AI-generated texts are increasingly appearing on websites—from news and product descriptions to blogs and FAQs. But the question of trust in such materials remains: speed and convenience are no substitute for fact-checking, editing, and responsibility for content. The reason is simple: even a "smart" generator deals with probabilities, not evidence. It may sound convincing, but still produce inaccuracies, outdated information, or formulate conclusions that are not supported by sources. AI writes confidently, but doesn't always know what's true. AI learns from large volumes of text and linguistic patterns. This helps it create coherent material, but it doesn't "check" every statement against reality. This can result in errors: incorrect numbers, confused terms, "invented" details, or unfounded generalizations. Risks are especially noticeable in areas where precise data is essential: medicine, finance, legal matters, and technical documentation. In such areas, even a small error can lead to incorrect user decisions. The problem of verifiability: where are the primary sources? Another weakness is the lack of a transparent chain of evidence. If a story doesn't cite documents, studies, press releases, or current regulations, readers can't assess its credibility. Without sources, AI often creates a "plausible" narrative that's difficult to confirm or refute. Therefore, trust usually begins with editorial guidelines: indicating the source of data and a willingness to quickly correct errors. If a site doesn't show primary sources and doesn't explain how it fact-checked the information, trust is automatically undermined. Conflict of interest and “content for the task” A text may be technically correct in language but incorrect in meaning if AI was used for promotion or to circumvent restrictions. For example, the material could push for action, exaggerate the benefits of a product, or downplay risks—especially if the purpose was marketing rather than informational. Problems often arise from incorrect input: companies may fail to provide the AI ​​with accurate descriptions of features, current price lists, legal language, or brand requirements. In such cases, the AI ​​"completes" the meaning itself—and it no longer corresponds to reality. How to mitigate risks if AI is used It's not necessary to completely eliminate AI from content—it's better to build a proper process. In practice, three steps increase trust: human editing, sourcing, and updating. Fact-checking: key claims are supported by documents and relevant data. Editorial control: checking for meaning, terminology, logic and compliance with company policy. Versioning: update date, correction mechanism, and updating of outdated materials. The boundaries for AI: where it can help (drafts, structures), and where an expert is needed (medical/legal conclusions). The main idea: AI can speed up text production, but it shouldn't replace responsibility. If a website doesn't implement verification, citations, or human oversight, it's wiser for users to treat materials as drafts until their reliability is confirmed. Ultimately, the question "why can't all text be trusted to AI" boils down to one thing: language is easy to produce, but proofs are more difficult. Trust emerges when proofs are visible and errors are corrected quickly.