How does the power and structure of the underlying computer impact what artificial intelligence can actually do?
Every AI solution, from your smartphone assistant to large-scale recommendation engines, is powered by a combination of hardware, software, and data. Understanding how these elements connect is essential to grasping what AI can really achieve — and where its limits lie.
To see AI at work, you need to have some sort of computing system, an application that contains the required software, and a knowledge base. The computing system can be anything with a chip inside; in fact, a smartphone does just as well as a desktop computer for certain applications. Of course, if you’re Amazon and you want to provide advice on a particular person’s next buying decision, the smartphone won’t do — you need a big computing system for that application. The size of the computing system is directly proportional to the amount of work you expect the AI to perform.
The world’s fastest supercomputers are often used to train advanced AI models — but your phone may still use AI for tasks like voice recognition, thanks to efficient, lightweight algorithms.
Think of an AI-powered tool you use — is it running on your device, or somewhere else? Why might that matter?
The application can also vary in size, complexity, and even location. For example, if you’re a business owner and you want to analyze client data to determine how best to make a sales pitch, you might rely on a server-based application to perform the task. On the other hand, if you’re a customer and you want to find products on Amazon to complement your current purchase items, the application doesn’t even reside on your computer; you access it via a web-based application located on Amazon’s servers.
When you ask your smartphone for directions, AI helps process your request right on your device. But when you use a streaming service’s recommendation engine, the heavy-lifting AI typically runs on cloud servers far away.
Why might some AI tasks be handled locally, while others require remote servers?
A database that holds information about the facts, assumptions, and rules that the AI can use to make decisions or answer questions.
The knowledge base (a database that holds information about the facts, assumptions, and rules that the AI can use), varies in location and size as well. The more complex the data, the more insight you can obtain from it, but the more you need to manipulate the data as well. You get no free lunch when it comes to knowledge management. The interplay between location and time is also important: A network connection affords you access to a large knowledge base online but costs you in time because of the latency of network connections. However, localized databases, though fast, tend to lack details in many cases.
The delay between sending a request and receiving a response, often caused by the time it takes data to travel across a network.
Want to go deeper? The science behind AI storage and speed
Storing AI knowledge locally (on your device) can make retrieval and processing fast, but the device may not have enough storage or power for complex AI tasks. Cloud-based knowledge bases can be massive and up-to-date, but every query has to travel across the internet — introducing latency. Modern AI systems sometimes use hybrid approaches: simple tasks run locally, while more demanding computations are handled remotely.
Many real-world AI systems are designed to balance speed (local processing) and depth (cloud-based analysis), aiming to deliver results that are both fast and insightful.
All AI needs extremely powerful computers to be useful.
Some AIs run efficiently on smartphones and small devices; only the most complex applications require massive computational resources.
How might the location and size of an AI’s knowledge base affect its performance or usefulness for you as a user?
- AI relies on a combination of hardware, software, and a knowledge base.
- The location and power of each component can change how you experience AI-powered solutions.
Explore how different AI applications run on different systems.
- List three AI-powered tools or apps you use regularly (e.g., voice assistant, recommendation system).
- For each, research or guess: Does it run on your device, on a remote server, or both?
- Discuss why the location might have been chosen for each application.
What three components are essential to seeing AI at work?
Tap to revealA computing system, an application with the required software, and a knowledge base.
Define “latency” in the context of AI systems.
Tap to revealLatency is the delay experienced due to the time needed for data to travel between devices or servers, especially over a network.
Why might a business use a server-based AI application?
Tap to revealTo analyze large or complex data sets that require more computing power than a local device can offer.
Reflect on a time when you noticed an AI tool working quickly or slowly. What do you think influenced its performance — the device, the application, or the knowledge base? Explain your reasoning.
Which of the following best describes the relationship between the size of a computing system and AI performance?
The effectiveness and speed of AI depend on how its computing system, application, and knowledge base are structured and connected.
There is no one-size-fits-all hardware for AI — solutions can run on devices as small as smartphones or as powerful as cloud supercomputers, depending on the task.
The size of the computing system is directly proportional to the amount of work you expect the AI to perform.
The Shift
- AI’s potential and limits are shaped by the computing system, application, and knowledge base working together.
- Not all AI requires powerful hardware — many tasks are efficiently handled on everyday devices.
- The location and complexity of an AI’s knowledge base can impact both its speed and the quality of its insights.