Another year in search has come and gone, and Google called it year three of a 10-year platform shift. In 2025, that shift became impossible to ignore. AI moved from experiments and previews into the core of how search actually works.
Below are the biggest SEO news stories of 2025 on Search Engine Land.
Note: This article doesn’t include any stories related to Google algorithm updates. Barry Schwartz wrote a separate recap on that, which will also publish today.
10. Perplexity ranking factors and systems
Independent researcher Metehan Yesilyurt analyzed browser-level interactions to reveal how Perplexity scores, reranks, and sometimes drops content. He uncovered a three-layer machine-learning reranker for entity searches, manual authority whitelists, and dozens of engagement and relevance signals.
Yesilyurt’s research also found boosts for authoritative domains, strong early performance, and topics centered on tech and AI. Rankings further reflected time decay, interconnected content clusters, and synchronized YouTube trends that increased visibility across platforms.
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9. Google Search Console Query groups
Google added Query groups to the Search Console Insights report. The feature uses AI to cluster similar search queries into clear audience topics and does not affect rankings. It rolled out gradually to high-volume sites and replaced long query lists with topic-level groupings that make performance shifts easier to spot.
HubSpot’s organic traffic appeared to fall from 13.5 million to 8.6 million in a month, with most of the losses coming from its blog. The drop followed several Google updates, and SEOs publicly pointed to thin, off-topic, traffic-at-all-costs content that drifted beyond HubSpot’s core expertise.
The SEO identity crisis continued as Google dismissed new acronyms like GEO (generative engine optimization) and AEO (answer engine optimization), arguing that good SEO is good GEO, and that the same fundamentals drive AI Overview rankings.
That stance collided with Google’s own admission that search traffic decline is inevitable as AI answers replace clicks, even while traditional search still dominates discovery at a massive scale.
Yet, search behavior is fracturing: users turn to AI for quick answers and to Google for deeper research, pushing brands to optimize for visibility, not just traffic.
Google rapidly expanded AI Mode from an opt-in experiment into a widely available, and possibly soon default, search experience. It added deeper research, agentic actions, personalization, and Gemini 2.5, signaling longer and more complex search behavior.
At the same time, AI Mode exposed major transparency gaps. It initially broke referral tracking and still blends performance data into standard Search Console reports, raising new concerns about visibility, attribution, and what SEO becomes as AI takes on a larger role in search.
Cloudflare CEO Matthew Prince said AI was breaking the web’s search-driven business model. He said Google scraped far more content while sending back much less traffic because of zero-click results. He added that AI companies deepen the imbalance by consuming huge amounts of content with little return to creators, putting original publishing at risk unless the economic model changes.
Statcounter data showed Google’s global search share fell below 90% in October, November, and December 2024, the first time its search share remained under 90% since early 2015. The decline was driven mainly by Asia, alongside a December U.S. dip to 87.39%. Bing, Yandex, and Yahoo captured much of the lost share.
Google tightened its stance on AI-generated content by telling quality raters to give the Lowest ratings to pages where most main content is auto- or AI-generated with little originality or added value. It also expanded its spam definitions to target scaled, low-effort AI use.
At the same time, Google tested AI-generated and AI-summarized search snippets, pointing to a future where AI both judges content more harshly and increasingly controls how that content appears in search.
Analyses from Seer, Ahrefs, Amsive, and BrightEdge all showed the same pattern. Google Search produced more impressions and more AI Overview visibility, but sent fewer clicks. The drop was sharpest on non-branded, informational queries, where AI Overviews pushed classic results down, and CTR fell hard.
The studies also found a winner-take-some dynamic. Brands cited in AI Overviews saw higher paid and organic CTR, while those left out lost ground, showing that AI visibility increasingly drives results.
Google’s removal of the long-standing &num=100 search parameter disrupted SEO data across the industry. It broke rank-tracking tools and coincided with sharp drops in Google Search Console impressions and query counts.
Early analysis showed most sites lost reported visibility, especially beyond Page 1. The change suggested years of inflated metrics from scrapers and a new, possibly more accurate, view of organic performance.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-29 13:00:002025-12-29 13:00:00Top 10 SEO news stories of 2025
OpenAI is laying the groundwork for an advertising business, signaling a potential shift in how ChatGPT and other products could be monetized beyond subscriptions and enterprise deals.
What’s happening. According to reporting from The Information, OpenAI has begun exploring ad formats and partnerships, with early discussions pointing toward ads that could appear within or alongside AI-generated responses. The effort is still in its early stages, but internal conversations suggest ads are becoming a more serious part of OpenAI’s long-term revenue strategy.
Why we care. OpenAI is exploring ads inside AI-generated responses, creating a new, highly contextual channel for reaching users at the moment they seek information. This could put OpenAI in direct competition with Google and Meta, but also raises questions about trust and user engagement. Early adoption could offer a first-mover advantage, while formats and metrics may differ from traditional digital ads. Overall, it’s a potentially transformative new frontier for advertising.
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Between the lines. OpenAI appears cautious, aiming to avoid disrupting user experience or undermining confidence in its models. Any ad product is likely to be tightly controlled, at least initially, and positioned as helpful or contextually relevant rather than overtly promotional.
The bigger picture. With soaring infrastructure costs and growing pressure to scale revenue, ads could become a key lever for OpenAI — especially as generative AI reshapes how people search for information and discover products.
What to watch. When ads move from internal planning to public testing, how clearly they’re labeled, and whether users accept advertising embedded in AI responses.
Bottom line. OpenAI isn’t rushing ads to market, but the foundations are being laid — and their eventual arrival could reshape both AI products and the digital advertising landscape.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-24 16:18:022025-12-24 16:18:02OpenAI discusses an ad-driven strategy centered on ChatGPT scale and media partnerships
Google reduced the minimum audience size requirement to just 100 active users across all networks and audience types, making remarketing and customer list targeting far more accessible—especially for smaller advertisers.
What’s new. Audience segments with as few as 100 users can now be used across Search, Display, and YouTube, including both remarketing lists and customer lists. The same 100-user threshold now applies for segments to appear in Audience Insights, down from 1,000.
Why we care. Smaller accounts and niche advertisers can now activate audience strategies that were previously out of reach due to size constraints. This change removes a long-standing barrier to personalization, remarketing, and first-party data activation within Google Ads.
What to watch. How advertisers use smaller, more precise segments—and whether performance or privacy safeguards evolve alongside the expanded access.
First seen. This update was first spotted by Web Marketing Consultant, Dario Zannoni, who shared it on LinkedIn.
SEO didn’t stand still in 2025. It didn’t reinvent itself either. It clarified what actually matters. If you followed The SEO Update by Yoast monthly webinars this year, you’ll recognize the pattern. Throughout 2025, our Principal SEOs, Carolyn Shelby and Alex Moss, cut through the noise to explain not just what was changing but why it mattered as AI-powered search reshaped visibility, trust, and performance. If you missed some sessions or want the full picture in one place, this wrap-up is for you. We’re looking back at how SEO evolved over the year, what those changes mean in practice, and what they signal going forward.
Key takeaways
In 2025, SEO shifted its focus from rankings to visibility management, as AI-driven search reshaped priorities
Key developments included the rise of AI Overviews, a shift from clicks to citations, and increased importance of clarity and trust
Brands needed to prioritize structured, credible content that AI systems could easily interpret to remain visible
By December, SEO transformed to retrieval-focused strategies, where success rested on clarity, relevance, and E-E-A-T signals
Overall, 2025 clarified that the fundamentals still matter but emphasized the need for precision in content for AI-driven systems
AI-powered, personalized search accelerated. Zero-click results increased. Brand signals, E-E-A-T, performance, and schema shifted from optimizations to requirements.
SEO expanded from ranking pages to representing trusted brands that machines can understand.
February
Massive AI infrastructure investments. AI Overviews pushed organic results down. Traffic dropped while brand influence and revenue held steady.
SEO outcomes can no longer be measured by traffic alone. Authority and influence matter more than raw clicks.
March
AI Overviews expanded as clicks declined. Brand mentions appeared to play a larger role in AI-driven citation and selection behavior than links alone. Search behavior grew despite fewer referrals.
Visibility fractured across systems. Trust and brand recognition became the differentiators for inclusion.
April
Schema and structure proved essential for AI interpretation. Multimodal and personalized search expanded. Zero-click behavior increased further.
SEO shifted from optimization to interpretation. Clarity and structure determine reuse.
May
Discovery spread beyond Google. AI Overviews reached mass adoption. Citations replaced visits as success signals.
SEO outgrew the SERP. Presence across platforms and AI systems became critical.
June – July
AI Mode became core to search. Ads entered AI answers. Indexing alone no longer offers guaranteed visibility. Reporting lagged behind reality.
Traditional SEO remained necessary but insufficient. Resilience and adaptability became essential.
August
Visibility without value became a real risk. SEO had to tie exposure to outcomes beyond the number of sessions.
Visibility without value became a real risk. SEO had to tie exposure to outcomes beyond sessions.
September
AI Mode neared default status. Legal, licensing, and attribution pressures intensified. Persona-based strategies gained relevance.
Control over visibility is no longer guaranteed. Trust and credibility are the only durable advantages.
October
Search Console data reset expectations. AI citations outweighed rankings. AI search became the destination.
SEO success depends on presence inside AI systems, not just SERP positions.
November
AI Mode became core to search. Ads entered AI answers. Indexing alone is no longer a guarantee of visibility. Reporting lagged behind reality.
Clarity and structure beat scale. Authority decides inclusion.
December
SEO fully shifted to retrieval-based logic. AI systems extracted answers, not pages. E-E-A-T acted as a gatekeeper.
SEO evolved into visibility management for AI-driven search. Precision replaced volume.
January: SEO enters the age of representation
January set the tone for the year. Not through a single disruptive update, but through a clear signal that SEO was moving away from pure rankings toward something broader. The search was becoming more personalized, AI-driven, and selective about which sources it chose to surface. Visibility was no longer guaranteed just because you ranked well.
From the start of the year, it was clear that SEO in 2025 would reward brands that were trusted, technically sound, and easy for machines to understand.
What changed in January
Here are a few clear trends that began to shape how SEO worked in practice:
AI-powered search became more personalized: Search results reflected context more clearly, taking into account location, intent, and behavior. The same query no longer produced the same result for every user
Zero-click searches accelerated: More answers appeared directly in search results, reducing the need to click through, especially for informational and local queries
Brand signals and reviews gained weight: Search leaned more heavily on real-world trust indicators like brand mentions, reviews, and overall reputation
E-E-A-T became harder to ignore: Clear expertise, ownership, and credibility increasingly acted as filters, not just quality guidelines
The role of schema started to shift: Structured data mattered less for visual enhancements and more for helping machines understand content and entities
What to take away from January
January wasn’t about tactics. It was about direction.
SEO started rewarding clarity over cleverness. Brands over pages. Trust over volume. Performance over polish. If search engines were going to summarize, compare, and answer on your behalf, you needed to make it easy for them to understand who you are, what you offer, and why you are credible.
That theme did not fade as the year went on. It became the foundation for everything that followed.
February: scale, money, and AI made the shift unavoidable
If January showed where search was heading, February showed how serious the industry was about getting there. This was the month where AI stopped feeling like a layer on top of search and started looking like the foundation underneath it.
Massive investments, changing SERP layouts, and shifting performance metrics all pointed to the same conclusion. Search was being rebuilt for an AI-first world.
What changed in February
As the month unfolded, the signs became increasingly difficult to ignore.
AI Overviews pushed organic results further down: AI Overviews appeared in a large share of problem-solving queries, favoring authoritative sources and summaries over traditional organic listings
Traffic declined while brand value increased: High-profile examples showed sessions dropping even as revenue grew. Visibility, influence, and brand trust started to matter more than raw sessions
AI referrals began to rise: Referral traffic from AI tools increased, while Google’s overall market share showed early signs of pressure. Discovery started spreading across systems, not just search engines
What to take away from February
February made January’s direction feel permanent.
When AI systems operate at this scale, they change how visibility works. Rankings still mattered, but they no longer told the full story. Authority, brand recognition, and trust increasingly influenced whether content was surfaced, summarized, or ignored.
The takeaway was clear. SEO could no longer be measured only by traffic. It had to be understood in terms of influence, representation, and relevance across an expanding search ecosystem.
March: visibility fractured, trust became the differentiator
By March, the effects of AI-driven search were no longer theoretical. The conversation shifted from how search was changing to who was being affected by it, and why.
This was the month where declining clicks, citation gaps, and publisher pushback made one thing clear. Search visibility was fragmenting across systems, and trust became the deciding factor in who stayed visible.
What changed in March
The developments in March added pressure to trends that had already been forming earlier in the year.
AI Overviews expanded while clicks declined: Studies showed that AI Overviews appeared more frequently, while click-through rates continued to decline. Visibility increasingly stopped at the SERP
Brand mentions mattered more than links alone: Citation patterns across AI platforms varied, but one signal stayed consistent. Brands mentioned frequently and clearly were more likely to surface
Search behavior continued to grow despite fewer clicks: Overall search volume increased year over year, showing that users weren’t searching less; they were just clicking less
AI search struggled with attribution and citations: Many AI-powered results failed to cite sources consistently, reinforcing the need for strong brand recognition rather than reliance on direct referrals
Search experiences became more fragmented: New entry points like Circle to Search and premium AI modes introduced additional layers to discovery, especially among younger users
Structured signals evolved for AI retrieval: Updates to robots meta tags, structured data for return policies, and “sufficient context” signals showed search engines refining how content is selected and grounded
March exposed the tension at the heart of modern SEO.
Search demand was growing, but traditional traffic was shrinking. AI systems were answering more questions, but often without clear attribution. In that environment, being a recognizable, trusted brand mattered more than being the best-optimized page.
The implication was simple. SEO was no longer just about earning clicks. It was about earning inclusion, recognition, and trust across systems that don’t always send users back.
April: machines started deciding how content is interpreted
By April, the focus shifted again. The question was no longer whether AI would shape search, but how machines decide what content means and when to surface it.
After March exposed visibility gaps and attribution issues, April zoomed in on interpretation. How AI systems read, classify, and extract information became central to SEO outcomes.
What changed in April
April brought clarity to how modern search systems process content.
Schema has proven its value beyond rankings: Microsoft has confirmed that schema markup helps large language models understand content. Bing Copilot used structured data to generate clearer, more reliable answers, reinforcing the schema’s role in interpretation rather than visual enhancement
AI-driven search became multimodal: Image-based queries expanded through Google Lens and Gemini, allowing users to search using photos and visuals instead of text alone
AI Overviews expanded during core updates: A noticeable surge in AI Overviews appeared during Google’s March core update, especially in travel, entertainment, and local discovery queries
Clicks declined as summaries improved: AI-generated content summaries reduced the need to click through, accelerating zero-click behavior across informational and decision-based searches
Content structure mattered more than special optimizations: Clear headings that boost readability, lists, and semantic cues helped AI systems extract meaning. There were no shortcuts. Standard SEO best practices carried the weight
What to take away from April
April shifted SEO from optimization to interpretation.
Search engines and AI systems didn’t just look for relevance. They looked for clarity. Content that was well-structured, semantically clear, and grounded in real entities was easier to understand, summarize, and reuse.
The lesson was subtle but important. You didn’t need new tricks for AI search. You needed content that was easier for machines to read and harder to misinterpret.
By May, it was no longer sufficient to discuss how search engines interpret content. The bigger question became where discovery was actually happening.
SEO started expanding beyond Google. Visibility fractured across platforms, AI tools, and ecosystems, forcing brands to think about presence rather than placement.
What changed in May
The month highlighted how search and discovery continued to decentralize.
Search behavior expanded beyond traditional search engines: Around 39% of consumers now use Pinterest as a search engine, with Gen Z leading adoption. Discovery increasingly happened inside platforms, not just through search bars
AI Overviews reached mass adoption: AI Overviews reportedly reached around 1.5 billion users per month and appeared in roughly 13% of searches, with informational queries driving most of that growth
Clicks continued to give way to citations: As AI summaries became more common, being referenced or cited mattered more than driving a visit, especially for top-of-funnel queries
AI-powered search diversified across tools: Chat-based search experiences added shopping, comparison, and personalization features, further shifting discovery away from classic result pages
Economic pressure on content ecosystems increased: Industry voices warned that widespread zero-click answers were starting to weaken the incentives for content creation across the web
May reframed SEO as a visibility problem, not a traffic problem.
When discovery happens across platforms, summaries, and AI systems, success depends on how clearly your content communicates meaning, credibility, and relevance. Rankings still mattered, but they were no longer the primary measure of success.
The message was clear. SEO had outgrown the SERP. Brands that focused on authenticity, semantic clarity, and structured information were better positioned to stay visible wherever search happened next.
By early summer, SEO entered a more uncomfortable phase. Visibility still mattered, but control over how and where content appeared became increasingly limited.
June and July were about adjustment. Search moved closer to AI assistants, ads blended into answers, and traditional SEO signals no longer guaranteed exposure across all search surfaces.
What changed in June and July
This period introduced some of the clearest operational shifts of the year.
AI Mode became a first-class search experience: AI Mode was rolled out more broadly, including incognito use, and began to merge into core search experiences. Search was no longer just results. It was conversation, summaries, and follow-ups
Ads entered AI-generated answers: Google introduced ads inside AI Overviews and began testing them in conversational AI Mode. Visibility now competes not only with other pages, but with monetized responses
Measurement lagged behind reality: Search Console confirmed AI Mode data would be included in performance reports, but without separate filters or APIs. Visibility changed more rapidly than reporting tools could keep pace.
Citations followed platform-specific preferences: Different AI systems favored different sources. Some leaned heavily on encyclopedic content, others on community-driven platforms, reinforcing that one SEO strategy would not fit every system
Most AI-linked pages still ranked well organically: Around 97% of URLs referenced in AI Mode ranked in the top 10 organic results, showing that strong traditional SEO remained a prerequisite, even if it was no longer sufficient
Content had to resist summarization: Leaks and tests showed that some AI tools rarely surfaced links unless live search was triggered. Generic, easily summarized modern content became easier to replace
Infrastructure became an SEO concern again: AI agents increased crawl and request volume, pushing performance, caching, and server readiness back into focus
Search moved beyond text: Voice-based interactions, audio summaries, image-driven queries, and AI-first browsers expanded how users searched and consumed information
What to take away from June and July
This period forced a mindset shift.
SEO could no longer assume that ranking, indexing, or even traffic guaranteed visibility. AI systems decided when to summarize, when to cite, and when to bypass pages entirely. Ads, assistants, and alternative interfaces now often sit between users and websites more frequently than before.
The conclusion was pragmatic. Strong fundamentals still mattered, but they weren’t the finish line. SEO now requires resilience: content that carries authority, resists simplification, loads fast, and stays relevant even when clicks don’t follow.
By the end of July, one thing was clear. SEO wasn’t disappearing. It was operating under new constraints, and the rest of the year would test how well teams adapted to them.
August: the gap between visibility and value widened
By August, SEO teams were staring at a growing disconnect. Visibility was increasing, but traditional outcomes were harder to trace back to it.
This was the month when the mechanics of AI-driven search became more transparent and more uncomfortable.
What changed in August
August surfaced the operational realities behind AI-powered discovery.
Impressions rose while clicks continued to decline: AI Overviews dominated the results, driving exposure without generating traffic. In some cases, conversions still improved, but attribution became harder to prove
The “great decoupling” became measurable: Visibility and performance stopped moving in sync. SEO teams saw growth in impressions even as sessions declined
Zero-click searches accelerated further: No-click behavior climbed toward 69%, reinforcing that many user journeys now ended inside search interfaces
AI traffic stayed small but influential: AI-driven referrals still accounted for under 1% of traffic for most sites, yet they shaped expectations around answers, speed, and convenience
Retrieval logic shifted toward context and intent: New retrieval approaches prioritized meaning, relationships, and query context over keyword matching
It reinforced the reality that SEO could no longer rely on traffic as the primary proof of value. Visibility still mattered, but only when paired with outcomes that could survive reduced clicks and blurred attribution.
The lesson was strategic. SEO needed to connect visibility to conversion, brand lift, or long-term trust, not just sessions. Otherwise, its impact would be increasingly hard to defend.
September: control, attribution, and trust were renegotiated
September pushed the conversation further. It wasn’t just about declining clicks anymore. It was about who controlled discovery, attribution, and access to content.
This was the month where legal, technical, and strategic pressures collided.
What changed in September
September reframed SEO around governance and credibility.
AI Mode moved closer to becoming the default: Search experiences shifted toward AI-driven answers with conversational follow-ups and multimodal inputs
The decline of the open web was acknowledged publicly: Court filings and public statements confirmed what many publishers were already feeling. Traditional web traffic was under structural pressure
Legal scrutiny intensified: High-profile settlements and lawsuits highlighted growing challenges around training data, summaries, and lost revenue
Licensing entered the SEO conversation: New machine-readable licensing approaches emerged as early attempts to restore control and consent
Snippet visibility became a gateway signal: AI tools relied heavily on search snippets for real-time answers, making concise, extractable content more critical
Persona-based strategies gained traction: SEO began shifting from keyword targeting to persona-driven content aligned with how AI systems infer intent
Trust eroded around generic, formulaic, AI writing styles: Formulaic, overly polished AI content raised credibility concerns, reinforcing the need for editorial judgment
Measurement tools lost stability again: Changes to search parameters disrupted rank tracking, reminding teams that SEO reporting would remain volatile
What to take away from September
September forced SEO to grow up again.
Control over visibility, attribution, and content use was no longer guaranteed. Trust, clarity, and credibility became the only durable advantages in an ecosystem shaped by AI intermediaries.
The takeaway was sobering but useful. SEO could still drive value, but only when it is aligned with real user needs, strong brand signals, and content that earned its place in AI-driven answers.
October marked a turning point in how SEO performance needed to be interpreted. The data didn’t just shift. It reset expectations entirely.
This was the month when SEO teams had to accept that AI-powered search was no longer a layer on top of results. It was becoming the place where searches ended.
What changed in October
October brought clarity, even if the numbers looked uncomfortable.
AI Mode reshaped user behavior: Around a third of searches now involve AI agents, with most sessions staying inside AI panels. Clicks became the exception, not the default
AI citations increasingly rivalled rankings: Visibility increasingly depended on whether content was selected, summarized, or cited by AI systems, not where it ranked
Search engines optimized for ideas, not pages: Guidance from search platforms reinforced that AI systems extract concepts and answers, not entire URLs
Metadata lost some direct control: Tests of AI-generated meta descriptions suggested that manual optimization would carry less influence over how content appears
Commerce and search continued to merge: AI-driven shopping experiences expanded, signaling that transactional intent would increasingly be handled inside AI interfaces
What to take away from October
October reframed SEO as presence within AI systems.
Traffic still mattered, but it was no longer the primary outcome. The real question became whether your content appeared at all inside AI-driven answers. Clarity, structure, and extractability replaced traditional ranking gains as the most reliable levers.
From this point on, SEO had to treat AI search as a destination, not just a gateway.
November: structure and credibility decided inclusion
If October reset expectations, November showed what actually worked.
This month narrowed the gap between theory and practice. It became clearer why some content consistently surfaced in AI results, while other content disappeared.
What changed in November
November focused on how AI systems select and trust sources.
Structured content outperformed clever content: Clear headings, predictable formats, and direct answers made it easier for AI systems to extract and reuse information
Schema supported understanding, not visibility alone: Structured data remained valuable, but only when paired with clean, readable on-page content
AI-driven shopping and comparisons accelerated: Product data quality, consistency, and accessibility directly influenced whether brands appeared in AI-assisted decision flows
Citation pools stayed selective: AI systems relied on a relatively small set of trusted sources, reinforcing the importance of brand recognition and authority
Search tooling evolved toward themes, not keywords: Grouped queries and topic-based insights replaced one-keyword performance views
What to take away from November
November made one thing clear. SEO wasn’t about producing more content or optimizing harder. It was about making content easier to understand and harder to ignore.
Clarity beats creativity. Structure beat scale. Authority determined whether content was reused at all.
This month quietly reinforced the fundamentals that would define SEO going forward.
Instead of introducing new disruptions, it clarified what 2025 had been building toward all along. SEO was no longer primarily about ranking pages. It was about enabling retrieval.
What changed in December
The year-end review highlighted the new reality of SEO.
Search systems retrieved answers, not pages: AI-driven search experiences pulled snippets, definitions, and summaries instead of directing users to full articles
Literal language still mattered: Despite advances in understanding, AI systems relied heavily on exact phrasing. Terminology choices directly affected retrieval
Content structure became mandatory: Front-loaded answers, short paragraphs, lists, and clear sections made content usable for AI systems
Relevance replaced ranking as the core signal: Being the clearest and most contextually relevant answer mattered more than traditional ranking factors
E-E-A-T acted as a gatekeeper: Recognized expertise, authorship, and trust signals determined whether content was eligible for reuse
Authority reduced AI errors: Strong credibility signals helped AI systems select more reliable sources and reduced hallucinated answers
What to take away from December
December didn’t declare the end of SEO. It defined its next phase.
SEO matured into visibility management for AI-driven systems. Success depended on clarity, credibility, and structure, not shortcuts or volume. The fundamentals still worked, but only when applied with discipline.
By the end of 2025, the direction was clear. SEO didn’t get smaller. It got more precise.
SEO evolved into visibility management for AI-driven search. Precision replaced volume.
2025 didn’t rewrite SEO. It clarified it.
Search moved from ranking pages to retrieving answers. From rewarding volume to rewarding clarity. From clicks to credibility. And from optimization tricks to systems-level understanding.
The fundamentals still matter. Technical health, helpful content, and strong SEO foundations are non-negotiable. But they are no longer the finish line. What separates visible brands from invisible ones now is how clearly their content can be understood, trusted, and reused by AI-driven search systems.
Going into 2026, the goal isn’t to outsmart search engines. It’s to make your expertise unmistakable. Write for humans, structure for machines, and build authority that holds up even when clicks don’t follow.
SEO didn’t get smaller this year. It got more precise. Stay with us for our 2026 verdict on where search goes next.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-24 12:52:512025-12-24 12:52:51The 2025 SEO wrap-up: What we learned about search, content, and trust
Social media evolves fast. What’s trending today could be outdated by next month. Brands that don’t adapt risk getting left behind. Marketers who fail to adapt will struggle to stay visible, let alone competitive.
Looking ahead to 2026, major shifts in content, engagement, and platform evolution are shaping the next era of social media. Brands that want to win need to stop chasing vanity metrics and focus on what actually works: valuable content, community building, and smart social listening.
The biggest change is how people use social platforms. They are not just scrolling. They are searching, comparing, researching, and asking questions across social channels the same way they use traditional search engines. AI is tightening this loop even more by summarizing information, elevating high-quality content, and influencing what people see first.
This means your strategy has to account for a world where social content fuels discovery, credibility, and even search visibility. The brands gaining ground are the ones creating content that answers real questions, serves real intent, and earns genuine engagement from the communities they want to reach.
In this post, we’ll break down the biggest social media trends for 2026 and how you can stay ahead of the social media marketing curve.
Key Takeaways
Social platforms are becoming search engines. People are using TikTok, YouTube, Reddit, and Instagram to look up answers, compare options, and make decisions faster than ever.
AI is reshaping what users see. Models summarize content, elevate clearer answers, and influence reach based on how well your content aligns with intent.
Discovery is starting on social. For many consumers and even B2B buyers, social platforms are now the first stop in the research process, not the last.
Video keeps winning, but format preference is shifting. YouTube is becoming a research destination, short-form video is still rising, and creators are shaping buying decisions more directly.
Platform behavior is fragmenting by generation. Younger users search differently than older ones, and brands need to adapt content, tone, and formats to match multi-platform habits.
Algorithms reward structure. Clear, searchable, high-quality content that answers real questions performs better than high-volume posting or vanity metrics.
Brands need to build for communities, not feeds. Authentic conversations, creator collaborations, and real user insights are driving trust and shaping perception.
Social Platforms Are Becoming Discovery Engines
People are using social media the way they once used search engines. Instead of going straight to Google, they turn to TikTok, YouTube, Reddit, and Instagram to find real experiences, quick breakdowns, and authentic recommendations. Your audience often discovers your brand before they ever hit your website.
This shift means discovery is now happening inside feeds and social search bars. If your content does not answer questions clearly or show real value, you miss the moment when people are actively looking for direction. The brands that win will be the ones creating content that shows up early, answers intent quickly, and earns trust before the research journey ever reaches traditional search.
AI Is Reshaping What Users See Across Social
AI and LLMs now play a major role in what users see. Models analyze clarity, structure, and usefulness more than sheer volume. Content that solves problems or answers questions travels further than posts engineered for empty engagement.
This puts a spotlight on quality. If your content is clear and intentional, algorithms are more likely to surface it. If it is vague or overly polished without substance, it gets buried. Treat every post as an answer to a user need, and the platforms will do more of the distribution work for you.
Social Search Is Overtaking Traditional Search for Early Research
More people start their research on social platforms than on traditional search engines. They want quick explanations, real user takes, and creator-driven insights. They type questions directly into TikTok or YouTube and expect clear, straightforward answers.
This makes social search the new top of funnel. If your brand is not showing up in these searches, you are missing the first stage of the buying journey. Focus on creating content that mirrors what users type into search bars. Clear titles, descriptive captions, and intentional phrasing help your content rise.
Short-Form Video Is Evolving Into a Research Format
Short-form video still dominates, but the reasons people engage with it are shifting. Users rely on short clips to learn, compare, and get clarity quickly. A thirty-second video can walk someone through a process or compare two options better than a long caption ever could.
To stand out, your videos need more than entertainment value. They need to be small teaching moments that help viewers make decisions. Simple visuals, strong hooks, and straightforward explanations make short-form video a stronger bridge between curiosity and action.
YouTube Is Becoming a Primary Research Destination
YouTube has become a go-to resource for deeper research. People watch step-by-step guides, product breakdowns, and long-form tutorials to understand topics more fully. This behavior often sits in the middle of the buying journey, where trust starts forming.
Brands that create educational content position themselves as credible guides. You do not need cinematic production. You need helpful structure, clear teaching, and content that answers the questions viewers care about most. When you do that, YouTube becomes a consistent driver of trust and high-intent traffic.
Creators Are Becoming the New Trust Layer
Creators continue to shape how people perceive brands. Users trust creators because they speak plainly, share real experiences, and explain products in ways that feel relatable. When a creator talks through a product naturally, it carries more influence than a polished brand ad.
This changes how brands build credibility. Working with creators who truly understand your audience can help you meet people where they already spend their time. Real voices, not brand scripts, are what people rely on when deciding whether a product is worth it.
Social Behavior Is Splitting by Generation
Different age groups now use social platforms for very different reasons. Younger users treat social as a search tool. They look for answers, reviews, and tutorials. Older users still rely on social for updates and connection, but their research patterns vary.
Because of this divide, brands need flexible content strategies. One format or tone will not fit every audience. You may need short explainer videos for younger users and in-depth guides or community conversations for older ones. The more you match the behavior of each group, the better your content performs across the board.
Algorithms are increasingly designed to recognize content that offers clarity and utility. Posts that are easy to understand, easy to categorize, and easy to match to user intent rise faster in feeds and search results.
This makes structure a competitive advantage. Strong hooks, organized ideas, helpful descriptions, and clear language tell platforms exactly what your content is about. When people and algorithms understand your message instantly, your reach naturally expands.
AI-Assisted Content Workflows Are Becoming Standard
Marketers are using AI for brainstorming, planning, drafting, repurposing, and analyzing content. It speeds up production and helps teams adapt quickly when trends shift. The real advantage is not automation. It is consistency.
Brands that use AI well publish with more clarity, more frequency, and more strategic alignment. AI removes bottlenecks but still relies on human direction. Teams that build smart workflows can create high-quality content at scale without sacrificing voice or relevance.
Community and Social Proof Are Replacing Vanity Metrics
Followers and likes do not matter as much as they used to. What matters now is whether people trust you. Community conversations, user-generated content, comment threads, creator mentions, and real reviews influence decisions far more than big follower numbers.
People look for proof from other users before they buy. If your brand creates spaces for conversation and encourages genuine participation, you build the kind of trust that turns attention into action. Social proof carries more weight than standard advertisements on social in many cases and industries.
FAQs
What is the future of social media in 2026?
Social platforms are becoming discovery engines. People search, research, and compare brands on TikTok, YouTube, Reddit, and Instagram before they ever hit Google. AI shapes what they see, so clear, useful content will win in 2026.
What social media trends should you be aware of in 2026?
Expect more social search, AI-assisted content planning, smarter algorithms, and a bigger push toward video and creator-led trust. Users want quick answers, not filler.
How important is short-form video in 2026?
Still essential. Short-form video now acts as a fast research tool, not just entertainment. Simple explanations, clear hooks, and searchable language make the biggest impact.
How can brands leverage social commerce?
Make buying feel seamless. Use strong visuals, creator content, real customer proof, and frictionless checkout. Keep answers clear so users feel confident buying without leaving the platform.
Why is brand authenticity so important on social media?
People trust humans, not polish. Authentic content builds credibility, fuels social proof, and helps algorithms understand what your brand stands for.
Conclusion
Social media is changing fast, but the biggest shift is how people use it. In 2026, social is not just where people scroll. It is where they search, learn, compare, and decide what brands they trust. AI is shaping what users see, which means your content needs to be clearer, more useful, and built around real questions people are asking.
Brands that focus on intent, structure, and community will win. Not because they post the most, but because they create content that helps people make better decisions. If you adapt early, stay curious, and keep testing new formats, you will stay ahead while everyone else plays catch-up.
The rules are changing. The opportunity is growing. Now is the time to rethink your social strategy and build for the way people actually use social today.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-23 20:00:002025-12-23 20:00:00The Future of Social Media Trends in 2026
I get it, these are uncertain times. Organic traffic is dropping like a rock, and new referral traffic coming in from LLMs like ChatGPT barely scratches the surface of what’s been lost.
The narrative of “traffic is simply coming from a new source” is not accurate. Search and engagement are happening in new ways, but CTRs are dropping significantly across nearly all industries.
It’s no surprise that many in the industry are feeling anxious about the future of SEO and whether AI might eventually render their roles obsolete. Bringing this up with your C-suite team might feel like the last thing you want to do.
But here’s the reality: Now is exactly the time to lean in.
Your leadership team needs to understand what’s happening, and, more importantly, what you’re doing about it.
Use this moment to educate, align expectations, and map out how your search strategy is evolving to meet the new landscape head on. Schedule the meeting. Start the conversation.
I’ll walk you through exactly what to do to maximize the value of this very important meeting.
Don’t avoid leadership — address AI visibility head-on
No, I’m not going to tell you to picture your leadership team in their underwear. That won’t make the conversation easier, it’ll just make it awkward.
What will help is showing up prepared to lead the conversation.
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Set the tone from the start. Your leadership team will already respect the fact that you’re raising this issue before they assign someone to investigate it.
Use this opportunity to guide the discussion and provide clarity, not excuses. This isn’t the time to sugarcoat or downplay what’s happening.
Let’s break down the key points to bring to leadership to provide clarity.
Why SEO is down and how that impacts business
This is your opportunity to lead with facts, not fear. Give an honest recap of the current state of the industry and how it’s affecting your business.
To start, here are a few critical events that may help explain shifts in performance:
Tools like ChatGPT, Gemini, and Perplexity are changing user behavior and pulling searches away from Google entirely.
Google has since rolled out AI Overviews (AIOs), which are appearing in more and more SERPs and driving fewer clicks to third-party sites. (Reports of -61% reduction in organic CTR have been reported).
LLMs are sending some traffic, but it’s a drop in the bucket compared to what’s been lost from traditional search.
Bing launched AI-powered search summaries, but the impact was limited due to its smaller market share.
Next, present a clear, data-driven overview of what’s changed at your company and how it’s already affecting your business. If organic traffic is down 30%, own it, and if revenue has dipped as well, own that too.
Keep the conversation grounded in measurable outcomes and alignment with company goals. And confirm in advance with your analytics team that data you are citing (in addition to LLM visibility metrics you are collecting) are accurate.
Here’s data that needs to be shared.
Discuss revenue, leads (or actions marked as key events), and organic traffic data over time, ideally including year-over-year numbers.
These numbers tie the discussion directly to business impact instead of rankings or other vanity metrics. Year-over-year views help distinguish seasonality and industry trends from real performance drops. Identifying these allows leadership to quickly understand when performance went down vs. a soft market (or shift to a new search ecosystem).
Export and review keywords you’ve been tracking. This is valuable for Google and Bing, and additional insights from LLM rank tracking can add more context.
No, I’m not going against my long standing take that rankings shouldn’t be used as a performance metric on their own. However, in situations like these, rankings are incredibly important to understand if the decrease in traffic is purely lost rankings, lost demand, or shifts in how people search.
Export click/impression and CTR data in Google Search Console and Bing Webmaster Tools. Isolate queries/URLs that saw a CTR decrease and determine if those SERPs are now displaying AIOs.
This further demonstrates when performance is truly down or if everyone playing the game has been impacted. If the pages that saw the biggest dips in clicks also display AI overviews, then the impact is likely very similar for your competitors as well. Just another valuable piece of the puzzle.
Once you deliver the current state of the business, questions will follow. Don’t wait to be asked, own the narrative. Explain the broader context, industry-wide shifts, and emerging technologies behind these changes. A few opportunities to consider:
Pull traffic estimates and keyword ranking reports for your top competitors. Are they seeing similar results?
Review Google Trends and Exploding Topics to identify increasing (or decreasing) demand for topics/products within your industry.
Leverage new AI visibility technology/reports to show your brand’s visibility where the conversation/research is happening (LLMs).
Remember, this isn’t about assigning blame. It’s about showing you understand the change in landscape and how it’s impacting overall performance.
What we’ve learned so far and where we’re going
This is the moment to show leadership that you are not just diagnosing a problem, you are actively working toward a solution. They might not love every answer, but they will respect that you are thinking three steps ahead.
Make it clear that while the rules are changing, your team is already adapting to win in the next era of search. Then be explicit about what you need from them, whether that’s budget, headcount, data support, or cross-functional alignment, so you can actually execute the plan instead of just presenting the problem.
Here are a few ideas to communicate the next plan of attack.
We are working to increase our brand’s presence outside of traditional search, focusing heavily on AI-generated answers and emerging discovery platforms.
That includes tracking which questions matter most to our buyers, understanding where our brand appears today, and prioritizing content, PR, and partnerships to increase our odds of being named in those answers.
The goal is simple: If people are getting answers without clicking, our brand still needs to show up in the answer. This is done by repetition and consistency in our brand mentions/citations across the web.
We are rethinking content strategy around entities and topics, not just keywords and rankings.
LLMs reward brands that have deep, consistent coverage of a topic and clear signals of expertise. That affects what we publish, how we structure content, and how we collaborate with PR, product, and subject matter experts to build authority over time. This is the 2.0 version of “SEO content” and it won’t be easy, but the results will be worth it.
We are investing in visibility measurement across both traditional and non-traditional search channels.
Google organic traffic is no longer the single source of truth. We are building reporting that accounts for AI surfaces, social discovery, referral ecosystems, and even offline demand, so the broader team sees the full picture instead of assuming “SEO is down, therefore demand is down.” This helps quantify the broader shift in search ecosystems.
AI Overviews are a permanent shift, not a test.
This means resetting traffic baselines, forecasts, and goals to reflect fewer clicks from classic blue links within the SERP. We are not planning our pipeline in the hope that Google turns AI Overviews off, we are planning for a world where this is the new normal.
Some version of “AI Mode” will likely become Google’s default experience in 2026.
If more searches are answered directly in Google’s interface, fewer visitors will hit our site. That changes how many leads or sales we can expect from SEO alone, and it will force us to rethink everything, including budgeting and how we attribute performance across channels.
How we’ll be proactive and adapt to the new search landscape
You’ve explained what’s happening, why it’s happening, and how your team is adapting. Now, make it clear to leadership that to succeed in this shifting landscape, it can’t be done in isolation. You’ll need alignment, resources, and ongoing support.
Use this opportunity to preemptively answer questions like “What do you need from us?” and to shape the path forward. Leaders like nothing more than an actionable plan that they simply have to bless to get done.
Here are some critical needs to outline.
Search success in the AI era looks different, is measured differently than we are used to, and will take time to optimize.
We should agree up front on realistic timeframes, what leading indicators we will track, and how often we will report back. Rankings, traffic, and last-click revenue will not always move neatly in sync, so leadership needs to be comfortable with a period where we are learning and recalibrating, not just chasing last year’s dashboards.
Executive buy-in is needed to prioritize long-term brand-building alongside short-term performance metrics.
This means leadership agrees that some SEO and content initiatives will not pay off in this quarter’s reporting but are required to keep the brand visible in search and AI-driven experiences over the next 12 to 24 months. It also means updating KPIs so the team is not punished for investing in assets that compound over time instead of quick, last-click wins.
Budget flexibility to invest in experimental channels, new content formats, and tools that help track AI visibility.
A portion of the marketing budget will need to be earmarked for testing, for example: new AI visibility tools, structured data implementations, interactive content, and partnerships that increase the odds of being cited in AI answers. The goal is to learn fast, kill what does not work, and scale what does.
Cross-functional collaboration with analytics, product, PR, and content teams needs to happen to shift how we measure and execute organic growth.
SEO can no longer operate in a silo. We need analytics to help us build new dashboards that track visibility and assisted impact, PR to prioritize stories and placements that feed both search and AI systems, and product and content teams to align roadmaps with the topics and entities that matter most. Without that alignment, we end up with fragmented efforts and noisy data that no one trusts.
This is your moment to lead the AI visibility discussion
You’re not just reacting to change but guiding your organization through it. AI and LLMs are rewriting how people search, discover, and click. This isn’t the time to panic, let alone support the “organic search is dead” rumor. It means the game has changed, and good businesses aren’t afraid. They adapt.
Part of that strategy is ongoing monitoring. One-time pitches for buy-in are great, but all marketing efforts need to be measured. Set a regular cadence—for example, a monthly AI visibility update metric alongside your “normal” SEO KPIs.
As AI and LLMs evolve, you can leverage the data you’ve measured to brief leadership on what has changed and how you have adapted to the situation.
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By getting ahead of the conversation, grounding your message in data, and proposing a realistic path forward, you’re showing exactly the kind of strategic thinking that executives value.
This is no longer only about SEO, it’s about future-proofing how your business earns visibility, trust, and traffic in a radically new environment. It doesn’t matter if that happens on Google, ChatGPT, Reddit, or anywhere else. What’s important is being visible in the spaces where your customers are hanging out.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-23 18:11:432025-12-23 18:11:43AI’s impact on search isn’t a secret (How to talk to execs about the new era of search)
Most business owners assume that if an ad is approved by Google or Meta, it is safe.
The thinking is simple: trillion-dollar platforms with sophisticated compliance systems would not allow ads that expose advertisers to legal risk.
That assumption is wrong, and it is one of the most dangerous mistakes an advertiser can make.
The digital advertising market operates on a legal double standard.
A federal law known as Section 230 shields platforms from liability for third-party content, while strict liability places responsibility squarely on the advertiser.
Even agencies have a built-in defense. They can argue that they relied on your data or instructions. You can’t.
In this system, you are operating in a hostile environment.
The landlord (the platform) is immune.
Bad tenants (scammers) inflate the cost of participation.
And when something goes wrong, regulators come after you, the responsible advertiser, not the platform, and often not even the agency that built the ad.
Here is what you need to know to protect your business.
Note:This article was sparked by a recent LinkedIn post from Vanessa Otero regarding Meta’s revenue from “high-risk” ads. Her insights and comments in the post about the misalignment between platform profit and user safety prompted this in-depth examination of the legal and economic mechanisms that enable such a system.
The core danger: Strict liability explained
While the strict liability standard is specific to U.S. law (FTC), the economic fallout of this system affects anyone buying ads on U.S.-based platforms.
Before we discuss the platforms, it is essential to understand your own legal standing.
In the eyes of the FTC and state regulators, advertisers are generally held to a standard of strict liability.
What this means: If your ad makes a deceptive claim, you are liable. That’s it.
Intent doesn’t matter: You can’t say, “I didn’t mean to mislead anyone.”
Ignorance doesn’t matter: You can’t say, “I didn’t know the claim was false.”
Delegation doesn’t matter: You can’t say, “My agency wrote it,” or “ChatGPT wrote it.”
The law views the business owner as the “principal” beneficiary of the ad.
You have a non-delegable duty to ensure your advertising is truthful.
Even if an agency writes unauthorized copy that violates the law, regulators often fine the business owner first because you are the one profiting from the sale.
You can try to sue your agency later to get your money back, but that is a separate battle you have to fund yourself.
The unfair shield: Why the platform doesn’t care
If you are strictly liable, why doesn’t the platform help you stay compliant? Because they don’t have to.
Section 230 of the Communications Decency Act declares that “interactive computer services” (platforms) are not treated as the publisher of third-party content.
The original intent: This law was passed in 1996 to allow the internet to scale, ensuring that a website wouldn’t be sued every time a user posted a comment. It was designed to protect free speech and innovation.
The modern reality: Today, that shield protects a business model. Courts have ruled that even if platforms profit from illegal content, they are generally not liable unless they actively contribute to creating the illegality.
The consequence: This creates a “moral hazard.” Because the platform faces no legal risk for the content of your ads, it has no financial incentive to build perfect compliance tools. Their moderation AI is built to protect the platform’s brand safety, not your legal safety.
The liability ladder: Where you stand
To understand how exposed you are, look at the legal hierarchy of the three main players in any ad campaign:
The platform (Google/Meta)
Legal status: Immune.
They accept your money to run the ad. Courts have ruled that providing “neutral tools” like keyword suggestions does not make the platform liable for the fraud that ensues.
If the FTC sues, they point to Section 230 and walk away.
The agency (The creator)
Legal status: Negligence standard.
If your agency writes a false ad, they are typically only liable if regulators prove they “knew or should have known” it was false.
They can argue they relied on your product data in good faith.
You (The business owner)
Legal status: Strict liability.
You are the end of the line.
You can’t pass the buck to the platform (immune) or easily to the agency (negligence defense).
If the ad is false, you pay the fine.
The hostile environment: Paying to bid against ‘ghosts’
The situation gets worse.
Because platforms are immune, they allow “high-risk” actors into the auction that legitimate businesses, like yours, have to compete against.
A recent Reuters investigation revealed that Meta internally projected roughly 10% of its ad revenue (approximately $16 billion) would come from “integrity risks”:
Scams.
Frauds.
Banned goods.
Worse, internal documents reveal that when the platform’s AI suspects an ad is a scam (but isn’t “95% certain”), it often fails to ban the advertiser.
Instead, it charges them a “penalty bid,” a premium price to enter the auction.
You are bidding against scammers who have deep illicit profit margins because they don’t ship real products (zero cost of goods sold).
This allows them to bid higher, artificially inflating the cost per click (CPC) for every legitimate business owner.
You are paying a fraud tax just to get your ad seen.
Because the platform is no longer a neutral host but is vouching for the business (“Guaranteed”), regulators can argue they have stepped out from behind the Section 230 shield.
By clicking “Auto-apply,” you are effectively signing a blank check for a robot to write legal promises on your behalf.
Risk reality check: Who actually gets investigated?
While strict liability is the law, enforcement is not random. The FTC and State Attorneys General have limited resources, so they prioritize based on harm and scale.
If you operate in dietary supplements (i.e., “nutra”), fintech (crypto and loans), or business opportunity offers, your risk is extreme. These industries trigger the most consumer complaints and the swiftest investigations.
If you are an HVAC tech or a local florist, you are unlikely to face an FTC probe unless you are engaging in massive fraud (e.g., fake reviews at scale). However, you are still vulnerable to competitor lawsuits and local consumer protection acts.
Investigations rarely start from a random audit. They start from consumer complaints (to the BBB or attorney generals) or viral attention. If your aggressive ad goes viral for the wrong reasons, the regulators will see it.
International intricacies
It is vital to remember that Section 230 is a U.S. anomaly.
If you advertise globally, you’re playing by a different set of rules.
The European Union (DSA): The Digital Services Act forces platforms to mitigate “systemic risks.” If they fail to police scams, they face fines of up to 6% of global turnover.
The United Kingdom (Online Safety Act): The UK creates a “duty of care.” Senior managers at tech companies can face criminal liability for failing to prevent fraud.
Canada (Competition Bureau): Canadian regulators are increasingly aggressive on “drip pricing” and misleading digital claims, without a Section 230 equivalent to shield the platforms.
The “Brussels Effect”: Because platforms want to avoid EU fines, they often apply their strictest global policies to your U.S. account. You may be getting flagged in Texas because of a law written in Belgium.
The advertiser’s survival guide
Knowing the deck is stacked, how do you protect your business?
Adopt a ‘zero trust’ policy
Never hit “publish” on an auto-generated asset without human eyes on it first.
If you use an agency, require them to send you a “substantiation PDF” once a quarter that links every claim in your top ads to a specific piece of proof (e.g., a lab report, a customer review, or a supply chain document).
The substantiation file
For every claim you make (“Fastest shipping,” “Best rated,” “Loses 10lbs”), keep a PDF folder with the proof dated before the ad went live.
This is your only shield against strict liability.
Audit your ‘auto-apply’ settings
Go into your ad accounts today.
Turn off any setting that allows the platform to automatically rewrite your text or generate new assets without your manual review.
Efficiency is not worth the liability.
Watch the legislation
Lawmakers are actively debating the SAFE TECH Act, which would carve out paid advertising from Section 230.
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Google expanded Demand Gen channel controls to include Google Maps, giving advertisers a new way to reach users with intent-driven placements and far more control over where Demand Gen ads appear.
What’s new. Advertisers can now select Google Maps as a channel within Demand Gen campaigns. The option can be used alongside other channels in a mixed setup or on its own to create Maps-only campaigns.
Why we care. This update unlocks a powerful, location-focused surface inside Demand Gen, allowing advertisers to tailor campaigns to high-intent moments such as local discovery and navigation. It also marks a meaningful step toward finer channel control in what has traditionally been a more automated campaign type.
Response. Advertisers are very excited by this update. CEO of AdSquire Anthony Higman has been waiting for this for decades:
Google Ads Specialist Thomas Eccel, who shared the update on LinkedIn said: “This is very big news and shake up things quite a lot!”
Between the lines. Google continues to respond to advertiser pressure for greater transparency and control, gradually breaking Demand Gen into more modular, selectable distribution channels.
What to watch. How Maps placements perform compared to YouTube, Discover, and Gmail—and whether Google expands reporting or optimization tools specifically for Maps inventory.
First seen. This update was first spotted by Search Marketing Specialist Francesca Poles, when she shared the update on LinkedIn
Bottom line. Adding Google Maps to Demand Gen channel controls is a significant shift that gives advertisers new strategic flexibility and the option to build fully location-centric campaigns.
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Search marketers are starting to build, not just optimize.
Across SEO and PPC teams, vibe coding and AI-powered development tools are shrinking the gap between idea and execution – from weeks of developer queues to hours of hands-on experimentation.
These tools don’t replace developers, but they do let search teams create and test interactive content on their own timelines.
In a zero-click environment, the ability to build unique, useful, conversion-focused tools is becoming one of the most practical ways search marketers can respond.
What is vibe coding?
Vibe coding is a way of building software by directing AI systems through natural language rather than writing most of the code by hand.
Instead of working line by line, the builder focuses on intent – what the tool should do, how it should look, and how it should respond – while the AI handles implementation.
The term was popularized in early 2025 by OpenAI co-founder Andrej Karpathy, who described a loose, exploratory style of building where ideas are tested quickly, and code becomes secondary to outcomes.
His framing captured both the appeal and the risk: AI makes it possible to build functional tools at speed, but it also encourages shortcuts that can lead to fragile or poorly understood systems.
Since then, a growing ecosystem of AI-powered development platforms has made this approach accessible well beyond engineering teams.
Tools like Replit, Lovable, and Cursor allow non-developers to design, deploy, and iterate on web-based tools with minimal setup.
The result is a shift in who gets to build – and how quickly ideas can move from concept to production.
That speed, however, doesn’t remove the need for judgment.
Vibe coding works best when it’s treated as a craft, not a shortcut.
Blindly accepting AI-generated changes, skipping review, or treating tools as disposable experiments creates technical debt just as quickly as it creates momentum.
Mastering vibe coding means learning how to guide, question, and refine what the AI produces – not just “see stuff, say stuff, run stuff.”
This balance between speed and discipline is what makes vibe coding relevant for search marketers, and why it demands more than curiosity to use well.
Vibe coding vs. vibe marketing
Vibe coding should not be confused with vibe marketing.
AI no-code tools used for vibe coding are designed to build things – applications, tools, and interactive experiences.
AI automation platforms used for vibe marketing, such as N8N, Gumloop, and Make, are built to connect tools and systems together.
For example, N8N can be used to automate workflows between products, content, or agents created with Replit.
These automation platforms extend the value of vibe-coded tools by connecting them to systems like WordPress, Slack, HubSpot, and Meta.
Used together, vibe coding and AI automation allow search teams to both build and operationalize what they create.
Why vibe coding matters for search marketing
In the future, AI-powered coding platforms will likely become a default part of the marketing skill set, much like knowing how to use Microsoft Excel is today.
AI won’t take your job – but someone who knows how to use AI might.
We recently interviewed candidates for a director of SEO and AI optimization role.
None of the people we spoke with were actively vibe coding or had used AI-powered development software for SEO or marketing.
That gap was notable.
As more companies add these tools to their technology stacks and ways of working, hands-on experience with them is likely to become increasingly relevant.
Vibe coding lets search marketers quickly build interactive tools that are useful, conversion-focused, and difficult for Google to replicate through AI Overviews or other SERP features.
For paid search, this means teams can rapidly test interactive content ideas and drive traffic to them to evaluate whether they increase leads or sales.
These platforms can also be used to build or enhance scripts, improve workflows, and support other operational needs.
For SEO, vibe coding makes it possible to add meaningful utility to pages and websites, which can increase engagement and encourage users to return.
Returning visitors matter because, according to Google’s AI Mode patent, user state – which includes engagement – plays a significant role in how results are generated in AI Overviews and AI Mode.
For agency founders, CEOs, CFOs, and other group leaders, these tools also make it possible to build custom internal systems to support how their businesses actually operate.
For example, I used Replit to build an internal growth forecasting and management tool.
It allows me to create annual forecasts with assumptions, margins, and P&L modeling to manage the SEO and AI optimization group.
There isn’t off-the-shelf software that fully supports those needs.
Vibe coding tools can also be cost-effective.
In one case, I was quoted $55,000 and a three-month timeline to build an interactive calculator for a client.
Using Replit, I built a more robust version in under a week on a $20-per-month plan.
Beyond efficiency, the most important reason to develop these skills is the ability to teach them.
Helping clients learn how to build and adapt alongside you is increasingly part of the value agencies provide.
In a widely shared LinkedIn post about how agencies should approach AI, Chime CMO Vinneet Mehra argued that agencies and holding companies need to move from “we’ll do it for you” to “we’ll build it with you.”
In-house teams aren’t going away, he wrote, so agencies need to partner with them by offering copilots, playbooks, and embedded pods that help brands become AI-native marketers.
Being early to adopt and understand vibe coding can become a competitive advantage.
Used well, it allows teams to navigate a zero-click search environment while empowering clients and strengthening long-term working relationships – the kind that make agencies harder to replace.
Top vibe coding platforms for search marketers
There are many vibe coding platforms on the market, with new ones continuing to launch as interest grows. Below are several leading options worth exploring.
AI development tool and experience level
Pros
Cons
Google AI Studio (Intermediate)
• Direct access to Google’s latest Gemini models. • Seamless integration with Google ecosystem (Maps, Sheets, etc.). • Free tier available for experimentation.
• Locked into Google’s ecosystem and Gemini models. • Limited flexibility compared to open platforms. • Smaller community/resources compared to established tools.
• Relatively new platform with less maturity. • Limited customization for complex applications. • Generated code may need refinement for production.
Figma Make (Intermediate)
• Seamless design to code workflow within. • Ideal for teams already using Figma. • Bridges gap between designers and developers.
• Requires Figma subscription and ecosystem. • Newer tool, still evolving features. • Code output may need developer review for production.
Replit (Intermediate)
• All-in-one platform (code, deploy, host). • Strong integration capabilities with third-party tools. • No local setup required.
• Performance can lag compared to local development. • Free tier has significant limitations. • Fees can add up based on usage.
Cursor (Advanced)
• Powerful AI assistance for experienced developers. • Works locally with your existing workflow. • Advanced code understanding and generation.
• Steeper learning curve, requires coding knowledge. • Need to download the software GitHub dependency for some features.
For beginners:
Lovable is the most user-friendly option for those with little coding experience.
Figma Make is also intuitive and works well for teams already using Figma.
Replit is also relatively easy to use and does not require prior coding experience.
For developers, Replit and Cursor offer deeper tooling and are better suited for integrations with other systems, such as CRMs and CMS platforms.
Google AI Studio is broader in scope and offers direct connections to Google products, including Google Maps and Gemini, making it useful for teams working within Google’s ecosystem.
You should test several of these tools to find the one that best fits your needs.
I prefer Replit, but I will be using Figma Make because our creative teams already work in Figma.
Bubble is also worth exploring if you are new to coding, while Windsurf may be a better fit for more advanced users.
Practical SEO and PPC applications: What you can build today
There is no shortage of things you can build with vibe coding platforms.
The more important question is what interactive content you should build – tools that do not already exist, solve a real problem, and give users a reason to return.
Conversion focus matters, but usefulness comes first.
Common use cases include:
Lead generation tools
Interactive calculators, such as ROI estimators and cost analyzers.
Quiz funnels with email capture.
Free tools, including word counters and SEO analyzers
Content optimization tools
Keyword density checkers.
Readability analyzers.
Meta title and description generators
Conversion rate optimization
Product recommenders.
Personalization engines.
Data analysis and reporting
Custom analytics dashboards.
Rank tracking visualizations.
Competitor analysis scrapers, with appropriate ethical considerations.
Articles can only take you so far in a zero-click environment, where AI Overviews increasingly provide direct answers and absorb traffic.
Interactive content should be an integral part of a modern search and content strategy, particularly for brands seeking to enhance visibility in both traditional and generative search engines, including ChatGPT.
Well-designed tools can earn backlinks, increase time on site, drive repeat visits, and improve engagement signals that are associated with stronger search performance.
For example, we use AI development software as part of the SEO and content strategy for a client serving accounting firms and bookkeeping professionals.
Our research led to the development of an AI-powered accounting ROI calculator designed to help accountants and bookkeeping firms understand the potential return on investment from using AI across different parts of their businesses.
The calculator addresses several core questions:
Why AI adoption matters for their firm.
Where AI can deliver the most impact.
What the expected ROI could be.
It fills a gap where clear answers did not previously exist and represents the kind of experience Google AI Overviews cannot easily replace.
The tool is educational by design.
It explains which tasks can be automated with AI, displays results directly on screen, forecasts a break-even point, and allows users to download a PDF summary of their results.
AI development software has also enabled us to design additional calculators that deliver practical value to the client’s target audience by addressing problems they cannot easily solve elsewhere.
Vibe coding works best when it follows a structured workflow.
The steps below outline a practical process search marketers can use to plan, build, test, and launch interactive tools using AI-powered development platforms.
Step 1: Research and ideation
Run SERP analysis, competitor research, and customer surveys, and use audience research tools such as SparkToro to identify gaps where AI Overviews leave room for interactive tools.
Include sales, PR, legal, compliance, and cybersecurity teams early in the process.
That collaboration is especially important when building tools for clients.
When possible, involve customers or target audiences during research, ideation, and testing.
Step 2: Create your content specification document
Create a content specification document to define what you want to build before you start.
This document should outline functionality, inputs, outputs, and constraints to help guide the vibe coding software and reduce errors.
Include as much training context as possible, such as brand colors, tone of voice, links, PDFs, and reference materials.
The more detail provided upfront, the better the results.
Begin with wireframes and front-end design before building functionality.
Replit prompts for this approach during setup, and it helps reduce rework later.
Getting the design close to final before moving into logic makes it easier to evaluate usability.
Design changes can always be made later.
Step 4: Prompt like a product manager
After submitting the specification document, continue prompting to refine the build.
Ask the AI why it made specific decisions and how changes affect the system.
In practice, targeted questions lead to fewer errors and more predictable outcomes.
Step 5: Deploy and test
Deploy the tool to a test URL to confirm it behaves as expected.
If the tool will be embedded on other sites, test it in those environments as well.
Security configurations can block API calls or integrations depending on the host site.
I encountered this when integrating a Replit build with Klaviyo.
After reviewing the deployment context, the issue was resolved.
Step 6: Update the content specification document
Have the AI update the content specification document to reflect the final version of what was built.
This creates a record of decisions, changes, and requirements and makes future updates or rebuilds easier.
Save this document for reference.
Step 7: Launch
Push the interactive content live using a custom domain or by embedding it on your site.
Plan distribution and promotion alongside the launch.
This is why involving PR, sales, and marketing teams from the beginning of the project matters.
They play a role in ensuring the content reaches the right audience.
The dark side of vibe coding and important watchouts
Vibe coding tools are powerful, but understanding their limitations is just as important as understanding their strengths.
The main risks fall into three areas:
Security and compliance.
Price creep.
Technical debt.
Security and compliance
While impressive, vibe coding tools can introduce security gaps.
AI-generated code does not always follow best practices for API usage, data encryption, authentication, or regulatory requirements such as GDPR or ADA compliance.
Any vibe-coded tool should be reviewed by security, legal, and compliance professionals before launch, especially if it collects user data.
Privacy-by-design principles should also be documented upfront in the content specification document.
These platforms are improving.
For example, some tools now offer automated security scans that flag issues before deployment and suggest fixes.
Even so, human review remains essential.
Price creep
Another common risk is what could be described as the “vibe coding hangover.”
A tool that starts as a quick experiment can quietly become business-critical, while costs scale alongside usage.
Monthly subscriptions that appear inexpensive at first can grow rapidly as traffic increases, databases expand, or additional API calls are required.
In some cases, self-hosting a vibe-coded project makes more sense than relying on platform-hosted infrastructure.
Hosting independently can help control costs by avoiding per-use or per-visit charges.
Technical debt
Vibe coding can also create technical debt.
Tools can break unexpectedly, leaving teams staring at code they no longer fully understand – a risk Karpathy highlighted in his original description of the approach.
This is why “Accept all” should never be the default.
Reviewing AI explanations, asking why changes were made, and understanding tradeoffs are critical habits.
Most platforms provide detailed change logs, version history, and rollback options, which makes it possible to recover when something breaks.
Updating the content specification document at major milestones also helps maintain clarity as projects evolve.
Vibe coding is your competitive edge
AI Overviews and zero-click search are changing how value is created in search.
Traffic is not returning to past norms, and competing on content alone is becoming less reliable.
The advantage increasingly goes to teams that build interactive experiences Google cannot easily replicate – tools that require user input and deliver specific, useful outcomes.
Vibe coding makes that possible.
The approach matters: start with research and a clear specification, design before functionality, prompt with intent, and iterate with discipline.
Speed without structure creates risk, which is why understanding what the AI builds is as important as shipping quickly.
The tools are accessible. Lovable lowers the barrier to entry, Cursor supports advanced workflows, and Replit offers flexibility across use cases.
Many platforms are free to start. The real cost is not testing what’s possible.
More importantly, vibe coding shifts how teams work together.
Agencies and in-house teams are moving from “we’ll do it for you” to “we’ll build it with you.”
Teams that develop this capability can adapt to a zero-click search environment while building stronger, more durable partnerships.
Build something. Learn from it. The competitive advantage is often one prompt away.
Brand-agency partnerships look very different today than they did even a few years ago, and by 2026 that gap will only widen.
Internal marketing teams are more sophisticated, digital channels are more specialized, and the role agencies play is no longer one-size-fits-all.
As a result, the companies that get the most value from agency relationships aren’t always the biggest spenders.
They’re the ones that are clear about what they need and what they don’t.
That clarity starts with understanding the true role an agency should play inside your organization.
Too many partnerships struggle because expectations and responsibilities were never properly aligned from the start.
When that foundation is off, even strong execution can fall flat.
After working with thousands of businesses across various industries and growth stages, we consistently observe that agency success falls into two distinct partnership models, primarily shaped by company size and internal marketing maturity.
Model 1: Execution-first partnerships (large companies)
If your company generates more than $50 million in annual online revenue, you likely already have a strong internal marketing team.
Strategy, goal-setting, and planning live in-house. What you need from an agency is deep platform expertise and consistent, high-level execution.
At this stage, agencies function as specialist operators that:
Activate the roadmap your team has already defined.
Optimize performance inside specific channels.
Bring advanced technical knowledge that would be inefficient to replicate internally.
When something underperforms, a strong agency partner doesn’t rush to tactics.
They help determine whether the issue lies in execution, shifting market conditions, or a broader strategic blind spot – and they bring the data needed to support course correction.
Model 2: Integrated growth partners (small to mid-size companies)
For companies under $50 million in annual online revenue, the agency relationship is different.
Internal teams are often lean, stretched, or still developing core digital expertise.
In these cases, agencies don’t just execute – they help shape the entire growth strategy.
Here, the right agency partner becomes an extension of the marketing department that can:
Guide platform selection.
Develop cross-channel strategies.
Execute campaigns.
Provide direction on tools, tracking, and infrastructure.
The relationship is more integrated because it has to be.
For many growing businesses, agencies offer access to senior-level expertise at a fraction of the cost of building a full in-house team.
That tradeoff often creates the best possible balance between speed, strategy, and financial reality.
Most companies approach agency selection the wrong way.
Here’s how to improve your odds of finding a partner that actually fits your needs.
Ditch the RFPs
Many large companies use the request for proposal (RFP) process to solicit potential partners.
However, RFPs often favor vendors that excel at paperwork over those that prioritize performance.
From an agency perspective, if you don’t already know you’ve won an RFP, you’re not going to win it.
They act more as rubber stamps for a decision that has already been made.
Large companies should instead leverage their connections.
If you’re running a large internal marketing department, you probably already know dozens of professionals who could provide referrals.
Use that network to find firms doing great work, then reach out to them directly.
Smaller businesses should talk to their peers about trusted marketing vendors and then check reviews to validate those recommendations.
No agency is perfect, and every agency will have some dissatisfied clients.
But if you see patterns of negative reviews emerge, you should stay away.
Request an audit
Once you’ve identified a few potential partners, ask them to audit your current marketing setup.
In most cases, digital marketing agencies conduct these audits for free.
Keep in mind that during an audit, many agencies will point out what you’re doing wrong.
But the goal is to receive honest, constructive feedback that offers insight into what’s working and what’s possible.
The audit process will look different depending on the company’s size.
For larger companies, agencies should only audit the platforms they’ll be working on.
Smaller companies need a broader audit across the entire marketing funnel.
These agencies won’t be working in a vacuum.
Every element of marketing is interrelated, so they’ll need to know who manages each stage of the funnel and whether they’re doing a good job.
Companies of all sizes should collect audits from multiple sources.
This enables you to compare recommendations and understand if the partnership will be a good fit.
Large companies need partners that can integrate with their internal processes.
Smaller companies need to pick vendors with people they actually want to work with.
Both considerations are critical in ensuring long-term success.
Setting achievable goals
Once you’ve selected the right agency partner, it’s time to define your goals.
It’s an unfortunate reality that most business leaders set marketing goals that don’t align with their business goals, which puts agency partners in an untenable position before the relationship even gets off the ground.
Good agencies should challenge your goals before you even sign a contract. They should push you to dream bigger or rein you in if your expectations are unrealistic.
If a potential client in the beauty space says they want a tenfold return on ad spend (ROAS) while jumping their non-brand spend from $20,000 to $100,000, a good agency should know enough to push back.
Your potential partner should understand the economics of your business and help ensure your marketing goals align with your business goals.
Often they don’t, which is where good agencies add immediate value.
Once the work begins, you need to keep your agency accountable. Here’s how.
Contract length
Larger companies typically sign 12-month contracts with their agency vendors.
They value stability and performance, and longer contract terms provide agencies with the time needed to establish themselves within the marketing operation.
Smaller companies can’t afford to bind themselves to an underperforming agency for an entire calendar year.
If you’re hiring an agency partner at a smaller company, opt instead for a three-month agreement that automatically renews to month-to-month.
Challenge and conflict are healthy
The most productive business-agency partnership often involves some conflict from time to time.
Great partners will challenge your thinking regularly, which can sometimes create discomfort.
But if everything is always smooth sailing, you probably aren’t growing or improving.
The goal instead is to have productive conversations that involve healthy disagreement and constant refinement.
Ongoing accountability
If you’re overseeing a brand-agency partnership, you should establish regular reviews that compare progress to the opportunities identified in the agency’s initial audit.
For smaller companies, quarterly reviews make sense. They align with the contract structure and allow you to recalibrate budget allocation.
Larger companies might review monthly or quarterly, depending on spend and complexity.
However, context here matters. You need to understand if your industry is growing or shrinking to judge your agency’s work.
For example, if your industry is down 10% year-over-year and your sales are flat, you’re outperforming your competitors.
Often, the agency or brand can obtain this information from their representatives on platforms such as Google, Microsoft, Amazon, or Meta.
Innovation and testing
Great agency partners will proactively bring new growth ideas to the table, which is particularly valuable for smaller businesses.
Large companies also benefit from outside ideas and should establish dedicated budgets for testing.
After all, if your agency isn’t investing at least a small portion of the budget into new, untested ideas, brands will find themselves falling behind competitors that are.
Innovation isn’t just about testing what works today. It’s about understanding what’s coming next.
Great agency partners should help you see what’s coming 6-12 months out, and prepare your marketing to meet those new conditions.
Businesses need an agency’s expertise, which becomes insight over the longer term.
Not every brand-agency partnership succeeds, even with the best intentions.
If your gut is telling you something isn’t working or that something could be working better, here are a few red flags that might indicate it’s time to make a change.
Your business isn’t growing
Your marketing efforts should revolve around finding new-to-brand customers. Full stop.
If your business isn’t growing and your industry is stable or growing, that’s a big red flag that marketing isn’t working.
Once an agency stops being a partner in growth, it’s time to make a change.
Your agency isn’t pushing innovation
The marketing ecosystem is constantly changing:
Customer needs evolve.
Platforms update features.
New tools emerge that upend old processes.
If your agency isn’t bringing new ideas or exploring new ways to reach customers, your marketing is stagnating.
In these instances, an outside audit can reveal deficiencies and potential opportunities.
Your agency can’t explain performance
If your agency can’t contextualize your performance – good or bad – within the broader marketing ecosystem, it’s a strong indication they don’t understand your sales funnel.
Channel experts should know how their performance is affected by upper-funnel activities and how those activities affect bottom-funnel activities.
Marketing agencies for smaller businesses should know enough about the entire marketing operation and understand how performance in one area impacts another.
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