6 Key AI Concepts for 2026 Explained
Dec 31, 2025
AI has rapidly changed throughout 2025, and as 2026 approaches, much of AI can be understood through six key concepts. At the core of these systems are models built on Neural Networks that learn patterns in data through training and apply that knowledge through inference. These elements form the foundations of Large Language Models, the technology behind apps such as ChatGPT, where ongoing optimizations improve both models' intelligence and speed.
Models
Models are the core components that make up AI systems. A model is a system that learns complex patterns from data and is able to use its knowledge to create outputs through making predictions, generating text, or supporting decision-making. What's commonly referred to as "AI" is almost always a model running behind the scenes.
Neural Networks
Neural Networks form the foundation of modern AI. Inspired by neurons in the brain, they consist of various layers and internal nodes that analyze and find patterns in data. Each layer identifies relationships in the inputs given to the network, eventually resulting in an output such as a prediction or generated text. Neural Networks are often used for tasks such as text generation, natural language understanding, image processing, and speech processing. For example, Neural Networks can be used to detect cancer from medical imaging based on patterns in them that they identify implying the presence of cancerous cells.
Training
Training is how AI learns. Neural Networks and other AI models are first given large datasets. From there, the models adjust their internal components to better identify patterns and produce accurate predictions with similar data. Essentially, this is how AI is able to mimic the way humans learn and understand new tasks. Large Language Models for example, are able to generate coherent and helpful responses through training on vast amounts of text from websites, news articles, and conversations.
Inference
Inference is how AI leverages its learned data in real-world applications. With the knowledge gained from training on new data, it can now produce predictions, classifications, and detections that are leveraged throughout various applications. With inference, AI can now function in a virtual assistant to answer questions, or in an autonomous vehicle using sensor data to navigate safely—inference bridges links both learning and application.
Large Language Models (LLMs)
Large Language Models or LLMs are one of the most well-known forms of AI, trained on vast corpora of text and conversations to generate human-like text. They are able to answer questions, creatively write, and do a lot of other more complex tasks. LLMs generate outputs by predicting and producing coherent sequences of words, making them fundamental to conversational AI, chatbots, and other automated or agentic tools—receiving all the attention in 2025. One notable instance is ChatGPT, powered by OpenAI's most recent GPT-5 LLMs that can generate contextually relevant and accurate text.
Optimization
Optimization is how AI is able to improve, whether in gaining more intelligence or operating more efficiently. Many types of optimization exist to improve existing models, whether in making them learn data faster or allowing them to operate on less computational resources, especially relevant as companies are spending billions on creating the smartest models and constructing large-scale data centers. One type of optimization is quantization, where a model is compressed so that running inference on it requires less compute.
All in all, AI has been through a lot in 2025, but it can be easily broken down into 6 core concepts—a quick read as you welcome the new year.