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Companies that employ black hat techniques or other spammy tactics can get their client websites banned from the search results. In 2005, the ''[[Wall Street Journal]]'' reported on a company, [[Traffic Power]], which allegedly used high-risk techniques and failed to disclose those risks to its clients.<ref>{{cite news|newspaper=[[Wall Street Journal]]|url=https://www.wsj.com/articles/SB112714166978744925?apl=y&r=947596|title=Sites Get Dropped by Search Engines After Trying to 'Optimize' Rankings|author=David Kesmodel|date=September 22, 2005|access-date=July 30, 2008|archive-date=August 4, 2020|archive-url=https://web.archive.org/web/20200804125356/https://www.wsj.com/articles/SB112714166978744925?apl=y&r=947596|url-status=live}}</ref> ''[[Wired (magazine)|Wired]]'' magazine reported that the same company sued blogger and SEO Aaron Wall for writing about the ban.<ref name="wired09082005">{{cite magazine|magazine=[[Wired Magazine]]|url=http://archive.wired.com/culture/lifestyle/news/2005/09/68799?currentPage=all|title=Legal Showdown in Search Fracas|date=September 8, 2005|author=Adam L. Penenberg|access-date=August 11, 2016|archive-date=March 4, 2016|archive-url=https://web.archive.org/web/20160304055056/http://archive.wired.com/culture/lifestyle/news/2005/09/68799?currentPage=all|url-status=live}}</ref> Google's [[Matt Cutts]] later confirmed that Google had banned Traffic Power and some of its clients.<ref>{{cite web|publisher=mattcutts.com/blog|author=Matt Cutts|url=http://www.mattcutts.com/blog/confirming-a-penalty/|title=Confirming a penalty|date=February 2, 2006|access-date=May 9, 2007|author-link=Matt Cutts|archive-date=June 26, 2012|archive-url=https://web.archive.org/web/20120626093828/http://www.mattcutts.com/blog/confirming-a-penalty/|url-status=live}}</ref>
== Optimization for AI-driven systems ==
With the rise of large language models (LLMs) such as ChatGPT, Gemini, and Claude, researchers and practitioners have begun exploring methods to improve how content is embedded, retrieved, and ranked in AI-generated outputs. Early proposals include ''Generative Engine Optimization'' (GEO), which sought to adapt content strategies for generative search systems.<ref>{{Cite book |last1=Aggarwal |first1=Pranjal |title=Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining |last2=Murahari |first2=Vishvak |last3=Rajpurohit |first3=Tanmay |last4=Kalyan |first4=Ashwin |last5=Narasimhan |first5=Karthik |last6=Deshpande |first6=Ameet |date=2024-08-24 |publisher=Association for Computing Machinery |isbn=979-8-4007-0490-1 |series=KDD '24 |___location=New York, NY, USA |pages=5–16 |chapter=GEO: Generative Engine Optimization |doi=10.1145/3637528.3671900 |chapter-url=https://dl.acm.org/doi/10.1145/3637528.3671900 |arxiv=2311.09735}}</ref>
''Artificial Intelligence Optimization'' (AIO) subsequently introduced formalized metrics and structures—such as the ''Trust Integrity Score'' (TIS)—to assess how reliably content is represented in LLM responses.<ref>{{Cite journal |last1=Bashir |first1=A |last2=Chen |first2=RL |last3=Delgado |first3=M |last4=Watson |first4=JW |last5=Hassan |first5=Z |last6=Ivanov |first6=P |last7=Srinivasan |first7=T |date=2025-02-03 |title=Trust Integrity Score (TIS) as a Predictive Metric for AI Content Fidelity and Hallucination Minimization |url=https://zenodo.org/records/15330846 |journal=National System for Geospatial Intelligence |doi=10.5281/zenodo.15330846}}</ref>
More recently, the concept of ''AI Visibility Optimization'' (AIVO) has been proposed, focusing on measurable indicators such as prompt-space occupancy and citation stability to evaluate brand persistence within AI-driven search environments.<ref>Independent secondary source needed</ref>
== Optimization for AI-driven systems ==
With the rise of large language models (LLMs) such as ChatGPT, Gemini, and Claude, researchers and practitioners have begun exploring methods to improve how content is embedded, retrieved, and ranked in AI-generated outputs. Early proposals include ''Generative Engine Optimization'' (GEO), which sought to adapt content strategies for generative search systems.<ref>{{Cite book |last1=Aggarwal |first1=Pranjal |title=Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining |last2=Murahari |first2=Vishvak |last3=Rajpurohit |first3=Tanmay |last4=Kalyan |first4=Ashwin |last5=Narasimhan |first5=Karthik |last6=Deshpande |first6=Ameet |date=2024-08-24 |publisher=Association for Computing Machinery |isbn=979-8-4007-0490-1 |series=KDD '24 |___location=New York, NY, USA |pages=5–16 |chapter=GEO: Generative Engine Optimization |doi=10.1145/3637528.3671900 |chapter-url=https://dl.acm.org/doi/10.1145/3637528.3671900 |arxiv=2311.09735}}</ref>
''Artificial Intelligence Optimization'' (AIO) subsequently introduced formalized metrics and structures—such as the ''Trust Integrity Score'' (TIS)—to assess how reliably content is represented in LLM responses.<ref>{{Cite journal |last1=Bashir |first1=A |last2=Chen |first2=RL |last3=Delgado |first3=M |last4=Watson |first4=JW |last5=Hassan |first5=Z |last6=Ivanov |first6=P |last7=Srinivasan |first7=T |date=2025-02-03 |title=Trust Integrity Score (TIS) as a Predictive Metric for AI Content Fidelity and Hallucination Minimization |url=https://zenodo.org/records/15330846 |journal=National System for Geospatial Intelligence |doi=10.5281/zenodo.15330846}}</ref>
More recently, the concept of ''AI Visibility Optimization'' (AIVO) has been proposed, focusing on measurable indicators such as prompt-space occupancy and citation stability to evaluate brand persistence within AI-driven search environments.<ref>Independent secondary source needed</ref>
== As marketing strategy ==
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