AI Speak Decoded: Essential Terms Every Founder Should Know
Artificial intelligence is rewriting the world—and inventing a new language as it goes. In product meetings, pitches, and panels, you'll hear LLMs, RAG, RLHF, and now terms like 'opaque recurrence,' the reasoning technique in OpenAI's new Astra model that has safety researchers rattled. The vocabulary moves fast enough to make even tech insiders feel insecure. This glossary is our attempt to fix that, with plain-English definitions of the AI terms you're most likely to encounter, whether you're building, investing, or just trying to keep up.
Core Concepts: From AGI to Inference
AGI, or artificial general intelligence, remains a nebulous term, but it generally refers to AI more capable than the average human at most tasks. Definitions vary: OpenAI's Sam Altman calls it the 'equivalent of a median human you could hire as a co-worker,' while Google DeepMind sees it as 'AI at least as capable as humans at most cognitive tasks.' Inference, on the other hand, is the process of running a trained model to make predictions—it can't happen without training. Understanding these basics is crucial for any founder evaluating AI's potential.
The Building Blocks: LLMs, Compute, and Deep Learning
Large language models (LLMs) power assistants like ChatGPT and Gemini. They are deep neural networks with billions of parameters that learn relationships between words. Compute refers to the hardware—GPUs, TPUs—that fuels AI training and deployment. Deep learning, a subset of machine learning, uses multi-layered neural networks inspired by the brain, allowing models to identify patterns from millions of data points. These foundations explain why AI development is resource-intensive and why startups often fine-tune existing models rather than train from scratch.
Making AI Work: Agents, Fine-Tuning, and Distillation
AI agents are tools that perform series of tasks autonomously, like booking tickets or writing code. A coding agent is a specialized version that can write, test, and debug software with minimal oversight. Fine-tuning adapts a pre-trained model to a specific domain by feeding it specialized data—a common strategy for startups. Distillation, meanwhile, extracts knowledge from a large 'teacher' model to train a smaller 'student' model, making AI more efficient. This is likely how OpenAI developed GPT-4 Turbo.
Staying Current: New Standards and Techniques
Model Context Protocol (MCP) is an open standard that lets AI models connect to external tools and data, like a USB-C port for AI. Introduced by Anthropic in 2024 and adopted by OpenAI, Google, and Microsoft, it's spreading fast. Also emerging are techniques like chain-of-thought reasoning, which breaks problems into intermediate steps for better answers, and 'opaque recurrence,' a reasoning method in OpenAI's Astra model that has safety researchers concerned. Keeping up with these terms is essential for informed decision-making.
Key Takeaways
- AI vocabulary is expanding rapidly; understanding key terms is essential for founders and investors.
- AGI definitions vary by company, but generally refer to AI at or above human capability in most tasks.
- Fine-tuning and distillation allow startups to build on large models without massive compute costs.
- Emerging standards like MCP and techniques like chain-of-thought are shaping AI's future.
- Terms like 'opaque recurrence' signal new capabilities and raise safety questions.
Source: TechCrunch • 🇺🇸 San Francisco
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