🧠 The Invisible Anatomy of AI (Part 1): The Layers that Sustain Artificial Intelligence
When we talk about artificial intelligence, we usually think of models like ChatGPT, Gemini or Copilot. But we rarely think about everything underneath: the physical infrastructure that allows these intelligences to exist.
AI doesn’t float in the cloud as an abstract concept. It lives in cables, chips, servers and energy, and every interaction with it travels through a chain of machines working in synchrony. Understanding this foundation is key to comprehending who truly holds the technological power of the future.
⚙️ Layer 1 — The Muscle: Chips, Energy and Computing
Everything starts here, in the specialized processors that allow AI to learn and generate responses.
The most used chips are GPUs (graphics cards), originally designed for video games, but perfect for AI because they can perform millions of calculations in parallel. A single modern GPU, like the NVIDIA H100, can reach over 80 teraflops (trillions of operations per second).
Large models use thousands of them connected to each other. To train GPT-4 it’s estimated that over 25,000 GPUs were used, running for weeks.
Alongside them are TPUs (from Google), ASICs (custom chips) and new hybrid processors that combine CPU + GPU + AI in the same module.
Each new generation improves power, but also doubles or triples energy consumption. Training a large language model can require over 30 gigawatt-hours, the same amount consumed by 7,000 European households in a year.
The race for AI chip supremacy is fierce:
- NVIDIA dominates the market with 95% of GPUs for AI
- AMD launched MI300X to compete directly
- Google develops its own TPU v5p
- Amazon designed Graviton4 chips for AWS
- Apple integrated Neural Engine into M3 processors
🔹 In summary: This is the “brute force” of AI. It’s measured in computing power (flops) and energy efficiency (performance per watt). The challenge is balancing power, heat and electrical cost, while NVIDIA, AMD, Google and Amazon compete to dominate the specialized chip market.
🌐 Layer 2 — The Veins: Connectivity and Networks
Having thousands of chips isn’t very useful if they don’t communicate quickly with each other. That’s why the network layer exists: the high-speed interconnection that links servers and data centers.
In distributed training, chips must constantly exchange data. Every microsecond counts: a small latency can mean extra hours of computing. Modern networks reach 400 to 800 gigabits per second of bandwidth per link, using fiber optics and technologies like InfiniBand or ultra-fast Ethernet.
There’s also a broader component: the global networks that connect data centers between countries. Cloud giants install dedicated submarine cables to move AI data between continents, with response times of just a few milliseconds.
🔹 In summary:
- It’s the circulatory system of AI: it moves information between processors
- Current speeds are thousands of times greater than a home connection
- A failure or congestion in this layer can slow down the entire training process
🏢 Layer 3 — The Lungs: Data Centers and Infrastructure
Data centers are the factories where AI “breathes”. They house the servers, networks and cooling systems that keep thousands of chips running 24 hours a day.
A single Microsoft Azure or Google Cloud data center can host over 100,000 servers and consume between 50 and 100 megawatts of continuous electrical power. That’s why they’re built near clean energy sources and in cold climates like Finland, Sweden or the Netherlands.
Efficiency is measured with a value called PUE (Power Usage Effectiveness): the most modern centers achieve figures close to 1.1, meaning almost all energy is used directly to process data, and very little is lost to heat.
Companies like Microsoft are experimenting with liquid cooling or even microfluidic cooling (circulation of liquids inside the chip) to maintain stable temperatures and reduce environmental impact.
🔹 In summary:
- This is where AI physically lives
- Each model depends on thousands of coordinated servers working together
- The big challenge: energy, sustainability and growing costs
🧩 Layer 4 — The Brain: Models and Software
At the top is what we all see: the AI models and the software that trains them. But even this layer totally depends on the previous ones.
The software is responsible for distributing tasks among chips, managing data and optimizing resources. There are techniques like:
Quantization → using smaller numbers to speed up calculations.
Distillation → creating lighter versions of the same model, with lower consumption.
Parallelism → dividing training among thousands of processors at once.
For example, a model can occupy hundreds of gigabytes of memory and process trillions of parameters, so it needs an architecture that allows parallel training without losing synchrony.
🔹 In summary:
- This is where “intelligence” itself is born
- But its performance depends on the physical infrastructure
- Software efficiency can reduce energy consumption by up to 30%
🏗️ The Complete Architecture of AI
| Layer | What it does | Example |
|---|---|---|
| 4. Models and Software | Where AI “thinks” | GPT-5, Gemini, Claude |
| 3. Infrastructure / Data Centers | Where it lives | Azure, AWS, Google Cloud |
| 2. Networks and Connectivity | Where information flows | Cisco, Arista, Huawei |
| 1. Hardware and Energy | The physical foundation | NVIDIA, AMD, TSMC |
Each layer depends on the previous one. A hardware improvement can reduce cloud costs; a software optimization can save megawatts. AI is an interconnected ecosystem of silicon, energy and data.
🔍 Conclusion — Intelligence is also built
Behind every brilliant model are thousands of tons of metal, kilometers of fiber optic cables and gigawatts of energy. AI isn’t just trained: it’s built.
In this first part we saw how that invisible architecture is organized: the layers that make it possible. In Part 2, we’ll explore who dominates each layer — the giants of hardware, cloud and telecommunications — and how they’re preparing to sustain the next wave of global intelligence.
📚 Recommended readings
The cost of compute: A $7 trillion race to scale data centers (McKinsey & Company)
Analysis of the massive infrastructure investments required to support AI growth and the $7 trillion data center scaling challenge.
👉 https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-cost-of-compute-a-7-trillion-dollar-race-to-scale-data-centers
AI power: Expanding data center capacity to meet growing demand (McKinsey & Company)
How data center infrastructure must evolve to meet the exponential energy and computing demands of artificial intelligence.
👉 https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/ai-power-expanding-data-center-capacity-to-meet-growing-demand
AI boom is infrastructure masquerading as software (Reuters)
Analysis of how the AI revolution is reshaping physical infrastructure requirements and investment patterns.
👉 https://www.reuters.com/commentary/breakingviews/ai-boom-is-infrastructure-masquerading-software-2025-07-23/
✍️ Claudio from ViaMind
Dare to imagine, create and transform.
