Large Language Models

Large language models (LLMs) are AI systems built on the transformer architecture, trained on vast corpora of text to understand and generate human language with remarkable fluency and reasoning ability.

The frontier LLMs of 2026—Anthropic's Claude, OpenAI's GPT series, Google's Gemini, Meta's Llama, and emerging competitors like DeepSeek—can write code, analyze complex documents, reason through multi-step problems, engage in nuanced conversation, and increasingly take autonomous action as AI agents. They are multimodal, processing not just text but images, audio, and video.

The economics of LLMs tell a story of radical deflation. Per-million-token pricing has fallen from $30 in early 2023 to $0.10–$2.50 by early 2026—a 92% decline in roughly three years. Open-source models have been the primary catalyst: DeepSeek's models match frontier quality at $1.50 per million tokens, forcing price competition across the industry. This cost curve is one of the steepest in computing history, and it's still accelerating.

LLMs are the foundation layer for a cascade of applications. They power generative AI tools for content creation, agentic engineering platforms for software development, generative engine optimization for marketing, and an emerging agentic web where AI mediates discovery, commerce, and creation. Long context windows (100k–200k tokens are now standard) enable LLMs to process entire codebases, legal documents, or research papers in a single pass.

The most significant frontier isn't raw capability but the gap between investment and impact. Despite $211 billion in AI venture funding in 2025, the vast majority of organizations haven't figured out how to capture value from these models. The winners are those who integrate LLMs deeply into workflows rather than treating them as novelties—a pattern that echoes every previous platform shift.