Scalenut

LinkedIn citations jumped 24.8% last month, reaching 100.2K citations. So, how do you get AI to cite your LinkedIn content? There’s no guaranteed formula, but recent research points to some clear patterns: 1. Answer specific questions Focus on the comparisons, how-tos, and problems your audience is actually asking about. 2. Make your content easy to extract Use clear structure, direct answers, examples, and useful data. 3. Make the topic clear from the start Hook the reader, but make it immediately obvious what your post is about. 4. Add something original First-hand experience, original data, specific examples, or a distinct point of view give AI something worth citing. 5. Use posts and articles together Use articles for depth and posts to distribute focused ideas around the same topic. The key takeaway? LinkedIn is no longer just a place to distribute content. It is increasingly becoming part of the source layer AI uses to build answers. So your LinkedIn strategy isn’t only about reach and engagement anymore. It’s also about making your expertise easier to find, understand, and cite.

AI referral traffic is up 117.4% YoY, reaching 770.7M monthly visits. Ignore GEO now, and you risk handing that growth to competitors. Similarweb found that every industry it tracked at least doubled its AI referral traffic YoY. At Scalenut, we’ve also seen our own ChatGPT referral traffic pick up consistently. The shift is becoming harder to ignore. Because when buyers use AI to research, compare, and discover products, your brand can either be: mentioned, cited, and clicked or missing while a competitor gets the visibility instead. That means marketers now need visibility into: - Which prompts are surfacing their brand? - Which pages and sources are getting cited? - Where are competitors appearing instead? - How much AI traffic is human referral traffic vs. crawler activity? - What needs to improve to grow that visibility? Scalenut can help you connect prompt-level visibility, citations, competitors, traffic signals, and recommendations, then helps turn those insights into content and optimization actions. So you are not just watching AI referral traffic grow. You can understand where your brand stands and what needs to change. AI search is already sending traffic. The bigger risk now may be letting your competitors capture it first. #GEO #AIVisibility #AIReferralTraffic #AEO #Scalenut

Google has finished rolling out first-party AI visibility reporting in Search Console. But seeing AI impressions is only useful if you know what to do with them. The new report shows how often your site appears across Google AI Overviews and AI Mode. For GEO teams, that means you can now: • Track whether your AI visibility is growing or declining • Identify which pages are getting the most AI exposure • Compare AI visibility with traditional search performance • Spot pages that may need more GEO attention • Combine GSC data with prompts, citations, competitors, traffic, and conversions for the bigger picture The important part? AI impressions are now a first-party signal from Google. But they’re still only one part of the GEO measurement stack. How are you using the new report so far? #GEO #AIVisibility #SearchConsole #AISearch #SEO

One of the most repeated AI prompting tips may no longer need to be in every prompt: “Think step by step.” For years, it became almost muscle memory. And for good reason. Asking models to reason through a problem step by step could improve performance on certain tasks. But newer reasoning models are changing that. OpenAI says reasoning models already reason internally, so explicitly asking them to “think step by step” is generally unnecessary. Google says Gemini 2.5 and 3 also use internal thinking. Anthropic goes a step further: with its newer thinking models, broader instructions can work better than prescribing the exact reasoning path. So the way we prompt may need to shift too. Instead of: “Think step by step. First do X, then Y, then Z…” Try giving the model: → The goal → The context → The constraints → What a good answer should achieve Then give it room to figure out the path. That does not mean detailed instructions are suddenly useless. If the order matters, the process must be followed exactly, or you are working with a model that does not reason internally, step-by-step guidance can still be useful. But it probably should not be something we add automatically anymore. The bigger shift in prompting may be this: Spend less time telling the AI how to think. Spend more time making it clear what good looks like. Have you changed the way you prompt newer reasoning models?

AI can put your brand on the buyer’s shortlist. The next prompt may also take it straight back off. A recent Semrush survey of 2,338 US adults found that among AI users: → 57.51% had bought something based on a chatbot recommendation. → 57.5% had decided against a purchase based on chatbot information. The matching percentages do not mean AI won and lost an equal number of sales. These are self-reported experiences, not transaction data. But they highlight an important gap in AI visibility tracking. A buyer may discover your brand by asking: “What are the best tools for X?” Then pressure-test that recommendation: → What are its limitations? → Are the negative reviews justified? → What do competitors do better? → Is it worth the price? → Who should avoid it? If you track only discovery prompts, you may know whether your brand appeared but not how it was evaluated later. So, track the full path: → Recommendation: Was the brand shortlisted? → Comparison: How was it positioned against competitors? → Objections: Which concerns appeared? → Accuracy: Were the claims current and correct? → Sources: Which pages shaped the answer? Being mentioned can earn consideration. What AI says next can influence whether the brand stays there. #AIVisibility #GEO #AEO #AISEO #DigitalMarketing

AI visibility is starting to look a lot more like search marketing. There is now an earned layer and a paid layer. Until recently, most GEO conversations focused on one question: Can your brand get mentioned or cited in the AI answer? ChatGPT Ads adds another route to visibility. Ads can appear below ChatGPT responses when users are exploring options, comparing alternatives, or moving closer to a decision. That creates 2 distinct layers of AI visibility: 1. Earned AI visibility Your brand gets mentioned, recommended, or cited in the response. 2. Paid AI visibility Your brand appears as a clearly labeled sponsored placement below the response. And these need different measurement frameworks. Earned visibility → mentions, citations, share of voice. Paid visibility → impressions, clicks, conversions. The interesting part is what happens when both compete for attention. A competitor may not earn a mention in the AI response. But it could still appear directly below that response as a sponsored option. SEO and PPC learned to work alongside each other years ago. GEO and AI advertising may be heading in the same direction. The next challenge for marketing teams will be understanding how these 2 visibility layers influence the same buyer journey. #GEO #AISearch #AIVisibility #DigitalMarketing

Your next customer could discover you in an AI answer. Your analytics might never show that first interaction. They might see your brand in a comparison, read a recommendation, or learn from your content without clicking through. Rankings and clicks still matter. But they capture only part of that journey. Here are 5 things to build into your approach: 1. Track brand mentions and citations separately to see where your brand and content appear. 2. Compare share of voice and prompt coverage across the same questions and platforms over time. 3. Measure identifiable AI referrals alongside organic traffic to understand which sources bring visitors. 4. Check engagement, leads, and sales by source before assuming AI traffic converts better. 5. Strengthen content with original data, practical examples, clear answers, and credible sources. Where available, add Search Console’s AI impression data to your reporting. These signals also give you a better starting point for your next content decision. With Scalenut, you can review mentions, citations, competitor gaps, and AI traffic trends together. Then use what you find to create content for missed questions, refresh existing pages, and strengthen internal links within the same platform. So when competitors keep appearing for a question your customers ask, you have a clear place to investigate and start improving. Your next customer’s journey may begin before the first click. Your visibility strategy should begin there, too. #SEO #GEO #AISearch #ContentMarketing

Most AI visibility advice starts with creating more content. But more content will not fix a brand AI cannot access, understand, or verify. Consistent visibility depends on 3 connected layers: → Technical access: Priority pages must be crawlable, indexable, internally linked, and available as readable text. → Brand clarity: AI must clearly understand what you do, who you serve, how you differ, and which evidence supports your claims. → External validation: Credible reviews, industry coverage, experts, partners, and customer discussions must support your story. That means: - A blocked page is not a content gap. - Unclear positioning is not a schema problem. - Weak credibility cannot be fixed by repeating claims on your own site. Search and training crawlers also require separate controls. OAI-SearchBot supports ChatGPT Search, while GPTBot is used for training. Google also says AI Overviews and AI Mode do not require special AI schema or llms.txt. A study found that branded web mentions correlated with visibility across every AI platform it studied. So, before publishing another page, ask: Can AI access the information? Can it understand the brand? Can it verify the claims elsewhere? #AIVisibility #GEO #AEO #AISEO #ContentStrategy

Answer engines were only the first step. Search is now moving toward agents and actions. We can already see the shift happening. - Text → Multimodal search - Answers → Actions - Generic results → Personalized responses - One-off queries → Continuous conversations - Web-only search → Connected apps and services - Source links → Stronger attribution - Search vs AI → One connected experience What’s changing is how much search can do beyond the first answer. It can carry context forward. Research further. Make recommendations. And increasingly, help users take the next step. Search is becoming part of the decision and action process, not just where it begins. Which of these shifts do you think will change search behavior the most?

AI may be one of the smallest channels in your report. It may also be sending some of your highest-intent visitors. Shopify’s Q2 commerce data found: → AI-referred sessions grew 197% YoY. → 50% landed directly on product pages. → Those visitors converted around 80% better than organic visitors. Organic search still drove far more traffic and grew 12% from a much larger base. This is not about AI replacing organic search. The channels are doing different jobs. Organic search drives discovery and scale. AI can handle research before the click, sending fewer visitors who are further along in their decision. If your report stops at sessions, AI traffic may look insignificant. Group identifiable AI referrals into a dedicated GA4 channel. Then compare them with organic traffic using: → Conversion rate: Which channel produces more meaningful actions? → Revenue or qualified leads per session: Which visitors create more value? → Landing-page performance: Where do visitors enter, and what happens next? Use a longer reporting window when AI traffic is low. Small samples can distort conversion rates. This still captures only identifiable AI referrals. Visits from Google’s AI features may be recorded as organic traffic. Small traffic does not always mean small impact. Track sessions for scale. Track outcomes for value. #AIVisibility #GEO #AEO #AISEO #DigitalMarketing