Navigating AI's Complex Landscape: The TechCrunch AI Glossary

Artificial intelligence (AI) has become a ubiquitous term in the tech industry, with companies large and small vying to develop and implement AI technologies. However, the field of AI is filled with jargon and complex concepts that can be challenging to understand, even for seasoned investors. To help investors better navigate the complex landscape of AI technologies, TechCrunch has compiled an AI glossary that defines key terms, concepts, and models. This article will explore the importance of the TechCrunch AI glossary and how it helps investors make informed decisions in the AI sector.



The TechCrunch AI glossary provides clear definitions and explanations of key AI terms, such as AI agents, chain-of-thought reasoning, deep learning, fine-tuning, large language models, and neural networks. By understanding these concepts, investors can better evaluate the potential of AI startups and their products. For example, AI agents are autonomous systems that can perform multi-step tasks on behalf of users, drawing on multiple AI systems. Investors should understand the capabilities and limitations of AI agents, as well as the infrastructure required to support them. Examples of AI agents include OpenAI's Operator and xAI's Grok 3.

Chain-of-thought reasoning is a technique used in large language models to break down complex problems into smaller, intermediate steps to improve the quality of the end result. Investors should be aware of the potential benefits and limitations of chain-of-thought reasoning, as it can significantly impact the performance of AI models. For instance, Anthropic's Claude sonnet 3.7 is a hybrid reasoning model that can both provide quick answers and engage in deep thinking when needed.

Deep learning is a subset of self-improving machine learning that uses multi-layered artificial neural networks to make complex correlations. Investors should understand the advantages and disadvantages of deep learning, such as its ability to identify important characteristics in data and learn from errors, as well as its high data and computational requirements. Examples of deep learning models include OpenAI's GPT-4.5 'Orion' and Google's Gemini 2.0 Pro.

Fine-tuning is the process of further training an AI model to optimize performance for a more specific task or area by feeding in new, specialized data. Investors should be aware of the potential benefits and limitations of fine-tuning, as it can help AI startups create commercial products tailored to specific sectors or tasks. For example, many AI startups are taking large language models as a starting point and fine-tuning them to build commercial products.

Large language models (LLMs) are AI models used by popular AI assistants, such as ChatGPT, Claude, and Google's Gemini. Investors should understand the capabilities and limitations of LLMs, as well as the importance of the data they are trained on. Examples of LLMs include OpenAI's GPT, Meta's AI Llama, and Mistral's Le Chat.

Neural networks are multi-layered algorithms inspired by the interconnected pathways of neurons in the human brain. Investors should understand the basic structure and function of neural networks, as they form the foundation of many AI models. For instance, deep learning models are a type of neural network.



By understanding these key terms and concepts, investors can better evaluate the potential of AI startups and make more informed decisions in the AI sector. The TechCrunch AI glossary helps investors identify promising investment opportunities by providing a clear understanding of the market landscape, the potential of AI models, the competition, emerging trends, and the specific capabilities of different AI technologies. This understanding enables investors to make more informed decisions about where to allocate their resources.

In conclusion, the TechCrunch AI glossary is an invaluable resource for investors looking to navigate the complex landscape of AI technologies. By providing clear definitions and explanations of key AI terms, concepts, and models, the glossary helps investors make informed decisions in the AI sector. As AI continues to evolve and grow, investors should stay up-to-date with the latest developments and use resources like the TechCrunch AI glossary to stay informed and make strategic investments in the AI sector.