AI EDUCATION: What Is AI Infrastructure?

Infrastructure brings out our inner nerd. 

Welcome to AI Education, where this week we’re talking about AI infrastructure. For anyone who needs to know, we selected this topic because we liked it. That’s how we got here, to AI Education. 

From our roots in Kentucky and rural northern New Jersey, where the skeletal remains of abandoned railroads stretch across the open landscape, to the highways and streets (transportation infrastructure), pipes and cables that our resources move through (water and electrical infrastructure), to the processors and wires our digital resources move through (IT infrastructure), we’re fascinated by infrastructure—the foundations upon which economic activities occur. Yet, when we started writing about financial services, it still took us a while to recognize financial infrastructure for what it was—the highways and pipes and cables that move money around the world and through economies. 

Why AI Has Its Own Infrastructure 

So, when we’re approached with artificial intelligence infrastructure as a concept, we do have some background to build on. AI infrastructure does encompass the processors and wires that make artificial intelligence and machine learning possible. However, it also encompasses the software environments in which AI is developed, trained and deployed. 

While there are some AI applications designed to run on personal computers and mobile devices, for the most part, building and using artificial intelligence requires specific, bespoke infrastructure. The main reason for this need remains AI’s large demands on energy and computing power—even now, at the beginning of 2026, most enterprises can’t concentrate the resources necessary to build and run their own AI, let alone retain the talent needed for doing so. 

The solutions and equipment offered by IT infrastructure—wired or wireless local networks, on-premise servers that can function as data centers, local computing power provided by central processing units (CPUs)—fall short of AI’s needs. Think of those narrow little century-old railroad tunnels under the Hudson River between New York and New Jersey and all the rickety bridges and sharp turns along the Northeast Corridor. There’s really no way modern high-speed rail pushing 260 miles per hour can run through those tunnels, across those bridges or around those turns—just as there’s really no way most AI applications can be run in a medium-sized business’s office or from an employee’s laptop. 

Why AI Technology Is Like an Onion (or a Parfait) 

We won’t call it an ogre. The AI technology stack is made of layers—Redhat actually divides the AI tech stack into three major layers:  Applications, Model and Infrastructure. 

Applications: technology through which humans collaborate and interact with machines. Human-facing technology, in other words. 

Model: Technology that helps AI function. Redhat names three different kinds of models—General, which mimic the human mind’s ability to think; Specific, which use specific data to produce precise results; and Hyperlocal, models superspecialized by task or subject area. 

Infrastructure: The hardware and software needed to build and train models. 

What AI Infrastructure Includes 

Processing power delivered by graphics processing units (GPUs) and AI-specific chips. This is particularly important because the chips that traditionally power your personal computers and mobile devices, CPUs, process information in a serial manner—one thing after another. GPUs and AI chips, on the other hand, are capable of what’s called parallel processing, handling more than one operation simultaneously. 

A cloud environment to distribute processing needs (and thus energy demand) via high-performance networks to locations with massive amounts of data storage and processing capability. This isn’t just a cloud where people share information, this is a cloud where software can intelligently move computing needs around, dividing the work between multiple sets of computers that may be geographically distant from each other, and then putting the results of their work back together in a coherent manner. It’s not your local WiFi network. 

AI also requires its own specialized software infrastructure: 

• One or more machine learning frameworks with which to build AI algorithms. Examples include TensorFlow and PyTorch  
• Optimization software to simplify the complex distributed hardware infrastructure that runs our AI. 
• Data processing and management tools, including data ingestion and cleaning, and orchestration and automation platforms. This is where tools like Snowflake and Apache come into play, as well as software that can combine AI models like Microsoft’s AutoGen.
• MLOps software to manage and produce AI models across their lifecycle, examples include DataBricks and MLFlow. 
• Security capabilities to protect sensitive data throughout an AI model’s lifecycle. 

Why Is AI Infrastructure So Difficult to Build 

First and foremost, we return to the reason AI has its own specific infrastructure in the first place: it is extremely demanding on energy and computing resources. Most individuals and small-and-medium sized enterprises lack the ability to concentrate the necessary resources to build and deploy AI from scratch. That’s not to say it can’t be done with limited applications, for example, my 16-year-old nephew is designing a bespoke smart home system for my brother’s new house that will help manage and automate climate control, energy use, entertainment features, and even groceries and other shopping needs, all using local technology. It’s not going to manage a complicated investment portfolio without a data center, but it can run a home on some advanced but widely available and affordable technology. 

There’s good reason that our AI data centers are built in close proximity to power generation facilities—it’s difficult, expensive and even dangerous to transmit large amounts of electricity over distances, while moving data over distances is relatively cheap and easy. 

But there’s also the time issue. We’re willing to send our data to the ends of the earth for processing and analysis—but only as long as it doesn’t take very long. Traditional processors and computing might get through all that data, but they’ll take much longer than AI-appropriate infrastructure would to complete the same task. Similarly, the old networking infrastructure on which we once relied is no longer up to the standards of artificial intelligence. 

Outside of personal AI uses, scalability becomes an issue. For one thing, business activity is rarely constant—it usually ebbs and flows, and in the best of times, it grows and grows. Locally built and managed AI solutions might work for a medium-sized business today, but after five or ten years of growth they could very well become unmanageable. Security and privacy are also issues for businesses, which are often required to safeguard against breaches.