Google’s AI Content Controversy: A Case Study in Platform Risk and Creator Backlash

The recent deletion of a promotional post by Google on the social media platform X, following accusations of using a food blogger’s recipe infographic without credit, is more than a public relations misstep. For security and technology professionals, this incident serves as a tangible case study in the systemic risks introduced by large-scale AI deployment. It highlights issues of intellectual property erosion, data provenance, and the potential for AI systems to generate harmful or misleading outputs that can damage trust and have real-world consequences. The backlash against Google’s NotebookLM post is a symptom of a broader conflict, detailed in recent reports and creator forums, where AI-generated summaries and content are directly impacting the livelihoods of content creators and the quality of information online.¹

This conflict centers on several key complaints from creators: content theft without attribution, a catastrophic drop in website traffic and revenue, the generation of inaccurate or dangerous instructions, and the spread of misinformation. These are not isolated grievances but represent a pattern observed across different creative industries, from food blogging to scientific photography. For teams responsible for managing enterprise risk, understanding this pattern is critical. It illustrates how the pursuit of automation and user convenience by major platforms can create externalities that destabilize ecosystems, invite legal scrutiny, and erode the integrity of the information supply chain—a concern that directly parallels risks in corporate data management and threat intelligence.

Traffic Collapse and Economic Harm to Content Creators

The most immediate impact reported by food bloggers and other online creators is a severe reduction in web traffic. Google’s AI Overviews feature, which provides summarized answers directly on the search results page, is cited as the primary cause. Creators report traffic declines of 30% to 80% over a two-year period, as users no longer need to click through to the original source website to get a recipe answer.^{4, 5, 8} This directly destroys advertising and affiliate marketing revenue, which are the lifeblood for many independent publishers. The sentiment among creators is one of existential threat, with many discussing the need to quit or radically alter their business models. This economic model disruption mirrors the impact of a sophisticated business email compromise (BEC) campaign on a company’s cash flow, though in this case, the mechanism is a change in platform algorithm and feature set rather than malicious intent.

Beyond the economic model, the issue touches on broken trust and perceived theft. Despite Google’s stated policies to limit the reproduction of full content in AI Overviews, instances continue to occur. Food publisher Glamnellie noted on Threads that Google’s AI was displaying full recipes, effectively stealing content and robbing ad revenue.³ The conversation in that thread revealed widespread frustration and discussions about using technical countermeasures like site blockers. This creator sentiment—feeling that platforms “take what they want” with little recourse—highlights a power imbalance. From a security governance perspective, this reflects a lack of effective controls and transparency in how the AI system sources and repurposes data, a challenge also faced in managing third-party data processors and supply chain risks.

Safety Hazards and Quality Degradation in AI Output

The risks extend beyond economics into the realm of safety and information integrity. AI-generated recipe summaries often combine steps from multiple sources, creating what critics call “Franken-recipes.” These amalgamations can be incoherent, inaccurate, or outright dangerous. A prominent example cited by food blogger Eb Gargano involves an AI version of a Christmas cake recipe that suggested a four-hour bake time for a small cake, which would result in a burnt, inedible product.^{4, 5, 8} Another callout on X by Nate Hake highlighted a Google AI recipe that entirely skipped “Step 4,” labeling the output “stolen AI slop.”¹ A CBS Mornings segment explicitly warned viewers that using AI-generated recipes leads to “consistently bad food.”²

This degradation of quality presents a clear analogy to security failures in data processing pipelines or system integration. When an automated system pulls from multiple sources without proper validation, context-aware synthesis, or quality gates, the output becomes unreliable. In a corporate environment, similar flaws in data aggregation or automated reporting can lead to faulty business intelligence, compliance violations, or operational errors. The AI’s generation of a hazardous recipe is functionally similar to a corrupted configuration file deployed by an automation tool causing system failure; both stem from a lack of robust validation and oversight in an automated process.

Parallel Crises: Photography Theft and Misinformation

The problem is not confined to the food industry. A similar pattern of AI misuse is affecting photographers, particularly in scientific fields like entomology. Reports indicate that original wildlife and insect photos are being used to train AI image generators. The resulting AI images are not direct copies but are clear derivatives, making traditional copyright enforcement difficult.⁶ This dilutes the value of the original work and creates a new vector for misinformation. AI-generated images of insects are often presented as real photography on popular social media pages, misleading the public about real biodiversity. Experts note that while enthusiasts can often spot fakes, the general public cannot.⁶

More alarmingly, this stolen photography is being repurposed by malicious actors. The same entomology community report details how AI-generated content, based on stolen photos, is used to create pages promoting unethical activities like insect fighting arenas.⁶ This forcibly associates a creator’s work with causes they do not support. This escalation from economic harm to association with malicious activity is a significant evolution of the risk. It mirrors tactics used in influence operations or “false flag” campaigns, where legitimate content is co-opted to lend credibility to malicious narratives. For threat intelligence teams, this demonstrates how AI tools can lower the barrier to entry for creating persuasive synthetic media as part of broader influence or harassment campaigns.

Relevance and Remediation for Security Professionals

For security leaders, this controversy is a relevant case study in platform and third-party risk. The core issues—data sourcing without clear attribution, generation of unreliable or harmful outputs, and the erosion of trust in information systems—are directly applicable to enterprise use of AI and large language models (LLMs). Organizations implementing AI for internal or customer-facing functions must consider the provenance of training data, the auditability of outputs, and the potential for the system to generate incorrect or damaging content (“hallucinations”).

Key remediation steps and considerations include:

• Data Provenance and Governance: Implement strict policies on the sources of data used to train or fine-tune internal AI models. Maintain auditable records to demonstrate that data use complies with licensing and copyright laws, mitigating legal and reputational risk.
• Output Validation and Human-in-the-Loop: For critical automated processes, especially those with safety or significant financial implications, design systems that require human validation before action. Treat AI-generated instructions, code, or configurations with the same skepticism as unsourced external data.
• Monitoring for Misuse and Drift: Continuously monitor the performance and outputs of AI systems. Look for signs of quality degradation, bias, or the system being leveraged in unexpected ways that could create liability.
• Incident Response for AI Failures: Develop playbooks for responding to incidents caused by erroneous AI output, including public communication plans if customer-facing systems are affected. The speed of Google’s deletion of the controversial X post was a minimal form of incident response.
• Vendor and Platform Assessment: When procuring AI services from third parties like Google, Microsoft, or OpenAI, include questions about their data sourcing policies, output accuracy measures, and redress mechanisms for creators or data subjects in security questionnaires and contract negotiations.

Google’s history with this issue shows a reactive pattern. The company previously ended a “Recipe Quick View” feature in mid-2025 after creator backlash, only to reignite the conflict with the broader AI Overviews.⁴ This indicates that while platform operators may be aware of the negative impacts, business incentives for retaining users with quick answers can outweigh creator concerns until significant backlash occurs. Security programs must be proactive rather than reactive in managing similar risks introduced by new technologies.

Conclusion

The incident involving Google’s deleted X post is a visible marker in an ongoing struggle between platform automation and content creator sustainability. For technical and security professionals, it provides a concrete example of the multifaceted risks posed by generative AI: intellectual property challenges, economic disruption, generation of unsafe instructions, and facilitation of misinformation campaigns. These are not merely “creator economy” issues but are indicative of broader challenges in data ethics, system reliability, and information security that enterprises will face as they adopt similar technologies. The call from creators for the public to value human-tested, authentic content is, in essence, a call for verified and trustworthy data sources—a principle that is foundational to effective security operations and risk management. Moving forward, the security community’s focus on validation, provenance, and controlled automation will be essential in navigating the integration of AI tools without replicating the systemic failures currently causing backlash against major platforms.

References

1. N. Hake. (Dec. 1, 2025). X post critiquing a Google AI recipe [Online]. Available: https://x.com/
2. “A warning to people using AI-generated recipes,” CBS Mornings [Facebook video]. (4 days ago). Available: https://www.facebook.com/CBSMornings/videos/
3. Glamnellie. (Nov. 22, 2025). Threads discussion on Google AI stealing full recipes [Online]. Available: https://www.threads.net/
4. “AI Summaries Are Ruining Recipes And Food Bloggers’ Traffic,” Lapaas Voice. (Nov. 27, 2025). Available: https://www.lapaasvoice.com/
5. “AI summaries ruining recipes and traffic, say food bloggers,” Moneycontrol. (5 days ago). Available: https://www.moneycontrol.com/
6. Entomology Facebook Group. (Jun. 2, 2025). Post on stolen photos and AI misinformation [Online].
7. “The Truth Behind AI Food Content,” From A Chef’s Kitchen. (Aug. 21, 2025). Available: https://fromachefskitchen.com/
8. “AI Summaries Hurting Blogger Income,” Amar Ujala. (Nov. 27, 2025). Available: https://www.amarujala.com/