This week
Suggest a nameObligation Marketing for AI Builders
The 7-Rung LadderThe 8-Type TaxonomyWrapper or WinnerThe Sample ColumnThe Signal
Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
This is a data quality catastrophe hiding in plain sight. If you've trained or fine-tuned on Common Crawl PDFs, your dataset is systematically biased toward short documents and missing more than half the available text in long ones. The TeX toolchain overrepresentation matters too. Go audit what you actually got versus what you thought you got.
This is SEO poisoning at scale, and it works because RAG systems citation-wash thin or fabricated content. For builders shipping search or recommendation products: you're running the engine that legitimizes this. The incentive structure is broken. For anyone relying on AI-generated recommendations, treat them as starting points, not ground truth. Perplexity's citation problem is everyone's problem now.
This is the slow-moving story that matters more than any single model release: the training data pool for future models is increasingly self-generated content, which raises real questions about model collapse and search quality over time. For builders relying on web-scraped data or search-grounded retrieval, this is a reason to weight source provenance and freshness more heavily. Watch for downstream effects on search engines and RAG pipelines before this becomes a bigger problem.