The AI industry has a language problem, and it starts at the top. When OpenAI CEO Sam Altman describes artificial general intelligence (AGI) as the "equivalent of a median human that you could hire as a co-worker," he's using different criteria than Google DeepMind, which views AGI as "AI that's at least as capable as humans at most cognitive tasks." Meanwhile, OpenAI's own charter defines it as "highly autonomous systems that outperform humans at most economically valuable work." Three definitions, three different meanings, one term that's supposed to represent the future of technology .

Sam Altman

Why Can't the AI Industry Agree on Basic Definitions?

This definitional chaos isn't just academic nitpicking. As AI companies race to build increasingly powerful systems and investors pour billions into the space, the lack of shared language creates real confusion about what we're actually building and when we'll get there. The problem runs deeper than AGI. The entire AI ecosystem relies on technical jargon that scientists use to explain their work, but when those terms get translated into news coverage, product marketing, and investor pitches, the meaning often gets lost or distorted .

TechCrunch recently published a comprehensive glossary of AI terms to help bridge this gap, recognizing that the industry's reliance on specialized language creates barriers for anyone trying to understand what's actually happening in artificial intelligence. The glossary covers everything from foundational concepts to emerging techniques, and it's designed to be updated regularly as researchers develop new methods and identify novel safety risks .

What Are the Most Misunderstood AI Concepts Today?

Beyond AGI, several other core concepts in AI are frequently misused or misunderstood. Consider AI agents, for example. The term refers to tools that use AI technologies to perform a series of tasks on your behalf, such as filing expenses, booking tickets, or writing and maintaining code. But as the industry has explained, "AI agent" means different things to different people, and the infrastructure to deliver on these capabilities is still being built out .

Similarly, chain-of-thought reasoning sounds like jargon, but it describes something intuitive: breaking down a problem into smaller, intermediate steps to improve the quality of the answer. Just as a human might need to write down equations to solve a complex math problem, reasoning models developed from large language models (LLMs) use this technique to think through problems step by step, making them more likely to arrive at correct answers, especially in logic or coding contexts .

How to Navigate AI Terminology Like an Insider

• Start with foundational concepts: Understand what compute means (the computational power that allows AI models to operate, typically provided by GPUs, CPUs, and TPUs), since it's the bedrock of the entire industry and affects everything from training costs to deployment speed.
• Learn the difference between deep learning and simpler machine learning: Deep learning uses multi-layered artificial neural networks inspired by the human brain, allowing AI to identify important characteristics in data without human engineers defining them first, but requires millions of data points and higher development costs.
• Recognize emerging techniques as they appear: Diffusion (the technology behind many art and music-generating AI models), distillation (extracting knowledge from large models to create smaller, more efficient ones), and fine-tuning (further training models for specific tasks) are reshaping what's possible in generative AI.

The glossary approach reflects a broader recognition in the AI industry that clarity matters. As researchers continuously uncover novel methods to push the frontier of artificial intelligence while identifying emerging safety risks, the language used to describe these advances needs to evolve alongside the technology itself .

Other frequently misunderstood concepts include generative adversarial networks (GANs), which involve two neural networks competing with each other to produce realistic data, and hallucination, the industry's preferred term for when AI models generate false information. These aren't just semantic distinctions; they shape how companies build products, how investors evaluate opportunities, and how the public understands what AI can and cannot do .

The real takeaway is this: as AI becomes increasingly central to business and society, the industry needs to establish shared definitions and clear language. Without it, we risk building products based on misunderstandings, making investment decisions on false premises, and setting expectations that don't match reality. The glossary is a start, but the work of standardizing AI terminology is far from over.