Original Title: AI Is a Five‑Layer Cake
Original Author: Nvidia
Compiled by: Peggy, BlockBeats
Editor's Note: Artificial intelligence is gradually evolving from a cutting-edge technology to an infrastructure that supports the operation of the modern economy. In its first long article released on its official account, Nvidia attempts to systematically analyze the industrial structure of AI from the first principles: from energy and chips, to data center infrastructure, 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 development involving energy, computing power, manufacturing, and applications, the scale of which could become one of the largest infrastructure expansions in human history. Through the perspective of this "five-layer cake," Nvidia tries to illustrate that the true meaning of AI is not just smarter software but a revolution in infrastructure comparable 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 or a single model; rather, it is an infrastructure as important as electricity and the internet.
AI operates on real hardware, real energy, and a real economic system. It transforms raw materials into scalable "intelligence." Every company will use it, and every country will build it.
To understand why AI is developing in this way, it is helpful to start from the first principles and look at the fundamental changes that have occurred in the field of computing.
From "Prepackaged Software" to "Real-Time Generated Intelligence"
For most of the history of computing, software has been "prepackaged." Humans first describe an algorithm, and then the computer executes it according to instructions. Data had to be carefully structured, stored in tables, and retrieved through precise queries. SQL is indispensable because it enables the entire system to operate.
AI breaks this pattern.
For the first time, we have a computer capable of understanding unstructured information. It can look at images, read text, listen to sounds, and understand their meaning; it can reason about context and intention. 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; it is software reasoning in real-time and generating intelligence as needed.
Because intelligence is generated in real-time, the entire computational technology stack that supports it must 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 electricity. The generation of each token means electrons are moving, heat is being managed, and energy is being transformed into computing power.
Below this layer, there is no abstraction. Energy is the first principle of AI infrastructure and is the fundamental constraint that determines how much intelligence the system can produce.
Chips
Above energy are chips. The design goal of these processors is to convert energy into computing power with extremely high efficiency and under large-scale conditions.
AI workloads require enormous parallel computing power, high-bandwidth memory, and high-speed interconnects. Advances at the chip layer determine the speed at which AI can scale and how cheap "intelligence" will ultimately become.
Infrastructure
Above chips is infrastructure. This includes land, electricity transmission, cooling systems, construction projects, network 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 reality itself.
Language models are just one category. One of the most transformative works is happening in the following fields: protein AI, chemistry AI, physical simulation, robotics, and autonomous systems.
Applications
The top layer is the applications layer, where real economic value is generated. For example, 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; it is just the final form that differs.
Thus, this is the five-layer structure of AI: energy → chips → infrastructure → models → applications. Each successful application pulls down all levels, all the way to the power plant at the bottom supporting it.
An Infrastructure Build Still in Its Early Stages
We have just begun this construction. The current scale of investment is only a few hundred billion dollars, while the future will still require building infrastructure on the scale of tens of trillions of dollars.
Globally, we are seeing: chip factories, computer assembly plants, AI factories.
Built on an unprecedented scale, this is becoming one of the largest infrastructure constructions in human history.
The Labor Demand of the AI Era
The scale of labor required to support this construction is enormous.
AI factories need: electricians, plumbers, pipeline installers, steel structure workers, network technicians, equipment installers, and operations personnel.
These are highly technical roles with generous salaries, and there is currently a severe shortage of them. Participating in this transformation does not necessarily require a Ph.D. in computer science.
Meanwhile, AI is driving productivity improvements in the knowledge economy. Taking radiology as an example. AI has begun to assist in medical imaging interpretation, yet the demand for radiologists continues to grow.
This is not contradictory.
The true responsibility of radiologists is to care for patients, and reading images is just one task among many. As AI takes over more repetitive tasks, doctors can invest more time in judgment, communication, and treatment.
Improved efficiency in hospitals can serve more patients, thus requiring more personnel. Productivity creates capability, and capability creates growth.
What Changes Have Occurred in the Past Year?
In the past year, AI has crossed a crucial threshold.
Models are now good enough to truly function 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 beginning to create real economic value.
There has already been a notable product-market fit in the following areas: drug research and 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. 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 cutting edge of technology, they not only change software but also activate demand across the entire technology stack.
DeepSeek‑R1 is a typical example. By making a powerful reasoning model widely available, it drives rapid growth at the applications layer while increasing demand for training computing power, 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:
· The way energy is produced and consumed
· The way factories are constructed
· The way work is organized
· The patterns of economic growth
The reason AI factories are being built is that intelligence can now be generated in real-time. The reason chips are being redesigned is that efficiency determines the speed of intelligence expansion. The reason energy is becoming core is that it determines how much intelligence the system can produce at most. The explosion of applications is due to models finally crossing the threshold of "scalable availability."
Each layer reinforces the other layers.
This is why this construction is so large in scale, 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.
Much infrastructure has yet to be built, many workers have 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 responsibility of deployment will determine what this era will ultimately become.
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