Each week we find a new topic for our readers to learn about in our AI Education column.
You don’t build something big without setting a good foundation first.
Welcome to AI Education, where, this week, we’re going to zoom back out and talk about a more general concept, artificial intelligence infrastructure. That’s essentially the foundation upon which the world’s most powerful and advanced software to date is being built and used.
Before we dive in, we’ll note that there’s another way to look at the intersection of AI and Infrastructure, where AI is being applied to infrastructure management in areas like logistics, traffic control, energy and communications. That’s not what we’re talking about this week—we’re talking about what AI needs to work—it’s IT infrastructure, but for AI. While IT infrastructure refers to the technology necessary for general computing and data storage for individuals, businesses and institutions, AI infrastructure is the same thing but for the specific needs of machine learning and artificial intelligence.
So our metaphor—which is also kind of how we got here—is an obvious one. We were stricken with a momentary curiosity about Dubai’s Burj Khalifa, the tallest building in the world, wondering to ourselves what kind of foundation holds up a building that huge in the desert. The answer was more interesting than we thought—rather than b uilding a conventional foundation as we would in a city like Chicago or New York, the Burj Khalifa is held up by a concrete “raft,” which itself is supported by scores of long piles bored up to 50 meters into the ground, where they reach a more stable layer of earth beneath Dubai’s sand.
And thus it is with AI infrastructure—while the problems are usually similar, related to the need to transmit and process large volumes of data quickly—there are different solutions applied to answering the technology’s demands.
Two Sides of AI Infrastructure
AI Infrastructure encompasses the massive amounts of hardware and software enabling AI to function—all of the things that underpin the building, training and implementation artificial intelligence models. AI infrastructure, then, can include the personal computers a user operates to interface with AI models like ChatGPT or Claude—but it more formally refers to the processing power, storage, networks and software that the AI model itself relies on, encompassing hardware and software demands far beyond the needs of traditional IT infrastructure.
AI Hardware Infrastructure
AI hardware includes AI chips like graphics processing units (GPUs) and tensor processing units (TPUs). AI chips are key in modern computing because they are capable of parallel processing—simultaneously performing many operations or sifting through different segments of data. Other AI-specific chips, like field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs) may be used as accelerators to help process AI workloads. Of course, all of this AI infrastructure is itself built on a foundation of energy infrastructure.
It also includes high-performance computing in general, which may either be concentrated into a single location like a supercomputer or a massive data center, or distributed geographically among many different computers which may be remote from one another. Similarly, data storage may be concentrated into a single location but is now more often distributed, due to the very large size of data sets being used to train AI.
AI systems also require the ability to move data between different computers very quickly—especially in distributed computing, where a task may need to be divided between different sets of computers in remote locations—and the ability to coherently divide tasks and reintegrate them. This not only requires advanced high-speed networking infrastructure, but also specialized software.
AI Software Infrastructure
On the software side of things, AI requires data, data storage and data management. While much of today’s cutting-edge AI is capable of ingesting raw data, training machine learning and artificial intelligence models still requires some ability to process and prepare data. On the other hand, the rise of AI infrastructure and models using unprocessed data has also led to increasing data storage burdens—millions of raw text, image, sound and video files take up a lot of space.
Developing artificial intelligence requires machine learning frameworks on which to build AI models like TensorFlow and PyTorch, and data processing libraries to help train AI like Pandas and SciPy. As we’ve mentioned, AI also requires some software tools to manage and divide AI workloads and data storage.
Keys to AI Infrastructure
- Well-built AI infrastructure should offer robust data storage, data is the lifeblood of artificial intelligence.
- It should be scalable—successful, useful AI models tend to go from small to very large very quickly.
- It should be efficient, powerful and fast—meaning that not only should AI be trained and operated on infrastructure optimized for artificial intelligence in general, but that infrastructure should be optimized for each unique deployment of AI. Ideally, this means that AI infrastructure will also be able to integrate with existing hardware and software to access and move data seamlessly. However, this also means that…
- It should be safe, compliant and secure by design. This extends from following best practices to keeping data secure and private to ensuring that privacy regulations like HIPPAA are followed to the letter.
Why AI Infrastructure Is Growing In Importance
AI infrastructure is what entities—individuals, businesses and institutions—need to build to overcome their shortcomings in computing power, data storage, data processing, and to some extent experience and expertise—wen it comes to building out their AI capabilities. AI infrastructure is still in most cases cost prohibitive—so complex and so immense that it is expensive to build and maintain—but that won’t always be the case. For the time being, smaller companies and individuals can access AI infrastructure—for a price—from the likes of Microsoft Azure, Amazon Web Services and Google Cloud.
AI Infrastructure is key to the future of AI for two reasons. One is that the quality of the AI models we build depend largely on the quality of the infrastructure that supports them. Two is that AI is growing in sophistication and resource demand so quickly that failure to build quality AI infrastructure with a view towards potential long-term needs threatens to throttle our ability to develop subsequent generations of AI models.
Think of tomorrow’s AI models like the next Burj Khalifa—if we’re going to build AI capable of artificial general intelligence or artificial superintelligence, we first need to build a solid, flexible foundation of AI infrastructure.
