When large language models (LLMs) are trained, they are trained primarily on human-produced or human-audited content. This is essential because if they were to train on content generated by other AIs—including their own previous outputs—they would risk recycling and amplifying their own errors. These errors could stem from hallucinations, misinformation, data poisoning, or deliberate exploitations. In essence, without human oversight, an AI system would begin to feed on its own inaccuracies, resulting in a self-reinforcing cycle of degradation in data quality and logical coherence.
Engineers at OpenAI and other major AI corporations are well aware of this issue. For that reason, they attempt to filter out AI-generated content from their training datasets unless it has been reviewed and verified by humans. To achieve this, modern AI systems embed subtle “digital signatures” or hidden identifiers within the text they generate. These invisible markers allow AI models to recognize and exclude their own unverified content when crawling the internet for new data. This filtering process helps prevent the models from training on what has been termed “AI slop”—machine-generated material that may appear coherent but lacks factual grounding or originality.
Because of these risks, both search engines like Google and conversational systems like ChatGPT prioritize human-authored content, particularly material that has been vetted by recognized institutions or authoritative sources. Academic studies, peer-reviewed papers, and content verified by domain experts are considered higher-quality data because they have undergone human scrutiny for accuracy and reliability. This human filter is vital to maintaining the integrity of knowledge systems and preventing an exponential spread of misinformation through recursive AI self-training.
Sam Altman and other AI leaders have publicly acknowledged these challenges. They have repeatedly urged users to remain creative and continue producing original human content to “keep civilization moving forward.” Altman has also admitted that AI developers do not yet have full solutions for the long-term risks associated with self-reinforcing AI feedback loops and the potential for models to diverge from factual reasoning.
Fundamentally, AI should be used only when it is both safe and necessary. The rule of thumb should always be that human judgment must remain the final authority. Every piece of AI-generated content should be carefully audited, verified, and fact-checked by a human before it is published, relied upon, or integrated into business operations.
AI systems—and the corporations that develop them—cannot and should not be trusted blindly. These tools are designed to serve human progress, not replace human reasoning. The moment we cease to verify, question, and oversee AI output, we risk allowing machines to rewrite the fabric of knowledge itself—based not on truth, but on probability, convenience, and corporate control.
