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Why Google’s Algorithm Will Eventually Need to Be Reset for Inclusivity

With AI-driven conversational search on the rise, this article provides expert-backed strategies for optimizing content, enhancing brand authority, and how minority-owned media and small and new businesses can leverage machine learning to compete in search, despite Google's bias for longevity markers. Will AI replace Google’s search dominance, or can small businesses fuel and future-proof their strategies? 


By Andrew Juma


Andrew is the founder of The AJ Center, a globally recognized, award-winning end-to-end digital marketing firm, scaling brands with SEO, PPC, and PR excellence.

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Google's algorithm has shaped the internet for nearly three decades, defining how information is ranked, retrieved, and monetized. Since its founding in 1996, Google has revolutionized search by prioritizing relevance and user experience, setting it apart from early competitors. Over time, it introduced complex ranking factors, penalized manipulation tactics, and integrated artificial intelligence to refine results. However, as SEO professionals adapt, concerns about declining organic reach and AI-driven alternatives like ChatGPT are raising questions about Google’s future.

The rapid evolution of AI search engines challenges Google’s ad-driven model, shifting how users seek and consume information. As Google's algorithm becomes increasingly opaque, issues like bias, content suppression, and the prioritization of ads over organic results fuel speculation that a fundamental reset is inevitable. This article traces Google’s algorithmic history, examines its current shortcomings, and explores whether AI search can overtake traditional SEO.

Google’s Search Algorithm: The Early Years (1996–2009)

Google's algorithm has shaped the internet for nearly three decades, defining how information is ranked, retrieved, and monetized. Since its founding in 1996, Google revolutionized search by prioritizing relevance and user experience, setting it apart from early competitors. Over time, it introduced complex ranking factors, penalized manipulation tactics, and integrated artificial intelligence to refine results. However, as SEO professionals adapt, concerns about declining organic reach and AI-driven alternatives like ChatGPT are raising questions about Google’s future.

The rapid evolution of AI search engines challenges Google’s ad-driven model, shifting how users seek and consume information. As Google's algorithm becomes increasingly opaque, issues like bias, content suppression, and the prioritization of ads over organic results fuel speculation that a fundamental reset is inevitable. This article traces Google’s algorithmic history, examines its current shortcomings, and explores whether AI search can overtake traditional SEO.

The Rise of AI and Personalization in Search (2010–2016)

The 2010s saw Google integrating machine learning and artificial intelligence into its search algorithm. The Panda update (2011) penalized low-quality content, while Penguin (2012) targeted manipulative link-building. These updates reshaped SEO by forcing content marketers to focus on value-driven material instead of shortcuts.

Google’s Hummingbird update (2013) marked a turning point by shifting from exact keyword matching to semantic search, interpreting user intent rather than specific words (Singhal, 2013). The RankBrain algorithm (2015) further revolutionized search, using AI-driven ranking models to adjust results dynamically.

During this period, personalization increased. Google collected user data, search history, and location to customize search results, creating an ecosystem where websites struggled to control their visibility. While SEO professionals adapted, Google's power over search visibility became absolute, sparking debates about bias, algorithmic favoritism, and declining organic reach.

The Current State of Google's Algorithm: Opportunities and Challenges (2017–2024)

By the late 2010s, Google faced growing scrutiny over its ranking system, monetization model, and impact on content creators. The BERT update (2019) improved natural language understanding, allowing Google to interpret conversational queries more accurately (Devlin et al., 2019). The Core Web Vitals update (2021) emphasized page experience, forcing websites to improve speed, responsiveness, and interactivity (Google, 2021).

However, concerns over declining organic traffic, excessive ads, and AI-generated summaries grew. Studies showed that over 65% of Google searches ended without a click, as Google provided direct answers within search results (Fishkin, 2023). This zero-click search phenomenon diminished SEO returns, frustrating businesses and content creators who relied on traffic from organic search.

Additionally, Google’s reliance on advertising revenue created conflicts of interest. While Google claimed to prioritize high-quality content, many argued that ad-driven results overshadowed independent publishers. The shift toward AI-powered search summaries, such as Google’s Search Generative Experience (SGE), raised questions about how content creators would be credited or compensated.

Why Resetting May Be Inevitable

Critics argue that Google's algorithm is facing fundamental problems that demand a complete overhaul. Three major issues highlight why a reset might be necessary:

Google’s Algorithmic Weaknesses

The Exclusion of Essential Service Providers in Google’s Rankings

Google’s algorithm prioritizes established authority and historical signals, often sidelining essential service providers that lack digital longevity. Small healthcare clinics, independent financial advisory firms, and local emergency response teams frequently struggle to rank despite offering critical public services. Because Google’s ranking model heavily favors websites with long-established credibility, newer providers must rely on paid search campaigns to gain visibility. This creates an uneven digital landscape where services essential to public welfare remain difficult to access organically.

For essential service providers, the impact of limited visibility extends beyond business performance. In crisis situations, immediate access to relevant, high-quality information is crucial. Yet, Google’s ranking system often amplifies large, well-known organizations while pushing smaller but highly specialized service providers lower in results. Users searching for localized or niche emergency services may instead encounter generic, outdated, or non-region-specific results, reducing the practical value of search.

The reliance on historical domain strength further compounds the issue. Newer institutions—despite meeting compliance standards and offering critical services—struggle to establish domain authority, particularly if their competitors have been online for decades. Without algorithmic adjustments that recognize real-time relevance and verified industry expertise, Google’s search system risks creating an information bottleneck, where access to essential services is dictated by domain age rather than service quality.

Addressing this issue would require recalibrating ranking factors to weigh real-time accuracy, user engagement, and localized relevance more heavily. Structured data enhancements, direct verification mechanisms, and increased emphasis on fresh, high-quality content could help bridge the gap between digital authority and practical necessity. Failure to address this imbalance could result in users seeking alternative search models that prioritize direct, on-demand information retrieval.

The Challenge of High-Value New Websites in an Authority-Dominated Search Landscape

Google’s preference for domain age and backlink history creates significant barriers for new high-value content providers. Platforms offering groundbreaking research, independent journalism, or expert-driven analysis often struggle to rank against legacy media institutions with extensive digital footprints. This structural disadvantage limits the visibility of emerging perspectives, reinforcing an ecosystem where older, well-funded platforms dominate search results.

While Google’s E-E-A-T framework was designed to improve content quality, its reliance on historical signals creates an inherent challenge for new entrants. A newly launched site by an industry expert may provide more accurate and specialized insights than an older, generalized publication, yet the latter is more likely to rank. This results in a digital environment where high-value knowledge struggles to surface organically, reducing the diversity of accessible information.

Another challenge lies in Google’s assessment of credibility. While traditional media outlets benefit from decades of accumulated trust signals, smaller, independently operated content platforms must navigate an extended period of low visibility before reaching competitive search rankings. The absence of immediate recognition for well-researched, expert-driven content diminishes the incentive for new, high-value information sources to compete in organic search.

Adjusting Google’s algorithm to account for dynamic credibility indicators—such as verified expertise, peer-reviewed citations, and real-time engagement metrics—could help address this issue. Incorporating more AI-driven context analysis to assess content quality in real-time rather than relying primarily on historical backlink structures would create a more equitable search ecosystem.

The Impact of AI-Optimized Companies on Google’s Search Model

The rise of AI-focused companies introduces an emerging challenge for Google’s search framework. As businesses increasingly develop AI-native content strategies, traditional ranking models may struggle to accommodate evolving search behaviors. AI-generated knowledge bases, proprietary AI-assisted customer support systems, and direct conversational AI models are shifting user interactions away from conventional web search dependency.

Companies leveraging AI-driven search models are already optimizing their content for ChatGPT, Perplexity, and other generative AI tools rather than traditional SEO principles. This shift suggests a growing disconnect between Google’s ranking system and real-world user behavior. While Google’s algorithm is structured around indexing and ranking static webpages, AI-driven search prioritizes real-time data synthesis, contextual understanding, and direct query responses.

Google’s continued reliance on its PageRank-based framework poses limitations in adapting to these shifts. AI-first search models focus on content relevance and intent-matching rather than backlink structures or domain authority. As users transition towards AI-assisted search engines, companies prioritizing AI optimization over conventional SEO may find Google’s ranking system increasingly obsolete.

To remain competitive, Google may need to integrate more AI-native ranking adjustments, placing greater emphasis on conversational context, real-time data processing, and user intent analysis. Failure to do so could accelerate the adoption of alternative AI-driven search platforms, reducing Google’s dominance in digital discovery.

The Representation Gap for Black-Owned Businesses and Minority-Led Media

Algorithmic bias remains a challenge within Google’s search ranking system, disproportionately affecting Black-owned businesses and minority-led media platforms. The algorithm’s reliance on historical authority and backlink-based credibility inadvertently reinforces systemic inequalities, as minority-led platforms often lack the same legacy web presence as mainstream institutions.

This disparity impacts both commercial and informational visibility. Black-owned businesses, particularly those in industries like finance, technology, and e-commerce, often face higher barriers to organic search visibility. Without established historical authority, these businesses are more likely to be excluded from high-ranking positions, limiting their ability to compete in digital markets.

Similarly, minority-led media outlets struggle to gain traction against long-established mainstream news sources. While these platforms may provide critical, underrepresented perspectives, Google’s ranking factors prioritize older, well-funded institutions with extensive digital histories. This dynamic creates a structural disadvantage, making it harder for diverse voices to shape digital discourse.

Addressing this issue requires recalibrating Google’s ranking signals to better assess content quality beyond traditional authority markers. Implementing diversity-aware ranking adjustments, incorporating alternative credibility assessments, and increasing transparency in ranking methodologies could help mitigate algorithmic bias. Without these changes, search results may continue to reinforce existing digital inequalities rather than promote a more inclusive information ecosystem.

The Challenges of Google’s E-E-A-T Framework for Emerging Thought Leaders

Google’s E-E-A-T framework—designed to prioritize experience, expertise, authoritativeness, and trustworthiness—has introduced new challenges for emerging thought leaders and non-traditional experts. While intended to improve content quality, its execution often favors well-established institutions, making it difficult for independent researchers, niche educators, and industry disruptors to gain visibility.

A subject-matter expert launching a new platform today must compete with organizations that have decades of accumulated ranking signals. Even if their insights are more innovative or accurate, the algorithm’s bias toward established credibility markers makes organic ranking difficult. This creates a system where expertise is often measured by institutional affiliation rather than content depth or relevance.

The issue is further compounded by Google’s trust assessment mechanisms. While major publishers benefit from historical credibility, independent experts must navigate extensive verification hurdles. The lack of alternative trust signals—such as verified academic contributions, industry peer recognition, or AI-enhanced content credibility—limits the ability of new thought leaders to gain recognition within Google’s search ecosystem.

Reforming E-E-A-T to incorporate more dynamic credibility assessments, real-time expertise verification, and AI-assisted quality analysis could help level the playing field. Failure to adapt may drive thought leaders toward alternative AI-based search models that better recognize evolving expertise.

The Shift Towards AI-Driven Search and Google’s Future

The rise of AI-driven search models presents a fundamental challenge to Google’s ranking system. As users increasingly engage with conversational AI tools for direct, contextual answers, the traditional page-ranking model faces obsolescence. Generative AI’s ability to synthesize real-time knowledge and personalized responses offers an alternative to static search rankings.

Google’s current algorithm remains largely structured around keyword indexing, backlink authority, and page relevance. AI search, by contrast, prioritizes natural language understanding, real-time content synthesis, and query intent resolution. This divergence raises concerns about Google’s ability to maintain search dominance in an AI-first digital landscape.

For marketers, the challenge lies in adapting to a search environment where ranking signals are no longer solely dictated by Google’s framework. AI search optimization involves training proprietary datasets, optimizing for AI model interactions, and leveraging structured data for contextual relevance. Companies investing in AI-native search strategies may eventually bypass Google altogether, accelerating the transition toward direct AI-driven discovery.

If Google fails to integrate AI-native search methodologies into its core ranking framework, a systemic reset may be inevitable. The growing gap between traditional search ranking mechanisms and evolving user behaviors suggests that Google must either adapt its algorithm to AI search principles or risk losing relevance in the digital ecosystem.

How Minority-Owned and New and Small Businesses Can Win with AI SEO

Most new, small, and minority owned businesses often feel left out in Google searches. It is now critical for them to begin to rethink their SEO strategies by shifting focus from traditional ranking factors to AI-driven principles. Unlike traditional SEO, which prioritizes keywords and backlinks, AI search engines emphasize contextual understanding, entity-based indexing, and personalized content delivery. This means marketers must optimize for knowledge graphs and semantic search rather than just keyword matching. Google’s advancements in Natural Language Processing (NLP), such as BERT and MUM, demonstrate a shift toward intent-based search, requiring a deeper approach to content structuring and optimization.

Vector Search

One key search computing concept that marketers must embrace is vector search—a model used in AI-driven search systems that retrieves data based on meaning rather than keyword matches. Platforms like OpenAI’s embedding models and Google's Multitask Unified Model (MUM) use vectors to understand relationships between concepts. Marketers can use tools like Pinecone or Weaviate to structure content for vector-based retrieval, ensuring that their information is indexed effectively by AI-powered search engines. This helps improve discoverability in AI-generated responses and search snippets.

For example, instead of relying on spreadsheets to track keyword performance, a marketer can input blog articles into Weaviate and use its search functionality to find content gaps. This allows them to enhance topic clustering and improve search engine rankings based on natural language relevance rather than outdated keyword density strategies.

Predictive search optimization

Another essential approach is predictive search optimization, where machine learning models anticipate user queries based on past behavior and search history. Marketers can integrate Google’s AutoML, which allows for custom AI model training, to analyze user behavior and tailor their content accordingly. By leveraging predictive modeling tools like TensorFlow or PyTorch, brands can train AI models to optimize content dynamically, ensuring it aligns with emerging search behaviors and trends.

Dynamic content updates

Additionally, real-time content adaptation is becoming crucial. AI-powered search engines like Perplexity AI and ChatGPT retrieve and summarize information dynamically. Marketers must continuously update content to reflect the latest trends and ensure that AI systems recognize their brand as a reliable source. Tools like Frase.io and SurferSEO help in real-time content optimization by analyzing AI search behavior and suggesting structured improvements, ensuring brand content remains relevant in an AI-driven landscape.

Building Brand Authority for AI Search

Brand authority has always been a ranking factor, but AI search engines now prioritize expertise, credibility, and trust (E-E-A-T). This means marketers must focus on high-authority citations, original research, and author expertise to be recognized as a trustworthy source by AI models. Unlike traditional link-building strategies, which emphasized volume, AI search prioritizes signals from authoritative sources, requiring marketers to build verifiable, high-trust digital footprints across platforms.

Wikidata, Wikipedia, and Schema

One way to achieve this is by leveraging entity-based SEO, which focuses on connecting a brand to recognized entities in Google’s Knowledge Graph. Marketers should ensure that their brand has a Wikidata entry, structured schema markup, and consistent entity linking across Wikipedia, LinkedIn, and authoritative industry publications. AI search engines prioritize content that is well-linked to existing knowledge bases, ensuring that brands appear in AI-generated results.

Industry white papers, research papers and govt reports

Another crucial factor is high-authority content collaboration. AI search models, like OpenAI’s GPT and Google’s Gemini, are trained on vast datasets, including academic research, government reports, and industry whitepapers. Marketers should contribute to peer-reviewed journals, Google Scholar-indexed publications, and authoritative research repositories to establish credibility. Platforms like ResearchGate, SSRN, and arXiv allow brands to publish research-driven content that AI models can cite as a credible source.

Author profiles

AI also favors expert-driven content, meaning marketers should highlight their brand’s expertise by featuring real experts with strong digital credentials. Google’s algorithm assesses author reputation, making it crucial for brands to showcase author profiles on Google Scholar, Forbes Councils, and industry conferences. Using structured data to tag content with author information via Schema.org’s Person markup helps AI models attribute credibility to published content, boosting AI search rankings.

Optimizing for Structured Data and AI-Friendly Content

AI search engines process information differently than traditional search algorithms, requiring marketers to structure data in machine-readable formats. Schema markup plays a vital role in helping AI understand and categorize content efficiently. JSON-LD (JavaScript Object Notation for Linked Data) is the preferred format for AI indexing, enabling structured representation of data entities across websites.

To implement AI-friendly structured data, marketers should utilize Google’s Structured Data Testing Tool and Schema.org markup validator. Essential schema types include FAQ schema (for conversational AI responses), How-To schema (for instructional content), and Review schema (for brand credibility signals). AI search engines rely on structured metadata to generate knowledge-rich answers, making structured data optimization a critical factor for AI-driven discoverability.

Another aspect of AI-friendly content is semantic SEO, which focuses on content relationships rather than exact keyword matches. AI search prioritizes topic clusters and contextual relevance, making traditional keyword stuffing obsolete. Marketers can use semantic NLP tools like IBM Watson’s Natural Language Understanding (NLU) and Google’s NLP API to analyze how AI interprets their content. These tools help optimize phrasing and entity relationships to align with AI-driven search behaviors.

In addition to text, multimodal AI search requires optimization for image and video content. Google’s MUM and Gemini AI models process images, videos, and spoken queries alongside text-based search. Marketers should incorporate alt text, image captions, and video transcripts optimized for AI comprehension. Tools like Cloudinary’s AI-based media optimization platform and YouTube’s automatic captioning services enhance AI-driven search visibility by ensuring multimedia content is well-indexed.

Preparing for Conversational and AI-Powered Search

AI-driven search is increasingly conversational, moving away from static query matching to dynamic, dialogue-based interactions. Voice search, chat-based queries, and AI-generated answers require marketers to structure content for natural language processing (NLP) models. Unlike traditional SEO, where exact-match keywords were critical, conversational AI demands long-form, question-driven content optimized for Google Assistant, Alexa, and ChatGPT-style AI search engines.

To prepare for conversational search, marketers must implement Conversational AI Optimization (CAO)—a strategy that aligns content with AI-driven query patterns. This involves using latent semantic indexing (LSI) tools like Clearscope and MarketMuse to analyze how AI interprets conversational intent. NLP-enhanced content structuring ensures that AI search engines provide direct, relevant answers to complex queries.

Another critical shift is multi-turn search optimization, where AI engines generate follow-up query suggestions based on user intent. Marketers must anticipate and structure content to answer sequential queries logically. Google’s LaMDA (Language Model for Dialogue Applications) and OpenAI’s ChatGPT-4 power multi-turn search, making FAQ-based content and internal linking structures essential for guiding AI-driven search pathways.

Furthermore, voice search optimization is a key component of AI-powered discovery. AI search engines process spoken queries using automatic speech recognition (ASR) and text-to-speech (TTS) models. Marketers should prioritize natural phrasing, featured snippets, and conversational schema markup to ensure AI systems retrieve their content effectively. Tools like Speakable schema from Schema.org and Google’s Actions on Google platform enable direct integration into AI-driven voice search frameworks.

As AI search evolves, brand-specific AI training will become a competitive differentiator. Businesses should consider building proprietary AI models using open-source frameworks like Hugging Face’s Transformers. Training AI on brand-owned datasets ensures that AI-driven search engines recognize their content as authoritative, future-proofing their discoverability in an AI-first world.

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