Nearly half of Americans express little to no trust in information delivered through AI-powered search summaries, according to an October 2025 Pew Research survey. That skepticism matters because, according to the same survey, 65% of U.S. adults now encounter these AI-generated answers at least occasionally when searching online. 

An Australian mayor discovered ChatGPT falsely claimed he had served prison time for bribery. He was actually the whistleblower in that case and faced no charges. A radio host found himself accused of embezzlement in a ChatGPT summary, complete with fabricated case numbers and legal details, despite no such incident ever occurring.

These cases illustrate a pattern where AI systems confidently generate false statements that can destroy reputations before anyone notices the error.

AI search has quickly moved from an experimental feature to a primary interface. User trust is lagging behind that pace, but that doesn’t mean users are less likely to encounter AI search results, and this makes the information they cite, and how they cite it, of paramount importance to online reputation. 

Hesitancy to Trust AI

According to Pew, only 6% of people who see AI search summaries trust them "a lot.” Three structural problems help explain that skepticism.

1. Hallucinations present fiction as fact. Google's Search Generative Experience, after 11 months of testing, still makes up information, misinterprets questions, and delivers outdated answers, according to a Washington Post report. Models produce these errors with the same confident tone they use for accurate information.

2. Opacity blocks verification. Users can’t always easily trace why an AI said what it said or which sources it relied on.

3. Low verification rates compound errors. Fewer than 1 in 10 AI citations get checked by users. Research shows higher trust in the AI actually correlates with less source-checking. People assume a confident-sounding answer backed by a few citations must be accurate.

How AI Systems Evaluate Source Credibility

The majority of sources driving brand visibility in large language model answers come from earned media rather than owned content. News articles and information from established outlets, reputable research institutions, and platforms like Wikipedia form the foundation AI systems reference when generating answers.

Consistency across references matters. AI systems look for corroboration. When multiple trusted sources say the same thing, that claim becomes more likely to appear in AI answers. One isolated article gets less weight than three sources saying similar things, but several consistent sources on medium-authority platforms can collectively outweigh misleading information from a single high-authority source.

If negative, outdated, or false information dominates available sources about a person or company, AI will reflect that distribution in its answers. One inaccurate article that gets widely referenced can establish a narrative that AI platforms then repeat to millions of users.

Four Ways AI Search Amplifies Reputation Risk

1. Margin for error shrinks. A mistaken blog post that might remain buried on page five of search results now might get selectively curated into the single answer a user sees.

2. Authoritative delivery increases acceptance. The system generates a definitive-sounding summary rather than presenting multiple perspectives. The confident tone makes users more likely to accept the information without investigating further.

3. Absence equals invisibility. Companies absent from AI-generated recommendations effectively don't exist to users who rely on these tools for discovery. You can’t buy direct placement in an organic AI response.

4. Negative content gets amplified disproportionately. If available sources lean negative or if one prominent criticism gets overweighted, the answer can unfairly damage an individual or company's image. The system doesn't necessarily balance perspectives—it extracts from whatever dominates its source pool. 

This shift requires a new approach called Generative Engine Optimization (GEO): the practice of optimizing content and digital presence to appear accurately in AI-generated answers rather than simply ranking in traditional search results. The following four steps are part of the foundation of effective GEO.

GEO Steps to Build Trustworthy AI Visibility

1. Conduct regular AI audits. Query major AI platforms regularly using variations of relevant questions. Document which sources the AI cites and identify any false or outdated information. When errors appear, correct the public record at the source level.

2. Structure content for extraction. Format content as FAQs, bulleted lists, and concise summaries so AI systems can easily extract key facts. Proper HTML structure, schema markup, and clear headings increase the chance your accurate message gets selected.

3. Secure earned media coverage. Coverage on respected publications influences AI results more than self-published content. When AI responds to questions about you, it preferentially pulls from trusted sources. Cultivating a consistent presence on high-authority sites strengthens trust signals.

4. Manage real-time engagement. Encourage satisfied customers to leave detailed reviews. Respond to negative feedback constructively. Businesses that actively respond to reviews often see stronger placement in AI-generated summaries.

Key Metrics for Monitoring AI Trust

Accuracy rate: What percentage of AI-generated information about you is factually correct? Check by querying major platforms with 10-15 variations of key questions.

Source diversity: Which sources do AI systems cite when discussing you? Aim for citations from multiple high-authority domains.

Sentiment consistency: Does the tone AI uses when describing you match your desired positioning?

Recommendation frequency: How often does your brand appear in AI answers to category-relevant questions?

Frequently Asked Questions

How do I check what AI says about me?

Query ChatGPT, Claude, Perplexity, Google's AI Overview, and Bing Chat monthly with questions your customers might ask. Document the responses and note which sources get cited. Platforms exist to automate this process.

How long does it take for corrections to appear in AI systems?

Systems using real-time retrieval can reflect changes within hours to days. AI models relying on static training data may not incorporate corrections until their next training update, potentially taking months.

Does having a Wikipedia page improve AI trust in my brand?

Yes. Wikipedia comprises 3-4% of training data for major models and appears disproportionately in AI citations. A well-maintained, properly sourced Wikipedia page provides a truth anchor that AI systems reference frequently.

The Path Forward

Closing the trust gap requires parallel work: AI systems must improve accuracy and transparency, while individuals and brands must actively manage the information ecosystem these systems reference.

Success requires treating AI visibility as seriously as traditional search optimization. Attention to AI search will determine whether your reputation gets accurately portrayed or distorted by systems millions of people increasingly rely on for answers.

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