What is Large Language Model (LLM)?
A Large Language Model is a type of artificial intelligence trained on vast amounts of text data to understand and generate human language. LLMs like GPT-4, Claude, Gemini, and Llama power chatbots, code assistants, content generation, and enterprise AI applications.
LLMs work by predicting the next token (word or word-piece) in a sequence. They're trained on billions of parameters using transformer architecture. The 'large' in LLM refers to both the training data (often trillions of tokens) and the model size (billions of parameters).
The economics of LLMs are unique: unlike traditional software with near-zero marginal cost, LLMs have significant variable costs that scale with usage. Every query costs compute. This creates what Richard Ewing calls the Cost of Predictivity — as you demand higher accuracy, costs scale exponentially.
Why It Matters
LLMs are the foundation of the 2026 AI revolution, but they introduce variable cost structures that traditional software economics don't account for. Understanding LLM pricing, capabilities, and limitations is essential for any team building AI features.
Frequently Asked Questions
What is an LLM?
A Large Language Model is AI software trained on massive text datasets to understand and generate human language. Examples include GPT-4, Claude, Gemini, and Llama.
How much do LLMs cost?
LLM costs range from $0.0001/query for small open-source models to $0.10+/query for frontier models like GPT-4. Cost depends on model size, input/output length, and whether you self-host or use APIs.
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Richard Ewing is a Product Economist and AI Capital Auditor. He helps companies translate technical complexity into financial clarity.
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