AI Strategy

The vocabulary of the AI era of startups. This cluster covers the foundational concepts of modern AI (foundation models, LLMs, generative AI), the architecture and operations that power AI applications (Transformer, training data, fine-tuning, prompt engineering, RAG, context window), the economics that determine viability (inference cost, GPU cost, token economics), the strategic moats AI companies build (data flywheel, AI moat, wrapper vs thick wrapper), the safety considerations (alignment, safety), and current-era terms (multimodal, agents, vibe coding). 22 entries.

This cluster is the freshest in the lexicon. If you're building anything AI-adjacent in 2025, every entry here is operational vocabulary.

Foundations

• AI Startup, the three categories (foundation, infrastructure, application).
• Generative AI, the post-2022 inflection.
• Foundation Model, GPT, Claude, Gemini, Llama, etc.
• Large Language Model (LLM), text-prediction at scale.
• Machine Learning, the broader parent field.

Architecture and how AI works

• Transformer Architecture, the 2017 paper that unlocked modern AI.
• Training Data, what the model learned from.
• Fine-Tuning, adapting foundation models.
• Prompt Engineering, writing effective prompts.
• Context Window, the LLM input size limit.
• Retrieval-Augmented Generation (RAG), vector search + LLM pattern.

Agents and capabilities

• AI Agent, LLMs that plan, use tools, take action.
• Multimodal AI, text + image + audio + video unified.

Economics

• Inference Cost, the per-token cost of running AI.
• GPU Cost, the underlying compute economics.
• Token Economics, modeling AI app unit economics.

Strategic moats

• AI Moat, the five real AI moats.
• Data Flywheel, the self-reinforcing data advantage.
• AI Wrapper, the critique and when it's fair vs lazy.

Safety and alignment

• AI Safety, preventing AI harms.
• AI Alignment, ensuring AI pursues intended goals.

Current concepts (2025)

• Vibe Coding, Karpathy's term for AI-driven software building.