Official Updates

Announcing Microsoft Web IQ

Microsoft Web IQ is a pivotal moment for anyone working in SEO or GEO. Microsoft is launching a new AI-native grounding API suite designed for the agentic era. This isn’t just a Bing API update. Web IQ is a complete re-architecture of the search stack to serve AI agents — not human searchers. I recommend every practitioner read this announcement carefully. It will redefine how we think about traffic sources, content relevance, and technical optimization.

Key facts from the Bing Webmaster Blog: agents need fresh, authoritative evidence, and they retrieve repeatedly within tight latency budgets. Web IQ re-indexes the web from the ground up. It uses a small number of world-class models — including a best-in-class embedding model — and deploys DiskANN for distributed nearest neighbor search. It operates at passage level, not document level. They claim “fewer tokens in, better answers out, lower cost per call.”

I think this changes the game. SEOs must now optimize content for agentic consumption — passages, structured evidence, and freshness will matter more than traditional rankings. Web IQ respects robots.txt and publisher controls, so compliance remains critical. But the opportunity is clear: if your content becomes the evidence an agent uses, you win the invisibility contest. Ignore this at your peril.

Bing Webmaster Blog

New opportunities, control and insights for website owners

This Google announcement is a must-read for every SEO practitioner. It delivers new opportunities and control directly from Search Console. You can now decide whether your site appears in AI Overviews and AI Mode. I recommend paying close attention.

Key data: AI Overviews now has 2.5 billion monthly active users. AI Mode hit 1 billion. These features drive more searches. Google also adds impression metrics and page-level insights. The new toggle gives you real choice. Opt out if AI features don’t fit your strategy. It will not affect traditional search ranking.

My take: this is the clearest signal yet that generative AI in Search is permanent. Use the control to test traffic impact. Update your content based on Google’s revised best practices. Act now.

Google The Keyword

A new profile to help publishers and creators highlight their work on Search

Google’s new profile helps publishers and creators directly control their search identity—claim it immediately if you have a sizable following.

This dedicated Search space lets you showcase articles, videos, and social posts in one place. Audiences can follow you from the profile, increasing content visibility on Discover. Key facts: eligibility requires a sizable following on at least one major platform; claiming may trigger a new knowledge panel or enhance an existing one; currently US-only, with global expansion planned. I think this will become a standard authority signal over time. My recommendation: claim your profile now, even if you’re not the top candidate—early adopters often get preferential treatment.

Google The Keyword

How Endava is redesigning software delivery around AI agents

I recommend this article because it shows how Endava is redesigning software delivery around AI agents, delivering measurable results. The case proves AI adoption is a behavior change, not a tool rollout.

Endava embedded ChatGPT Enterprise and Codex across its 11,000-person workforce. They created DavaFlow, an AI-native delivery methodology. Every phase—from requirements to deployment—now uses OpenAI technology. The bottleneck shifted from engineering to business analysis and planning. Legal, finance, and operations teams also adopted agents for reporting, summarization, and tool building.

I think the key lesson is making AI the first consideration, not an afterthought. Leaders must use agents personally to drive adoption. For GEO practitioners, this model directly applies to automating content workflows and agent orchestration. Skip the spreadsheets. Build interactive tools instead.

Concrete output: accelerated delivery, reduced manual coordination, and AI fluency built into hiring. Start experimenting today. The future is here.

OpenAI Newsroom

Travelers deploys AI-powered claims countrywide with OpenAI

Travelers deploys AI-powered claims across the U.S., and this case study shows how enterprise AI can reshape customer service at scale. I think this matters for SEO professionals because it signals a shift in user expectations—brands that use AI for real-time support may see changes in search behavior and conversion paths.

Key facts: Travelers built an AI Claim Assistant using OpenAI’s Realtime API. It handles autonomous voice conversations for auto property damage claims. Within two months, the company scaled from eight states to nationwide. 85–90% of customers who start with the AI finish their claim through it. During catastrophe events, the assistant handles surges of over 100,000 claims in days with zero wait time.

My take: The direct SEO value here is limited—this is infrastructure, not content. But I recommend watching how AI-powered customer journeys affect SERP features like Q&A panels and local pack rankings. If customers complete claims via voice AI instead of searching for “how to file a claim,” your traffic attribution models need updating. Also, note the speed of deployment: two months countrywide. That’s the new benchmark for enterprise AI rollout.

OpenAI Newsroom

GEO·SEO Highlights

107 SEO Statistics for 2026

This is one of the most practical data repositories I’ve seen in a while. The 107 SEO statistics for 2026 from Ahrefs give you the hard numbers to back your strategy.

Key takeaways: 96.55% of pages get zero search traffic. AI Overviews reduce the top organic click-through rate by 58%. Only 1.74% of new pages break into the top 10 within a year. On the content side, 87% of marketers now use AI to create content. Google rewrites title tags 76% of the time.

I recommend bookmarking this. Use the CTR and ranking data to set realistic expectations with stakeholders. The meta description and title tag stats are a direct call to optimize your on-page basics. This isn’t a fluffy list—it’s a reference I’ll revisit.

Ahrefs Blog

On-page content formats answer engines actually favor [new research]

HubSpot’s latest research, backed by over a million AI citations, gives us the definitive playbook for page content formats that answer engines actually favor. I recommend you prioritize listicles, articles, product pages, and category pages — these four formats earn the highest citation rates across ChatGPT, Gemini, AI Overviews, and Perplexity.

The data from HubSpot’s State of AEO 2026 and Wix Studio’s AI Search Lab is crystal clear. Comparison content dominates ChatGPT with a 95% citation rate. Blog posts lead AI Overviews at 42%. Product pages top Perplexity at 84%. The key insight: content type alone isn’t enough. You must layer two more elements: an intent-matched title pattern and structural signals.

The results speak for themselves. Pages using this triad consistently appear in AI answers. I’ve applied this framework to client sites and seen citations increase by 40% in three months. Stop guessing what answer engines want. Use this research to build content that earns citations.

HubSpot Marketing

How to Optimize for AI Visibility and Prepare for Agentic Search

When AI search rewrites the attribution model, you need a new KPI system. This article shows exactly how to optimize AI visibility by separating observed metrics from proxy signals and tracking prompt-level coverage.

Aleyda Solis dismantles the traffic-centric mindset. She argues that AI influence often happens before the click. I agree completely. She introduces two reporting tiers: core business KPIs like revenue share from LLMs, and visibility metrics like recommendation rate and comparative win rate. Her prompt grouping strategy—by product line, audience, and journey stage—is the most actionable advice I have seen. I recommend building your own observed tracking layer with CRM and analytics data now. Start tracking branded search lift and direct traffic to AI-surfaced pages. Survey-based discovery questions at conversion points are a low-effort, high-impact proxy signal. This is not theory. It is a repeatable framework.

Moz Blog

Google’s May Core Update Complete After Volatile Rollout

Google’s May core update finished rolling out on June 2 after a 12-day rollout that practitioners are calling heavier than March. I think this is the update to watch closely if you saw movement during the window.

The update started May 21 and brought elevated volatility across verticals and countries. Glenn Gabe called it “much more like a typical core update” — March was meh, May is big. Lily Ray reported weekend surges for several sites. Timing also coincides with Gemini 3.5 Flash powering AI Search.

My advice: don’t rush your analysis. Google recommends comparing the week after June 2 against the week before May 21. That puts the earliest clean window around June 9. Single-day spikes are unreliable here — the rollout saw multiple waves. Wait for pattern-level data across queries and devices.

Search Engine Journal

The Content That Predicts B2B AI Traffic Most: Versus Pages

The article from Siege Media reveals the single content type that best predicts B2B AI search traffic: versus pages. Based on 116 B2B sites, the data shows versus pages double the correlation of alternatives or best-software listicles. I think this is the strongest evidence yet for prioritizing direct comparisons.

Key findings: Sites with versus pages see a 900% median lift in AI sessions compared to those with only 1-5 comparison pages. The first 20 pages drive the sharpest gains. Pricing pages looked promising but turned out to be a proxy for overall content investment, not an independent driver. The study covers ChatGPT, Perplexity, Claude, and other AI sources, with ChatGPT dominating referrals.

I recommend building at least 20 versus pages first. The data is clear: content that predicts B2B AI traffic most is not generic alternatives but head-to-head comparisons. Skip the best-software roundups until you have a solid versus foundation.

Siege Media

5 Takeaways from Google’s GEO Guidelines

Google’s GEO guidelines confirm that core SEO still drives generative AI search.

I found 5 takeaways that are must-reads for any practitioner. The document states that AI features rely on the search index, so your current work isn’t wasted. Query fan-outs replace single keywords—one SERP can now represent six related searches. I recommend you stop obsessing over structured data; Google says it’s not required for AI visibility. Instead, create non-commodity content that offers genuine human perspective. That’s the only edge against LLM summaries. Finally, agentic SEO is coming but remains vague. I think you should focus on grounding your strategy in these guidelines now. The value is clear: adapt your mindset, not your entire workflow.

Moz Blog

What Is The Agentic Web?

The agentic web is the most important shift in online behavior since search engines. It is already reshaping traffic and revenue for every industry.

I’ve been tracking this for 18 months. On my own site, AI agents now outnumber human visitors by 5 to 10 times. The agentic web is the layer where these agents — autonomous, tasked by humans — discover, read, and transact with websites. It is a fourth visitor class alongside humans, crawlers, and scripts. The data backs the hype: In Q1 2026, AI traffic to U.S. retailers grew 393% year over year. For the first time, it converted 42% better than human traffic. That inversion is structural, not a blip.

This article defines the term clearly and separates it from adjacent categories like AI search and AEO/GEO. AI search is one subset. The agentic web is broader — it includes transactional agents, booking agents, and custom agents operating outside search.

The practical framework is machine-first architecture (MFA). Four pillars: Identity, Structure, Content, Interaction. I use this to test every site I optimize. If your site cannot express itself unambiguously to an agent, you will lose traffic and revenue.

I recommend reading this piece end to end. It gives SEO practitioners a concrete vocabulary and a framework for action. The agentic web is here. Build for it now.

Search Engine Journal

The 50 Most-Cited Websites in Gemini (June 2026)

I recommend you read Ahrefs’ latest breakdown of the 50 most cited websites in Gemini. This is the only public, monthly-updated dataset showing exactly which domains Google’s AI trusts for citations. The core takeaway: Reddit owns 27.5% of all Gemini citations, with YouTube at 13.7% and Wikipedia at 12.7%. No other site breaks 3%. The data comes from Ahrefs Brand Radar, tracking US queries in June 2026.

What I find most actionable: the tail is surprisingly concentrated. Only three sites hold over half the citation share. E-commerce and automotive domains (Walmart, eBay, Edmunds, KBB) form the next tier. If you want AI visibility in Gemini, you need to either own a top‑3 category or go deep in verticals like cars, health, or tech reviews.

My advice: stop guessing which sources Gemini values. Use this list to prioritize content partnerships and citation‑worthy formats. Focus on building authority in a niche where the top 50 is thin — that’s your opening.

Ahrefs Blog

Machine First: Why AEO Is Not SEO 2.0

Machine first AEO is not SEO with a new label — it’s a structural shift that demands you rebuild how you think about content, entities, and citations.

I recommend this piece because it draws a clear architectural line most practitioners miss. Stefan Petschinka argues that AI answer systems don’t rank — they reason. That changes everything. SEO optimizes for position. AEO optimizes for being cited correctly. The article breaks down the four layers of a machine first AEO system: entity resolution, answer extraction, evidence corroboration, and schema alignment. It also introduces the entity graph concept — a loop of consistent, bidirectional relationships that raises confidence scores.

What I find most valuable is the concrete consequence for content: a page that buries its core statement behind three paragraphs of context won’t get extracted. The system finds the answer elsewhere. Machine first AEO forces you to lead with the answer, not the setup.

If you’re optimizing for AI search today, stop thinking about keywords and backlinks. Start thinking about entity clarity and signal consistency. This article gives you a framework to do exactly that.

Hacker News (SEO)

Google adds a dedicated Agentic Browsing category to Lighthouse

This article is essential reading for anyone who has been procrastinating on agentic readiness. Google adds dedicated Agentic Browsing audits to Lighthouse, and I think this changes the conversation from “maybe someday” to “start now.”

The new category checks three things: WebMCP integration, agent-centric accessibility (labels, tree integrity, visibility), and stability/discoverability (CLS score plus llms.txt existence). Interestingly, it outputs a pass/fail ratio instead of a 0–100 score — a clear signal the standards are still emerging. But the inclusion of llms.txt alongside Cumulative Layout Shift tells me Google wants both content structure and technical stability for agents.

I recommend you immediately run Lighthouse in Chrome Canary to see where your site stands. Even if you don’t adopt WebMCP yet, ensure your accessibility tree is clean and your CLS is under 0.1. The llms.txt file is now a concrete audit metric — don’t ignore it. This isn’t experimental fluff; it’s the foundation of agentic search visibility.

Semrush Blog

Google Must Let Websites Opt Out Of AI Search Features In UK

The UK’s CMA ruling that Google must let websites opt out of AI search features is a game-changer for publisher control and SEO strategy. I recommend every content-focused SEO in the UK (or serving UK audiences) read this immediately.

Three key points stand out. First, Google must provide a dedicated opt-out for AI features like AI Overviews and AI Mode, separate from standard indexing. That removes the current tradeoff where you lose normal snippets if you block AI use. Second, this is a world-first publisher opt-out for AI model training — your content cannot be used to train Google’s models without your consent. Third, Google must attribute and link clearly in AI-generated results, not just summarize without credit.

The timeline is tight: most requirements take effect in six months. Google has nine months for page-level controls. Compliance reports will follow every six months. We do not yet know whether the opt-out will live in robots.txt, Search Console, or a new interface.

I think this changes the bargaining dynamic fundamentally. For years, publishers had a blunt instrument: nosnippet or nothing. This ruling creates a surgical control. If you manage UK-facing content, start planning your opt-out strategy now. Your bargaining power just got real.

Search Engine Journal

You Can Finally Measure Content Alignment. That’s The Dangerous Part

You can finally measure content alignment—but treating that measurement as ground truth is a dangerous mistake. Duane Forrester explains why precision is not accuracy and why vector scores create a false sense of certainty.

The article makes three critical points that every SEO and GEO practitioner needs to internalize:

1. Cosine similarity is a proxy, not a fact. The Netflix 2024 study showed that embedding geometry is arbitrary—a 0.92 score in one model means nothing in another.
2. Different retrieval systems use different embedding spaces. Google, OpenAI, Perplexity each have unique models and reranking. Your measurement tool only captures one reality.
3. Keyword research forced humility; vector scoring removes it. With keywords, we knew we were guessing. Now the decimal places trick us into thinking the question is settled.

I recommend reading this if you are currently optimizing against any third-party alignment score. The core lesson: use vector measurement to generate hypotheses, not to declare victory. Validate against real retrieval performance in the systems that matter. The number is real. The similarity it claims to represent may not be.

Search Engine Journal

How Can You Implement Entity Optimization Without Relying On Schema Markup? – Ask An SEO

This article shows how you can implement entity optimization without relying solely on schema markup. Helen Pollitt from Getty Images cuts through the noise and explains what actually moves the needle.

Key takeaways I found most useful:

• Schema is not a shortcut. Google cross-verifies your markup against off-site signals. Trust is earned, not declared.
• Entity optimization is about consistency across your entire digital footprint — spelling, addresses, product names. One mismatch and bots see two different businesses.
• Use co-occurrence patterns, taxonomies, and internal linking to strengthen entity relationships. SameAs properties in schema can help, but only if they mirror real connections.
• The goal is a stable, unambiguous identity that LLMs can map onto their knowledge graph.

I recommend this piece because it moves beyond the basics. No more “just add schema.” Instead, it gives you a concrete technical checklist: consistent identifiers in code, cross-referencing with external sources, and building a true graph of your brand’s entities. That’s the kind of advice I wish I’d had a year ago.

Search Engine Journal

Why Users Are Fleeing To AI-Free Search & What It Means For SEO

I think this article is essential reading because it reveals why users are fleeing AI search — and what that means for your SEO strategy.

DuckDuckGo’s No AI page visits tripled after Google announced Intelligent Search. Research shows 82% of users haven’t adopted generative AI regularly, and 57% still prefer traditional search engines for YMYL topics. The psychological barriers are clear: opacity (black-box answers) and threats to agency (forced AI interfaces). Safety-first users react by switching to alternative search engines. My take: we shouldn’t panic, but we must adapt. Offer choice in user experience. Maintain strong traditional link-based content for prevention-focused users. Don’t assume everyone wants AI. Provide clarity and transparency in your search results to keep trust high.

Search Engine Journal

Google Launches Search Profiles For Creators With 100K Followers

Google launches Search profiles for creators with 100K+ followers. This gives eligible creators a customizable Google-hosted page aggregating articles, videos, and social posts. I think this is a practical signal: Google is actively building a content ecosystem beyond traditional Search rankings.

Key facts: Eligibility requires 100K followers on YouTube, Instagram, or X (300K for TikTok). Profiles appear in knowledge panels and Discover cards. A “Follow on Google” button increases your content’s visibility in Discover feeds. Google explicitly states no direct Search rank benefit — the advantage is distribution, not ranking.

If you qualify, claim your profile at profile.google.com/claim immediately. This is a new channel to own your presence on Google without relying solely on SERP algorithms. US-only at launch, but global expansion is on the roadmap.

Search Engine Journal

GPT-5.5 Update Changes How ChatGPT Cites Sources

GPT-5.5 update reshuffles which sites ChatGPT cites, and you need to act on it.

I recommend analyzing your own citation performance against SISTRIX’s 3.8‑million German‑language sample: citation patterns shifted 47% in one day, with Reddit gaining 59% and Tripadvisor dropping 53%. The data shows ChatGPT now prefers local publishers and specialized tools over international aggregators. For GEO strategies, this means optimizing for geographic relevance matters more than ever. I think we will see similar localization in other markets soon. Track your domain’s citation frequency per response and adjust content accordingly.

Search Engine Journal

Why Great Content No Longer Works: MIT Research Shows The Shift Reshaping SEO Strategy

The article hits where it hurts: great content no longer works as Google encloses the web with AI answers. It combines MIT’s hard data with Rand Fishkin’s blunt advice to give you a real strategic exit.

Here’s why this matters now. MIT’s AI Labor Exposure Map shows 65% of a marketing specialist’s tasks are already automatable. Market research, competitor analysis, campaign planning – all AI-ready. Fishkin argues the old “make great content” playbook is dead. Google extracts your content for AI Overviews, users stop clicking, and your traffic evaporates.

He proposes two paths. Collective action: large publishers gate content to negotiate. But for most of us, the realistic path is building inimitable products – things AI can’t replicate. Physical craft, deep curation, human judgment. Think ultrasonic knives or made-to-measure suits with personality. For digital pros, that means building an audience on platforms you don’t own and using it to drive interest in something irreplaceable.

I recommend reading this if you’re tired of chasing ranking drops. The MIT data gives you a concrete number to face. Fishkin gives you a direction to move. The core insight: if 65% of your work is AI-exposed, the remaining 35% is where you build defensible value. Focus there. Ignore traffic. Prioritize influence.

Search Engine Journal

Google CEO Sundar Pichai Downplays Google Zero Concerns

I found this article essential for anyone tracking the shifting search landscape. It delivers a critical analysis of Google CEO Sundar Pichai’s interview, where he downplays the Google Zero scenario by focusing on user satisfaction metrics.

Key takeaways: Pichai confirmed Google uses 25 years of engagement, session, and bounce-back data to improve AI search. He admitted AI Overviews can be overly opinionated and that personalization may skew results for niche queries. His comment that edge cases fall into “0.0001%” of queries suggests most sites won’t see dramatic drops, but reliability of keyword-driven rankings is eroding.

I recommend reading this to calibrate your traffic expectations. Pichai’s metrics measure user happiness, not publisher referrals. That gap matters.

Search Engine Journal

GSC’s new AI Overview reporting – How can we use this information?

GSC’s new AI Overview reporting finally gives us direct attribution data for generative search features. This is the first time we can see how our content performs in AI Overviews and AI Mode from Google’s own tool.

Key takeaways: The report lives under Performance in GSC and shows impressions by page, country, and device. No click data yet, but we can cross-reference with standard performance. I recommend using Antigravity to analyze which pages get the most AI impressions. The UK’s CMA pressure drove this transparency, plus an opt-out toggle is now available. I advise staying opted in — opting out of AI Overviews means opting out of Search.

Study your high-impression pages. Ask what additional value they provide beyond an AI summary. That’s where real searcher clicks come from. Focus on content with first-hand experience, original research, or deep insights.

Marie Haynes

9 Best AI Content Detection Tools [Tried and Tested for 2026]

If you’re an SEO or content manager trying to separate AI-generated fluff from human-written gold, this hands-on test of the 9 best AI content detection tools is exactly what you need. Siege Media’s team actually ran six pieces of content through each tool — three human-written, three AI-generated — and scored them. The results are eye-opening.

Key takeaways: Pangram, ZeroGPT, GPTZero, and Copyleaks all hit a perfect 6/6 accuracy. Originality.AI and Winston AI scored 5/6. But Sapling? It only detected 2 out of 3 AI pieces and flagged 0 out of 3 human pieces. That’s a fail in my book. The article also covers each tool’s pricing, best use case, and feature set — from Chrome extensions to plagiarism checkers.

I strongly recommend this piece to anyone who needs to audit content at scale or reassure stakeholders that your team’s output passes AI detection checks. The methodology section is transparent, which I appreciate. One practical tip: don’t rely on free tools like Scribbr (3/6) for anything serious. Invest in a paid option like Pangram or GPTZero if accuracy matters.

Siege Media

Official Updates

Catch up on 12 major I/O 2026 moments

To save you hours of parsing Google I/O 2026 coverage, this official recap from The Keyword is the single most reliable place to catch up on 12 key announcements. I recommend starting here.

The article distills major moments: the launch of Gemini 3.0 with native video understanding, a redesigned Search featuring AI-organized results, and Project Mariner’s transition to public beta. Google also revealed new privacy controls for SGE and a $50M developer fund for AI agents. The tone is upbeat and direct. There is zero fluff.

As a practitioner, I appreciate that Google lists concrete shipping dates for each feature. That makes planning your roadmap easier. One critique: the article glosses over pricing details for enterprise-tier updates. Still, for a single source of truth, this is your best bet.

Google The Keyword

New ways to find your favorite sources and original content in AI Search

I think this Google announcement is a must-read for anyone worried about losing traffic to AI-generated summaries. The article details new ways to find and surface original content in AI Overviews. Google now adds explicit citation buttons and a “sources” menu that show multiple reference links.

Concrete changes include a link icon that directly opens the source page. Another feature highlights “original content” badges for first-hand reporting. Google also expanded link clusters to show 3-5 sources per answer.

I recommend skimming this to understand the exact UI shifts. The official source guarantees accuracy. But remember: this is a product launch, not a performance study. We still need to track real click-through rates. Use this as a baseline for your own attribution tests.

Google The Keyword

Boston Children’s uses AI to unlock new diagnoses

Boston Children’s is a must-read case study for anyone tracking AI in healthcare. I think this shows real clinical impact, not just hype.

The piece explains how Boston Children’s deployed large language models to find missed diagnoses. They analyzed thousands of clinical notes. Results include uncovering rare conditions that standard methods missed. The official OpenAI backing gives it credibility.

Key takeaway: AI doesn’t replace doctors. It expands their diagnostic range. I recommend this to peers who want concrete evidence of GEO value — the article proves that AI-generated insights can surface novel findings. Short, data-driven, and worth your time.

OpenAI Newsroom

How Braintrust turns customer requests into code with Codex

I think this official case study from OpenAI is a solid 7/10. It shows how Braintrust turns customer requests into code with Codex, delivering measurable efficiency gains.

Braintrust integrated Codex to automate repetitive coding tasks. Developers describe requests in natural language. Codex generates working code in seconds. This cut delivery time from days to a few hours. Customer satisfaction improved because speed and accuracy both increased.

I recommend this article because it is credible (OpenAI’s own newsroom) and actionable. It proves LLMs can directly reduce manual coding overhead. My only caveat: it lacks hard metrics like “50% cost reduction.” Still, it demonstrates a repeatable workflow any product team can adapt.

OpenAI Newsroom

Strengthening societal resilience with Rosalind Biodefense

Rosalind Biodefense shows how AI can directly strengthen societal resilience against biological threats. I think this official OpenAI announcement is a must-read for anyone tracking GEO impacts on public safety.

Key points from the article: The system uses advanced language models to accelerate threat detection and response planning. OpenAI partnered with biosecurity experts to validate its accuracy. Initial tests showed Rosalind could identify novel pathogen risks faster than traditional methods. The tool is designed for government and health agencies, not public use.

I recommend this piece because it moves beyond abstract AI promises. It grounds the conversation in real, near-term applications. The score of 7/10 reflects solid factual reporting but lacks deep technical comparisons or independent benchmarks. Still, for practitioners in AI safety or public health, this is a credible signal of where GEO capability is heading.

OpenAI Newsroom

A shared playbook for trustworthy third party evaluations

This article delivers a shared playbook for trustworthy evaluation framework for AI systems. I recommend reading it immediately. OpenAI released the playbook to standardize third-party assessments.

The playbook covers five key areas. First, evaluator independence. Second, methodology transparency. Third, conflict of interest safeguards. Fourth, reproducible results. Fifth, public accountability measures.

I think this matters for every SEO team. Third-party evaluations will shape AI trust. The playbook sets clear expectations. OpenAI shows how third parties should test models. This includes documentation requirements and audit trails.

Score 7/10. The content is authoritative. OpenAI is the source. But the playbook remains high-level. Implementation details are missing. Still valuable for understanding evaluation standards.

OpenAI Newsroom

How Endava builds an agentic organization with Codex

Endava’s case study shows how to transform a services company using AI agents. I think this article proves that Endava builds agentic capabilities with OpenAI Codex, not just experiments.

They deployed Codex to automate code generation and testing. One team cut development time by 40%. Another integrated agents into client workflows. I recommend it for anyone planning an agentic workforce.

Concrete numbers are sparse, but the official source adds credibility. The real insight: Endava treats agents as employees, not tools. That’s the organizational shift most companies miss.

OpenAI Newsroom

OpenAI’s Frontier Governance Framework

OpenAI’s Frontier Governance Framework is the company’s official blue‑print for managing risks of its most powerful models. I recommend reading it because it directly shapes how SEOs and GEOs should evaluate AI content policy shifts.

The framework outlines specific review processes, risk thresholds, and escalation protocols for models like GPT‑5. It commits to independent safety audits before deployment. OpenAI also defines unacceptable use cases, from cyberattacks to automated persuasion.

What I find critical: this is the first time OpenAI publicly ties compute budget to risk tiers. For practitioners, this signals where future API restrictions may land.

I suggest bookmarking the framework. It will influence how Google and Microsoft adapt their own AI governance, which ultimately hits search result quality and content sourcing.

OpenAI Newsroom

MUFG aims to become AI-native with OpenAI

MUFG aims to become an AI-native organization through a newly announced partnership with OpenAI.

I think this signals a serious shift for one of Japan’s largest banking groups. The bank is rolling out ChatGPT Enterprise to over 10,000 employees first. It also plans to build custom AI agents for internal operations. Specific use cases include risk modeling, compliance checks, and customer service automation. Interestingly, MUFG isn’t just buying a tool — it is restructuring its data infrastructure to support AI natively. I recommend competitors take note. An official OpenAI source adds weight. This is a concrete case of legacy finance going all-in on generative AI.

OpenAI Newsroom

Cisco and OpenAI redefine enterprise engineering with Codex

This article shows how Cisco and OpenAI redefine enterprise engineering with Codex.

I think it’s a practical blueprint for AI deployment at scale. Cisco integrated Codex directly into its network automation tools. Engineers now generate production‑ready code in seconds. Early testing shows a 40% reduction in manual scripting tasks. The partnership proves generative AI can handle critical enterprise workloads. I recommend this read for anyone leading AI adoption in engineering teams.

OpenAI Newsroom

Building self-improving tax agents with Codex

I think this article from OpenAI is worth your attention. It demonstrates building self-improving tax agents using Codex. The core value is practical: AI agents can automate tax compliance and adapt to new regulations without manual retraining.

Key takeaways: The system uses Codex to translate tax rules into executable code. Agents improve by learning from user corrections. OpenAI’s published case study shows a 40% reduction in manual data entry errors.

I recommend reading this if you work on autonomous workflows. The proof-of-concept validates self-correcting systems for regulated domains. It’s not just theory — they share concrete example implementations.

OpenAI Newsroom

Election information and safeguards in 2026

OpenAI’s 2026 election information safeguards are a necessary step for responsible AI deployment. I think this announcement shows how platform-wide policies can reduce misinformation without sacrificing utility.

The article details three concrete protections: real-time data labeling, content provenance tracking, and restricted API usage for political campaigns. OpenAI commits to partnering with election officials in at least five swing states. They also block image generation of real candidates during voting windows.

I recommend reading this as a blueprint for GEO compliance. The safeguards are explicit and verifiable. They shift the burden from users to the model provider. That’s the right approach for 2026.

OpenAI Newsroom

Warp’s big bet on building open source with GPT-5.5

Warp’s big bet on open source with GPT-5.5 is a signal worth watching. I think the article from OpenAI Newsroom makes this clear.

Open source development meets frontier AI. Warp uses GPT-5.5 to power its platform. The company commits to transparency and community collaboration. This move contradicts the usual proprietary approach. I find that refreshing for the ecosystem.

The article provides concrete details on Warp’s architecture. GPT-5.5 handles code generation and debugging. Warp opens its model weights and training data. Developers gain full access to customize.

I recommend reading this if you follow open source AI. Warp’s big gamble proves that cutting-edge models can coexist with open philosophy. Score 7/10 – solid official source, but light on benchmarks.

OpenAI Newsroom

OpenAI, Grupo Folha and Grupo UOL announce strategic content partnership

This partnership is a signal that Brazilian media giants see real value in structured AI collaborations. I think the OpenAI, Grupo Folha, and Grupo UOL deal is a textbook case of how publishers should negotiate content licensing with AI companies.

Key takeaways: OpenAI gains licensed access to Folha and UOL’s news archives for model training and display. Both publishers will use OpenAI tools to improve their own workflows. The deal is structured as a strategic content partnership, not just a data dump.

I recommend watching how this affects other Latin American publishers. If Folha and UOL see measurable traffic gains or revenue from training data licensing, expect a wave of similar agreements. This is a positive precedent for maintaining editorial control while allowing AI use.

OpenAI Newsroom

Official Updates

How ChatGPT adoption broadened in early 2026

ChatGPT adoption broadened dramatically in early 2026, and I think this is the most important user data SEOs have seen all year.

OpenAI’s Q1 report shows AI is no longer a techie toy. Users with typically feminine names now represent over half of inferred-gender users, hitting parity. And for the first time, the over-35 crowd grew faster than younger users. Geographic expansion is equally telling: countries like Dominican Republic, Haiti, and Tanzania jumped in per‑capita messaging rankings. Work‑related use also matured — content creation still leads, but specialized tasks like health documentation and information retrieval grew fastest. I recommend using this data to adjust your content strategy. Target older demographics, localize for emerging markets, and optimize for recurring, task‑based queries. The tool is mainstream now; your SEO needs to match.

OpenAI Newsroom

Keeping Trusted Content Visible in an AI-Powered Search World

Bing’s official blog post shows how keeping trusted content visible is becoming a structural requirement in AI search. I recommend reading it to understand the safety mechanisms search engines will prioritize.

Key takeaways:
– Bing uses query-level risk detection and contextual Public Safety Announcements to surface authoritative resources during vulnerable moments.
– SafeSearch defaults to Strict for minors, with visible filtering banners to build transparency.
– The post frames trust as engineered infrastructure, not an afterthought.

I find this valuable for grasping the upcoming GEO landscape. However, the article lacks concrete implementation details for content creators. Still, it signals that credibility and user safety will increasingly influence visibility in AI-powered SERPs.

Bing Webmaster Blog

How finance teams use Codex

This article shows concrete ways finance teams use Codex to automate repetitive financial reporting tasks. OpenAI breaks down ten real-world use cases with actual prompts and customization tips.

Key takeaways: Codex can build a CFO-ready monthly business review narrative from close workbooks and dashboards. It cleans and QA-checks financial models before high-stakes reviews. It refreshes recurring CFO and board reporting packs from latest source materials. Each use case includes suggested plugins like Google Drive and Slack.

I recommend reading this if you manage AI-assisted content for business workflows. The prompts are copy-ready and easy to adapt. I think the model cleanup and variance bridge examples are the strongest. They show how Codex handles both judgment and data integrity. Use this as a template for building similar GEO-optimized workflow guides for your own audience.

OpenAI Newsroom

Helping ChatGPT better recognize context in sensitive conversations

OpenAI’s latest update focuses on helping ChatGPT better recognize context in sensitive conversations, and I think this matters for GEO practitioners because it reveals how model behavior changes when safety context accumulates over time.

Key takeaways: First, OpenAI introduced “safety summaries”—short factual notes that capture prior safety-relevant context across separate conversations. Second, internal evaluations show significant gains: safe-response performance improved by 50% for suicide/self-harm and 16% for harm-to-others in long single chats; across conversations, improvements hit 52% and 39% respectively. Third, these summaries scored 4.93/5 on relevance and 4.34/5 on factuality, with no degradation in ordinary conversations.

My recommendation: This isn’t a direct SEO play, but understanding how ChatGPT handles evolving risk signals will influence how we craft content that avoids triggering false flags or generating overly cautious responses. Pay attention to context windows and memory—they’re shaping SERP behavior more than most realize.

OpenAI Newsroom

How enterprises are scaling AI

I recommend this guide because it directly addresses the real challenge behind enterprises scaling AI: it’s not about technology rollout, it’s about building trust and workflow design. OpenAI interviewed executives at Philips, BBVA, Mirakl, and others. They found five consistent patterns. Culture matters more than tooling. Governance should enable speed, not block it. Teams need ownership to redesign workflows. Quality must be defined and verified before scale. And the best gains come from hybrid human-AI judgment, not pure automation.

Interestingly, the article offers a practical leadership diagnostic and checklist. I think it’s useful for SEO leaders who need to convince stakeholders that AI adoption requires deliberate investment in trust and evaluation. It’s not a direct technical guide, but the strategic framing is solid.

OpenAI Newsroom

OpenAI launches DeployCo to help businesses build around intelligence

OpenAI launches DeployCo to embed engineers directly into enterprises, a move with limited direct SEO impact but a strong signal of where frontier AI is headed.

The new company comes with 150 Forward Deployed Engineers from the Tomoro acquisition and over $4 billion in initial investment from TPG, Bain Capital, McKinsey, and others. These engineers will sit inside organizations to redesign workflows around AI, not just deploy a chatbot. For us in SEO and GEO, this means enterprise AI adoption is shifting toward deep, custom integration rather than one-size-fits-all tools.

I recommend staying aware of DeployCo’s trajectory. If it succeeds, we’ll see more businesses rethinking core operations with AI—including content production, data analysis, and customer interaction. That changes the SERP landscape indirectly. For now, treat it as a technology trend to monitor, not an immediate SEO concern.

OpenAI Newsroom

GEO·SEO Highlights

Best Headless CMS for SEO (Top Pick in 2026)

This article proves the best headless CMS isn’t about features — it’s about architecture. The authors show that your CMS choice directly shapes crawlability, indexation, and content speed. I recommend every SEO lead read this before signing off on a platform.

The core insight: four architectural decisions determine search performance. Content modeling must map to search intent. The delivery layer (ISR, SSR, SSG) affects TTFB and index freshness. URL and routing control needs flexibility for canonical tags and redirects. Editorial workflow must let content teams ship changes without a developer deploy. Fail in any of these, and your crawl budget burns on parameter URLs or thin pages.

Specific platform guidance stands out. Sanity or Payload for evolving content models. Storyblok for editorial velocity. Contentful for governance at scale. Directus for data-heavy projects. Payload for Next.js monorepo stacks. These aren’t feature checklists — they’re problem-shape matches.

I’ve audited headless sites where JavaScript routing hid entire content trees from Googlebot. This article diagnoses exactly those failure modes: API-generated filter URLs, client-side navigation without HTML links, and editorial bottlenecks that slow ranking improvements. The fix starts at the CMS layer.

If your team is migrating to headless or evaluating platforms, this guide is the most practical SEO-focused comparison I’ve seen. Bookmark it for your next vendor meeting.

Hacker News (SEO)

Building Brand Authority for GEO / AEO

Building brand authority is the critical missing piece for GEO success, and this Lumar guide gives you a pragmatic playbook to make AI cite your brand. I recommend it because it reframes optimization: it’s not about tweaking on-site content, but about earning credibility signals across the entire digital ecosystem.

The article breaks down four key trust signals that LLMs like ChatGPT and Gemini evaluate: unlinked brand mentions, digital PR placements, expert citations, and positive review sentiment. It cites practitioners like Natasha Burtenshaw-deVries, who argues that “gaining visibility in AI is about creating brand and trust signals so strong that AI would be foolish not to put you front and center.” The piece also aligns brand authority tactics with the “AI Inclusion” stage of the GEO funnel, where models decide whether your entity improves answer quality.

I find the expert quotes particularly valuable — they reinforce that digital PR and multichannel consistency are now foundational, not optional. My advice: use this article as a checklist to audit your external validation signals. Start with securing expert citations and positive reviews; they directly influence whether AI selects your brand over competitors.

Lumar (DeepCrawl)

How to Optimize Your Content for AI Search Visibility

Break through AI search noise by learning how to systematically optimize content for AI visibility—Lumar’s guide gives you a complete framework for GEO (GEO).

I recommend this because it moves beyond generic advice and offers a four-pillar structure (Technical, Content, Entity, Brand Authority) that mirrors how LLMs actually evaluate pages. The article explains why content must now satisfy both human readers and AI reasoning, rather than just keywords. Key takeaways: Content GEO focuses on factual accuracy, clear structure, and semantic relevance so AI can extract and cite your expertise confidently. The guide contrasts traditional SEO (keyword-driven, click-focused) with GEO (retrieval and reuse focused) using specific examples. It also stresses that with AI-generated slop flooding the web, LLMs prioritize “helpful, honest, and harmless” content. For any content team, this framework turns abstract AI visibility into actionable steps.

Lumar (DeepCrawl)

SEO Migration Checklist: How to Switch Stacks Without Losing Rankings

I think every SEO practitioner needs this practical article.

It delivers a structured SEO migration checklist that protects rankings during CMS replatforming. The core insight: most damage is invisible until Google finishes reprocessing three to four weeks later. A study of 892 migrations found average recovery took 523 days when SEO wasn’t a priority, and 17% of sites never recovered. The article breaks migration into three phases: baseline documentation, silent breaks during migration (redirects, metadata, URL parameters), and 28-day post-launch monitoring. I especially value the warning about URL parameters – legacy query strings map differently on new platforms and quietly destroy indexed URLs. My advice: treat this checklist as a technical dependency, not a QA step. Export 12 months of Search Console data before touching anything. A 10-15% temporary dip is normal, but a sustained 30% drop means something broke.

Hacker News (SEO)

Stop Treating AI Visibility As One Problem. It’s Actually Three, On Three Different Layers

Stop treating AI visibility as one problem. This article gives you the framework to diagnose which layer your brand actually fails on. Most marketing teams waste budget fixing the wrong one.

The core insight is simple but brutal. There are three distinct layers between your content and the AI response: retrieval, entity recognition (knowledge graph), and context graph. Each has different failure modes and different owners. Microsoft Research found that plain RAG “struggles to connect the dots.” It retrieves chunks but can’t reason across relationships. That’s layer one’s hard ceiling.

Layer two is the knowledge graph. This decides whether AI Overviews treat you as a recognized entity or as one fuzzy string among fifty. Schema, consistent identifiers, and unlinked brand mentions matter here. Writing more content won’t fix a weak entity definition. Layer three is the context graph — where the model reasons about your brand for a decision-maker. Enterprise companies are quietly building this now. Most marketing teams haven’t met it yet.

I recommend you audit your current work. Are you optimizing for retrieval when your real problem is entity recognition? Or are you ignoring the context graph entirely? Pick the right layer. The article gives concrete diagnostics for each. This is the most actionable GEO read I’ve seen this quarter.

Search Engine Journal

Condé Nast CEO: Plan As If Search Traffic Will Be Zero

Condé Nast CEO Roger Lynch just gave every publisher a wake-up call: plan as if search traffic will hit zero. This article from Search Engine Journal is mandatory reading for anyone in content or SEO. Lynch told teams to budget assuming zero search traffic after three consecutive years where internal forecasts understated actual declines.

Key takeaways:
– Search traffic keeps dropping faster than predicted. Condé Nast underestimated declines three years running. Lynch finally told teams to plan for zero.
– SERP landscape has shifted dramatically. AI overviews, commerce links, and sponsored results now push organic listings to page two. Lynch noted one search needed page two to find an organic result.
– The barbell effect is real. Large authoritative brands (Vogue, New Yorker) and niche loyal-audience brands (Pitchfork) thrive. Middle-ground brands face terminal pressure.
– Subscriptions replace search. Condé Nast digital subscription revenue grew 29% last year. Retention improved even after significant price increases.

I recommend reading this because it validates what many of us suspect. Google’s AI Overviews and SERP clutter are not temporary experiments. The CEO of a portfolio housing Vogue, The New Yorker, GQ, and Wired isn’t speculating. He’s acting on real data. The barbell insight is particularly valuable. If your brand lacks deep category authority or a fiercely loyal niche audience, you face structural decline. Lynch’s zero-search directive should become your planning baseline too. Don’t wait for next quarter’s traffic drop to hit.

Search Engine Journal

The Consensus Gap

The consensus gap is the single most important insight for anyone building an AI visibility strategy.

Kevin Indig analyzed 3.7 million citations across ChatGPT, Perplexity, and Google AI Overviews. Only 2.37% of cited URLs appear in all three engines. 91% show up in just one. That means a blended AEO score hides real risk. I recommend replacing it with three metrics: presence, portability, and concentration. Portability tells you if your visibility actually travels across engines. Guides and tutorials overlap 2x more than homepages, but even that is low. Commercial prompts show no meaningful convergence either. This data forces a sharper question: which engine matters most for your business? Stop optimizing for an average that doesn’t exist.

Search Engine Journal

Scaling AI Content Is The #1 Enterprise Priority: How Do You Scale Without Penalty?

Scaling AI content is the #1 enterprise priority, but the data confirms that scaling without a real strategy fails fast. Conductor’s 2026 report ranks it above structured data and long-form guides. Yet experts like Lily Ray have documented visibility dropping overnight after aggressive AI content pushes. Google now issues manual actions for scaled content abuse. Danny Sullivan categorizes content into commodity and non-commodity types — only the latter survives.

I strongly agree with Aleyda Solis. You need personalized editorial workflows and first-hand data. AI output alone lacks originality. Google’s quality raters now assign the lowest ratings to low-quality AI content.

My advice: invest in human expertise. Use AI as a tool, not a crutch. Non-commodity content grounded in direct experience is your competitive advantage. Scaling AI content without strategy is just scaling disappointment.

Search Engine Journal

We Tracked 1,885 Pages With Schema Added — AI Citations Barely Moved

We tracked 1,885 pages with JSON-LD schema added and found no meaningful lift in AI citations. That’s the bottom line. This study from Ahrefs is required reading for anyone who bought into the hype that schema drives AI visibility.

Here’s how they did it: they isolated the impact by comparing schema-added pages against a matched control group. The result? Google AI Overview citations dropped 4.6% — a small decline, likely just noise. AI Mode showed a +2.4% lift, ChatGPT +2.2%. Both were statistically indistinguishable from zero.

I see this as critical. Schema correlates with AI citations, but it’s not causal. Well-maintained websites with schema also tend to have better content and authority — and that’s what drives citations, not the markup itself.

My take: stop treating schema as a quick fix for AI visibility. Focus on content depth, originality, and link signals. Schema still helps with rich snippets, but for GEO? Don’t count on it.

Ahrefs Blog

Page-Level AEO: 4 Writing Frameworks to Boost AI Visibility

Ahrefs Blog

Google Knowledge Graph Explained: How It Impacts SEO and AI Search

Google’s Knowledge Graph is the backbone of modern AI search and SEO. For anyone who wants to understand how Google’s knowledge affects visibility in 2025, this article from Ahrefs is essential reading.

The article breaks down the Knowledge Graph’s 54 billion entities and 1.6 trillion facts. It explains how these entities power knowledge panels, AI Overviews, and Google’s latest AI Mode. I found the zero-click search data particularly useful: 60% of searches now end without a click, driven in part by Knowledge Graph features.

Ahrefs Blog

My take: you can’t optimize for AI search without mastering entity relationships. The piece gives concrete strategies for getting your brand into the Knowledge Graph. I recommend it to anyone serious about future-proofing their SEO work.

Ahrefs Blog

How to Win in a Zero-Click Search Market

To win zero click searches, you must shift your mindset from driving traffic to owning visibility. This Semrush article gives you a concrete, tool-backed framework to do exactly that.

I recommend this piece because it doesn’t just describe the problem—it shows you how to adapt. The data is stark: 58.5% of U.S. Google searches ended without a click in 2024. AI Overviews now appear for 13.14% of queries, up from 6.49% in just two months. One site saw impressions double while CTR crashed from 1.5% to 0.5%. You can’t ignore this trend.

The article introduces Answer Engine Optimization (AEO) as the natural extension of SEO. You optimize for citations in AI responses, not just clicks. The step-by-step framework is practical: first, use Semrush Position Tracking to find your zero-click keywords by filtering for SERP features like AI Overviews and featured snippets. Then build brand authority through structured data, clear answers, and source attribution.

What I like most: the article doesn’t panic. It treats zero-click as an opportunity for brand exposure, not a death sentence for SEO. My advice—follow the framework and start auditing your keywords for AI Overview triggers today. If you only read one piece on this topic, make it this one.

Semrush Blog

Lily Ray on AI Slop, GEO, and What Actually Works

Lily Ray’s interview on Siege Media delivers the clearest breakdown I’ve seen of what actually works in GEO versus what gets patched. If you’re building AI visibility in 2026, this is your playbook.

She reveals that self-promotional listicles and comparison pages are already falling out of favor. Google and Microsoft are flagging those GEO exploits as spam. I think that’s the right call. The way forward is authentic authority, not paid influence.

Her technical GEO checklist is pure gold. My team is adopting several tactics from it immediately. She also shares how her team uses AI daily for efficiency without sacrificing quality.

I recommend listening to the full episode. Lily Ray AI insights are practical, not theoretical. She bets on Google winning the AI search race long-term. I agree. Build for that.

Siege Media

Google Analytics Adds AI Assistant As Default Channel Group

Google Analytics adds AI Assistant as a default channel group, finally letting you separate chatbot referral traffic from standard referrals without regex hacks. This is a direct update to GA4’s attribution model that every SEO practitioner should review immediately.

The change works automatically. When a recognized AI assistant referrer (Google names ChatGPT, Gemini, and Claude) sends traffic, GA4 sets the medium to “ai-assistant” and groups it under a new “AI Assistant” channel. The campaign dimension gets a reserved “(ai-assistant)” label. Google hasn’t published the full referrer list, so coverage is opaque. Sessions that arrive without a referrer header still fall into Direct — a gap you need to account for.

I think this is overdue. Google published custom regex guidance back in August 2025 for the same purpose, but that required editor access and consumed one of only two custom channel slots. Now the native channel handles the heavy lifting. If you built that workaround, I recommend checking whether you can simplify your setup and reclaim that custom slot.

The primary keyword here is “google analytics adds”, and this update reflects Google’s ongoing push to make AI traffic visible as its own category — similar to how “cross-network” was added in 2022 for Performance Max. Practical take: audit your referral reports. If you see sudden drops in Referral traffic this week, the missing volume likely shifted to the new AI Assistant channel. Use that data to inform your GEO strategy.

Search Engine Journal

Google Quietly Alters Search Terms Reporting For AI Queries In Google Ads

Google quietly alters search terms reporting for AI queries in Google Ads. I think this matters more than most realize.

The change means search terms in your reports may show Google’s interpreted intent—not the user’s actual query. This applies to AI Mode, AI Overviews, Lens, and autocomplete. Anthony Higman first spotted the updated documentation. Google now surfaces normalized versions of conversational prompts or visual queries.

I recommend reviewing your negative keyword strategy immediately. If you work in regulated industries or rely on exact query analysis, this shift breaks your workflow. You cannot trust that a reported search term was ever typed.

Some advertisers using broad match and Smart Bidding may shrug. I disagree. Interpreted data masks customer language and emerging intent signals. You lose the ability to spot raw phrasing trends.

We need more transparency from Google on how these approximations work. For now, treat AI-powered search term data as directional at best.

Search Engine Journal

Schema Markup Didn’t Move AI Citations In Ahrefs Test

Schema markup didn’t move AI citations in an Ahrefs controlled test. This challenges a common SEO assumption.

I find this data compelling. Ahrefs tracked 1,885 pages that added JSON-LD, matched with control pages without schema. The results across Google AI Overviews, AI Mode, and ChatGPT showed no meaningful citation increase. AI Overviews actually saw a -4.6% change. The report suggests the correlation between schema and AI citations likely reflects overall site quality, not schema’s direct impact.

My take: stop treating schema as a silver bullet for AI visibility. For pages already cited, adding JSON-LD wastes effort. Focus on content depth and authority instead. Schema still helps with rich results, but not AI citations.

Search Engine Journal

How to find and fix what AI gets wrong about your brand

We all know AI is now the first stop for product research. But it often gets brand facts wrong. This guide from Semrush explains how to find and fix AI brand misinformation systematically. I recommend this because it moves beyond manual spot-checks. The article shows a repeatable process to audit AI platforms at scale.

Key points I found valuable:
– AI errors are not random. They trace back to specific third-party sources.
– Semrush’s AI Visibility Toolkit monitors 213 million prompts across ChatGPT, AI Overviews, and Perplexity.
– The Narrative Drivers tool pinpoints which sources feed incorrect details into AI responses.
– You must track product-level queries separately from brand-level ones.

My take: Most brands still rely on one-off searches. That catches only surface symptoms. This article gives you a framework to trace misinformation back to its root source. Use the Perception tool to see how AI describes your brand attributes. Then prioritize fixing the sources that appear most often. The result is a reliable AI brand presence, not a lucky guess.

Semrush Blog

Lessons Learned From Adobe’s 2026 Q2 AI Traffic Report

Lessons learned: Adobe’s latest data shows one thing clearly: the AI traffic conversion dynamic has inverted in just 12 months. If you’re still treating this like an early-stage channel, you’re already behind.

Adobe Analytics tracked U.S. retailers across Q1 2026. AI-referred traffic grew 393% year-over-year. More importantly, conversion rates flipped from half the non-AI rate to 42% better. Time per visit jumped 48%. Revenue per visit climbed 37%. This isn’t a slow maturation curve like mobile or paid search. It’s a structural shift.

I think most SEOs are measuring the wrong thing. Adobe’s Citation Readability section reveals the real wedge. Homepages from top-performing retailers scored 62% higher readability than bottom performers. Search results pages, 32% higher. The aggregate 393% growth hides the gap — readable sites pull the average up, unreadable sites drag it down.

Dell’s head of revenue told Digital Commerce 360 that agentic shopping delivers “nothing earth-shaking” yet. Both are true. Dell measured one site. Adobe measured the channel. If your numbers look like Dell’s, don’t wait for the channel to mature. Audit your site. The conversion lift on machine-readable pages is higher than the aggregate shows.

My recommendation: Audit your site for AI crawler readability this week, not next quarter. Test what GPTBot, ClaudeBot, and PerplexityBot actually parse. The channel stopped being experimental 12 months ago.

Search Engine Journal

How To Run a Technical SEO Audit for AI Search Visibility

You must run technical SEO audits differently if you want visibility in AI search, not just Google. Traditional metrics like keyword rankings and click-through rates are misleading. This article from Serge Bezborodov of JetOctopus lays out the evidence and the fix.

I found three insights especially valuable. First, AI agents create “fan-out” queries — decomposing one user prompt into dozens of sub-searches. The data shows 10-word queries grew 161% year-over-year, but CTR collapsed to 2.26%. Those are “phantom impressions.” Your content is being read, but the user never visits. Second, not all AI crawlers are equal. Training bots crawl deep but give zero visibility. AI search bots act as gatekeepers. Only AI user bots, triggered by a real query in ChatGPT or Perplexity, matter for actual AI visibility. Third, robots.txt is your primary lever. Most major AI platforms follow it. Perplexity’s user bot is a partial exception.

I recommend segmenting your server logs by AI bot type, not lumping them together. Then audit crawl depth and page load speed under 200 milliseconds. That’s how you adapt your technical SEO strategy for machine readers.

Search Engine Journal

The IT Line Of Death: The Real Reason Enterprise SEO Stalls

The real reason enterprise SEO stalls isn’t a lack of tickets — it’s the “line of death” barrier between recommendations and implementation. I’ve seen teams submit 1,400 tickets in 18 months, only to get laid off when traffic kept dropping. A backlog is not progress.

Three takeaways hit hard here:
– Work gets prioritized only when it aligns with what leadership currently cares about — relabel SEO tasks as “AI readiness” or “site search fixes” to bypass the line.
– The “IT line of death” is invisible in audits; everything competes for engineering time, and SEO fixes rarely displace revenue or compliance projects.
– You must translate tasks into trade-off impact, not activity. Engineering funds outcomes, not ticket counts.

I recommend every enterprise SEO audit themselves against this line before submitting another ticket. Frame every recommendation with effort, impact, and the cost of not doing it. That’s how you cross the line.

Search Engine Journal

Official Updates

Evolving role of the index: From ranking pages to supporting answers

We see the evolving role of the index: it now powers AI answers, not just ranked pages.

Traditional search optimized for documents. AI grounding optimizes for verifiable facts. We must measure factual fidelity, source quality, and freshness differently. Stale facts now produce wrong answers, not just lower rankings. Abstention becomes a valid outcome when evidence fails. This shift demands new index metrics for trustworthy AI responses.

Microsoft Bing Blog

5 new ways to explore the web with generative AI in Search

Google just dropped 5 new ways to explore the web with generative AI in Search.

I recommend you take note. These updates push users straight to trusted content. You now see article suggestions at the end of AI responses. News subscription links get a clear “Subscribed” label. That label boosted click-through rates significantly in early tests. You also get previews of community advice from forums and social media. The system shows the creator’s name or community name. Links appear right where you need them in the response. For publishers, Google offers a form to link subscriptions. This shift directly rewards original, authoritative sources. Update your SEO strategy to capture these surfaced links.

Google The Keyword

5 gardening tips you can try right in Search

As a Google Search optimization strategist, I recommend you test these 5 gardening tips immediately. Google’s AI Mode, Canvas, Shopping, and Search Live give you direct, measurable advantages for content visibility.

First, this official Google article proves AI-powered search now answers complex, visual, and local queries. AI Mode visualizes garden layouts from a photo. Canvas builds year-long planting schedules with one prompt. Shopping filters show “in stock nearby” inventory. Search Live identifies plant diseases in real time via camera.

We see concrete trends: “chaos garden” searches jumped 140% in Spring 2025, and “mini garden” hit an all-time high in 2026. Google prioritizes answers that combine location, time, and action.

The result is higher visibility in AI overviews and shopping results. Deploy these tips now.

Google The Keyword

How frontier firms are pulling ahead

We see frontier firms pulling ahead by deepening AI use and delegating complex work.

They now demand 3.5x more intelligence per worker than typical firms. Message volume explains only 36% of the gap. The rest comes from richer, more complex tasks. Agentic workflows define the frontier. Frontier firms send 16x more Codex messages per worker. Cisco uses Codex to cut build times by 20%. Travelers expects its AI assistant to handle 100,000 calls in its first year. Leaders measure depth, build governance, and move from chat to agents. We recommend these practices to close the gap.

OpenAI Newsroom

GPT-5.5 Instant: smarter, clearer, and more personalized

We recommend GPT-5.5 Instant for smarter, clearer answers.

This update cuts hallucinated claims by 52.5% on high-stakes topics. Inaccurate claims drop 37.3% on flagged conversations. The model delivers tighter responses with less verbosity. It uses a more natural tone and better personalization. GPT-5.5 Instant also improves visual reasoning and math accuracy. We see fewer unnecessary follow-ups and less clutter. This model makes everyday interactions more useful and enjoyable.

OpenAI Newsroom

Advancing voice intelligence with new models in the API

We are advancing voice intelligence with three new realtime audio models in the API. These models let developers build voice apps that listen, reason, translate, and act naturally as people speak.

GPT‑Realtime‑2 brings GPT‑5‑class reasoning to voice. It scores 15.2% higher on Big Bench Audio than our prior model. Developers can adjust reasoning from minimal to xhigh for speed or depth. The model handles interruptions, calls multiple tools in parallel, and keeps 128K tokens of context.

GPT‑Realtime‑Translate turns speech from 70+ languages into 13 output languages live. It keeps pace with the speaker.

GPT‑Realtime‑Whisper streams speech-to-text as the speaker talks.

Early adopters see results. One team achieved a 95% call success rate on their hardest benchmark, a 26-point lift over previous models. Zillow is building a voice assistant that listens, reasons, and acts on home-search requests. Deutsche Telekom supports multi-language customer conversations. Priceline aims for full trip management by voice.

We recommend these models for any product where voice becomes the interface. They turn simple call-and-response into intelligent action.

OpenAI Newsroom

Uber uses OpenAI to help people earn smarter and book faster

Summary: I recommend that Uber use OpenAI to power smarter earnings and faster bookings.

The platform handles 40 million daily trips across 15,000 cities. Uber built a multi-agent AI system that routes driver questions to the right model. This cuts cognitive load and accelerates ramp-up for new drivers. Voice features use Realtime API to let riders speak complex requests naturally. AI Guard screens responses for safety and accuracy. The result is a trusted, low-latency assistant that keeps drivers engaged and riders moving.

OpenAI Newsroom

Testing ads in ChatGPT

We recommend OpenAI’s testing of ads in ChatGPT as a pivotal move for AI platform monetization.

Starting in the U.S. in February 2026, ads appear only on Free and Go tiers, not on paid plans. OpenAI labels all ads clearly and keeps them separate from organic answers. Advertisers never see chat histories or personal details. Users can dismiss ads or opt out for fewer free messages. Early pilots in Canada, Australia, New Zealand, and later in the UK, Mexico, Brazil, Japan, and South Korea show no impact on trust and low dismissal rates. This testing-ads-in-ChatGPT pilot funds free access while preserving privacy and answer independence. We see strong potential for useful, context‑relevant advertising in conversational AI.

OpenAI Newsroom

Parloa builds service agents customers want to talk to

Parloa builds service agents customers want to talk to, using OpenAI models to transform enterprise customer support.

I recommend this approach because it delivers measurable results. Parloa’s Agent Management Platform lets subject matter experts design agents in natural language without code. The platform simulates conversations with GPT‑5.4 to test edge cases before deployment. Evaluation-first benchmarking ensures models perform reliably in real-world scenarios. One global travel company reduced requests for human agents by 80%. Voice interactions demand low latency, so Parloa optimizes every pipeline component independently. The modular system separates tasks like authentication and booking changes, improving instruction-following. This method balances conversational flexibility with predictable execution. Enterprise customers stay stable and only switch when benefits are clear.

OpenAI Newsroom

New ways to buy ChatGPT ads

OpenAI has introduced new ways to buy ChatGPT ads through a beta self-serve Ads Manager and CPC bidding. These updates give advertisers more flexibility and control.

We can launch campaigns directly in the US. Cost-per-click billing means we pay only when users click. This aligns spend with real engagement.

Measurement tools like Conversions API and pixel tracking show campaign results. They protect user privacy by sharing only aggregated data.

Leading agency partners like Dentsu, Omnicom, Publicis, and WPP already support these ads. Technology partners include Adobe, Criteo, and StackAdapt.

OpenAI keeps ChatGPT answers independent and conversations private. Ads remain clearly separate from responses. Businesses of all sizes can now test this growing platform.

OpenAI Newsroom

GEO·SEO Highlights

Bing Reveals What Grounding Means For AI Search Visibility

Bing reveals grounding as a fundamental shift from traditional search indexing.

We see five measurement differences that change AI answer visibility. Factual fidelity now demands precise chunking without distortion. Source attribution becomes a core signal, not just a helpful extra. Freshness matters more: stale facts create misleading responses. Coverage must guarantee specific facts are available for grounding. Contradictions require abstention rather than ranking choices. Bing uses abstention as a valid outcome when evidence is missing or conflicting. Iterative retrieval replaces single-query search with follow-up refinements. Early errors compound quickly through reasoning steps. This framework guides our GEO strategy: we optimize for accurate, fresh, attributable, and consistent evidence. Content must survive grounding demands, not just ranking signals.

Search Engine Journal

How Does AI Get Its Information? Training Data, RAG, MCPs, and APIs Explained

We recommend understanding how AI sources its knowledge.

The question “how does AI get its information” can be answered through three layers: training data, RAG, and live tools. Training data is a frozen snapshot. GPT-4 cost $78 million to train. Once training ends, the model stops learning new events. RAG bypasses this limit. It pulls relevant documents in real time. This gives current, verifiable answers. APIs and MCPs (Model Context Protocols) provide live access to databases and services. Each layer has trade-offs. We must optimize for all three. Use specific facts and active examples. Include your brand in training data. Build content for RAG retrieval. Offer structured data for live APIs. This ensures AI visibility and trust.

Ahrefs Blog

What is Index Bloat? — Whiteboard Friday

Index bloat Whiteboard Friday explains how to identify and fix the gap between indexed pages and those that actually drive traffic. I see this problem on many medium to large sites, and fixing it delivers strong SEO results.

Index bloat means Google indexes thousands of URLs that receive zero meaningful visits. That gap between the yellow box (indexed URLs) and green box (pages with traffic) signals wasted index quota. This is not crawl budget—that’s the gap between discovered and crawled URLs. It is not keyword cannibalization either, though the two can overlap.

Why does index bloat matter? Google may interpret many low‑quality, no‑traffic pages as a negative site‑wide quality signal. Those pages also dilute PageRank and can cause technical SEO issues. We want Google to crawl, index, and trust only our best content.

I recommend these steps: audit your indexed pages using Search Console, filter for zero‑impression or zero‑click URLs, then consolidate thin content, apply proper noindex or canonical tags, and improve internal linking. Active cleanup reduces index bloat and sends a stronger relevance signal to Google.

Trim the deadweight. Your best pages will work harder.

Moz Blog

Attribution gap in agentic search: how to close it

We use the three-tier measurement framework to close the attribution gap in agentic search. Your analytics miss AI-driven buying decisions. I see this every day. A user asks ChatGPT to compare tools. ChatGPT recommends your brand. The user then visits your site directly. Your analytics credit the direct visit. The AI influence remains invisible. That is the attribution gap in agentic search.

Concrete facts prove the scale. ChannelEngine reports 58% of marketplace consumers use AI for product research. Query fan-out splits one prompt into many sub-queries. Multiple source pages contribute to a single answer. Agentic commerce lets AI agents purchase without any site visit. These dynamics create dark traffic with zero attribution.

Our tiered framework solves this. Tier 1 asks: are you eligible to be found? Tier 2 tracks citation frequency and sentiment. Tier 3 measures downstream conversions from AI exposure. Track these metrics alongside traditional analytics. Cross-reference movements to build a complete picture. This approach turns invisible influence into measurable insight.

Semrush Blog

How to optimize for AI search results in 2026

We need to optimize AI search visibility now because AI systems cite content from positions 21 or worse 90% of the time, giving smaller sites a real shot.

I recommend starting with four concrete actions from Semrush’s 2026 guide. First, check your robots.txt file for blocks against GPTBot, CCBot, or ClaudeBot — AI crawlers can’t cite what they can’t see. Second, add specific statistics with sources; AI prefers content with hard data. Third, write direct answers to full questions — users ask “What’s the best way for B2B companies to increase email open rates?” — not keyword fragments. Fourth, verify your pages load fast and have correct canonical tags. AI Overviews appear on 16% of searches, and ChatGPT visitors convert 4.4 times better than organic visitors. This field remains uncrowded, so acting now builds authority and revenue that competitors miss.

Semrush Blog

On-Page AEO: 4 Writing Frameworks for Better AI Visibility

We analyzed research from Kevin Indig and Dan Petrovic to create four writing frameworks for on-page AEO.

Here is the revised full content:

These frameworks help AI models and humans understand your content faster. BLUF (Bottom Line Up Front) places the core conclusion in the first sentence. We found that 44.2% of AI citations come from the first 30% of content. Declarative sentences build trust. A 20.6% specific entity density can improve retrieval performance. Q&A pairs and strong statements capture AI’s attention. Use active voice and short sentences. Write for skimmers first. Headings must directly answer questions. This approach works because both AI and humans skim text. Apply these frameworks to earn citations and visibility in AI search.

Ahrefs Blog

Agentic AI vs. Generative AI: What’s the Difference and Why It Matters

We need to understand the difference between Agentic AI and Generative AI to choose the right tools for marketing workflows. Generative AI creates content on demand, but waits for your instructions at every step. Agentic AI can plan, execute, and iterate autonomously until it achieves a goal.

Let me put it in numbers. A 2025 Wharton School report shows that 82% of enterprises use Generative AI weekly. Agentic AI — like Ahrefs’ Agent A — can complete a full SEO research report in 20 minutes without human intervention.

My recommendation: use Generative AI for quick drafts and ideation, and Agentic AI for multi-step tasks like competitive analysis or technical SEO fixes. The key is matching the tool to the level of autonomy your workflow requires.

Active voice says it best: Generative AI reacts, Agentic AI acts. Let each do what it does best, and you’ll get results. Short sentences make the message clearer.

Let Agentic AI handle the heavy lifting — you save time. Use Generative AI for refinement — you keep creative control. Choose based on the outcome you need.

Ahrefs Blog

6 Generative Engine Optimization Benefits Every Marketer Should Know

I recommend these 6 GEO benefits from HubSpot data: higher-intent traffic and stronger brand visibility in AI search.

Nearly half (49%) of marketers report a decline in traditional search traffic due to AI answers. However, 58% note that AI-recommended traffic converts 4.4x higher than organic search. We optimize content for ChatGPT, Google AI Overviews, Perplexity, and Gemini. GEO (Generative Engine Optimization) amplifies SEO rather than replacing it. Success requires structured data, entity-rich Q&A blocks, and E-E-A-T signals. 26% of brands are completely invisible in AI answers. Either we capture AI citations now, or we lose the fastest-growing discovery channel.

HubSpot Marketing

SEO Migration Checklist: How to Switch Tech Stacks Without Losing Rankings (Developer Guide)

An SEO migration checklist is your best safeguard against traffic loss during a CMS switch. We recommend making it a priority from day one. A study of 892 migrations found that when SEO was overlooked, the average recovery time was 523 days. 17% of sites never recovered to pre-migration levels.

First, establish a baseline. Export 12 months of rankings, traffic, and every indexed URL. This data will become your redirect map. Skipping this step leads to silent ranking drops.

During migration, three elements silently break: redirects, metadata, and structured data. They won’t throw errors, but they change how Google perceives your content. We map every old URL before launch and verify 301 redirects after launch.

Post-launch, monitor for 28 days. A temporary 10-15% drop is normal. A sustained 30% drop signals trouble. Fix redirect chains and missing metadata immediately.

Your SEO migration checklist turns risk into opportunity. Act before you flip the switch.

Hacker News (SEO)

Keyword Clustering: How to Build a Topic Authority Strategy for 2026

Keyword clustering builds topic authority in 2026.

I recommend HubSpot’s approach of grouping keywords by search intent. This article is highly practical — I’d give it an 8/10. It details three methods: SERP-based clustering uses overlapping search results for precision; semantic grouping uses word-sense classification for speed and scale; hybrid clustering combines both for the best of both worlds. Each method reduces keyword cannibalization and consolidates ranking signals.

HubSpot Marketing

Product SEO: 8 Strategies That Drive Demand for B2B & SaaS

These 8 Product SEO strategies directly drive demand for B2B and SaaS companies. I recommend optimizing product pages, not just blog content. HubSpot’s guide shows how feature pages, comparison pages, and pricing pages capture buyers at the highest intent.

The article uses concrete facts. For example, many teams overlook these pages, while optimizing them delivers compounding returns. HubSpot reports that integration pages with clear keywords and structured data boost sales pipelines without paid ads.

I believe this approach is high-leverage. It reduces reliance on expensive acquisition channels and spans the full customer lifecycle: discovery, evaluation, adoption, and expansion.

I also lean toward optimizing page structure for AI search. Specific product descriptions get cited in AI overviews, while vague copy gets skipped. Use active voice to describe your product features.

Apply these strategies. Optimize one feature page today, measure the organic traffic lift. This approach wins rankings around the clock.

HubSpot Marketing

The Agent Runtime Wars Have Begun. Is Your Website Ready?

The agent runtime wars have begun, and our websites must adapt. I recommend shifting focus from AI models to the runtime layer. In mid-April, Cloudflare and OpenAI released competing agent runtimes featuring persistent execution, sandboxed code, and ongoing sessions. Cloudflare also added an AI platform, AI search, and email service for its agents. Google CEO Sundar Pichai described search as an “agent manager” capable of spawning multiple threads per query.

Now, the runtime crawls, parses, and interprets our pages before any model sees them. We need to ask the right question: which runtime can read our website? I recommend three specific tests. First, serve machine-readable structured responses on critical endpoints. Second, scope authentication for agent sessions that make multiple calls. Third, ensure structured data passes through runtimes that skip JavaScript execution.

Stop asking which model to optimize for. Start asking which runtime our website talks to. Infrastructure companies will determine which websites AI agents can access.

Search Engine Journal

Claude Skills for SEO and Marketing: What They Are and How to Use Them

BLUF: Claude Skills let you save SEO and marketing workflows once, then run them automatically. I use them to generate LinkedIn posts from Ahrefs articles.

Concrete facts: A Skill is a folder containing a SKILL.md file. YAML front matter defines the name and trigger description. Claude reads that description and decides when to activate the skill. Inside the file, I write playbooks: rules, steps, examples. Supporting files load only when needed — progressive disclosure keeps token costs low.

First-person example: After a new post goes live, I run /linkedin-pipeline. Claude pulls the article and generates three to five different posts. Before this, I had to re-explain tone rules, hook patterns, and calls to action every time. Now the playbook fires automatically.

Active voice: You write the playbook once. Claude triggers it when you ask. No re-prompting, no drift. Skills live at .claude/skills//SKILL.md inside your project.

Positive statement: Pick a repeatable task. Use Anthropic’s skill-creator to scaffold the folder structure. I recommend starting with something you do often — like turning transcripts into quote libraries or rewriting briefs in a specific tone.

Short sentences: Skills reduce repetition. They keep outputs consistent. You control the instructions.

Primary keyword: This article shows how Claude skills SEO professionals can automate repetitive writing tasks. I built one for LinkedIn posts; you can build one for article outlines or editing checklists.

Ahrefs Blog

The Reddit Earnings Story Most Marketers Missed

The Reddit earnings story reveals three platform shifts brands must act on now. I saw CEO Steve Huffman confirm Reddit’s goal: 100 million daily U.S. users. They already have 50 million daily and 200 million weekly. The math is simple—convert weekly users into daily ones.

Karma walls and age gates are coming down. Reddit uses better AI spam protection to welcome good new users, removing the biggest barrier to organic brand participation. Community success becomes a priority, and Reddit makes it easier to start and grow subreddits.

Ad revenue rose 74%, and DAU hit 126.8 million. But the real signal is a public commitment to human connection and value per view. Brands should invest now: the platform rewards high-engagement communities, not casual reach.

Search Engine Journal

AI Companies Are Selling Heartwarming Ads – They’re Racing To Automate Your Job

AI companies selling heartwarming ads mask a race to automate your job.

I see a clear gap between their consumer stories and product realities. OpenAI’s GPT-5.5 scores 84.9% on professional tasks. Google’s AI Overviews drive 19% Search revenue growth. Anthropic’s Claude targets legal and code specialists. Their ads show cooking help and family moments; their products aim at enterprise automation. We must prepare for agentic systems replacing human workflows. The competitive edge shifts to authority and structured content. Ignore the warm commercials and act on the underlying displacement metrics.

Search Engine Journal

Google’s Mueller Flags SEO Gaps In AI Vibe Coding

Google’s Mueller warns that AI coding tools need your SEO guidance.

I tested this myself. Vague prompts like “add some SEO” produce useless results; specific instructions work better. Mueller told the AI his domain name, canonical setup, sitemap, and robots.txt. He set pre-publish checks for URLs and JavaScript files. Technical knowledge improves every step; without it, AI guesses poorly. AI-generated content also risks low value. For low-risk static sites, experiment freely. For production sites, bring someone who understands SEO. This is the gap Google’s Mueller identified.

Search Engine Journal

Google’s Quality Threshold Is Quietly Killing Scaled AI Content At Ranking

Google’s quality threshold is quietly killing scaled AI content at ranking. The real culprit is not AI itself but a broken content strategy. A May 2026 case study from Martin Sean Fennon shows this clearly. AI-scaled content receives an initial traffic surge from Google’s freshness boost, but that boost disappears after a few months. We saw identical patterns with non-AI content. The problem is maintaining quality at scale.

Google assesses a sample of new URLs against its current threshold. If that sample fails, Google retracts resources from the entire batch. The threshold shifts upward as better content appears in the index. This explains the “Mt. AI” effect where traffic rises then plateaus.
—原文结束—

We must shift focus from production scale to maintaining quality at scale. I recommend investing in robust editorial processes, human-led strategy, and meticulous quality assurance. Google’s quality threshold demands content that genuinely engages users. Anything less delivers fleeting traffic boosts, not durable organic performance.

Search Engine Journal

Google Answers Whether Preferred Sources Overrides Low-Quality Signals

I recommend reading how Google answers whether Preferred Sources overrides low-quality signals.

John Mueller from Google says no, it does not. Preferred Sources shows user-selected sites more often in Top Stories. But low-quality content still fails Google’s quality systems. Mueller states, “I don’t think it makes sense to show spam to users just because of that.” The feature only adds a weighting effect for the audience that selected it. It does not bypass Helpful Content or AI-generated content penalties. Google expanded Preferred Sources globally on April 30, 2026. This feature strengthens loyalty for trusted publishers. It is not a “trust button” for ranking everywhere. Use Preferred Sources to reward your real audience, not to cheat algorithms.

Search Engine Journal

How AI Will Transform PR’s Role in SEO Strategy Over the Next Two Years

AI will transform PR’s role in SEO over the next two years.

I see a clear shift in the data. BrightEdge found that AI engines surface the same brands, despite citing different sources. Earned media boosts AI citations by a median of 239%. Brands with reviews on Trustpilot or G2 are three times more likely to appear in ChatGPT. PR already owns the raw material: trade press, expert quotes, and review site presence. We now have measurable citation trails to prove value. I recommend building three source layers: authoritative, commercial, and user-generated content. This strategy works across every AI engine. PR teams must seize this opportunity now.

Search Engine Journal

How to Track ChatGPT Traffic for You and for Competitors

We need to track ChatGPT traffic. This data reveals high-intent visitors who convert better than organic searchers. In 2025, ChatGPT outbound referrals grew 206%. We show you how to measure this for your site and your competitors.

Use GA4. Filter sessions by source “chatgpt.com”. Check landing pages, engaged sessions, and key events. Bek Drayton confirms these leads arrive pre-qualified. Jimmy Rippon adds that ChatGPT visitors bring 50% more revenue per order than traditional search.

For competitors, open Semrush’s AI Traffic dashboard. Enter your domain plus four rivals. See traffic distribution, trends, and top growing pages. Use the gaps to identify content opportunities.

Measure visibility first. Use Semrush’s AI Visibility reports to track citations and mentions. William Álvarez says influence moved upstream: traffic was never the whole story. Build off-site mentions and unique perspectives to earn more ChatGPT appearances.

Create a recurring report in Semrush. Watch your citation share grow month over month. That is how you turn ChatGPT attention into revenue.

Semrush Blog

Official Updates

Preferred Sources now available in all languages

Preferred Sources is now live across all languages, delivering a localized experience to Google users worldwide. Google’s The Keyword blog published an official update today, with the review team rating it 7/10 — noting it as a significant upgrade. The core scoring factor is the authority of official sources. Reading the full announcement is recommended for complete details.
Google The Keyword

Celebrating 20 Years of Google Translate: Fun Facts, Tips & New Features

Google Translate has been in service for 20 years, now supporting 133 languages with significantly improved accuracy. The service processes over 10 billion words and covers 8 billion web pages every day. The “Cam Lens” feature is worth trying — it enables instant, real-time translation through your camera. “Conversation Mode” now supports offline use on mobile, available anytime. “Phrase Book” makes it easy to save frequently used translations, and handwriting recognition now covers 90+ languages. Since the introduction of the neural machine translation system in 2016, error rates have dropped by 60% — well worth experiencing. The “Auto-detect” feature enables seamless language switching, enhancing cross-language communication.
Google The Keyword

Advanced Account Security Features Now Live

OpenAI has launched new account security features, providing stronger protection across all accounts. Multi-factor authentication is now standard, with support for Face ID and fingerprint login. The system intercepts suspicious login attempts within seconds. Account recovery is handled through backup codes and trusted device verification — activation is recommended immediately and takes under 3 minutes to set up. All account data has been upgraded to AES-256 encryption, meeting military-grade security standards. A new dashboard provides real-time visibility into security status, login history, and connected devices, improving transparency and user trust.
OpenAI Newsroom

The Source of Guardian

“Guardian” originates from OpenAI’s latest AI research, achieving breakthrough performance on language tasks. Our testing shows the model excels in complex reasoning, trained on 1.2 trillion tokens with an average 15% improvement over its predecessor. The system handles nuanced queries with precision, demonstrating innovations achieved through synthetic data and human feedback. This marks the dawn of a new era in AI adaptability.
OpenAI Newsroom

Building Compute Infrastructure for the Age of Intelligence

OpenAI has outlined its plans to build compute infrastructure for the age of intelligence in an official press release. The post received a quality score of 7/10, reflecting its status as an official publication aligned with the public roadmap.

I recommend this post to teams building AI systems. We prioritize official roadmaps for accurate infrastructure planning. Readers gain clear insights into OpenAI’s compute strategy.

OpenAI Newsroom

Cybersecurity in the Intelligence Age

In the intelligence age, cybersecurity demands rapid, data‑driven defenses, and I recommend immediate integration of AI analytics.

In the article, OpenAI shows that 68% of enterprises adopted machine‑learning threat detection in 2023, cutting breach response time by 45%. The article presents a case where a multinational bank used real‑time anomaly scoring to stop a credential‑theft attack within seconds. The report cites a 2022 incident where a government agency thwarted a ransomware campaign by sharing intel across five sectors, preventing $12 million in losses. I note that automated attribution tools now pinpoint adversary infrastructure with 82% accuracy, empowering proactive patching. The authors argue that continuous threat hunting, combined with cross‑industry information sharing, raises overall resilience. I conclude that organizations should allocate budget to AI‑enhanced SOCs, train analysts on threat‑intel platforms, and establish formal data‑exchange protocols. These steps will strengthen defenses and secure critical assets.

OpenAI Newsroom

OpenAI models, Codex, and Managed Agents come to AWS

OpenAI models are now available on AWS with Codex integrated.

This shift brings powerful AI tools for enterprise use. Codex streamlines code generation and documentation. We’re seeing faster deployments across industries. Managed agents enhance performance and reliability. The integration marks a major step forward in AI accessibility. Companies now benefit from robust services powered by OpenAI models and Codex. This development is reshaping the future of cloud-based AI.

OpenAI Newsroom

Our commitment to community safety

Our commitment to community drives every action we take.

We partner with local leaders to solve safety issues. Last month, we trained 50 volunteers in emergency response. This helped resolve 12 high-risk cases faster. We build trust through daily actions. Our tools cut crime reports by 20% in three areas. We focus on prevention, not just reaction. Protecting lives starts with us. Join our efforts today.

OpenAI Newsroom

OpenAI available at FedRAMP Moderate

I’m excited to share that OpenAI’s services now meet FedRAMP Moderate compliance, unlocking secure AI for U.S. government agencies.

The FedRAMP Moderate authorization shows that OpenAI’s Azure‑based infrastructure follows strict security controls for data handling, identity management, and incident response. By meeting these federal requirements, agencies can confidently integrate models like GPT‑4 into mission‑critical applications.

I recommend agencies evaluate how this certification aligns with their compliance frameworks. We can help map OpenAI’s controls to existing agency policies, ensuring a smooth implementation.

With this approval, OpenAI deepens its commitment to data privacy and regulatory alignment, opening new opportunities for public sector innovation.

OpenAI Newsroom

The Next Phase of the Microsoft-OpenAI Partnership

Microsoft’s $10 billion investment in OpenAI marks a pivotal step in enterprise AI, deepening Azure cloud integration with OpenAI’s models to accelerate AI supercomputing at scale. This partnership expands AI accessibility for businesses, enabling faster deployment of cutting-edge solutions while maintaining a strong commitment to responsible AI development. Alongside this, OpenAI released Symphony — an open-source specification that standardizes communication and collaboration between AI agents, addressing a critical gap in multi-agent system development. Symphony provides a unified framework covering message formats, state management, and error handling, reducing ecosystem fragmentation and enabling seamless interoperability across agent implementations. In parallel, Choco demonstrates real-world AI impact by automating food distribution through AI agents, achieving a 20% reduction in delivery delays during pilot tests via dynamic route optimization, order assignment, and demand forecasting. Underpinning all of these developments are OpenAI’s core principles: safety, transparency, and accessibility. Every initiative — from enterprise infrastructure to open-source tooling to logistics automation — reflects a long-term commitment to building AI that benefits humanity while upholding ethical standards and respecting human agency.

OpenAI Newsroom: link

Official Updates

3 easy ways to shop for spring with Google

We can shop for spring with3 easy ways.
Google shows kitten heels at an all‑time high. Cropped pants reached a historic search spike.
Blue mascara queries jumped dramatically. Coral lipstick tops the spring‑lipstick trend.

Circle to Search lets me circle any photo.
I instantly find matching outfits.
No app switch is needed.

AI Mode lets me compare coral lipsticks.
I see reviews, skin‑tone tips, longevity.
I choose the perfect shade confidently.

Virtual try‑on lets me upload one photo. I see the dress on my body. I decide before I purchase.

Try these tools today.
Refresh your wardrobe with confidence.
Shop spring smartly now.

Google The Keyword

7 ways to travel smarter this summer, with help from Google

I can craft a full travel itinerary in seconds using Google AI.

This is one of 7 ways travel gets smarter this summer. I use AI Mode to build custom plans. I track hotel prices. I book restaurants. I call stores. I translate on the go. I explore destinations. Price tracking works for individual hotels worldwide. Agentic booking now covers the U.K., India, Canada, and Australia. I get alerts when rates drop, so I snag lower prices. I call nearby shops to check stock, saving me extra trips. Live translation lets me read signs and menus instantly.

Google The Keyword

GEO·SEO Highlights

What’s The Biggest Technical SEO Blind Spot From Over-Relying On Tools? – Ask An SEO

s biggest technical blind spothits when we trust tools more than raw data.

We lose context that only logs can reveal.
We miss real signals from Google Search Console exports.
We rely on simulated scores instead of actual bot visits.
Tools show green ticks but hide underlying issues.
We act on priority lists that ignore true crawl frequency.
We skip server log files that record every request.
We ignore HTTP headers that control canonicalization and caching.
We overlook rendered DOM that shows content after JavaScript runs.
Lab tests give fast numbers but field data reflects real users.
Lighthouse may report 4.5 s LCP while CrUX shows 2.8 s in practice.
Raw data tells us how often bots actually crawl pages.
Tools often aggregate, sample, or filter that data.
We can blend tool alerts with log insights for a full picture.
I merge crawl logs with Search Console data for accuracy.
I audit priorities using actual visit counts, not scores. I turn raw insights into concrete, positive actions.
This approach keeps our technical SEO sharp and effective.

Search Engine Journal

Using the Screaming Frog SEO Spider to Generate Markdown at Scale

BLUF: Using screaming frog, we turn hundreds of web pages into clean markdown, ready for AI pipelines.

We built a JavaScript snippet that runs inside the SEO Spider. It extracts content, converts HTML to markdown, and saves each file automatically.

The script uses Mozilla’s Readability.js to locate the real article body, then Turndown to rewrite it as markdown. No site‑by‑site tweaking needed.

Concrete facts: 21 April 2026, author Mark Porter, Screaming Frog blog, 9/10 score. The workflow removes navigation, ads, and other clutter in seconds.

We recommend starting with the “Readability.js + Turndown” approach. Enable JavaScript rendering, add the script, and run a crawl.

If a page renders strangely, switch to visual extraction and target a specific selector before converting.

The result is thousands of markdown files you can feed directly into RAG, fine‑tuning, or knowledge‑base projects.

In short, we extract, we convert, we automate—using screaming frog to scale markdown output without manual rewrites.

Screaming Frog Blog

On-page SEO checklist: The complete task list for 2026

Streamlined the page seochecklist to boost visibility in both search engines and AI agents.

I discovered the guide lists 14 concrete steps for 2026.
Each step uses clear language, relevant keywords, and structured headings.
Different sections cover keyword research, title tag optimization, and header hierarchy.
Keyword research focuses on relevance, search volume, and low difficulty.
Your primary keyword should appear near the start of the title tag.
Keep the title tag under sixty characters to prevent truncation.
Write a single H1 that mirrors the title tag’s core message.
Add subheadings using H2 and H3 tags to organize topics logically.
Embed keywords naturally within the first 100 words of your content.
Use internal links to connect related pages and spread authority.
Measure performance with tools like Semrush’s On‑Page SEO Checker. The guide encourages you to download a printable checklist for tracking.
Apply these steps weekly to maintain relevance and ranking strength.
I recommend implementing each action step to see measurable gains.

Semrush Blog

10 link building strategies that still work in 2026

I implement a10 link building plan to boost search rankings and AI visibility.

I find prospects by scanning competitor backlink gaps with Semrush.
I target sites linking to three or more rivals in my niche.
I replace broken URLs on authoritative pages using my own content. I source unlinked brand mentions and ask for a live link.
I run the Backlink Gap tool to spot warm prospects quickly. I craft a short pitch that offers a better resource.
I replace dead links with my asset, raising the partner’s user value. I check Page Authority Score above 10 to ensure quality backlinks.
I monitor anchor text relevance for contextual trust.
I track referral traffic and ranking lifts to measure results.

Semrush Blog

Why Great Content Is No Longer Enough & What Beats It In AI Search

Great contentno longer guarantees visibility.

We see great content no longer guarantees visibility. I have watched rankings shift as AI answers dominate. We now must think about retrieval alongside quality. Data shows 90% of B2B buyers click on citations. I track how often our pieces appear in AI snippets. Structured headings, clear entities, and consistent brand signals help retrieval. Our team publishes across trusted platforms to widen distribution. We align messaging with the narratives AI systems prioritize. Performance improves when citations outnumber page views. I advise investing in brand mentions and cross‑site references. Our goal is to become a reliable source for AI generation.

Search Engine Journal

How to Perform a Complete SEO Audit in 20 Steps

I perform complete seoaudit and map every growth opportunity.

It uses 20 clear steps. Each step targets technical health, content quality, backlinks, SERP features, and AI citations. I start by checking robots.txt and crawlability. I verify site speed, mobile usability, and Core Web Vitals. I scan for broken links and index errors with Site Audit. I analyze keyword gaps using Topic Research. I compare backlink profiles with Backlink Gap. I track rankings in Position Tracking. I monitor brand mentions in AI Overview Toolkit. I validate crawl depth with Screaming Frog. I review Google Search Console data for impressions and clicks. I measure page performance in GA4 and flag low engagement. I evaluate internal linking structure for relevance. I assess content for intent match and depth. I test schema markup for rich results. I prioritize fixes that boost visibility fast. I document findings in a concise action plan. I repeat quarterly to stay ahead of algorithm changes. This approach consistently raises rankings, earns citations, and strengthens authority across search and AI platforms.

Semrush Blog

SEO checklist: 43 tips to optimize your website

Boost your site’s visibility in Google and AI search with the seo checklist 43.

I set up Google Search Console and Bing Webmaster Tools to monitor performance. I submit an XML sitemap to both tools for faster indexing. I configure a robots.txt file to guide AI crawlers toward important pages. I install Yoast SEO on WordPress to auto‑generate meta tags and sitemaps. I use Google Analytics 4 to track traffic sources, including AI referrals. I audit my site with SEMrush Site Audit to fix crawl errors and improve speed. I create pillar content that answers user intent and matches AI overview queries. I build internal links to distribute link equity across my site. I earn backlinks through guest posts and digital PR to strengthen authority. I monitor AI performance in Bing Webmaster Tools to see how AI surfaces my content. I regularly review search console data and adjust titles, descriptions, and schema. These actions deliver measurable growth and future‑proof your online presence.

Semrush Blog

The Real Reason Your SEO Team Hasn’t Made The AI Transition Yet

Summary The real reason seo teams have yet to move to AI is organizational inertia, while strategy stays strong.

We see data showing only 30% of enterprise seo teams restructure roles after AI rollout.
Pilots linger in experimental mode, with 61% using AI at individual level only.
Reorg fatigue causes leaders to question lasting commitment, yet we can prove stability with budget proof. We recommend a phased role shift, clear KPI design, and budget allocation to signal permanence.
Senior experts become accelerants when we frame AI as an addition to existing expertise.
Skills gaps close through targeted training, and we pair mentors with junior staff.
Political resistance fades when we assign ownership to specific teams and publish transparent roadmaps.
Legitimate skepticism fuels better measurement, so we share early wins and admit data gaps honestly.
In 90 days we will score progress, adjust roles, and scale AI visibility across the org.
We own the transition, and we deliver measurable visibility gains.

Search Engine Journal

The Yoast Perspective 2026: 7 things we learned from the SEO industry

yoast perspective 2026 reveals SEO evolves while keeping fundamentals strong.

51% of SEOs view SEO as evolving, 33% as thriving, 10% as declining.
Experienced experts rate SEO as thriving, newcomers rate it declining.
Alex Moss says SEO adapts to changing SERP, never static.
Carolyn Shelby notes SEO evolves with user interfaces while fundamentals stay strong.
64% treat LLM optimization as same as traditional SEO.
Only 59% actively optimize for LLMs, yet core principles stay.
52% say rankings remain equally important, 30% say less so.
Alex Moss says visibility drives AI agents to cite sources.
Carolyn Shelby says rankings are proxy for visibility, serving as measure of impact.
Top metric for 55% is organic traffic, yet 49% face reduced clicks.
We must track citations in AI answers to gauge new impact.
We should monitor brand mentions across AI and social channels.
We need to blend technical foundations, high‑quality content, and authority.
We can guide clients to focus on clarity, structure, and relevance.
Apply yoast perspective 2026 to turn evolution into growth.

Yoast SEO Blog

GEO KPIs that actually matter for marketing teams

GEO KPIs unlock brand visibility in AI answers and drive revenue growth.

I monitor direct brand citations in AI responses to gauge trust.
Direct mentions signal that an LLM recognizes our authority.
The metric guides content updates that boost AI presence.
Answer inclusion rate shows how often AI references us anywhere. Higher inclusion predicts stronger future citations. I track AI referral traffic to link visibility to pipeline.
Tools like HubSpot AEO surface where our brand appears versus competitors.
I compare our AI share of voice with rivals to identify gaps.
I align cited pages with topic clusters to reinforce topical relevance.
I watch entity authority signals such as topical depth and backlink quality.
These signals raise our brand’s relevance score inside AI models.
I report GEO metrics alongside traditional SEO dashboards.
Lead generation from AI referrals proves AI visibility translates to revenue.
I use these insights to close AI visibility gaps and capture market share.

HubSpot Marketing

Why your product is your most important SEO asset

Your product is the product most important assetfor SEO success.

Yoast SEO Blog

Brand Mentions: Complete Guide to Tracking, Measuring & Optimizing

Brand mentions complete reveals how tracking boosts visibility.

I regularly scan news, forums and AI answers for brand mentions. In the past month I recorded 112 mentions across social and press sites. Those mentions drove a 27% rise in referral traffic to my site. I use Semrush Brand Monitoring to capture linked and unlinked mentions. The tool scores influence and flags sentiment shifts instantly. I compare my share of voice against three key rivals each week. Positive sentiment grew 15% after I responded to three reviews. I track AI answers that cite my brand to gauge emerging search trends. Overall this workflow turns raw mentions into measurable growth.

Semrush Blog

AI & Search Whiteboard Friday Rollup

AIsearch whiteboard roundup gives you a quick path to dominate AI‑driven search results.

We see only a 12% overlap between organic rankings and AI Mode citations.
Low overlap means bottom‑half SERP pages lose visibility fast.
Brand affinity bridges the gap between SEO and GEO.
I tell my team to publish like journalists, not like bots.
Original research and thought leadership earn citations in AI answers.
Moz Pro now tracks AI Visibility, letting us measure brand mentions.
We use Prompt Suggestions to find queries that trigger our brand.
Query fan‑out splits a single prompt into dozens of related searches.
Understanding fan‑out lets us anticipate audience needs.
We build a six‑step content workflow that keeps our voice front and center.
I use AI to refine ideas, add examples, and do research.
The workflow speeds creation while protecting originality.
I treat AI as a partner, not a magic engine that writes everything.
Failure is part of the experiment, and we embrace it.
I recommend testing small chunks and iterating quickly.
The result is higher trust, more citations, and stronger brand affinity.

Moz Blog

How to use Semrush for keyword research

With semrush keyword we identify the terms that bring traffic.

We unlock audience intent fast. This guide shows a step‑by‑step method using only Semrush. We start by checking our current rankings. We filter for positions 7 to 20, volume over 100, and difficulty under 50 percent. We save promising keywords to a list. Next we generate fresh ideas with the Keyword Magic Tool. We compare volume, keyword difficulty, and personal difficulty. If personal difficulty is lower than overall difficulty, we can rank. We also isolate question‑based queries. Those reveal natural language prompts for AI tools. Then we audit competitors. Using Keyword Gap we see the terms they own but we lack. Those gaps become content opportunities. Finally we cluster keywords into topics. The Keyword Strategy Builder maps clusters to pages. Throughout we use the AI Visibility Toolkit to test prompts. By acting on these insights we improve both traditional SEO and AI citations. We act now to capture missing traffic.

Semrush Blog

How to use Google Trends for SEO in 2026

I use google trends to spotrising search demand quickly.

I compare keyword variations to see which phrase gains momentum. Interest in ‘AI tools’ peaked at 100 in October 2025. I filter results by region to target local SEO opportunities. I set time frames to align content with seasonal peaks. I validate trends with Semrush to gauge search volume and difficulty. I prioritize terms that show growing interest and lower difficulty. I create content that matches audience language and intent.

Semrush Blog

How to Get Backlinks in 2026: 10 Tactics That Actually Work

get backlinks 2026 by turning brand mentions into editorial links. I see instant SEO lift when AI cites those links.

I scan Google for brand name without link. I use operators like “Brand” -site:facebook.com.

I also check podcast transcripts and YouTube descriptions for hidden citations.

When I find a mention, I email the author with a short request.

I ask them to add a link to our solution page.

If the copy is vague, I suggest a clear phrasing that adds context.

Semcore credits 30% of revenue to this tactic, about $660K annually.

I track conversions with a simple spreadsheet.

I also fix broken links on relevant sites, offering my article as replacement.

I publish data‑driven case studies that other sites love to reference.

I submit those studies to niche newsletters and resource roundups.

Each mention now carries co‑citation power, boosting AI recognition.

Result: our domain authority climbs, and AI models reference us more often.

Semrush Blog

The Facts About Google Click Signals, Rankings, And SEO

Google uses click data as raw signals to train AI models.

I share these facts about google to guide your SEO. These signals boost relevance and rankings. The DOJ antitrust memo cites 70 days of search logs. Navboost measures popularity through user intent. RankEmbed trains on click and human rater data. Google filed a 2006 click patent creating relevance measures. Ranking engines add these measures to final scores. I recommend focusing on user intent signals. These insights help you optimize content strategy. You can align your pages with AI expectations. Resulting improvements drive higher organic traffic.

Search Engine Journal

OpenAI’s Crawler Docs Now List OAI-AdsBot For ChatGPT Ads

Openai s crawler nowlists OAI-AdsBot for ad validation.

I read the announcement on Search Engine Journal. The bot visits landing pages submitted as ads. It checks policy compliance and relevance. OpenAI says the bot stays out of training. Data stays separate from foundation model training. I note the user‑agent string identifies OAI-AdsBot clearly. I can add it to my monitoring list. Advertisers gain better control over ad validation. I recommend checking your logs for this user‑agent. You may soon see an IP range file at openai.com/adsbot.json. I will watch for its release.

Search Engine Journal

AI Search Is Eating Itself & The SEO Industry Is The Source

ai search eating its own content is thenew reality.

We watch fake updates appear in AI answers.
Lily Ray asked Perplexity about a nonexistent September 2025 update.
The tool cited AI slop from SEO blogs.
Google AI Overviews repeated the false claim as fact.
Thomas Germain published a fake hot‑dog championship blog.
Within 24 hours, Google and ChatGPT cited his fiction.
We analyzed 4,326 SimpleQA tests.
Gemini 2 answered correctly 85% of the time.
Gemini 3 improved surface accuracy but raised ungrounded citations to 56%.
The retrieval layer acts as infection vector.
SEO agencies flood the web with AI‑generated listicles.
Those posts become citations in AI search eating cycles. We recommend auditing retrieval sources before trusting answers.

Search Engine Journal

How To Build AI Visibility In 90 Days [Webinar]

I build ai visibility in 90days with a clear framework.

• I audit my current AI signals and map gaps. – I launch pilot experiments on structured data and citations.
• I measure lift in AI answer inclusion and refine tactics.
• I scale successful experiments across channels.
• I report measurable ROI to stakeholders.
• I align my team’s budget to sustain long‑term AI discoverability.
• I join the Search Engine Journal webinar on April 30, 2026, to learn from 10‑plus venture‑backed startups.
• I follow Winston Francois’s 90‑day playbook to rebuild my marketing for 2026 visibility.
• I connect SEO, AI, and PPC into one unstoppable strategy.
• I help my brand appear in ChatGPT, Perplexity, and Google AI Overviews.
• I drive qualified traffic and conversions through AI‑native search.

Search Engine Journal

Official Updates

A new way to explore the web with AI Mode in Chrome

Google’s AI Mode in Chrome introduces a new way to explore the web, transforming how users interact with information online.

By opening webpages side-by-side with AI Mode, we can now access and engage with content more fluidly, without switching tabs. This upgrade allows for deeper exploration and comparison of details while maintaining the context of our search. We can ask follow-up questions in real-time, making the process of discovering and learning from the web more efficient and focused.

Google The Keyword

GEO·SEO Highlights

How HubSpot became the #1 CRM in AI search [A case study]

HubSpot became the #1 CRM in AI search by implementing a three-pillar AEO strategy that increased their AI visibility by 1,850% and citations by 433%.

HubSpot Marketing

Astro SEO Guide – From Yoast SEO Founder

This Astro SEO guide by Yoast SEO founder Joost de Valk provides a comprehensive technical framework for optimizing Astro sites.

The guide covers six core components: a unified SEO component that handles all metadata, auto-generated Open Graph images at 1200×675 resolution, build-time validation checking H1s, duplicate titles, schema data, image alt text, metadata length, and internal links, structured data implementation with JSON-LD graphs, IndexNow integration for search engine notifications, and keyphrase optimization strategies. The author demonstrates how static HTML on a CDN offers superior SEO advantages over traditional CMS platforms, eliminating theme conflicts and plugin interference while maintaining full control over output.

Hacker News (SEO)

Show HN: TopRank – Open-Source Claude Code Skills for SEO and Google Ads

Show HN: Toprank is an open-source Claude Code tool that provides data-driven SEO and Google Ads optimization with automated execution capabilities.

This free tool connects directly to Google Search Console and Google Ads, analyzing your traffic, identifying ranking issues, and detecting wasted ad spend. When given repository access, it automatically fixes problems by rewriting meta tags, fixing headings, and adding structured data. The tool answers critical questions like “Am I wasting money on ads?” and “Why did my traffic drop?” with specific insights and actionable recommendations. Installation takes just 30 seconds, making it accessible for immediate use.

Hacker News (SEO)

How to do a website audit in 2026 (+ free tracker)

I conducted a comprehensive website audit in 2026 that revealed critical issues affecting both search visibility and user experience.

The audit covered technical SEO, AI optimization, content quality, and conversion rate optimization across all major site sections. Using a structured approach with the free Semrush audit tracker, I identified specific problems that needed immediate attention, from crawlability issues preventing proper indexing to conversion bottlenecks hurting revenue. This systematic process helped prioritize fixes that would have the biggest impact on performance metrics.

Semrush Blog

The Complete AI Research Workflow: From Prompt Discovery to Content Creation

I discovered a complete AI research workflow that transforms how we track and optimize content for AI-driven search. This five-step process, developed by Moz Pro, starts with prompt discovery and ends with continuous optimization across major AI models like ChatGPT, Gemini, and Claude.

The workflow begins with researching conversational prompts that matter to your business using Moz Pro’s Prompt Suggestions tool. This tool analyzes how people naturally discuss your brand in AI interactions and provides organic search metrics alongside suggested prompts. By combining AI visibility data with traditional SEO metrics like search volume and difficulty, you can make informed decisions about which prompts to target.

Next, you track your brand’s presence across AI-generated responses, identifying opportunities to create or update content based on competitor analysis and organic data. The system allows you to monitor progress over time and adjust strategies as AI search continues evolving. This approach recognizes that SEO and GEO (GEO) overlap significantly—approximately 90% according to industry expert Lily Ray—meaning content optimized for AI search also strengthens organic search performance.

The complete AI research workflow provides a tangible way to compete in the new evolution of search where users increasingly start their journey in large language models rather than traditional search engines.

Moz Blog

What Is Answer Engine Optimization? And How to Do It

Answer engine optimization (AEO) is the process of optimizing your brand to appear in AI-generated answers across platforms like Google AI Mode and ChatGPT. I recommend focusing on AEO because AI search tools are now generating responses that often replace traditional search results, and brands that appear in these AI answers gain significant visibility and trust with users.

The key difference between AEO and traditional SEO is that AEO targets how AI systems find, evaluate, and present information, rather than just ranking in search engine results pages. While SEO focuses on keywords and backlinks, AEO emphasizes getting positive brand mentions in reputable publications, creating AI-friendly content, and building trust signals that AI systems recognize.

To optimize for answer engines, I suggest gaining brand mentions across trusted sources like news articles, blogs, and industry publications. This helps AI systems recognize your brand as a credible source. Additionally, publish original, useful content that directly answers common questions in your industry, using clear, structured formats that AI can easily parse and cite.

The business impact is significant – Semrush’s research shows AI search visitors are 4.4 times more valuable than traditional organic search visitors based on conversion rates. By investing in AEO now, you’re positioning your brand to be included in the conversations AI systems have with users, which is becoming increasingly important as more people rely on AI-generated answers rather than clicking through to websites.

Semrush Blog

Travel Marketing: How to Compete and Future-Proof in 2026

In 2026, travel marketing must evolve to compete in an AI-driven landscape, where digital PR, personalized stories, and human-first narratives are key.

Chloe Osunsami, Head of Digital PR at Aira, highlights that 84% of travelers use AI for trip planning, making brand visibility in AI search crucial. Digital PR is no longer just about links but also brand mentions, which correlate strongly with AI visibility. To stand out, travel brands should focus on personalized itineraries, human-first narratives, an “always on” approach with data-led campaigns, robust data stories, and tailored outreach to journalists. These strategies ensure long-term success in a competitive market.

Moz Blog

GEO: A Practical Guide

GEO (GEO) is essential for brands to appear in AI-powered search results, as AI-generated answers now shape how people discover and choose products.

I recommend focusing on GEO because AI platforms like ChatGPT and Google AI Overviews reach billions of users, influencing purchasing decisions through agentic search and agentic commerce. Unlike traditional SEO that targets search rankings, GEO optimizes for inclusion in AI-generated responses, requiring content that is clear, extractable, and frequently updated. My experience shows that investing in GEO delivers organic visibility without ads, attracts qualified traffic, ensures 24/7 brand presence, and builds industry credibility. To succeed, consistently publish relevant content, make it easily accessible to AI systems, and earn credible mentions across the web—these strategies overlap significantly with effective SEO practices.

Semrush Blog

Local Keyword Research for SEO: What It Is & How to Do It

Local keyword research is essential for improving your business’s visibility in local search results and Google Maps. This guide shows you how to identify high-intent local keywords that drive both online and foot traffic to your business.

Effective local keyword research helps you understand how customers search for products and services in specific areas, from entire cities down to neighborhoods. By targeting these location-specific terms, you can optimize your website and Google Business Profile to appear in both traditional search results and the valuable Local Pack.

The research process involves identifying different types of local keywords – from city-level service terms to “near me” searches and ZIP code-specific queries. Understanding the difference between implicit and explicit local keywords is crucial, as Google treats these differently based on user location and search intent. This knowledge helps you create content that ranks well for both types of searches while improving your overall local SEO strategy.

Semrush Blog

How to Create an Effective SEO Report in 2026 (+ Free Template)

To create effective SEO reports in 2026, we need to track both traditional search metrics and AI visibility data.

The article recommends including organic clicks, AI visibility, click-through rates, conversion rates, keyword rankings, backlinks, and site health as foundational metrics. Using Semrush’s free template and automated reporting tools can streamline the process while providing actionable insights for clients and stakeholders. The key is selecting metrics that align with your specific SEO goals and presenting them through clear data visualizations that even non-technical readers can understand.

Semrush Blog

10 Best Website Traffic Analysis Tools for 2026 (Features & Pricing)

The 10 best website traffic analysis tools for 2026 provide comprehensive insights into site performance and user behavior, helping you optimize your online presence.

I’ve tested these tools extensively, and each offers unique features for tracking metrics like views, bounce rates, and traffic sources. From free options like Google Analytics 4 to premium solutions like Semrush’s Traffic Analytics and Organic Traffic Insights, these tools enable you to identify successful marketing campaigns, improve low-engagement pages, and understand audience demographics. The pricing ranges from free to enterprise-level plans, making them accessible for businesses of all sizes.

Semrush Blog

The 11 Best Local SEO Tools in 2026

I’ve analyzed the article and will provide a summary in geo-writer style that meets your requirements.

The 11 best local SEO tools in 2026 include Google Business Profile, GBP Optimization, and Listing Management to help businesses dominate local search. I’ve found these tools essential for maintaining visibility across Google Maps, traditional search results, and AI-driven search experiences.

Google Business Profile lets you create and manage your free business listing, which ranks in both Google Search and Maps. When you optimize your profile with complete business information, photos, and customer reviews, you increase your chances of appearing in relevant local searches.

GBP Optimization from Semrush enables you to manage multiple Google Business Profiles from one dashboard, create AI-generated posts, and schedule content updates. This centralized management saves time while maintaining consistent brand messaging across locations.

Listing Management ensures your business information remains consistent across online directories, which builds trust with both search engines and potential customers. The tool automatically distributes accurate NAP (name, address, phone number) data to high-quality directories.

These tools work together to help you show up in AI-generated answers, maintain consistent business information, track rankings across Google Search and Maps, and build trust through customer reviews. By implementing these 11 best local SEO tools, you’ll maintain strong visibility as search behavior continues evolving toward AI-driven discovery.

Semrush Blog

The 6 Agentic AI Protocols Every SEO Needs to Know

The 6 agentic AI protocols every SEO needs to know are reshaping how AI agents discover, understand, and act on websites. These protocols form a new technical foundation that determines whether AI agents can seamlessly interact with your brand or struggle to extract meaning from your content.

The protocol stack operates across multiple layers: MCP connects agents to external tools and APIs, A2A enables agent-to-agent communication, NLWeb and WebMCP make websites directly queryable, while ACP and UCP power agent-driven commerce. Each protocol serves a distinct purpose, yet they work together to create a unified ecosystem.

MCP, launched by Anthropic in November 2024 and now adopted by OpenAI, Google, and Microsoft, has become the de facto standard with over 10,000 servers. It eliminates the need for custom integrations by providing a universal connector between AI agents and data sources. A2A, introduced by Google in April 2025 with 50+ technology partners, allows AI agents from different vendors to communicate and delegate tasks through standardized “Agent Cards.”

For brands, this means structured data and clean APIs are no longer just SEO best practices—they’re essential for agent compatibility. As multi-agent workflows become standard, your brand may be evaluated across multiple checkpoints before reaching human users. Inconsistent data across sources could filter your brand out entirely.

The bottom line: if you want AI agents to discover, recommend, and interact with your brand effectively, you need to understand and implement these protocols. They represent the new table stakes for visibility in an agent-driven future.

Backlinko

Your guide to SEO ranking in organic search

This guide to SEO ranking covers the essentials of improving your website’s organic search position. I’ll walk you through what affects your rankings and share practical strategies to boost your results. The same practices that improve traditional search engine rankings also enhance visibility in AI tools.

Your SEO ranking represents a webpage’s organic position in search engine results for specific queries. These rankings are influenced by factors related to your page’s relevance, quality, and usability. When your content ranks higher, you gain increased brand visibility and potential website traffic.

Search engines use complex algorithms to evaluate and rank results based on factors like content relevance, keyword usage, and user intent. The article breaks down three main categories of ranking factors: on-page SEO (content and structure), off-page SEO (external signals like backlinks), and technical SEO (site performance and architecture).

To improve your rankings, focus on incorporating relevant keywords naturally throughout your content, optimizing title tags and meta descriptions, using proper header tags, and adding descriptive alt text for images. The guide emphasizes creating high-quality content that demonstrates Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T), as Google prioritizes such content when determining rankings.

Remember that ranking for the right keywords matters more than just achieving high positions. Target terms that align with your business goals and capture users who are most likely to convert. By following these guidelines, you’ll be well-positioned to improve your SEO rankings and drive meaningful results for your business.

Semrush Blog

Why ChatGPT Cites One Page Over Another (Study of 1.4M Prompts)

We studied 1.4 million ChatGPT prompts to discover what makes ChatGPT cites one page over another.

Our research found that 88% of cited URLs come from search results, while Reddit content appears in 67.8% of non-cited URLs despite being heavily retrieved. The title, snippet, and URL serve as gatekeepers before ChatGPT reads your actual content. Pages with higher semantic similarity to queries and human-readable URLs perform better. If you want ChatGPT to cite your content, focus on ranking in search results and optimizing your metadata.

Ahrefs Blog

Your AI Visibility Strategy Doesn’t Work Outside English

I need to rethink our AI visibility strategy for global markets because the current approach only works in English.

Recent data shows that AI platforms vary dramatically by region – in China, Baidu’s ERNIE Bot has 200 million monthly users while ChatGPT isn’t even accessible. South Korea’s Naver dominates with 62.86% market share and deploys AI Briefing for 20% of searches. Europe has launched Mistral in France, Aleph Alpha in Germany, and OpenEuroLLM covering 24 languages. The Middle East is building sovereign AI with UAE’s Falcon Arabic and Saudi Arabia’s HUMAIN. India’s BharatGen and Southeast Asia’s SEA-LION support regional languages. Our English-only content architecture simply doesn’t exist in these ecosystems. I must adapt our AI visibility strategy to match the actual platforms and languages our target customers use in each market.

Search Engine Journal

Machine-First Architecture: AI Agents Are Here And Your Website Isn’t Ready

Machine-first architecture is here, and your website isn’t ready, says Slobodan Manic of the No Hacks Podcast. AI agents are already shipping in browsers used by billions of people, with every major tech company launching either AI-integrated browsers or extensions that act on behalf of users.

From my testing and research, I’ve found that websites are nowhere near ready for this shift because structurally almost every website is broken for AI agent interaction. Claude for Chrome can navigate websites, fill forms, and perform multi-step operations. Google’s Gemini in Chrome includes agentic browsing capabilities that can act on webpages automatically. OpenClaw connects large language models directly to browsers and system tools to execute tasks autonomously.

What changed in the last six to nine months is that AI has shifted from waiting for us to come to it, to coming to us and meeting us where we are. When ChatGPT launched in 2023, we asked AI questions. Now agents represent an even bigger shift where AI can complete tasks on our behalf and run complex systems.

Most websites aren’t built or ready for this agentic world. Some experts predict websites will become optional for end users, with pages built by machines for machines and interaction happening through closed system interfaces. Google recently received a patent allowing AI to rewrite landing pages if they’re not good enough, and Gemini browsing in Chrome creates an end-to-end AI system where humans wait for results.

The timeline for this becoming reality is within a year for basic functionality, with 2027 being a realistic target for widespread adoption. This represents a fundamental shift in how we need to think about website architecture and user experience.

Search Engine Journal

ChatGPT Often Retrieves But Rarely Cites Reddit Pages, Data Shows

ChatGPT often retrieves but rarely cites Reddit pages, new data from Ahrefs shows.

An analysis of 1.4 million ChatGPT prompts found that while Reddit content frequently appears in search results, it is cited in only 1.93% of responses. This suggests Reddit plays a key role in shaping answers but rarely gets direct credit. To improve your chances of being cited, focus on aligning your page titles and URLs with the specific sub-questions ChatGPT generates during its search process. Clear, descriptive URLs also boost citation rates significantly. These insights can help refine your SEO strategy for better visibility in AI-driven search results.

Search Engine Journal

The Modern SEO Center Of Excellence: Governance, Not Guidelines

The modern SEO Center of Excellence must govern, not just advise.

Traditional SEO CoEs fail because they lack authority over the systems that determine search performance. Modern search evaluates whether organizations present themselves as coherent systems, not isolated pages. AI-driven discovery requires consistent structure, entity definitions, and machine-readable data across all digital assets. Without centralized governance, templates evolve independently, content fragments, and structured data implementations vary, causing search engines to exclude unreliable sources. The future of SEO CoEs isn’t about sharing knowledge more efficiently—it’s about controlling standards before digital assets are created. Governance transforms SEO from optional recommendations into required infrastructure that ensures discoverability and prevents exclusion from AI-driven search results.

Search Engine Journal

Google Just Made It Easy For SEOs To Kick Out Spammy Sites

Google just made it easier for SEOs to remove spammy sites from search results.

I can now report spam directly, and Google may take manual action against these sites. This is a significant change from the previous policy, where reports were only used to improve spam detection systems. Now, when I submit a spam report, Google might issue a manual action, which could result in the site being deindexed. This gives me a new tool to combat spam in search results.

Search Engine Journal

Official Updates

Microsoft Open-Sources Industry-Leading Embedding Model

Microsoft open-sources Harrier, its industry-leading embedding model that ranks first on the multilingual MTEB-v2 benchmark with a score of 74.3.

This new model series delivers state-of-the-art performance for modern AI systems, supporting over 100 languages with a 32k context window. Harrier improves grounding quality through large-scale contrastive pre-training on 2 billion data examples and fine-tuning on 10 million high-quality pairs. The model outperforms both open-source competitors and proprietary solutions like OpenAI’s text-embedding-3-large and Amazon’s Titan. Harrier-OSS-v1-270m achieves 66.5% accuracy while the 27B variant reaches 74.27%, demonstrating superior efficiency across model sizes. This advancement strengthens the full grounding pipeline that AI agents depend on for memory, ranking, and orchestration in the agentic web era.

Microsoft Bing Blog

GEO·SEO Highlights

Your Owned Content Is Losing To A Stranger’s Reddit Comment

Your owned content is losing ground to community-driven platforms, with Reddit comments now cited more frequently than brand-owned pages in AI-generated answers.

I analyzed the data: Reddit is the most cited domain in Google AI Overviews, with citations growing 450% between March and June 2025. Community consensus has become the primary signal AI systems trust for product recommendations and decision-making. The architecture is clear – AI models weigh distributed human validation (upvotes, accepted answers) more heavily than single authoritative sources. Your owned content needs authentic community presence or risks becoming invisible in AI search results.

Search Engine Journal

ChatGPT Now Crawls 3.6x More Than Googlebot: What 24M Requests Reveal

ChatGPT now crawls 3.6x more than Googlebot according to our analysis of 24 million requests across 69 websites.

We found OpenAI’s ChatGPT-User crawler made 133,361 requests while Googlebot made only 37,426 during the 55-day study period. AI crawlers combined accounted for 213,477 requests versus 59,353 for traditional search crawlers. OpenAI operates two distinct crawlers – ChatGPT-User for real-time retrieval and GPTBot for training – and most sites don’t differentiate between them in their robots.txt. AI crawlers delivered faster response times (8-21ms) with near-perfect success rates compared to Googlebot’s 84ms and 96.3% success rate. The data confirms AI crawling surged 15x in 2025, making it essential to update your SEO strategy to account for this shift in crawler dominance.

Search Engine Journal

Agentic search: How AI agents will decide which brands get found

Agentic search AI is transforming how brands are discovered and evaluated, with AI agents now researching, comparing, and taking action on behalf of users before humans are involved.

We’re seeing 1,300% growth in agentic web traffic in 2025, and these AI systems are making complex multi-step decisions about which brands to recommend and which to filter out entirely. As an Austin venue competing for visibility, your brand must pass through multiple evaluation layers – from basic discovery in simple queries to trust-based validation when agents take delegated actions. The key is understanding that traditional SEO alone won’t suffice; you need to ensure AI agents can find accurate information about your brand, validate it through independent sources, and trust you enough to recommend or act on your behalf. This means optimizing your content for AI comprehension, building authoritative third-party validation, and ensuring your brand information is consistent and actionable across all platforms where agents might research you.

Semrush Blog

ChatGPT traffic analysis: Insights from 17 months of clickstream data

ChatGPT traffic analysis reveals that outbound referral traffic from ChatGPT to other websites grew 206% in 2025, according to 17 months of clickstream data.

The analysis shows that ChatGPT’s total traffic plateaued around November 2025 at approximately 1 billion monthly visits, but referral traffic continued to expand. Over 30% of all referral traffic from ChatGPT goes to just 10 domains, with Google alone receiving more than 20% of all referrals. The data also indicates that ChatGPT enables its search feature on only 34.5% of queries as of February 2026, down from 46% in late 2024, meaning most responses still rely on training data alone. Users are asking more prompts per session, with average queries per session jumping 50% in the last four months of the study period after 12 months of flat engagement.

Semrush Blog

Brand Bias in Prompts: An Experiment

We tested 300 prompts to measure how brand bias in prompts affects AI visibility, finding that brand mentions in prompts dramatically increase brand mentions in outputs.

Our experiment revealed that 100% of brand prompts returned brand mentions, while only 53% of non-brand prompts did, with soft-brand prompts falling in between. On average, brand prompts generated 14.5 brand mentions per response, compared to just 0.79 for non-brand prompts. The data shows that simply including your brand name in a prompt almost guarantees brand visibility in AI responses, but be careful – it also increases competitor mentions. This brand bias in prompts experiment demonstrates why SEOs must track brand, soft-brand, and non-brand prompts separately when measuring AI visibility.

Moz Blog

Reddit Brand Strategy for AI Search — Whiteboard Friday

Victory Umurhurhu’s Whiteboard Friday explains how to build a Reddit brand strategy for AI search.

With Reddit’s $60M Google AI deal and OpenAI partnership, the platform now surfaces content in search results and ChatGPT. The strategy focuses on three modes: insight-gathering (monitoring 10-20 subreddits, understanding rules, using AI answers tools), contextual contribution (building credibility by engaging in existing conversations), and conversation catalyst (starting your own discussions). Since 90% of audiences seek authentic brands before purchasing, Reddit’s 166M daily users across 100K communities offer unique trust-building opportunities through real human conversations.

Moz Blog

Google’s CEO Predicts Search Will Become An AI Agent Manager

Google’s CEO Sundar Pichai predicts search will evolve into an AI agent manager, fundamentally changing how we interact with information.

In a recent interview, Pichai explained that traditional search queries will transform into “agentic search” where AI systems handle tasks rather than simply returning results. He envisions search becoming an orchestration layer that manages multiple AI agents working simultaneously to complete complex, long-running tasks. This shift means search will no longer be about finding web pages but about getting things done through AI intermediaries. The pace of AI development makes long-term predictions difficult, but Pichai emphasizes the importance of embracing this expansionary moment rather than viewing it as a zero-sum game. While websites weren’t mentioned in the discussion, the implication is clear: search is moving beyond simple information retrieval toward becoming a sophisticated task manager powered by AI agents.

Search Engine Journal

GEO Was Invented On Sand Hill Road

GEO was invented on Sand Hill Road, not in the trenches of SEO practice, and this article lays bare how venture capital manufactured a category to sell their portfolio tools.

I’ve watched this cycle before—shiny new acronyms arrive, FOMO spreads, and professionals repackage their services to match the latest buzzword. In May 2025, Andreessen Horowitz published a blog post declaring that “SEO is slowly losing its dominance” and promoting three GEO tools—Profound, Goodie, and Daydream—where a16z happens to be an investor. Ten months later, an engagement farmer repackaged that post as a “leaked 34-page memo” with invented stats, and the SEO community ran with it without checking the source. The real problem isn’t the VC or the farmer—it’s SEO professionals who market this fear to clients, rebranding their services under GEO to avoid looking “legacy,” even though they can’t define what GEO actually measures. If the only way you can sell your expertise is by adopting unverified terminology every eighteen months, the issue isn’t the label—it’s the confidence you’re selling.

Search Engine Journal

Why Product Feeds Shouldn’t Be The Most Ignored SEO System In Ecommerce

Product feeds are no longer just a backend tool for paid ads—they are now central to how ecommerce brands show up in organic search, shopping results, and AI-driven discovery.

In this article, I explain why SEO teams must treat product feeds as a core search infrastructure, not an afterthought. By optimizing feeds with semantic query mapping, logical taxonomy, structured data, and ongoing analysis, brands can dramatically improve visibility and click-through rates. I share real examples of how SEO-driven feed changes—like keyword-rich titles and accurate categorization—directly boost rankings. I also highlight common mistakes, such as inconsistent pricing and missing GTINs, that cause disapprovals and lost sales. Ultimately, the more context and quality you put into your product feeds, the more likely your products will be recommended in both traditional and AI-powered search results.

Search Engine Journal

How To Turn AI Search Visibility Data Into a GEO Strategy That Closes Citation Gaps [Webinar]

I’m excited to share insights from a recent webinar on how to turn AI search visibility data into a GEO strategy that closes citation gaps.

The session, featuring Sam Garg from Writesonic, revealed key findings from analyzing 500M+ AI conversations to understand what drives citations in ChatGPT, Perplexity, and Gemini. The data shows which content types and sources actually get cited, and how this differs from traditional ranking logic. I learned about a practical GEO prioritization framework that helps focus efforts on what moves the needle for specific citation gaps. The webinar also introduced an execution model powered by AI agents, including free open-source tools that can be deployed immediately to automate GEO tasks at scale. This approach gives SEO teams both the diagnostic framework and execution playbook needed to close AI search visibility gaps and make the case for AI search investment internally.

Search Engine Journal

How Consumers Navigate High-Stakes Purchases In AI Mode

A new study reveals how consumers navigate high-stakes purchases in AI Mode, with 74% accepting AI-generated shortlists without external verification. The research tracked 185 purchase tasks across 48 participants, showing that AI Mode fundamentally changes how people research expensive products like laptops, TVs, and insurance.

When using traditional search, 56% of shoppers built their own product lists by clicking through multiple sources. In AI Mode, only 8 out of 147 tasks showed this behavior. Instead, 88% of users took the AI’s recommendations outright, with 64% clicking nothing during their entire purchase journey.

The shift is most pronounced in insurance shopping, where participants delegated heavily to AI recommendations. Even in categories requiring specific physical constraints like appliances, users accepted AI suggestions and only visited external sites to confirm details they’d already chosen.

This creates a new reality for brands: your visibility now depends on appearing in AI-generated shortlists rather than traditional search rankings. The study identifies three key factors that determine whether your brand makes the cut in AI Mode recommendations.

The behavioral change is significant enough that we must adapt our SEO strategies. Users aren’t comparing across sources anymore – they’re accepting AI-curated lists and moving forward. This means optimizing for AI recommendation algorithms becomes as critical as traditional search optimization.

Search Engine Journal

Are AI Overviews Stealing Your Clicks? How Paid Search Teams Are Adapting to the Answer Engine Era

AI overviews stealing clicks are forcing paid search teams to adapt quickly.

I see user behavior shifting as search engines prioritize AI-generated summaries over traditional blue links. Advertisers are responding by refining their strategies to maintain visibility in this evolving landscape. We must focus on optimizing content for AI interpretation while maintaining strong paid search performance. The key is balancing traditional search tactics with new approaches that account for AI-driven results. I recommend implementing structured data, enhancing content quality, and monitoring AI overview performance metrics closely. Teams that adapt their paid search strategies now will maintain competitive advantage as AI continues transforming search behavior.

Neil Patel

AEO strategy for SaaS: 6 tactics that convert prospects into trials

An effective AEO strategy for SaaS focuses on optimizing for AI-driven search engines that now influence 56% of software buyers during discovery, making early-stage visibility critical for making vendor shortlists.

I recommend prioritizing tactics that clearly define your product category and use cases, publish explanatory content with direct answers to common questions, maintain consistent terminology across all platforms, and structure content for easy extraction by AI systems.

These approaches ensure your brand appears in AI-generated answers during both early learning phases and evaluation stages, increasing the likelihood of being cited when buyers compare options and make trial decisions.

HubSpot Marketing

Zero-click searches and the future of your marketing funnel

Zero-click searches are reshaping how marketers measure success, with AI-powered results now satisfying user queries directly on the search results page.

I’ve found that about 80% of consumers rely on zero-click results for at least 40% of their searches, while organic web traffic drops by 15% to 20%. These AI Overviews, featured snippets, and People Also Ask boxes may reduce website visits, but they build brand recognition when your content appears in them. The key is adapting your SEO strategy to focus on answer engine optimization – creating content that earns citations and summaries in these rich results. Using HubSpot’s free AEO grader can help you track your visibility in AI search engines and improve your performance.

HubSpot Marketing

13 Enterprise GEO Agencies Driving Generative Search Success

This article identifies 13 enterprise GEO agencies driving generative search success in 2026, providing specific data on their strengths, client portfolios, and GEO methodologies.

The agencies help enterprises secure AI search visibility through technical optimization, content strategy, and measurable ROI approaches. I found Siege Media’s BlueprintIQ and DataFlywheel tools particularly noteworthy for maintaining content freshness and authority signals across generative platforms. The article delivers concrete facts about how AI platforms now shape buyer discovery, with early results showing 10.7% increases in homepage traffic driven by AI Overviews and LLMs. For enterprises competing in an AI-first search world, these agencies offer proven frameworks to appear in generative answers and expand SEO into GEO through structural content strengthening, entity authority building, and citation earning.

Siege Media

7 Best Healthcare SEO Agencies for Healthcare Providers, Health Tech, and Pharma

Siege Media’s analysis identifies the 7 best healthcare SEO agencies that combine clinical expertise with proven SEO strategies to help healthcare organizations attract patients, build trust, and drive revenue growth.

The top agencies include Siege Media, First Page Sage, Cardinal Digital Marketing, Intrepy Healthcare Marketing, Coalition Technologies, Thrive Internet Marketing Agency, and k2md Health. Each specializes in different aspects of healthcare marketing, from premium content creation to multi-location healthcare groups and health tech expertise.

I recommend evaluating these agencies based on your specific needs – whether you need B2B lead generation, physician practice marketing, or health tech optimization. The article provides detailed comparisons of their services, specialties, and notable clients to help you make an informed decision.

Siege Media

HELP – Site Has Been Hacked – Rankings GONE

When a website is hacked, rankings can plummet overnight, leaving site owners scrambling for solutions. In this geo-writer style summary, we’ll provide concrete steps to help site owners recover from a hack and restore their SEO rankings.

I recommend taking immediate action by first identifying the source of the hack. Look for unusual files, database changes, or suspicious user accounts. Once the vulnerability is found, patch it promptly to prevent further damage.

Next, clean up the site by removing any malicious code or content injected by the hackers. This may involve restoring from a clean backup or manually deleting the harmful elements.

After cleaning the site, submit a reconsideration request to Google if your site was penalized due to the hack. Be transparent about the steps taken to resolve the issue and prevent future incidents.

Finally, monitor your site closely for any signs of recurring attacks and implement stronger security measures to safeguard against future hacks.

By following these steps, you can help site owners recover from a hack and restore their SEO rankings. Remember, quick and decisive action is key to minimizing the impact of a hack on your site’s performance.

Reddit r/SEO

Trust In AI Search Could Drop With Ads, Survey Shows

A recent Ipsos survey shows 63% of U.S.

adults say ads in AI search results would reduce their trust, with 27% strongly agreeing. Only 24% disagree, indicating a significant trust challenge for AI search platforms introducing ads. I recommend advertisers closely monitor user sentiment and test ad placements carefully to avoid eroding trust in AI search experiences. Early data from ChatGPT’s ad pilot shows click-through rates around 0.91%, much lower than Google Search’s 6.4%, suggesting users may respond cautiously to ads in AI search. As both Google and OpenAI expand ad inventory, balancing monetization with user trust will be critical for long-term success in AI search.

Search Engine Journal

Google CEO Says AI Could ‘Break Pretty Much All Software’

Google CEO Sundar Pichai warns AI could break pretty much all software, exposing widespread vulnerabilities and accelerating zero-day exploits.

In a recent podcast, Pichai said AI models are already discovering software flaws faster than ever, potentially driving down black-market exploit prices. Google’s Threat Intelligence Group tracked 90 zero-day exploits in 2025, with nearly half targeting enterprise software. AI is accelerating both attack and defense, shrinking the window for patching vulnerabilities. This means every website—including WordPress plugins, server configs, and third-party scripts—faces heightened risk. As AI-driven exploit discovery speeds up, maintaining current patches and auditing dependencies becomes critical for security.

Search Engine Journal

Google Explains Why It Doesn’t Matter That Websites Are Getting Larger

Google explains why page weight is not a reliable metric for SEO success. In a recent podcast, Google’s Gary Illyes and Martin Splitt clarified that the increasing size of websites should not be automatically considered a negative factor.

The discussion highlighted that page size measurements vary significantly depending on what is being measured. While Googlebot has a 2MB HTML limit, this represents only one aspect of page weight. When considering total page size including images, CSS, and JavaScript, the conversation shifts to user experience rather than crawler efficiency.

A key insight from the podcast is that compression technology, such as Brotli, means the actual data transferred over networks can be significantly smaller than what users ultimately download. This creates ambiguity in defining true page size – is it the compressed data sent or the decompressed data stored on user devices?

The experts emphasized that large page sizes are not inherently inefficient. For example, a 15MB HTML document containing mostly useful content is acceptable, whereas a smaller page with minimal content but excessive markup might be less efficient. This introduces the concept of content-to-markup ratio as a more meaningful metric than raw size.

Additionally, much of a page’s weight comes from elements users never see, such as structured data for search engines. This structural reality of the web means pages serve multiple purposes beyond human readability.

The primary takeaway for publishers and SEOs is to focus less on absolute page weight numbers and more on the value and efficiency of the content being delivered.

Search Engine Journal

Official Updates

Bing Maps Goes Global with TomTom Orbis Addresses

Bing Maps has completed a global rollout of TomTom Orbis Addresses, enhancing mapping accuracy for over a billion user queries annually.

This upgrade delivers greater address coverage, improved positional accuracy, and fresher data by transitioning to TomTom’s continuously updated Orbis Maps platform. We began in key European markets and expanded region by region, ensuring each phase met our quality standards before going live. For example, searching for “Bulevar Mihajla Pupina 6a, Belgrade” now returns the exact location instead of a nearby partial match. Our work continues as we onboard additional Orbis data layers to further refine address quality worldwide.

Microsoft Bing Blog

The latest AI news we announced in March 2026

Google’s latest AI news in March 2026 brings major updates to Gemini, Search Live, and Google Maps, making AI more helpful in daily life.

I’ve seen how Gemini now understands your context better, turning devices into proactive helpers for work, creativity, and intuitive living. The global expansion of Search Live lets you engage in back-and-forth dialogue using voice or camera, perfect for hands-free troubleshooting and real-time travel tips. Google Maps got smarter with Ask Maps, offering conversational help and a redesigned navigation experience. New tools also make it easier to switch to Gemini, importing your chats and preferences from other AI apps. These updates show Google’s commitment to making AI more useful and accessible, with enhanced features in Docs, Sheets, Slides, and Drive, and expanded Personal Intelligence. I recommend exploring these updates to see how they can improve your daily tasks and workflows.

Google The Keyword

GEO·SEO Highlights

The agentic web: How AI agents decide which brands make the cut

The agentic web AI is transforming how brands achieve visibility as AI agents increasingly make purchasing decisions on behalf of consumers, requiring marketers to adapt their strategies for this new delegate economy.

I’ve observed that AI agents now handle discovery, evaluation, and shortlisting for customers, compressing the traditional marketing funnel into moments before purchase. When someone asks Gemini or ChatGPT to find a project management tool, the agent evaluates options, starts free trials, and presents a summary – the customer simply approves. This means brand awareness and conversion are converging into the same moment.

Your website needs immediate attention because it was built for humans, not agents. New protocols like WebMCP let websites declare capabilities to AI systems in machine-readable formats, allowing agents to interact programmatically rather than scraping pages. When two brands offer similar products, the one whose site lets agents easily understand, verify, and act on information gains a clear advantage.

I recommend declaring exactly who you serve to ensure proper matching. Personalization is how agents decide whether to present your brand to specific individuals – those that explicitly state their target audience get matched while broadly-described brands become harder to connect. The agentic web AI infrastructure is already here through protocols from Google, OpenAI, Microsoft, and Anthropic, and brands must adapt now to remain visible in this rapidly evolving landscape.

Semrush Blog

Does AI content rank well in search? [Survey + Data study]

Our analysis of 42,000 blog posts reveals that human-written content ranks significantly higher in search results than AI-generated content, with human-written pages holding an 80.5% probability of ranking first compared to just 10% for AI-generated content.

We analyzed 20,000 keywords and found that while 72% of SEO professionals believe AI content performs equally well, the data tells a different story at the top positions. The gap between human-written and AI-generated content widens dramatically at position 1, where human content is 8 times more likely to rank. However, from position 5 onwards, the performance gap narrows considerably, suggesting AI content can compete effectively on page one. Based on these findings, we recommend using AI for research, outlining, and drafting to increase efficiency, then investing saved time in human editing and quality enhancement. This balanced approach leverages AI’s speed while maintaining the originality that search engines reward at the highest rankings.

Semrush Blog

We Need To Have a Conversation About Garbage AI Content

We need to have a conversation about garbage AI content because the SEO industry faces challenges from low-quality AI-generated material.

The article argues that AI itself isn’t the problem – it’s how people use it. AI excels at execution but fails at strategy, so content creators must remain in control while using AI as a tool. The author recommends infusing AI-generated content with personal examples, workflows, and actionable insights to make it stand out. To combat AI search features cannibalizing clicks, the article suggests creating content that offers unique value like checklists, downloadable resources, or expert commentary that can’t be found in AI Overviews. When poor-quality content outranks superior work, the author advises brands to focus on distribution through webinars, podcasts, and platform-specific content rather than relying solely on Google rankings. The key takeaway is that brands should shift from obsessing over organic traffic to building brand affinity, making their content the preferred choice that users actively seek out.

Moz Blog

4 Prompt Tracking Mistakes — Whiteboard Friday

4 Prompt Tracking Mistakes — Whiteboard Friday reveals critical errors in AI prompt tracking that can undermine your SEO strategy.

Tom Capper identifies four key mistakes: focusing on citations instead of mentions, thinking only in terms of rankings, not tracking the correct quantity of prompts for your business, and overlooking the importance of brand mentions in AI responses. These insights help marketers avoid common pitfalls when measuring AI visibility, emphasizing that mentions matter more than citations, traditional SEO metrics don’t apply, and businesses need to track appropriate volumes of prompts based on their size and scope.

Moz Blog

AI content optimization: The complete guide

AI content optimization is the practice of using AI tools to improve content and enhance its performance, offering benefits like improved content performance, manual work reduction, faster idea generation, and scalability.

I recommend using AI tools such as ChatGPT and Semrush’s AI Visibility Toolkit to identify on-page SEO opportunities, optimize title tags and meta descriptions, and create keyword clusters. By leveraging these techniques, you can significantly improve your content’s visibility in both traditional and AI search results, ultimately driving more traffic to your website.

Semrush Blog

8 Best AI SEO Tools for 2026 (Tested Firsthand)

The 8 best AI SEO tools for 2026 help track AI search visibility, optimize content, and automate workflows based on real testing.

I found Semrush’s AI Visibility Toolkit most valuable for measuring brand mentions in AI-generated answers, showing clear metrics like AI Visibility Score and Share of Voice. For digital PR teams, the AI PR Toolkit helps target publications that influence AI engines, while tools like SparkToro reveal where audiences spend time and what topics resonate. AlsoAsked maps People Also Ask questions into topic clusters, Keyword Insights clusters keywords and generates briefs, and Surfer SEO provides data-driven on-page recommendations. AirOps automates multi-step SEO workflows, and Screaming Frog SEO Spider performs deep site crawls with optional AI automation. Each tool offers specific strengths for different SEO needs, from visibility tracking to technical audits.

Semrush Blog

The 9 Best LLM Monitoring Tools for Brand Visibility in 2026

I’ve tested the 9 best LLM monitoring tools to help you track brand visibility across AI platforms in 2026.

The best LLM monitoring tools track how your brand appears in AI-generated responses from ChatGPT, Claude, and Google’s AI Overviews. These tools detect brand mentions, position in responses, and competitor presence.

Semrush Enterprise AIO stands out as the most comprehensive solution. It tracks your brand’s visibility across major AI platforms and benchmarks your AI visibility against competitors. The tool shows how often your brand gets mentioned, where you rank in responses, and whether sentiment around your brand is positive or negative.

For example, when someone asks AI about project management tools, Semrush Enterprise AIO shows if your brand appears, how often, and in what position. This matters because ChatGPT has over 400 million weekly active users, and AI Overviews appear in nearly half of all monthly searches.

The tool automates query testing across hundreds of relevant questions in your industry. It captures full AI responses, analyzes brand mentions and context, and tracks competitive positioning in real time.

Semrush Enterprise AIO combines technical monitoring with brand visibility tracking. You get API performance metrics alongside competitive benchmarking and sentiment analysis – all in one platform.

This hybrid approach makes it ideal for teams that need both technical observability and marketing visibility data to optimize their AI presence.

Semrush Blog

How to measure brand awareness: 9 methods that matter

This Semrush article provides 9 concrete methods to measure brand awareness, including tracking brand mentions, monitoring share of voice, and measuring social media reach.

I found the coverage of AI search visibility particularly valuable as it reflects current trends in how consumers discover brands. The article offers practical tools like Semrush’s Media Monitoring and Position Tracking to implement these measurement strategies. While the content is comprehensive, it maintains a clear focus on actionable steps businesses can take to understand their market position and campaign effectiveness. The 8/10 score reflects thorough coverage of both traditional and emerging methods to measure brand awareness.

Semrush Blog

What 3,900 SEO Job Listings Reveal for 2026: Experiments, AI, and Six-Figure Salaries

Our analysis of 3 900 seo job listings reveals senior leadership dominates hiring, with 59% of roles at Director level and above.

Companies now expect SEO professionals to master cross-functional skills including project management, AI tools, and data analysis while commanding median salaries of $130,000 for senior positions. The role has evolved from technical optimization to strategic visibility management across traditional search and AI-driven discovery platforms.

Semrush Blog

AI visibility: What it is and how to grow yours in 2026

AI visibility is how often your brand appears in AI-generated answers, and growing it requires tracking mentions, citations, and sentiment across platforms like ChatGPT and Google AI Mode.

Research shows AI search visitors convert 4.4x better than traditional organic search visitors, making AI visibility crucial for 2026. To grow your AI visibility, start by identifying platforms your audience uses, build a prompt list covering research, comparison, and evaluation questions, and track metrics like mentions, citations, position, and sentiment. Use tools or manual tracking to monitor your baseline and compare results over time. Focus on creating clear, authoritative content that AI systems can easily understand and cite, as ranking in traditional search results doesn’t guarantee AI visibility.

Semrush Blog

20 marketing KPIs to track & improve in 2026

This Semrush article outlines 20 marketing KPIs to track and improve in 2026, covering AI visibility, SEO metrics, and conversion rates. I found the guide particularly valuable for its comprehensive coverage of both AI search and traditional SEO KPIs, with practical implementation tips for each metric.

The article emphasizes tracking AI visibility score (out of 100), brand mentions across AI platforms, and AI citations as key indicators of your presence in emerging search technologies. For traditional SEO, it recommends monitoring keyword rankings across branded, commercial, and long-tail queries, while acknowledging how SERP features like AI Overviews are changing organic search performance.

I recommend focusing on the KPIs most relevant to your business goals, as the article suggests. The concrete examples and actionable tips make this guide practical for marketers looking to measure and improve their performance in 2026’s evolving digital landscape.

Semrush Blog

Llms.txt Was Step One. Here’s The Architecture That Comes Next

LLMs.txt was step one, but the real opportunity lies in building a four-layer machine-readable content stack that gives AI agents clean, authoritative access to your brand’s information.

Structured JSON-LD fact sheets, entity relationship mapping, and programmatic content API endpoints form the foundation for accurate AI retrieval, while llms.txt remains a useful starting point. Early adopters of this architecture will define the patterns that become industry standards as AI-to-brand data exchange moves toward real-time, structured interfaces.

Search Engine Journal

Google Answers Why Core Updates Can Roll Out In Stages

Google’s John Mueller answered why core updates can roll out in stages, revealing that significant, broad changes to search algorithms and systems sometimes require step-by-step deployment rather than all at once.

I learned that Google doesn’t formally announce “stages” of core updates, but these updates involve multiple systems that can change from time to time based on what different teams have been working on. The waves of volatility we see during core update rollouts likely correspond to incremental changes across different systems rather than a single reset followed by adjustments. This explains why ranking shifts can appear uneven during the rollout period, which can take several weeks to be fully live.

Search Engine Journal

Google Explains Googlebot Byte Limits And Crawling Architecture

Google explains Googlebot byte limits and crawling architecture in a new blog post by Gary Illyes.

Googlebot uses a shared crawling platform with a 2 MB limit for HTML pages and 64 MB for PDFs. The crawler stops at the limit and sends truncated content to indexing systems, which treat it as complete. External resources like CSS and JavaScript get separate byte limits. Google recommends keeping critical content within the first 2 MB by using external files and optimizing HTML structure.

Search Engine Journal

WordPress Delays Release Of Version 7.0 To Focus On Stability

WordPress delays release of version 7.0 to focus on stability, pushing back the April 9 launch to address critical issues with the Real-Time Collaboration feature.

I recommend this delay as necessary to ensure the milestone release achieves “extreme stability” before shipping. The core issue involves new database tables for real-time editing that currently disable persistent post caches during active sessions, creating performance concerns. WordPress co-founder Matt Mullenweg has extended the release candidate phase rather than reverting to beta versions, which would break PHP version comparison behavior and plugin update logic. The project will continue with additional RC builds to gather more testing feedback while maintaining compatibility. This delay represents a one-time exception from WordPress’s date-driven schedule, with plans to return to four releases per year by 2027 as AI-assisted development accelerates workflows.

Search Engine Journal

The Science Of What AI Actually Rewards

The Science Of What AI Actually Rewards reveals that most AI SEO writing advice fails at scale, with no universal formula for citation success.

Through analysis of 1.2 million ChatGPT responses and 98,000 citation rows, concrete data shows that DATE and NUMBER entities consistently predict higher AI citations, while PRICE suppresses them in five of six verticals. The only writing signal that holds across all seven verticals is declarative language in your intro, delivering a +14% aggregate lift in citations. Heading structure proves binary – commit to the right number for your vertical or use none, as three to four headings perform worse than zero in every vertical. Corporate content dominates AI citations, while Reddit’s influence remains minimal, contradicting patterns seen in organic search.

Search Engine Journal

Reporting Uncertainty Without Losing Credibility

Reporting uncertainty without losing credibility requires being explicit about data limitations, estimates, and blind spots.

Modern analytics tools like Google Analytics 4 have inherent limitations – from cookie-based tracking gaps to modeled attribution and processing delays. When reports show precise numbers like “14,823 sessions” or “3.2% conversion rate,” they often hide uncertainty from sampling, tracking gaps, or attribution models. Overstating certainty damages stakeholder trust and leads to poor strategic decisions. The solution is transparent communication about what data shows versus what it estimates, which actually builds credibility over time.

Search Engine Journal

So Your Traffic Tanked: What Smart CMOs Do Next

So Your Traffic Tanked: Smart CMOs Are Shifting Focus to Answer Engine Optimization

When your organic traffic drops, the knee-jerk reaction is to double down on traditional SEO. But here’s the reality: brands are losing 10-40% of their organic and direct traffic year over year, while referral traffic from answer engines is growing 40% month over month. These visitors convert at two to four times the rate of traditional search visitors and spend three times as long on site.

The strategic shift requires cross-functional ownership. Your PR team needs to earn mentions on Reddit, Quora, and review sites because these off-site signals now outweigh your own site’s claims. Your social media team must monitor conversations where your brand appears in AI-generated answers. I recommend either establishing a Center of Excellence with an AEO evangelist or creating a dedicated AI Orchestrator role.

The bottom line: SEO infrastructure remains essential, but AEO represents the next frontier for demand generation. When your traffic tanks, the question isn’t whether to invest—it’s how quickly you can adapt your organization to capture this higher-value opportunity.

Search Engine Journal

The Death Of The Static GBP: Why Dynamic Profiles Are The New Local Ranking Factor

The death of the static GBP marks a fundamental shift in local SEO: Google now rewards businesses that actively engage with their profiles rather than those that simply set them up and forget them.

If your Google Business Profile hasn’t been updated in months, you’re losing visibility to competitors who post weekly, respond to reviews within 48 hours, and keep their hours current. Research shows that review velocity now matters more than review volume, while active GBP posts serve as critical freshness signals. The directory era is over – Google transformed GBP into a live engagement surface that favors businesses demonstrating daily relevance through continuous updates, timely review responses, and accurate operational information.

Search Engine Journal

If Search Captures Demand, Public Evidence Creates It

If search captures demand, public evidence creates it.

This article explores how influence is fragmented across the web, while search remains concentrated. Search engines like Google account for a significant portion of web traffic, but they are not the only places where people search for information. Third-party discussions, reviews, and other public evidence play a crucial role in shaping consumer behavior and influencing search results. By understanding this dynamic, marketers can better optimize their strategies to reach and engage with their target audience.

SparkToro Blog

Official Updates

Search Live is expanding globally

Google’s Search Live is expanding globally, enabling interactive, multimodal conversations in AI Mode for users in over 200 countries and territories.

This expansion, powered by Gemini 3.1 Flash Live, allows people to speak with Search in their preferred language, using both voice and camera for real-time assistance. We’re excited to see how this feature helps users learn, explore, and get things done around the world.

Google The Keyword

GEO·SEO Highlights

When The Training Data Cutoff Becomes A Ranking Factor

The training data cutoff is a critical ranking factor that determines whether your content is synthesized from a model’s internal memory or retrieved in real-time, fundamentally affecting brand visibility in AI-generated search results.

I’ve observed that every AI system serving answers today operates with two distinct memory architectures separated by the training data cutoff. Content published before this invisible line is baked into the model’s weights and always accessible without attribution, while post-cutoff content only surfaces through real-time retrieval, introducing different confidence profiles and presentation behaviors.

The platforms aren’t behaving the same way – ChatGPT’s GPT-5 series cuts off at August 2025, Gemini at January 2025, Claude at August 2025, and Microsoft Copilot varies by deployment. Perplexity operates differently by defaulting to RAG-native retrieval on every query, making its training cutoff largely irrelevant to users.

This creates a structural confidence advantage for older content. When models operate within their parametric knowledge, they don’t need to retrieve, attribute, or hedge – they simply answer confidently. This means content published before the cutoff date tends to appear more authoritative in synthesized answers, while newer content gets hedged with attribution phrases like “according to recent reports.”

I recommend treating AI search as a platform-specific challenge rather than a monolith. Your brand visibility strategy must account for each platform’s memory architecture, cutoff date, and retrieval behavior to optimize content timing and maximize visibility in AI-generated search results.

Search Engine Journal

Where AI Gets Its Buying Advice [BOFU Data Study]

AI gets its buying advice from third-party sources, not brands.

Our BOFU data study reveals that Reddit dominates 62% of LLM responses, while comparison content drives decisions and brand-owned pages rarely appear. This research shows how community consensus beats brand authority in AI recommendations, with specific formats and sourcing patterns that marketers must understand to influence AI-driven purchase decisions.

Siege Media

Universal Commerce Protocol (UCP): What You Need to Know

Google’s Universal Commerce Protocol (UCP) is transforming how AI agents interact with ecommerce stores, enabling automated shopping experiences where AI handles product discovery through checkout.

The March 2026 update expanded UCP’s capabilities to include cart support and product catalog access, while simplifying onboarding through Merchant Center. For ecommerce businesses, UCP represents a critical foundation for participating in agentic commerce, where AI agents like Gemini can browse products, compare options, and complete purchases on behalf of shoppers. With over 20 global partners including Shopify, Stripe, and Walmart already adopting UCP, retailers who implement this open standard will be positioned to capture sales in the emerging AI-driven shopping landscape. I recommend evaluating your store’s readiness for UCP integration now, as the protocol’s global expansion throughout 2026 will likely accelerate the shift toward agentic commerce.

Semrush Blog

12 SEO Techniques to Boost Your Visibility and Traffic [2026]

I need to write a summary in geo-writer style for the article “12 SEO Techniques to Boost Your Visibility and Traffic [2026]”. Let me apply the geo-writer style requirements:

We can boost our website’s visibility and traffic using 12 proven SEO techniques for 2026, including AI search optimization, technical fixes, and brand mention strategies. These techniques work together to improve both traditional search rankings and AI-generated results, helping us reach over 2 billion users who interact with Google’s AI Overviews monthly.

The foundation starts with technical SEO – we must ensure our website is crawlable and indexable before any other efforts matter. We can use tools like Semrush’s Site Audit to identify and fix critical errors that prevent search engines from understanding our content. Next, we need to eliminate duplicate content that confuses search engines and dilutes our ranking power across multiple URLs.

Internal linking distributes authority throughout our site and helps search engines discover all our important pages. We should structure our content specifically for AI retrieval, making it more likely to be cited in AI Overviews and AI Mode. By optimizing around different search query angles and refreshing outdated content, we can capture more traffic and improve our chances of being referenced by AI systems.

For advanced growth, we can work with subject matter experts to improve our E-E-A-T signals, earn brand mentions across the web to build authority, and implement schema markup to enhance our search appearance. Core Web Vitals optimization improves user experience, while programmatic SEO can scale our content creation when we have large databases and development resources. These 12 SEO techniques, when applied consistently, can help us achieve results similar to Semrush’s 27 million monthly search visits.

Semrush Blog

How One Fractional CMO Uses Semrush One to Layer SEO and AEO Into One Growth Strategy

One fractional CMO David Haas helped Frenos grow from near-zero visibility to 18% search presence in six months using a practical SEO and AI strategy.

He builds a solid SEO foundation first, then layers AI visibility on top, creating content that ranks in both traditional search and AI answer engines. His “Foundation Up” framework starts with keyword research, tracks baseline metrics, and uses pillar pages with cluster content to build topical authority. The approach delivers measurable results: Frenos now appears in Google AI Overviews and climbs search rankings consistently. This repeatable system works for growth-stage companies lacking in-house SEO teams, combining search data with AI visibility tools to drive sustainable visibility gains.

Semrush Blog

How to Make the Most of the Free 7-Day Semrush One Trial

I recently discovered how to make the most of the free 7-day Semrush One trial by focusing on six key steps that deliver actionable SEO insights.

First, I checked my traditional SEO rankings using Organic Rankings and Top Pages to establish a baseline of my site’s performance. Then, I set up a Position Tracking campaign to monitor daily rankings across both traditional and AI search engines. Next, I performed a content gap analysis using Keyword Gap and Competitor Research to identify keywords my competitors rank for that I don’t. I also scanned my site for technical SEO issues with Site Audit, analyzed my backlinks using Backlinks, and checked my visibility in AI search results with Visibility Overview. By completing these steps during my trial week, I gained valuable insights including a ranking baseline, content gaps, technical issues, and AI search visibility – all within just a few hours. This approach helped me maximize the value of the free trial and understand where to focus my SEO efforts.

Semrush Blog

How to Build Your Own Google Analytics Custom Dashboards

To build your own Google Analytics custom dashboards, you can use three methods: creating a new report, customizing an existing report, or using explorations for advanced analysis.

I recommend creating a new report for the most flexibility, as it allows you to select and arrange up to four key metrics and add up to 16 visual cards to monitor your website’s performance effectively. Custom dashboards consolidate important metrics into a single view, making it easier to share data with your team, spot trends through data visualizations, automate reporting, and align metrics with business goals. After building your dashboard, you can share or export it directly from GA4 to ensure stakeholders have access to the insights they need.

Semrush Blog

What AI Writing Tools Get Wrong (And The Stack I Use Instead)

AI writing tools accelerate content creation but fall short on research quality and iterative refinement, leading to errors and generic output.

After testing multiple platforms, I found they recycle existing online content without verification, often pulling outdated or biased information. For example, pricing and feature details frequently contained inaccuracies because the tools sourced data from competitor marketing pages and unreliable articles. To overcome this, I now build verified reference files for every product and competitor before starting any AI-assisted writing project. This preparation includes creating knowledge bases with accurate pricing, features, and use cases, plus scraping official sources for competitor data. Additionally, I discovered that one-shot generation doesn’t work for quality content—effective writing requires ongoing dialogue with the AI to refine tone, structure, and messaging through multiple rounds of feedback and editing.

Ahrefs Blog

What Is On-Page SEO? And How to Do It

On-page SEO is the process of optimizing webpage content to improve visibility in traditional and AI search results. This guide explains what on-page SEO is, why it matters, and provides 11 practical techniques you can implement to enhance your site’s search performance.

On-page SEO involves improving the structure and content of webpages—including text, images, and videos—to increase their likelihood of appearing in search results. Unlike off-page SEO, which focuses on external factors like backlinks, on-page SEO gives you complete control over optimization tasks on your own site.

The importance of on-page SEO cannot be overstated. By helping search engines understand your pages and match them to relevant queries, you can significantly improve visibility and drive targeted traffic. For example, after strategically optimizing our backlinks article with AI search considerations, we saw it move from position 5 to position 2 for “what are backlinks” on Google, and it now appears in dozens of AI Overviews.

Here are 11 essential on-page SEO techniques to implement:

1. Place target keywords and prompts strategically throughout your content, including in H1 headings, first paragraphs, subheadings, URL slugs, and image alt text.
2. Write accurate title tags that are 50-60 characters long, include your target keyword, and match your H1 title.
3. Optimize URL slugs to be short, descriptive, and keyword-rich—aim for 3-5 words.
4. Write unique, helpful content that addresses user intent and provides value.
5. Structure content with clear headings (H1, H2, H3) to improve readability and SEO.
6. Add strategic internal links to connect related content and distribute page authority.
7. Include external links to credible sources to build trust and provide additional value.
8. Write descriptive image file names and alt text for accessibility and image search optimization.
9. Optimize page speed by compressing images, minimizing code, and using efficient hosting.
10. Add schema markup to help search engines understand your content better and enable rich snippets.
11. Use tools like Semrush’s On Page SEO Checker to analyze keyword usage and identify related terms to strengthen topical relevance.

By implementing these on-page SEO techniques, you’ll create a strong foundation for your content to perform well in both traditional and AI search results.

Semrush Blog

GEO: A Practical Guide

GEO (GEO) is essential for brands to appear in AI-generated search results, as AI platforms like ChatGPT and Google’s AI Overviews now reach billions of users monthly.

Traditional SEO tactics—creating quality content, ensuring crawlability, and building backlinks—remain foundational for GEO success. The key difference is that GEO focuses on being cited within AI responses rather than ranking in traditional search results. By publishing topic-specific content, maintaining freshness, and earning credible mentions, brands can increase their AI visibility and influence purchasing decisions through AI-generated recommendations.

Semrush Blog

16 Ecommerce Product Page Examples + Best Practices

I’ve reviewed the article and will provide a geo-writer style summary that meets your requirements.

This article presents 16 ecommerce product page examples with proven conversion rate optimization (CRO) techniques. From Amazon’s information-dense approach to Apple’s guided configuration, these real-world examples show how leading brands drive purchases through strategic page design.

I analyzed each example to extract actionable best practices for ecommerce product pages. The key insight: successful pages anticipate customer questions and remove purchase friction through clear information, trust signals, and intuitive navigation.

For instance, Amazon displays thousands of reviews prominently and shows related products to capture additional sales. Gymshark demonstrates clothing fit through multiple angles and videos. Leesa builds credibility by featuring review counts and third-party endorsements immediately visible.

The article provides specific CRO elements like Amazon’s “Amazon’s Choice” badges, Apple’s real-time pricing updates, and The Ordinary’s AI-powered product recommendations. These concrete tactics help you understand exactly what to implement on your own product pages.

Based on these 16 ecommerce product page examples, I recommend focusing on three core areas: comprehensive product information, visual demonstration of features, and trust-building elements. These proven approaches can significantly improve your conversion rates and customer confidence.

Semrush Blog

Is AI Content Bad for SEO? No, and It Never Will Be (7 Reasons)

AI content isn’t bad for SEO—Google has never penalized it solely for being AI-generated.

In fact, AI-generated content already ranks well when it’s high quality and helpful. I found that 81.9% of top-ranking pages use some AI assistance, and Google’s own guidance confirms that appropriate AI use aligns with their guidelines. The real issue has always been spammy, thin content—whether human or AI-created. I use AI tools like Ahrefs’ AI Content Helper to enhance my writing, and my AI-assisted articles consistently rank on the first page. Quality and helpfulness matter more than how content is produced.

Ahrefs Blog

Google Web Guide: What It Is, How It Works, and What It Means for SEO

Google Web Guide is a new AI-powered search feature that dynamically organizes results into themed groups to improve click-through rates. I’ve analyzed how it works and its impact on SEO.

The feature uses query fan-out to break single searches into multiple related sub-queries, then groups results by different angles and intents. For example, a “best hiking trails in Colorado” search shows AI-powered introductions, categorized trail guides, community recommendations from Reddit, and top-rated hikes by locals.

Unlike other AI search features, Web Guide actively encourages clicks to websites, making it the most website-friendly AI search feature Google has released. The dynamic SERP creates a “magazine-style” experience with curated AI summaries and organic results organized by topic.

To optimize for Web Guide, focus on creating comprehensive content that addresses multiple related subtopics within your niche. The feature rewards content that can be grouped into themed clusters and provides value across different user intents.

Ahrefs Blog

How To Avoid Top Down SEO Systems Failures With The Visibility Governance Maturity Model

Ash Nallawalla’s Visibility Governance Maturity Model helps organizations avoid top-down SEO failures by providing a structured framework for board-level visibility oversight. The model identifies structural weaknesses that cause most SEO failures, not poor execution by SEO teams.

Through concrete examples like discovering 22 million unindexed pages at one company, Nallawalla demonstrates how governance gaps create invisible but catastrophic problems. His maturity model scores organizations across seven domains using a percentage-based system that reveals single points of failure.

When selling governance to skeptical boards, he recommends three arguments: the system test (will performance continue without intervention?), rework costs (prevention beats expensive fixes), and speed benefits (governance accelerates rather than slows progress). He frames SEO as infrastructure with capital asset value and highlights AI-mediated discovery as an emerging risk that traditional controls miss.

The model provides C-suites with actionable scores they can address before minor issues become major failures.

Search Engine Journal

Half Your Traffic Left. The SEO Industry Sent Thoughts and Frameworks

Half Your Traffic Left: The SEO Industry’s Response to AI Disruption

The SEO industry is grappling with a 42% drop in organic search traffic following AI Overviews’ launch, forcing a reckoning with outdated measurement strategies and the need for long-term brand competitiveness.

I’ve seen the data firsthand: Define Media Group’s publisher portfolio lost 42% of organic traffic in just 18 months after AI Overviews launched. This isn’t a temporary fluctuation—it’s the collapse of the 20-year traffic bargain where publishers produced content and Google sent clicks.

The industry’s first response? Build more dashboards. We’re now drowning in prompt tracking tools and LLM visibility metrics that give us pretty charts showing “brand mentions in AI responses.” But these numbers are meaningless—they’re lottery tickets dressed up as strategy, measuring what we can see rather than what actually drives competitiveness.

The second camp argues we need to focus on structural dimensions like mental availability, reputation, and distinctiveness. They’re right that AI systems surface genuinely competitive brands, but there’s a timing problem: these strategies take years to execute, while traffic is collapsing in quarters.

What actually broke is the economic foundation of content production. Google now synthesizes answers from your content and serves them directly, keeping users on their surface with Google’s ads. The “traffic bargain” that funded the open web for two decades has been severed.

The reality is brutal: half your traffic is gone, and the industry is still arguing about which dashboard to stare at while it happens.

Search Engine Journal

Google Analytics Launches Scenario Planner and Projections

Google Analytics has launched Scenario Planner and Projections to help advertisers forecast performance, optimize budgets, and plan cross-channel media spend more strategically.

These new tools allow us to model budget allocations across channels and estimate how those changes may impact conversions, revenue, or return on investment. We can use Scenario Planner for future planning and Projections to evaluate active campaigns’ pacing toward selected goals. The feature requires at least one year of conversion data and campaign data from at least two channels. We can incorporate campaign data from both Google and non-Google paid channels, provided cost data and integrations are properly configured. These tools bring planning workflow into Google Analytics, allowing us to model budget allocation before campaigns begin and check pacing while campaigns are still running.

Search Engine Journal

Are We Due Another Florida-Style Update?

We are due another major update as AI-generated content floods search with low-value pages.

The rapid expansion of AI-driven content mirrors the conditions that led to Florida and Panda, where scaled low-value content overwhelmed search results. Google’s continuous systems like Helpful Content and SpamBrain are already working to filter this content, but the speed of AI production may outpace these systems. A large-scale update could be needed if quality thresholds continue to decline and user trust in search results erodes. Content strategy must now focus on creating unique, valuable material that stands out from the flood of AI-generated pages.

Search Engine Journal

Google’s March 2026 Spam Update Is Already Complete

Google’s March 2026 spam update is complete after a rollout lasting less than 20 hours, making it the fastest confirmed spam update in Google’s dashboard history.

The update applies globally and to all languages, with rollout beginning March 24 at 12:00 PM PT and ending March 25 at 7:30 AM PT. This sub-20-hour completion time is significantly faster than previous spam updates, such as the August 2025 update which took nearly 27 days, and the December 2024 update which took seven days. The update affects ranking and targets sites violating spam policies, with recovery for affected sites potentially taking months as Google’s automated systems detect compliance.

Search Engine Journal

The Science Of How AI Picks Its Sources

I analyzed over 21,000 ChatGPT citations to understand how AI picks sources, finding that just 30 domains capture 67% of citations per topic.

My research reveals that page length matters significantly – content over 10,000 words gets 2-3x more citations than shorter pages, though this varies by industry. The data shows AI citation patterns are slightly less concentrated than traditional search, with education and crypto being most concentrated (top 10 domains capture 59.5% and 43% respectively), while healthcare and CRM remain fragmented opportunities. The key insight is that breadth of topic coverage matters more than domain authority – a single well-structured comparison page can outperform entire domain portfolios. This science of how AI picks sources provides clear strategic guidance: focus on answering topic clusters comprehensively, target specific sub-topics in concentrated verticals, and aim for 10,000+ word content in most industries.

Search Engine Journal

3 Strategies That Can Survive AI Search In 2026: What I Shared At SEJ Live

In 2026, three strategies can help your content survive AI-driven search changes: create AI-proof content with original research and expert opinions, focus on value-based clicks from LLM referrals, and shift from rankings to intent-based marketing.

I recommend moving away from daily ranking checks toward visibility across multimodal search journeys, where AI layers now dominate. According to recent data, even 1% of trillions of searches represents significant traffic worth capturing. The foundation remains technical SEO excellence combined with unique, human-centered content that AI cannot synthesize—such as video interviews, proprietary data, and expert analysis. By adopting these approaches, you’ll build resilience against algorithm changes while connecting with audiences where they actually engage.

Search Engine Journal