When you ask a question to an artificial intelligence, something very concrete happens—not something abstract or “in the cloud” in the magical sense of the term.
It happens in very specific physical places: edge routers, high-capacity fiber optic links, regional data centers, and large GPU clusters designed to operate at the edge of their limits. Your query is analyzed, routed, and processed based on latency, complexity, system load, and resource availability.
Modern AI is not just software. It’s a distributed infrastructure designed to respond in milliseconds at global scale.
1️⃣ The Real Journey of an AI Query
Let’s simplify the typical path of a query:
- You type a question from your device (mobile, PC, app, or API)
- The request enters your operator’s or connectivity provider’s network
- It arrives at an edge point (border node or edge POP)
- A system decides where to process it
- The inference runs
- The response comes back to you
Several things can happen on that journey:
- The query might travel a few kilometers or cross continents
- It might touch several different operator networks
- It might run on hardware nearby or in a data center thousands of miles away
All of this happens in fractions of a second, but it’s carefully orchestrated. Nothing is random.
The Role of Intelligent Routing
When your query leaves your device, it immediately enters a routing decision process that considers:
- BGP (Border Gateway Protocol): The protocol that determines routes between autonomous networks
- Peering agreements: Direct interconnections between providers that reduce hops
- Internet Exchange Points (IXP): Where multiple operators exchange traffic directly
- Real-time latency measurement: Every hop is constantly monitored
Major AI providers maintain direct agreements with telecommunications operators in dozens of countries. This means your query can avoid public internet transit and travel through optimized routes from the first hop.
A real example: A query from Santiago, Chile to an AI service can take two completely different paths depending on the operator. With direct peering, it can reach the regional data center in São Paulo in 30-40ms. Without it, it might transit through Miami first, adding 100ms or more to the total time.
2️⃣ Border and Edge: Where Time Matters More Than Power
At the edge of the network, tasks that can’t wait are processed.
We’re talking about:
- Basic data classification
- Event detection
- Filtering
- Inferences with small, specialized models
Real-world example: manufacturing
In an industrial plant, an artificial vision system inspects pieces moving along a production line. Each piece has just milliseconds to be evaluated before continuing on its way.
That model runs on local hardware, consumes relatively little power, and responds almost in real time. Sending each image to a remote data center would technically be possible, but it introduces latency, network dependency, and unnecessary costs.
That’s why that kind of AI lives on the edge.
3️⃣ Regional Data Centers: The Point of Balance
Most of the AI we use daily is processed in regional data centers.
This includes:
- Chatbots
- Document summarization
- Text and image analysis
- Enterprise AI services
- General inference APIs
These centers are designed specifically for AI workloads:
- High density of GPUs
- Internal networks with very low latency
- Direct interconnection with telecommunications operators
- Advanced cooling systems
A modern regional data center can host tens of thousands of GPUs and operate continuously. They’re not “server rooms”—they’re industrial facilities designed to sustain constant load.
Internal Network Architecture
What happens inside the data center is as critical as external connectivity.
Communication between GPUs in a cluster requires:
- Very high bandwidth networks: 400Gbps, 800Gbps or more per link
- Ultra-low latency: Measured in microseconds, not milliseconds
- Specialized topologies: Fat-tree, leaf-spine, or custom configurations designed for all-to-all traffic
- AI-dedicated switches: Hardware specifically designed for machine learning traffic patterns
When a model is distributed across multiple GPUs for inference (or worse, for training), the synchronization speed between them directly determines final performance. A bottleneck in the internal network can waste the potential of hundreds of GPUs.
That’s why AI infrastructure operators invest as much in internal networking as in the GPUs themselves.
This tier allows serving millions of users with reasonable response times without always depending on more distant global infrastructure.
4️⃣ Hyperscale: Where Large Models Are Trained
Training foundational models happens in only a handful of places worldwide.
Not for lack of knowledge, but because of physical and operational requirements:
- Massive clusters of GPUs working in parallel
- Internal networks of extremely high speed
- Capacity to run processes for weeks without interruption
- Infrastructure prepared for failures and automatic recovery
Training a large model is not something you do “when you have time.” It’s a planned, expensive, and highly concentrated process.
That’s why we see that:
- Training centralizes
- Inference distributes
It’s not an ideological decision—it’s a direct consequence of necessary infrastructure.
5️⃣ GPUs and Accelerators: The New Center of Design
Modern AI revolves around specialized accelerators.
Today, designing a data center starts by answering questions like:
- How many kilowatts per rack can we support?
- What kind of cooling do we need?
- How do we distribute power throughout the building?
A single high-performance GPU can consume hundreds of watts. A server with multiple GPUs easily exceeds several kilowatts. A complete rack can concentrate a density unthinkable a decade ago.
The Evolution of Accelerators
In recent years we’ve seen an explosion of specialized hardware:
- NVIDIA H100: 700W TDP, designed specifically for transformers and large models
- Google TPUs (v5): Optimized for Google workloads, with custom interconnect
- AMD Instinct MI300: Direct competition in the large-scale training market
- Custom chips from AWS, Microsoft, Meta: Designed for their specific workloads
The trend is clear: major AI operators are designing their own silicon.
Not because commercial GPUs don’t work, but because when you operate at a scale of millions of queries per second, every percentage point of efficiency translates into megawatts of energy consumption and millions of dollars.
A concrete data point: A cluster of 10,000 H100 GPUs can consume 7-10 megawatts in GPUs alone, not counting networking, storage, or cooling. That’s equivalent to the consumption of a small town.
This has completely changed how data centers are built.
6️⃣ Cooling and Physical Design: Back to Basics
With so much computational density, heat becomes a central problem.
That’s why it’s common today to see:
- Direct liquid cooling to the chip
- Immersion systems
- Closed-loop water circuits
- Locations chosen for climate and environment
AI data centers increasingly resemble industrial plants less and less like traditional IT infrastructure, where physical design is as important as the software running inside.
7️⃣ How the System Decides Where to Process Your Query
One of the least visible—but most important—parts is orchestration.
Every query is evaluated in real time based on criteria like:
- Task complexity
- Size of the required model
- Sensitivity to latency
- Current system load
- Regional availability
Practical examples:
- Simple, frequent queries → edge
- Common user tasks → regional data center
- Heavy or infrequent processes → hyperscale
These decisions aren’t made by a person. They’re made by the infrastructure system itself.
8️⃣ The Critical Role of Telecommunications
None of this would work without high-capacity networks.
Fiber optics, interconnections between operators, neutral traffic exchange points, and redundant links are fundamental pieces of the system. As AI increasingly distributes toward the edge, the network stops being a “medium” and becomes an active part of the intelligence architecture.
Submarine Cables and Global Connectivity
When we talk about global AI infrastructure, it’s impossible to ignore submarine cables.
More than 95% of international traffic travels through these cables. And the major cloud and AI service providers don’t just use them, they build them:
- Google: Has invested in more than 30 submarine cables owned or shared
- Meta: Participates in consortia deploying tens of thousands of kilometers of subsea fiber
- Microsoft and Amazon: Equally active in new transoceanic cable projects
Why? Because relying on third parties to connect data centers between continents introduces:
- Congestion risk: Sharing bandwidth with public traffic
- Variable costs: Paying for transit at massive volumes
- Less control: Dependence on external SLAs for critical services
A modern submarine cable can carry more than 400 terabits per second. To put that in perspective, it’s enough to transmit millions of simultaneous AI queries between continents without additional latency.
Latency, resilience, and network capacity directly influence the AI experience.
9️⃣ Infrastructure and Strategy
A broader dimension appears here.
Building AI infrastructure means coordinating:
- Specialized hardware
- Telecommunications networks
- Physical design
- Continuous operation
- Long-term scalability
Not all countries, companies, or regions can do it at the same pace. That’s why AI infrastructure has become a strategic factor.
It’s not just technology. It’s planning, investment, and sustained operational capacity.
🔟 Why Understanding This Matters
Because using AI without understanding its infrastructure leads to poor decisions:
- Unrealistic expectations
- Poorly designed architectures
- Unexpected costs
- Latency or scalability problems
Understanding how infrastructure works enables you to design better products, make better technical decisions, and understand where the ecosystem is really heading.
Conclusion
When you ask an AI a question, you’re not just interacting with a model.
You’re activating a global network of data centers, telecommunications links, physical systems, and orchestration software designed to respond in milliseconds.
Artificial intelligence doesn’t live in the cloud. It lives in infrastructure.
And understanding that infrastructure is key to understanding the true future of AI.
✍️ Claudio from ViaMind
“Dare to imagine, create, and transform.”
Recommended resources on AI infrastructure:
-
Data Center Solutions — NVIDIA
Enterprise infrastructure solutions for AI and high-performance computing. -
What is a CDN? — Cloudflare
Explanation of how content delivery networks work and latency on the internet. -
White Papers on 5G and Infrastructure — Ericsson
Technical analysis of mobile networks and their role in delivering AI services.
If you have questions about infrastructure, edge computing, or how distributed technology impacts the future, let me know in the comments or connect with me on LinkedIn.
