In five years, large language model (LLM) companies are expected to prioritize crawling the following types of content to enhance their models:
1. High-Quality, Human-Generated Content
- Long-form articles (e.g., journalism, editorials, in-depth guides)
- Blogs and opinion pieces that provide nuanced perspectives
- Creative works like fiction, poetry, and storytelling
- Educational resources and textbooks covering various domains
2. Dynamic and Up-to-Date Web Content
- News sites for real-time information and event coverage
- Frequently updated product pages, reviews, and comparison sites
- User-generated forums (Reddit, Quora), Q&A databases, and comment sections
- Tech documentation and changelogs reflecting latest software and hardware changes
3. Multimodal Content
- Text paired with images (e.g., explainers, infographics, instructions)
- Video and audio transcripts to capture conversational and explanatory styles
4. Structured and Semantically Rich Content
- Pages with extensive schema markup and rich metadata that clarify context (e.g., semantic SEO and structured data)
- Datasets and code repositories, especially for training AI models in coding and research tasks
5. Specialized and Niche Content
- Domain-specific resources (medical, legal, financial, scientific publications)
- Niche communities and language variants to ensure global representation and diversity
6. Permissioned and Licensed Data
- Content flagged as AI-friendly via new standards—such as llms.txt, which proposes to let website owners specify LLM-crawlable areas and signal full consent for specific pages or data types
- Licensed, paywalled, or premium content included via direct partnerships, or “publisher fences” for high-value exclusive data
7. Fresh Signals and Update-Frequency Sources
- Sites with high update frequency or signals of topicality and recency, as LLMs will be optimized for timely information discovery
- Index/portal pages that serve as hubs to trending and newly published resources
Trends Shaping Future LLM Crawling:
- LLMs will focus on relevance and meaning, not just keywords—semantic understanding of content will drive crawling priorities.
- Growing use of standards like llms.txt will let publishers grant or restrict AI access at granular levels.
- There will be increased tension—and negotiation—between content creators “fencing” off data and LLM builders seeking broad, diverse, and high-quality information.
- Partnerships, marketplaces, or licensing agreements will likely become a foundation for LLM access to valuable proprietary data.
In summary, LLM companies will seek to crawl not just more content, but smarter content: high-quality, frequently updated, properly licensed, semantically optimized, and representative of a wide array of domains, languages, and formatsts.
Sources include: Search Engine Land, ProjectPro and AIMultiple