How many AIs does it take to read a PDF?

The digital age has brought unprecedented access to information, but sometimes that access comes with frustrating user experience challenges. When the House Oversight Committee released 20,000 pages of documents from Jeffrey Epstein's estate last November, researchers and journalists found themselves wrestling with outdated tools rather than the sophisticated AI-powered solutions they might have expected. The struggle wasn't just about the volume of information, but about the tools available to process it. Luke Igel and his colleagues encountered this exact frustration while navigating through the Epstein documents, which included countless email threads and PDF files that were nearly impossible to parse efficiently. What began as a simple research task evolved into a testament to how far digital document processing tools still have to go. The PDF viewer they were using was described as "gross," highlighting the gap between the data explosion we're experiencing and the tools that help us make sense of it. This situation reflects a broader challenge in the AI landscape: while artificial intelligence has advanced dramatically in recent years, the practical applications for everyday tasks like document analysis remain underdeveloped. The Department of Justice and similar organizations are beginning to recognize that traditional approaches to document management are inadequate for handling massive datasets. As AI becomes more integrated into research workflows, the expectation for seamless, intelligent document processing tools continues to grow, suggesting that the next wave of innovation will likely focus on improving these foundational tools rather than just advancing the AI capabilities themselves. The Epstein document release serves as a case study in how even well-funded organizations struggle with basic digital research infrastructure, pointing toward a future where AI-powered document analysis tools become essential rather than optional.