Original Compilation: Peggy, BlockBeats
Editor’s Note: Artificial intelligence is evolving from a cutting-edge technology into the foundational infrastructure supporting the modern economy. In its first long-form article published on its official account, Nvidia attempts to systematically outline 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 in software or models, but a global industrial construction involving energy, computing power, manufacturing, and applications. Its scale may become one of the largest infrastructure expansions in human history. Through the lens of this “five-layer cake,” Nvidia seeks to illustrate that the true significance of AI is not merely smarter software, but an infrastructure revolution on a scale 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 clever application, nor a single model, but an infrastructure as vital 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, and every nation will build it.
To understand why AI is unfolding this way, it is helpful to start from first principles and examine what fundamental changes are actually happening 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 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 makes this entire system work.
AI breaks this pattern.
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 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 pre-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
Viewing AI from an industrial perspective, it can actually be deconstructed into a five-layer structure.
Energy
The bottom 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 a 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. Progress at the chip layer determines 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 like: 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 carried by a body.” The underlying technology stack is the same, only the final form differs.
Thus, this is AI’s five-layer structure: Energy → Chips → Infrastructure → Models → Applications. Every successful application pulls down through all layers, all the way to the power plant supplying its electricity at the very bottom.
An Infrastructure Buildout Still in Its Early Stages
We have only just begun this buildout. Current investment is merely in the hundreds of billions of dollars, while trillions of dollars worth of infrastructure still need to be built.
Globally, we are seeing: Chip factories, Computer assembly plants, AI factories.
Being constructed at an unprecedented scale. This is becoming one of the largest infrastructure builds in human history.
Labor Demand in the AI Era
The scale of labor required to support this buildout is enormous.
AI factories need: Electricians, Plumbers, Pipefitters, Steelworkers, Network technicians, Equipment installers, Operations and maintenance personnel
These are skilled, well-paying jobs, and they are currently in extreme shortage. Participating in this transformation does not 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 assisting with medical image interpretation, yet the demand for radiologists is still growing.
This is not contradictory.
A radiologist’s true duty is patient care, and reading scans is just one part of that job. 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, thus requiring more personnel. 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 substantially
- “Grounding” in the real world greatly enhanced
For the first time, AI-based applications are starting to create real economic value.
Clear product-market fit has emerged in areas like: Drug discovery, Logistics, Customer service, Software development, Manufacturing
These applications are strongly 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 at the core because it determines the maximum intelligence a system can produce. Applications are exploding because models have finally crossed the “usable 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 a single country or domain.
Every company will use AI.
Every nation 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.
本文来源于互联网： Jensen Huang’s Latest Article: The “Five-Layer Cake” of AI
Key Takeaways Bitcoin fell from $87,000 to $81,000 on January 29th and continued to drop below the $80,000 mark. Microsoft’s disappointing earnings report dragged down the Nasdaq, leading to a breach of Bitcoin’s key realized price support near $87,000. 市场 speculation about Kevin Warsh’s potential nomination as Fed Chair triggered downward pressure, although actual policy might not be as hawkish as feared. Regulatory sentiment towards crypto remains friendly, but with the $84,000 level lost, near-term downside risks cannot be ignored. Bitcoin Lags in the Rebound Bitcoin experienced two sharp declines in a short period. Around 9 AM ET on January 29th, Bitcoin began sliding from around $87,000; by 10 AM the next day, it had dropped to approximately $81,000, a decline of about 7%. The overall crypto market weakened, and…
简体中文 
English 
繁體中文 
日本語 
Tiếng Việt 
العربية 
한국어 
Bahasa Indonesia 
हिन्दी 
اردو 
Русский