4 AI Concepts Every Investor Should Know
Posted: Thu Sep 03, 2026 8:00 pmThe rapid adoption of AI is driving investment in everything from semiconductors and cloud computing to data centers, networking equipment, and power infrastructure. At the same time, new technologies such as AI agents are changing how businesses and consumers interact with artificial intelligence.
For investors trying to make sense of the rapidly evolving industry, here are four important AI concepts to know.
1. What Are AI Agents?
AI agents are artificial intelligence systems designed to complete tasks and make decisions with varying degrees of autonomy. Unlike a traditional chatbot that primarily responds to individual prompts, an AI agent can potentially perform a series of actions to accomplish a broader goal.
For example, instead of simply helping a user research flights, an AI agent could be tasked with planning an entire trip. Depending on the system and permissions it is given, the agent could research destinations, compare flights and hotels, create an itinerary, and potentially make reservations.
Businesses are also exploring AI agents for customer service, software development, sales, research, and other repetitive or time-consuming tasks. The goal is generally to automate more complicated workflows that previously required multiple steps of human input.
AI agents have consequently emerged as another potential growth area for the technology sector. Greater adoption could increase demand for AI models, cloud computing, software, and the computing infrastructure required to run these systems.
2. What Is an AI Data Center?
A data center is a facility that houses computing equipment used to store, process, and distribute data. AI data centers serve the same basic purpose but are designed to handle the particularly demanding computing workloads associated with artificial intelligence.
Training and running sophisticated AI models can require thousands of specialized chips working together. These systems also need high-speed networking equipment to quickly move enormous amounts of data between processors.
All of that computing power generates considerable heat and requires substantial amounts of electricity. As a result, the AI data center boom has created demand beyond the traditional technology sector, including for cooling equipment, electrical components, construction, and power generation.
For investors, this means the expansion of AI infrastructure can potentially benefit a much wider group of companies than the businesses directly developing AI models.
3. What Are AI Chips?
AI chips are semiconductors designed or optimized to perform the massive number of calculations required by artificial intelligence applications.
Graphics processing units (GPUs) have become particularly important to the AI industry. Originally developed to handle computer graphics, GPUs can perform many calculations simultaneously, making them well suited for training and running AI models.
However, GPUs are only one piece of the equation. Central processing units (CPUs), memory chips, networking chips, and application-specific integrated circuits (ASICs) can all play roles in AI computing systems.
Some technology companies are also developing their own custom AI processors. These chips can be designed for particular workloads and potentially offer advantages in areas such as performance, cost, or energy efficiency.
For investors, understanding the different types of AI chips can help illustrate why the semiconductor industry has become one of the most important areas of the broader AI buildout.
4. What Is the AI Supply Chain?
The AI supply chain encompasses the companies, products, and infrastructure needed to develop and operate artificial intelligence.
At the beginning of that chain are the companies that design and manufacture advanced semiconductors. Chipmaking also depends on specialized semiconductor equipment, materials, memory, and packaging.
Those chips are then incorporated into servers and connected through networking equipment inside data centers. Data centers themselves require cooling systems, electrical equipment, physical construction, and enormous amounts of power.
Cloud computing providers make much of this computing capacity available to businesses and developers, which can then use the infrastructure to build AI models, applications, and services.
A simplified AI supply chain might look something like this:
This broad supply chain is one reason the AI investment theme has expanded well beyond the most recognizable AI stocks. Increased spending on artificial intelligence can potentially ripple through numerous industries as companies build the infrastructure necessary to support growing AI demand.
Why These AI Concepts Matter to Investors
AI agents, data centers, chips, and the AI supply chain represent different parts of the same rapidly expanding ecosystem. AI agents illustrate how the technology may ultimately be used, while chips and data centers provide the computing power required to make those applications possible. The supply chain connects those technologies with the companies providing everything from semiconductor equipment to electricity.
Understanding these concepts can also help investors look beyond individual AI stocks and better evaluate where companies fit within the larger industry. As artificial intelligence continues to evolve, knowing how these pieces work together can provide valuable context for following one of Wall Street's biggest long-term investment themes.
From Schaeffer's Digital Content Team
For investors trying to make sense of the rapidly evolving industry, here are four important AI concepts to know.
1. What Are AI Agents?
AI agents are artificial intelligence systems designed to complete tasks and make decisions with varying degrees of autonomy. Unlike a traditional chatbot that primarily responds to individual prompts, an AI agent can potentially perform a series of actions to accomplish a broader goal.
For example, instead of simply helping a user research flights, an AI agent could be tasked with planning an entire trip. Depending on the system and permissions it is given, the agent could research destinations, compare flights and hotels, create an itinerary, and potentially make reservations.
Businesses are also exploring AI agents for customer service, software development, sales, research, and other repetitive or time-consuming tasks. The goal is generally to automate more complicated workflows that previously required multiple steps of human input.
AI agents have consequently emerged as another potential growth area for the technology sector. Greater adoption could increase demand for AI models, cloud computing, software, and the computing infrastructure required to run these systems.
2. What Is an AI Data Center?
A data center is a facility that houses computing equipment used to store, process, and distribute data. AI data centers serve the same basic purpose but are designed to handle the particularly demanding computing workloads associated with artificial intelligence.
Training and running sophisticated AI models can require thousands of specialized chips working together. These systems also need high-speed networking equipment to quickly move enormous amounts of data between processors.
All of that computing power generates considerable heat and requires substantial amounts of electricity. As a result, the AI data center boom has created demand beyond the traditional technology sector, including for cooling equipment, electrical components, construction, and power generation.
For investors, this means the expansion of AI infrastructure can potentially benefit a much wider group of companies than the businesses directly developing AI models.
3. What Are AI Chips?
AI chips are semiconductors designed or optimized to perform the massive number of calculations required by artificial intelligence applications.
Graphics processing units (GPUs) have become particularly important to the AI industry. Originally developed to handle computer graphics, GPUs can perform many calculations simultaneously, making them well suited for training and running AI models.
However, GPUs are only one piece of the equation. Central processing units (CPUs), memory chips, networking chips, and application-specific integrated circuits (ASICs) can all play roles in AI computing systems.
Some technology companies are also developing their own custom AI processors. These chips can be designed for particular workloads and potentially offer advantages in areas such as performance, cost, or energy efficiency.
For investors, understanding the different types of AI chips can help illustrate why the semiconductor industry has become one of the most important areas of the broader AI buildout.
4. What Is the AI Supply Chain?
The AI supply chain encompasses the companies, products, and infrastructure needed to develop and operate artificial intelligence.
At the beginning of that chain are the companies that design and manufacture advanced semiconductors. Chipmaking also depends on specialized semiconductor equipment, materials, memory, and packaging.
Those chips are then incorporated into servers and connected through networking equipment inside data centers. Data centers themselves require cooling systems, electrical equipment, physical construction, and enormous amounts of power.
Cloud computing providers make much of this computing capacity available to businesses and developers, which can then use the infrastructure to build AI models, applications, and services.
A simplified AI supply chain might look something like this:
This broad supply chain is one reason the AI investment theme has expanded well beyond the most recognizable AI stocks. Increased spending on artificial intelligence can potentially ripple through numerous industries as companies build the infrastructure necessary to support growing AI demand.
Why These AI Concepts Matter to Investors
AI agents, data centers, chips, and the AI supply chain represent different parts of the same rapidly expanding ecosystem. AI agents illustrate how the technology may ultimately be used, while chips and data centers provide the computing power required to make those applications possible. The supply chain connects those technologies with the companies providing everything from semiconductor equipment to electricity.
Understanding these concepts can also help investors look beyond individual AI stocks and better evaluate where companies fit within the larger industry. As artificial intelligence continues to evolve, knowing how these pieces work together can provide valuable context for following one of Wall Street's biggest long-term investment themes.
From Schaeffer's Digital Content Team