Response from Claude.ai:
Based on current trends and developments, LLMs will likely expand to crawl several new types of content sources over the next 5 years:
Multimodal Content Integration – Large multimodal models are already integrating diverse data types like text, images, audio, and video, surpassing text-only capabilities. This means LLMs will increasingly crawl video platforms, podcast archives, audio content libraries, and image repositories to create more comprehensive training datasets.
Real-Time and Dynamic Sources – LLMs will move beyond static web crawling to access live data streams, social media feeds in real-time, news APIs, and continuously updated databases. The emergence of standards like llms.txt is already creating frameworks for AI-friendly web content crawling, making it easier for models to identify and process dynamic content.
Specialized Professional Data – Expect expanded crawling of scientific databases, medical literature, legal document repositories, patent databases, and industry-specific knowledge bases. Medical applications are already exploring synthetic data generation and federated learning approaches to address data limitations.
Interactive and User-Generated Content – LLMs will likely access forum discussions, Q&A platforms, code repositories with version histories, collaborative documents, and user interaction logs to understand conversational patterns and real-world problem-solving approaches.
Synthetic and Augmented Data – Synthetic data generation is being combined with traditional training approaches, meaning LLMs will increasingly train on artificially generated content, simulation outputs, and augmented datasets created specifically for model training.
Private and Permissioned Sources – With proper agreements, LLMs may gain access to enterprise knowledge bases, proprietary databases, internal documentation systems, and curated professional content that’s currently behind paywalls or access controls.
The key shift will be from primarily crawling static web text to incorporating dynamic, multimodal, and specialized content sources that provide richer, more current, and more diverse training data.
Sources provided by Claude included: Nvidia, Search Engine Land, xFunnel and AI Multiple