Observed Signal · Jul 3, 2026 · Publication · Source: techcrunch · Impact: 3/5 · Sentiment: Neutral
TechCrunch publishes updated AI glossary
TechCrunch published an updated, regularly maintained AI glossary on 2026-07-03 that explains core AI and generative-AI concepts in plain language. The guide defines terms ranging from AGI, LLMs, chain-of-thought and reinforcement learning to infrastructure topics like compute, token throughput, KV caching and supply-chain concerns dubbed “RAMageddon.” It highlights interoperability developments such as the Model Context Protocol (MCP) — introduced by Anthropic in 2024, handed to the Linux Foundation, and adopted by OpenAI, Google and Microsoft — and discusses model architectures like Mixture of Experts (MoE). The piece is positioned as a living reference for builders, investors and readers trying to keep pace with AI terminology and technical trends.
The glossary consolidates and clarifies widely used AI terms for practitioners and decision-makers and highlights infrastructure and interoperability developments (notably MCP adoption by major AI vendors and MoE usage) that affect how AI is integrated into products and services.
Track TechCrunch Signals & Market Shifts in Real-Time
Polaris7 autonomous intelligence agents track regulatory filings, primary sources, executive changes, and deal flow 24/7. Create your free Explorer workspace to monitor these entities.
Key Takeaways & Evidence Grounding
- TechCrunch published an AI glossary article on 2026-07-03 that is updated regularly.
- Anthropic introduced the Model Context Protocol (MCP) in 2024, later handed it to the Linux Foundation, and MCP has been adopted by OpenAI, Google and Microsoft.
- Mixture of Experts (MoE) is described as a model architecture used by Mistral AI’s Mixtral and believed to be used in some OpenAI GPT models.
- The article identifies an industry memory shortage trend labelled “RAMageddon,” attributing rising RAM prices and supply constraints to AI infrastructure demand.
- The glossary defines many operational AI concepts relevant to production systems, including KV (key-value) caching, token throughput, inference and fine-tuning.
Connected Companies & Entities
9 Entities mapped“Sit in on any product meeting, pitch, or panel these days, and you’ll hear people toss around LLMs, RAG, RLHF, and a dozen other terms that ...”
“OpenAI CEO Sam Altman once described AGI as the “equivalent of a median human that you could [hire as a co-worker].” Meanwhile, OpenAI’s cha...”
“Anthropic introduced MCP in 2024 and later handed it over to the Linux Foundation, and it’s since been adopted by OpenAI, Google, and Micros...”
“Google DeepMind’s understanding differs slightly from these two definitions; the lab views AGI as “AI that’s at least as capable as humans a...”
“Model Context Protocol, or MCP, is an open standard that lets AI models connect to outside tools and data — your files, databases, or apps l...”
“Anthropic introduced MCP in 2024 and later handed it over to the Linux Foundation, and it’s since been adopted by OpenAI, Google, and Micros...”
“Anthropic introduced MCP in 2024 and later handed it over to the Linux Foundation, and it’s since been adopted by OpenAI, Google, and Micros...”
“Mixture of Experts is a model architecture that splits a neural network into many smaller specialized sub-networks, or “experts,” and only a...”
“Open source refers to software — or, increasingly, AI models — where the underlying code is made publicly available for anyone to use, inspe...”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
TechCrunch glossary explains common AI terms
TechCrunch published a living glossary of common artificial intelligence terms on May 29, 2026. The article provides plain-language definitions and context for dozens of AI concepts — from AGI, LLMs, and hallucinations to technical topics like inference, KV caching, token throughput, distillation, and recursive self-improvement. It cites example systems (ChatGPT, Claude, Google’s Gemini, Meta’s Llama, Microsoft Copilot, Mistral’s Le Chat), contrasts differing AGI definitions from OpenAI and Google DeepMind, and highlights infrastructure issues such as compute demands and RAM supply constraints (coined “RAMageddon”). The glossary is described as a living document that TechCrunch updates regularly as the field evolves.
TechCrunch Guide: Common AI Terms Explained
TechCrunch published a regularly updated glossary that defines core artificial intelligence terms used across its coverage. The glossary explains concepts ranging from high-level goals like AGI and AI agents to technical building blocks such as neural networks, weights, tokens, distillation, fine-tuning, diffusion models, GANs, and inference. It cites differing AGI definitions from OpenAI (including a Sam Altman quote and OpenAI’s charter) and Google DeepMind, gives examples of popular LLMs and assistants (ChatGPT, Claude, Gemini, Meta’s Llama, Microsoft Copilot, Mistral’s Le Chat), and covers operational topics such as compute, memory caching (KV caching), and supply pressures dubbed “RAMageddon.” The article frames hallucinations as a major quality risk and notes model-development techniques (transfer learning, distillation, fine-tuning) used to specialize or compress models.
TechCrunch AI Glossary of Common Terms
TechCrunch published a living glossary of common artificial intelligence terms on 2026-05-09. The article defines and explains a wide range of AI concepts — including AGI, AI agents, LLMs, chain-of-thought reasoning, RAG, RLHF, inference, training, fine-tuning, distillation, diffusion, GANs, hallucinations, KV caching, token throughput and a coined supply-term “RAMageddon.” It cites definitions and perspectives from sources such as OpenAI (including a quote from CEO Sam Altman) and Google DeepMind, provides practical context for developer-facing concepts (e.g., API endpoints, coding agents), and notes the glossary is updated regularly as the field evolves.
Track Real-Time Market Signals & Shifts
Set up custom watchlists to receive automated, evidence-grounded executive digests whenever material signals or shifts occur across your tracked landscape.