AI agents can reason over enormous amounts of information, but much of the world's actual work is still trapped in PDFs, spreadsheets, scans, and other formats that models struggle to reliably understand. Reducto co-founder and CEO Adit Abraham argues that solving this document layer is essential if agents are going to take on consequential work in healthcare, legal, finance, and insurance. Adit joins a16z General Partner Jennifer Li to explain why traditional OCR breaks down on messy real-world documents, how Reducto combines vision-language models with specialized infrastructure to reach high accuracy without sacrificing cost or latency, and why frontier models can silently drop information on long extraction tasks. Reducto now processes billions of pages each month, and crossed its 7 billionth page the morning of this recording. 0:00 Why AI agents need to read PDFs 2:12 From 1990s OCR to vision models 5:00 Training R1 on the messiest docs 7:46 When agents beat humans at extraction 9:45 A penny a page for 7 billion pages 12:47 Why legal and finance lag coding AI 15:01 The stakes of AI errors in healthcare 16:25 Agents that can edit Word and Excel 17:58 Why the labs won't go deep on PDFs 20:14 Frontier models silently drop data 22:21 Benchmarks have become marketing 25:45 Why charts and Excel stump models 28:46 Infra for 10M documents at once 31:03 How "chet pain" gets claims denied 34:32 The paperwork AI should kill first YouTube: https://youtu.be/xuczlbVVLsQ @aditabrm @reductoai @JenniferHli