, plumbers, pipe installers, steel structure workers, network technicians, equipment installers, and operations staff.

These are all highly technical, well-paid positions, and there is currently a severe shortage. Participating in this transformation doesn’t necessarily require a Ph.D. in computer science.

At the same time, AI is driving productivity increases in the knowledge economy. Take radiology as an example. AI has already begun assisting with medical image interpretation, yet the demand for radiologists continues to grow.

This is not contradictory.

The true role of radiologists is to care for patients, and reading images is only one aspect of that work. As AI takes over more repetitive tasks, doctors can devote more time to judgment, communication, and treatment.

Increased efficiency in hospitals can serve more patients, thus requiring more staff. Productivity creates capability, and capability creates growth.

What Changes Have Occurred in the Past Year?

In the past year, AI has crossed a critical threshold.

Models have become good enough to truly make an impact in large-scale scenarios.

• Reasoning capabilities have significantly improved
• Hallucinations have significantly decreased
• "Grounding" with the real world has greatly enhanced

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

There is already clear product market fit 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 key role in this. The vast majority of AI models in the world are free. Researchers, startups, enterprises, and even entire countries rely on open source models to participate in advanced AI competition.

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 typical example. By making a powerful inference model widely available, it has driven rapid growth at the application layer while also increasing demand for training computing power, infrastructure, chips, and energy.

What Does This Mean?

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

It is an industrial-scale transformation that will reshape:

• The way energy is produced and consumed
• The way factories are built
• The way 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 at which intelligence can scale. Energy is at the core because it determines the maximum amount of intelligence the system can produce. Applications are exploding because models have finally crossed the threshold of "scale-available."

Each layer reinforces the others.

This is why the scale of this buildout is so massive, why it will simultaneously impact so many industries, and why it will not be limited to any one country or field.

Every company will use AI.

Every country will build AI.

We are still in the early stages.

A great deal of infrastructure has yet to be built, a lot of labor has yet to be trained, and many opportunities have yet to be realized.

But the direction is already 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 responsibilities of deployment will determine what this era ultimately looks like.