The dark data problem hiding inside your AI agents

OpenClaw recently crossed 250,000 GitHub stars in just 60 days, surpassing React’s decade-long record to become the most-starred software project in GitHub’s history. At GTC 2026 , in his keynote speech, NVIDIA CEO Jensen Huang called it “the operating system for personal AI” and said, “For the CEOs, the question is, what’s your OpenClaw strategy?” I was at NVIDIA’s Hack for Impact hackathon at GTC, building alongside engineers who were using NemoClaw, OpenClaw and Nemotron to tackle real-world problems. One team built a wildfire detection system ingesting live NASA satellite data; another analyzed crime patterns across police jurisdictions; a third forecasted anomalies in energy grids. Every project was impressive. But watching them, I kept thinking about what happens the day after the hackathon. Where does all the data go? Autonomous AI agents continuously produce outputs—reports, analyses, alerts, processed video, audio and images. They accumulate memory, conversation histories and