A new metric is emerging in the AI optimization space: Inference Cost. How much compute (FLOPs) does it take for a model to process, understand, and answer a question using your content?

This sounds abstract, but it translates directly to money for the AI provider.

  • High Entropy Content: Convoluted sentences, ambiguous grammar, poor structure. Requires more “attention heads” and potentially multiple passes (Chain-of-Thought) to parse. Cost: High.
  • Low Entropy Content: Simple, declarative sentences. Subject-Verb-Object. Cost: Low.

The Economic Bias

Models are optimized for efficiency. We hypothesize that retrieval systems will deprioritize sources that consistently require high inference compute. If your content is “hard to read” for the machine, it is expensive to serve.

Calculating Your Content Entropy

You can estimate this by running your content through a tokenizer (like tiktoken) and checking the perplexity score using a small model like GPT-2.

  • Low Perplexity: The model is not surprised by your next word. It flows logically.
  • High Perplexity: The model is constantly guessing.

Strategy: The Hemingway Method

Writing for AI is like writing for a strict editor.

  1. Short Sentences: Under 20 words.
  2. Active Voice: “The agent fetched the data” (Good) vs. “The data was fetched by the agent” (Bad).
  3. Logical Connectors: Use “Therefore,” “Because,” “However” explicitly. Don’t leave the logic implicit.

Making your content “cheap” for the model to use is the ultimate optimization.

The Future of “Green SEO”

As the carbon footprint of AI inference grows, we predict a “Green SEO” movement where models are penalized for retrieving “heavy” content. If your page loads 4MB of JavaScript just to display 500 words of text, the cost-to-serve is high. Models optimizing for “Tokens per Watt” will prefer:

  1. Plain Text
  2. Markdown
  3. Static HTML

This aligns perfectly with the “Low Carbon Web” design philosophy. Optimizing for the planet and optimizing for the AI Agent are now the same constraint.

Glossary of Terms

  • Agentic Web: The specialized layer of the internet optimized for autonomous agents rather than human browsers.
  • RAG (Retrieval-Augmented Generation): The process where an LLM retrieves external data to ground its response.
  • Vector Database: A database that stores data as high-dimensional vectors, enabling semantic search.
  • Grounding: The act of connecting an AI’s generation to a verifiable source of truth to prevent hallucination.
  • Zero-Shot: The ability of a model to perform a task without seeing any examples.
  • Token: The basic unit of text for an LLM (roughly 0.75 words).
  • Inference Cost: The computational expense required to generate a response.