Can human verification make AI answers more reliable? Geo founder explains

Geo founder Yaniv Tal has identified four weaknesses in online information that he says make AI answers unreliable: lost provenance, flattened authority, hidden disagreement, and repeated model-generated errors. Summary Geo separates claims, sources, and supporting evidence inside community-governed knowledge Spaces. Tal says human judgment can help rank credible reasoning without removing competing views. A Nature study found that repeated training on synthetic material can cause model collapse. NIST recommends tracking training sources and incorporating expert human feedback into AI systems. Geo founder Yaniv Tal told crypto.news that unreliable AI answers often begin with the material models receive, arguing that the internet was designed to distribute information rather than preserve its authority, origin, or accountability. “AI doesn’t have a truth problem, the internet does,” Tal said. According to Tal, information loses critical context as websites scrape and republish it. A claim may pass through several pages before entering a training set, leaving a model with the statement but no clear route back to its original source. Authority also becomes difficult to measure when a ...