AI Glossary: Essential Terms for 2026

It's becoming increasingly impossible to ignore artificial intelligence in our everyday lives. From your bank and doctor to the streaming services you use and even your car, AI is woven into the fabric of modern life. This rapid integration has created a new vocabulary that tech CEOs, Wall Street analysts, and politicians are using daily.

Do you know what an LLM is? How about a GPU? From agentic AI to UBI, new terms and concepts are being created seemingly every day. This comprehensive glossary will help you understand the most common AI-related terms to speak about this technology with authority.

Understanding the fundamental building blocks of AI is the first step. These terms describe how AI systems function and their core capabilities.

Machine learning, also known as deep learning, refers to AI systems that can adapt and learn on their own, without following human instructions or explicit programming. At the heart of many modern systems are neural networks, programs designed to think and learn like a human brain.

When discussing AI's potential, two key concepts often arise:

• AGI (Artificial General Intelligence): The ability of AI to perform complex cognitive tasks like displaying self-awareness and critical thinking the way humans do.
• Singularity: A hypothetical moment where artificial intelligence becomes so advanced that it surpasses human intelligence.

However, AI isn't perfect. A phenomenon called hallucinations occurs when a large language model generates inaccurate information that it presents as a fact. For example, during an early demo, Google's AI chatbot Bard hallucinated by generating a factual error about the James Webb Space Telescope.

The landscape of AI models is competitive and rapidly evolving. Large language models (LLMs) are complex programs designed to understand and generate human-like text by training on massive datasets.

The most prominent models include:

• ChatGPT: OpenAI's signature chatbot, launched in 2022, which is often credited for kickstarting the AI race.
• Gemini: Google's flagship model, first launched in 2023 under its former name "Bard."
• Claude: Anthropic's flagship model, praised for its ability to write code.

These models rely on specialized hardware. A GPU (Graphics Processing Unit) is a computer chip used to train and deploy AI models. Companies like Microsoft and Meta use Nvidia's GPUs to run their AI systems. The computing resources needed for this work, including GPUs, servers, and cloud services, are collectively known as compute.

Behind the scenes, data centers—large warehouses filled with tens of thousands of advanced chips—handle the massive amounts of data and processing required. These facilities require significant space and energy, with leading tech CEOs using the gigawatt measurement to put the scale of their plans into perspective.

As AI capabilities grow, so do the applications and ethical considerations surrounding its use. Agentic AI represents a significant evolution, defined as a type of AI that can make proactive, autonomous decisions with limited human input. Unlike generative models, agentic AI does not need a human prompt to take action.

Other key applications include:

• Deepfakes: AI-generated images, videos, or voices meant to appear real, often used to deceive.
• Optical character recognition (OCR): Technology that recognizes text within images and extracts it into a machine-readable format.
• Prompt engineering: The process of asking AI chatbots the right questions to produce desired responses.

Ethical concerns are paramount. Bias is a major issue, as AI models trained on human data can adopt the same fallible human prejudices. To counter this, alignment is a field of AI safety research aiming to ensure AI systems' goals are consistent with human values. Furthermore, responsible scaling policies are guidelines designed to mitigate safety risks and ensure the ethical development of AI systems.

The AI revolution is driven by a handful of influential companies and visionary leaders shaping the future of technology.

The competitive landscape features major players like OpenAI, Google, Microsoft, and Anthropic. The Frontier Model Forum, an industry nonprofit launched by these companies in 2023, defines the most advanced AI technology.

Key figures in the industry include:

• Sam Altman: CEO and cofounder of OpenAI.
• Elon Musk: CEO of Tesla and SpaceX, who founded AI startup xAI and cofounded OpenAI.
• Demis Hassabis: CEO of Google DeepMind.
• Jensen Huang: CEO and cofounder of Nvidia.
• Mark Zuckerberg: CEO of Meta, investing heavily in AI capabilities.

Many of these leaders identify with movements like effective altruists, a social movement focused on using AI to safely reduce suffering from issues like climate change and poverty.

The rapid evolution of artificial intelligence presents both unprecedented opportunities and complex challenges. Understanding this new lexicon is the first step toward meaningful engagement with the technology that is reshaping our world.

As AI systems become more capable, discussions around capability overhang—the gap between what AI models can do and what applications are using—and federal preemption regarding regulation will become increasingly critical. The debate over whether AI will lead to widespread unemployment, prompting discussions about Universal Basic Income, or create an abundance of wealth remains ongoing.

Staying informed is no longer optional. Whether you're a professional, a policymaker, or simply a citizen, grasping these fundamental concepts is essential for navigating the future.