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Insights on AI Training Data

Data Quality AI Training Cost Optimization

The Hidden Cost of Low-Quality Training Data

Cheap data isn't free. Models trained on noisy web scrapes spend more compute on cleanup, produce weaker outputs, and require expensive fine-tuning to fix.

March 28, 2026

Data Pipeline Catalog Management AI Training

What Makes a Book Catalog AI-Ready?

Not every book collection is useful for AI training. Format, metadata, deduplication, and category diversity all determine whether a catalog creates value or headaches.

March 22, 2026

AI Training Data Quality LLMs

Why Books Are the Gold Standard for AI Language Training

Web-scraped text is abundant but noisy. Books offer something rarer: edited, intentional, long-form human thought at scale.

March 15, 2026

Web Scraping LLMs Data Strategy

Why AI Labs Are Moving Beyond Web Scraping

Web scraping built the first generation of LLMs. But the limitations are showing, and the most serious AI teams are diversifying their data sourcing strategies.

March 8, 2026

LLMs Training Data Foundation Models

How Wholesale Book Data Is Shaping the Next Generation of LLMs

The foundation model companies betting big on book data aren't doing it by accident. Here's what the research says and why the supply chain matters.

February 28, 2026

Training Data Data Diversity Model Performance

Building a Diverse Training Corpus: Lessons from Book Data

Model capability is shaped by training data composition. Here's what we've learned about how category mix, era coverage, and linguistic variety affect outcomes.

February 15, 2026

Rare Books Unique Data Competitive Advantage

The Role of Rare and Out-of-Print Books in AI Training

The most valuable training data is often the hardest to find. Rare and out-of-print titles give AI models access to knowledge that exists nowhere else in digital form.

February 1, 2026