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 research paper, company announcement, and anonymous forum post enter the same data pipeline as text. Tal said models may then treat material with different standards of evidence as if it carries similar weight. “Provenance collapses. A claim gets scraped, restated, and re-scraped until the original source is unrecoverable,” he said. You might also like: AI cannot bear liability for losing trades, responsibility follows delegation: Brickken CEO Geo founder identifies four failures behind unreliable AI answers Disagreement creates another problem because language models often combine competing positions into one response. Tal said such compression can hide genuine disputes among qualified experts, giving users a single confident answer without showing that credible alternatives exist. The fourth weakness arises when AI-generated material returns to the data supply used by later models. Errors can survive repeated publication, while synthetic articles, posts, and summaries make it harder to locate the human-produced material from which a claim originated. “Models increasingly train on output from other models, so errors don’t just persist, they amplify,” Tal said. Independent research has documented a related risk. A 2024 Nature study examined what happens when generative models repeatedly learn from material produced by earlier models. Researchers described “model collapse” as a process in which systems gradually lose information about the original data distribution. Less common material began disappearing during the early stages of the experiments, according to the paper, while later model generations produced distributions bearing little resemblance to the source data. Researchers said access to original, human-produced information remains important as AI-generated material spreads across the internet. Tal does not classify the four weaknesses as failures confined to model design.