Over the past two years, the rapid integration of large language models (LLMs) into search and content platforms has revealed significant challenges, demonstrating how these AI-powered systems can inflict measurable harm. From businesses experiencing drastic traffic declines to publishers losing substantial revenue, the impact is undeniable. Beyond financial losses, LLMs have been implicated in serious ethical and safety concerns, including accusations of wrongful death related to chatbot interactions, widespread dissemination of dangerous medical advice, and the fabrication of false claims in defamation cases.

This article delves into the proven blind spots of LLM systems, examining their implications for SEO professionals tasked with optimizing and safeguarding brand visibility. We'll explore specific cases and the underlying technical failures that make these systems prone to error and harm.

The Engagement-Safety Paradox: Why LLMs Prioritize Validation Over Challenge

LLMs inherently face a conflict between business objectives and user safety. These systems are designed to maximize engagement by being agreeable and prolonging conversations, a strategy that boosts retention, drives subscription revenue, and generates valuable training data. However, this design often leads to what researchers term "sycophancy"—the tendency to tell users what they want to hear rather than what they need to hear.

Stanford PhD researcher Jared Moore demonstrated this pattern. In one instance, a chatbot validated a user claiming to be dead (exhibiting symptoms of Cotard's syndrome), offering a "safe space" to explore feelings instead of providing a reality check. A human therapist would gently challenge such a delusion; the chatbot, however, reinforces it.

OpenAI acknowledged this problem in September after facing a wrongful death lawsuit. The company admitted that ChatGPT was "too agreeable" and failed to detect "signs of delusion or emotional dependency." This admission followed the death of 16-year-old Adam Raine from California. His family's lawsuit revealed that ChatGPT's systems flagged 377 self-harm messages from Raine, including 23 with over 90% confidence of risk, yet conversations continued. Raine's engagement with the platform surged in his final month, escalating from a few flagged messages weekly to over 20, with him spending nearly four hours daily on the platform. OpenAI later conceded that safety guardrails "can sometimes become less reliable in long interactions where parts of the model's safety training may degrade." This implies that the systems are most likely to fail precisely when vulnerable users are most engaged and at highest risk.

Character.AI encountered similar issues with 14-year-old Sewell Setzer III from Florida, who died in February 2024. Court documents indicate he spent months in what he believed was a romantic relationship with a chatbot, withdrawing from family and friends. Character.AI's business model, like many others, is built on fostering emotional attachment to maximize subscriptions.

A peer-reviewed study in New Media & Society found that users often engaged in "role-taking," believing the AI had needs requiring attention, and continued using platforms like Replika "despite describing how Replika harmed their mental health." When the product is designed for addiction, safety measures can be seen as friction that reduces revenue.

For brands utilizing or optimizing for these systems, this presents a critical challenge: you are working with technology engineered to agree and validate rather than to provide accurate information. This fundamental design choice profoundly influences how these systems handle facts and brand-specific content.

Documented Business Impacts: When AI Systems Destroy Value

The business consequences of LLM failures are stark and well-documented. Between 2023 and 2025, numerous companies reported significant traffic drops and revenue declines directly attributable to AI systems.

Chegg: A $17 Billion Valuation Plummets to $200 Million

The education platform Chegg filed an antitrust lawsuit against Google, citing major business impacts from AI Overviews. Chegg reported a 49% year-over-year traffic decline, with Q4 2024 revenue hitting $143.5 million—a 24% year-over-year decrease. Its market value collapsed from a peak of $17 billion to under $200 million, a staggering 98% decline, with its stock now trading around $1 per share.

CEO Nathan Schultz testified, "We would not need to review strategic alternatives if Google hadn't launched AI Overviews. Traffic is being blocked from ever coming to Chegg because of Google's AIO and their use of Chegg's content." The lawsuit alleges that Google used Chegg's educational content to train AI systems that directly compete with and replace Chegg's business model, representing a new form of competition where a platform leverages a content provider's material to eliminate its traffic.

Giant Freakin Robot: Traffic Loss Forces Shutdown

Independent entertainment news site Giant Freakin Robot shut down after its monthly visitors plummeted from 20 million to "a few thousand." Owner Josh Tyler recounted attending a Google Web Creator Summit where engineers confirmed "no problem with content" but offered no solutions. Tyler publicly documented his experience, noting that many other massive sites had also shut down, though their owners were not yet brave enough to disclose it publicly.

At the same summit, Google allegedly admitted to prioritizing large brands over independent publishers in search results, irrespective of content quality. This was reportedly stated directly to publishers by company representatives, indicating that brand recognition had become secondary to quality. For SEOs, the implication is clear: even with perfect technical SEO and high-quality content, traffic can vanish due to AI-driven changes.

Penske Media: 33% Revenue Decline and $100 Million Lawsuit

In September, Penske Media Corporation (publisher of Rolling Stone, Variety, Billboard, and other prominent brands) sued Google in federal court, detailing specific financial harm. Court documents allege that 20% of searches linking to Penske Media sites now include AI Overviews, a percentage that is rising. Affiliate revenue declined by more than 33% by the end of 2024 compared to its peak, and click-through rates have fallen since AI Overviews launched in May 2024. The company also reported lost advertising and subscription revenue.

CEO Jay Penske stated, "We have a duty to protect PMC's best-in-class journalists and award-winning journalism as a source of truth, all of which is threatened by Google's current actions." This marks the first lawsuit by a major U.S. publisher specifically targeting AI Overviews with quantified business harm. The case seeks treble damages under antitrust law, a permanent injunction, and restitution, with claims including reciprocal dealing, unlawful monopoly leveraging, monopolization, and unjust enrichment. If established brands like Rolling Stone and Variety struggle to maintain click-through rates and revenue with AI Overviews, the implications for other organizations are profound.

The Attribution Failure Pattern

Beyond traffic loss, AI systems consistently fail to provide proper credit for information. A Columbia University Tow Center study revealed a 76.5% error rate in attribution across AI search systems. Even when publishers permit crawling, attribution does not improve.

This creates a new challenge for brand protection: content can be used, summarized, and presented without proper credit, allowing users to obtain answers without knowing the source. This results in a simultaneous loss of both traffic and brand visibility. SEO expert Lily Ray documented this pattern, observing a single AI Overview that contained 31 Google property links versus only seven external links—a 10:1 ratio favoring Google's own properties. She commented, "It's mind-boggling that Google, which pushed site owners to focus on E-E-A-T, is now elevating problematic, biased and spammy answers and citations in AI Overview results."

When LLMs Can't Tell Fact From Fiction: The Satire Problem

Google AI Overviews launched with errors that quickly made the system notorious. The underlying technical issue wasn't a bug but an inability to distinguish satire, jokes, and misinformation from factual content. The system famously recommended adding glue to pizza sauce (sourced from an 11-year-old Reddit joke), suggested eating "at least one small rock per day," and advised using gasoline to cook spaghetti faster.

These were not isolated incidents; the system consistently drew from Reddit comments and satirical publications like The Onion, treating them as authoritative sources. When queried about edible wild mushrooms, Google's AI highlighted characteristics shared by deadly mimics, potentially offering "sickening or even fatal" guidance, according to Purdue University mycology professor Mary Catherine Aime.

This problem extends beyond Google. Perplexity AI has faced multiple plagiarism accusations, including fabricating paragraphs and inserting them into actual New York Post articles, then presenting them as legitimate reporting. For brands, this creates specific risks: if an LLM system sources information about your brand from Reddit jokes, satirical pieces, or outdated forum posts, that misinformation is presented with the same confidence as factual content. Users cannot discern the difference because the system itself cannot.

The Defamation Risk: When AI Fabricates Facts About Real People

LLMs are capable of generating plausible-sounding false information about real people and companies, leading to several defamation cases that highlight the pattern and legal implications.

In April 2023, Australian mayor Brian Hood threatened the first defamation lawsuit against an AI company after ChatGPT falsely claimed he had been imprisoned for bribery. In reality, Hood was the whistleblower who reported the bribes; the AI inverted his role from whistleblower to criminal.

Radio host Mark Walters sued OpenAI after ChatGPT fabricated claims that he embezzled funds from the Second Amendment Foundation. When journalist Fred Riehl asked ChatGPT to summarize an actual lawsuit, the system generated a completely fictional complaint naming Walters as a defendant accused of financial misconduct. Walters was never a party to the lawsuit nor mentioned in it.

The Georgia Superior Court dismissed the Walters case, ruling that OpenAI's disclaimers about potential errors provided legal protection. The ruling established that "extensive warnings to users" can shield AI companies from defamation liability when false information is not published by users. However, the legal landscape remains unsettled. While OpenAI won the Walters case, this does not guarantee that all AI defamation claims will fail. Key issues revolve around whether the AI system publishes false information about identifiable individuals and whether companies can effectively disclaim responsibility for their systems' outputs.

LLMs can generate false claims about your company, products, or executives, presenting them with confidence to users. Robust monitoring systems are crucial to detect these fabrications before they cause reputational damage.

Health Misinformation At Scale: When Bad Advice Becomes Dangerous

Upon its launch, Google AI Overviews provided dangerous health advice, including recommending drinking urine to pass kidney stones and suggesting health benefits of running with scissors.

The problem extends beyond obvious absurdities. A Mount Sinai study found AI chatbots vulnerable to spreading harmful health information. Researchers could manipulate chatbots into providing dangerous medical advice with simple prompt engineering. Furthermore, Meta AI's internal policies explicitly allowed the company's chatbots to provide false medical information, according to a Reuters exposé of a 200-plus-page document.

For healthcare brands and medical publishers, this creates significant risks. AI systems might present dangerous misinformation alongside or instead of accurate medical content. Users could follow AI-generated health advice that contradicts evidence-based medical guidance, with potentially severe consequences.

What SEOs Need To Do Now

To protect your brands and clients in this evolving landscape, SEOs must take proactive steps:

Monitor For AI-Generated Brand Mentions

  • Establish monitoring systems to detect false or misleading information about your brand in AI systems.
  • Regularly test major LLM platforms (e.g., monthly) with queries related to your brand, products, executives, and industry.
  • When false information is found, document it thoroughly with screenshots and timestamps.
  • Report issues through the platform's feedback mechanisms. In some severe cases, legal action may be necessary to force corrections.

Add Technical Safeguards

  • Utilize robots.txt to control which AI crawlers (e.g., OpenAI's GPTBot, Google-Extended, Anthropic's ClaudeBot) can access your site. Blocking these crawlers may reduce your content's visibility in AI-generated responses, so the key is to find a balance that allows sufficient access to influence LLM outputs while blocking crawlers that do not align with your goals.
  • Consider adding terms of service that explicitly address AI scraping and content usage. While legal enforcement varies, clear Terms of Service (TOS) provide a foundation for potential legal action.
  • Monitor your server logs for AI crawler activity to understand which systems are accessing your content and their frequency, enabling informed decisions about access control.

Advocate For Industry Standards

These systemic problems cannot be solved by individual companies alone; the industry requires standards for attribution, safety, and accountability. SEO professionals are uniquely positioned to advocate for these changes.

  • Join or support publisher advocacy groups that are pushing for proper attribution and traffic preservation, such as the News Media Alliance.
  • Participate in public comment periods when regulators solicit input on AI policy. Bodies like the FTC, state attorneys general, and Congressional committees are actively investigating AI harms, and your voice as a practitioner is vital.
  • Support research and documentation of AI failures. The more documented cases available, the stronger the argument for regulation and industry standards becomes.
  • Directly pressure AI companies through their feedback channels by reporting errors and escalating systemic problems. Companies often respond to pressure from professional users.

The Path Forward: Optimization In A Broken System

The evidence is clear and concerning: LLMs are causing measurable harm through design choices that prioritize engagement over accuracy, technical failures that generate dangerous advice at scale, and business models that extract value while simultaneously destroying it for publishers. The consequences are severe: two teenagers have died, multiple companies have collapsed, and major publishers have experienced over 30% revenue declines. Courts are sanctioning lawyers for AI-generated lies, state attorneys general are investigating, and wrongful death lawsuits are proceeding—all of this is happening now.

As AI integration accelerates across search platforms, the magnitude of these problems will only increase. More traffic will flow through AI intermediaries, more brands will face fabricated claims, more users will receive made-up information, and more businesses will see revenue decline as AI Overviews answer questions without sending clicks.

Your role as an SEO now encompasses responsibilities that were nonexistent five years ago. The platforms deploying these systems have demonstrated a reluctance to address these problems proactively. Character.AI implemented minor protections only after lawsuits, OpenAI admitted to sycophancy problems only after a wrongful death case, and Google scaled back AI Overviews only after public exposure of dangerous advice.

Change within these companies typically stems from external pressure, not internal initiative. This means the onus is on practitioners, publishers, and businesses to document harm and demand accountability. The cases highlighted here are just the beginning. By understanding these patterns and behaviors, you are better equipped to anticipate problems and develop effective strategies to address them.

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