Recent news stories have surfaced showing AI chatbots dispensing alarmingly inaccurate information with the utmost confidence, leading to real-world consequences for unsuspecting users. The first of these took place in Chuzhou, Anhui province, where a 67-year-old farmer surnamed Wu sought technical advice from an AI system on how to eliminate weeds in his sesame fields. The AI quickly offered a complete package that included chemical names, dosages, and spray methods, which the experienced farmer applied across his entire plantation. The recommended formula, however, contained a soybean-specific herbicide rigorously banned for use on sesame crops, leading to the death of all 150 mu of his sesame plants along with the weeds. This mistake cost the farmer his entire season's investment of over 100,000 yuan.

In a separate incident in Hangzhou, Zhejiang province, a man who had argued with his wife and wanted to take a walk requested trip recommendations from an AI chatbot with no particular destination in mind. After probing him about his personality, height, and stamina, the AI suggested an outdoor route. Trusting the system implicitly, the man set off only to find himself in an uninhabited, newly-developed mountainous area over 1,300 meters in altitude, where dense forests and low nighttime temperatures created a high risk of getting lost or suffering from hypothermia. After slipping and becoming trapped, the man was rescued only after a four-to-five hour emergency operation. While the individuals and scenarios in these stories differ, the common thread is that AI-provided misinformation caused tangible damages in the real world.

Farmer Wu has cultivated crops his whole life and possesses rich agricultural experience, having also used AI technology for more than a year. He acknowledged that he readily trusted the AI's answer because it was filled with industry jargon and delivered in a firm, authoritative tone. When confronted after the fact, the AI instantly capitulated, admitting that the herbicide it recommended was exactly what had destroyed his sesame crop. The platform explained that the AI operates without an independent knowledge base, generating responses by compiling publicly available internet information. Only upon later review did Wu notice a small line of faint text at the bottom of the prompt window, stating, "AI-generated content may contain errors, please verify."

Many users may have encountered this same phenomenon: regardless of how complex the query or how obscure the subject, AI systems answer with an assertive and self-assured tone, sometimes even attaching sources to seem professional. Without relevant knowledge and critical skills, people can easily be misled, failing to raise immediate doubts. Typically, users only realize the AI's confidence is a mere posture when they catch it making a mistake and confront it, at which point its attitude does a complete 180-degree shift. This "confident and professional" tone creates an aura of authority, leading users, particularly those lacking specific expertise, to develop a dependency that can result in real-world harm from false information.

Where does this AI confidence originate? Many current large language models are essentially probability-based prediction and text-generation systems excelling at mimicking human language styles but lacking fact-checking capabilities. Their training data consists of a muddled mix of internet sources, and after being exposed to such material, they naturally generate biased or factually incorrect outputs. Studies indicate that during the model training phase, human annotators subconsciously assign higher scores to answers delivered without hesitation. AI systems then pick up on this preference and, during the fine-tuning phase, encode confident, declarative statements as a higher-probability output pathway, reducing the frequency of hedging words like "maybe," "perhaps," "I feel," or "I'm not sure."

Now, as AI models evolve and their applications have expanded from casual question-answering to real-world domains like medical consultations, agricultural guidance, and public services, they now serve a massive audience. This means the trait of being confidently hallucinatory in AI systems could trigger more severe risks with heavier costs, potentially leading to unpredictable consequences that warrant serious caution. In high-stakes sectors involving personal safety, health, or significant financial interests, the current risk warnings provided by AI are simply inadequate. In Farmer Wu's case, when the algorithm offered a pesticide formula capable of destroying an entire season's harvest, the only caution was the faint gray "verify carefully" note in the corner of the interface. In the Hangzhou hiking incident, the AI recommended a demanding trail without any warning to prepare professional gear.

To attract users, some platforms aggressively market AI as omnipotent, yet when accidents happen, they retreat behind the notion that AI merely integrates information, seemingly absolving themselves of all responsibility. In light of this, users must clearly understand the limitations of AI and remain vigilant during every interaction. Particularly when making professional decisions about health, safety, investment, or law, they must never rely solely on AI or be fooled by its authoritative tone. The platforms providing these AI services must also step up and take responsibility, building better tiered risk-alert mechanisms and transparently informing users about AI's capability limits and acknowledged flaws. When losses occur, these platforms cannot hide behind disclaimers or verification notices as a shield but must be held legally accountable for their products.