In the world of Agentic SEO, not all bot traffic is created equal. For years, we treated “Googlebot” as a monolith. Today, we must distinguish between two fundamentally different types of machine visitation: Training Crawls and Inference Retrievals. Understanding this distinction is critical for measuring the ROI of your AI optimization efforts.

Training Crawls: Building Long-Term Memory

Training crawls are performed by bots like CCBot (Common Crawl), GPTBot (OpenAI), and Google-Extended. These bots are gathering massive datasets to train or fine-tune the next generation of foundational models.

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.