TechCrunch released a comprehensive AI glossary aimed at demystifying the rapidly expanding vocabulary surrounding artificial intelligence. The resource covers essential terminology that has flooded mainstream discourse as AI adoption accelerates across industries.
The glossary addresses a real gap in accessibility. As startups and established tech companies race to integrate AI into their products, the jargon has become a barrier for investors, operators, and customers trying to understand what these companies actually do. Terms like transformer models, retrieval-augmented generation, fine-tuning, and prompt engineering now appear routinely in pitch decks and product announcements.
TechCrunch's approach focuses on practical definitions rather than academic precision. The glossary explains concepts that matter for founders pitching to VCs, for investors evaluating AI startups, and for executives building go-to-market strategies around AI capabilities. It covers both foundational concepts like neural networks and large language models, as well as emerging practices like chain-of-thought prompting and agent-based systems.
The timing reflects where the market stands. Twelve months ago, most startup pitches leaned heavily on explaining what large language models were. Today, founders assume baseline knowledge and move faster into vertical applications, pricing models, and competitive positioning. This shift requires a shared vocabulary that goes deeper than headline terms.
The resource serves multiple audiences. For venture capitalists, it provides a reference point for due diligence conversations. For journalists covering the space, it enables faster, more accurate reporting. For founders building AI products, it offers language that resonates with both technical and non-technical stakeholders.
TechCrunch's glossary reflects how AI has become infrastructure rather than novelty. When terminology becomes standardized enough to warrant a public reference guide, it signals the technology has moved past hype cycle inflection and into adoption phase. The existence of such a resource itself indicates that AI vocabulary is now foundational