AI Search Barely Cites Syndicated News Or Press Releases via @sejournal, @MattGSouthern

A BuzzStream report analyzing 4 million AI citations found that press releases distributed through syndication channels barely appear in AI-generated answers.

Background

Press release distribution services have been marketing AI visibility as a selling point.

For example, ACCESS Newswire offers an “AI Visibility Checklist” for press releases. eReleases published a guide positioning press releases as tools for AI search visibility. Business Wire has written about optimizing releases for answer engine discovery.

BuzzStream’s data offers a different perspective.

What They Found

The report’s authors used XOFU, a citation monitoring tool from Citation Labs, to track where AI platforms pull their sources across ChatGPT, Google AI Mode, Google AI Overviews, and Google Gemini. BuzzStream ran 3,600 prompts across 10 industries and collected data for one week.

Overall, news publications accounted for 14% of all citations in the dataset. But within that news category, the numbers drop off quickly for syndicated and distributed content.

Press releases published through syndication channels like Yahoo and MSN accounted for 0.32% of news citations and 0.04% of the entire dataset.

Direct citations from newswire services like PRNewswire made up 0.21% of the full dataset. They appeared most often in exploratory and informational prompts, but even there they only reached 0.37%.

Syndicated news content overall, including articles republished through MSN and Yahoo networks, accounted for 6.2% of news citations and 0.9% of the total dataset.

To identify syndicated content, BuzzStream cross-referenced author names against publications using its ListIQ tool and manually confirmed cases where the author name didn’t match the publication. The company acknowledged this method has limits, since some sites repost press releases without labeling them as such.

What The Data Shows About What Works

The report’s more interesting finding is what does get cited.

Original editorial content made up 81% of news citations in the dataset. Affiliate and review content accounted for the rest. The split held across prompt types, though affiliate content had its strongest showing in evaluative prompts at 39%.

The report broke prompts into three categories. Evaluative prompts like “Is Sony better than Bose?” generated the most news citations at 18% of all citations. Brand awareness prompts like “What is Chase known for?” generated the fewest at 7%. Informational prompts fell in between.

Editorial content that appeared most often in evaluative citations included head-to-head comparisons and cost analysis from outlets like Reuters, CNBC, and CNET.

The ChatGPT Newsroom Exception

One platform-level finding stood out. Internal press releases and newsroom content on company-owned domains accounted for 18% of ChatGPT’s citations in the dataset.

On Google’s AI platforms, that number dropped to around 3%.

BuzzStream cited examples including Iberdrola’s corporate press room and Target’s corporate subdomain. When prompted about Iberdrola’s role in renewables, ChatGPT cited a press release from Iberdrola’s own website. When asked about Target’s products, ChatGPT cited a 2015 press release from Target’s corporate domain.

BuzzStream said most earlier trends looked fairly uniform across platforms, with newsroom content on ChatGPT standing out as a clearer exception.

Why This Matters

The data challenges a premise that press release distribution services have been promoting. Multiple distribution platforms now market press releases as a path to AI visibility.

BuzzStream’s data suggests the distributed version of a press release, the one that lands on Yahoo Finance or MSN through a wire service, rarely becomes the version AI platforms cite. Original editorial coverage and owned newsroom content performed better by wide margins.

This connects to patterns we’ve been tracking. A BuzzStream report we covered in January found 79%of top news publishers block at least one AI training bot, and 71% block retrieval bots. Hostinger’s analysis of 66 billion bot requests showed AI training crawlers losing access while search bots expanded their reach.

The citation data suggests that even when syndicated content is accessible to AI crawlers, it rarely gets cited.

Google’s VP of Product for Search, Robby Stein, said in an interview we covered that being mentioned by other sites could help with AI recommendations, comparing AI’s behavior to how a human might research a question. That comparison favors earned editorial coverage over distributed press releases.

Adam Riemer made a related point in his Ask an SEO column, drawing a line between digital PR that builds brand coverage in publications and link building that focuses on placement metrics. BuzzStream’s data suggests that line extends to AI citations too.

For transparency, BuzzStream sells outreach and digital PR tools, so the finding that earned media outperforms distribution aligns with its business model. The company partnered with Citation Labs and used Citation Labs’ XOFU monitoring tool for the data collection.

Looking Ahead

This is part one of a multi-part analysis from BuzzStream. The single-week data window and large-brand focus are limits worth noting. Smaller brands with less existing editorial coverage may see different results.

Businesses investing in digital PR may want to look more closely at how different distribution channels perform in their category. Data suggests the channel you use can affect where your brand gets cited.


Featured Image: Cagkan Sayin/Shutterstock

How To Track AI Visibility & Prompts The Right Way via @sejournal, @lorenbaker

AI search has changed the rules, but has your tracking? 

How do you measure visibility without rankings?

Which prompts actually reflect real buyer intent?

And how do you avoid AI tracking data that looks useful, but isn’t?

Learn how to set up AI prompt tracking you can trust for smarter decisions.

ChatGPT, Google AI Overviews & Perplexity Are Reshaping Discoverability

In this on-demand webinar, Nick Gallagher, Sr. SEO Strategy Director at Conductor, breaks down how AI prompt tracking really works, why topics matter more than individual prompts, and how to avoid common mistakes that skew insights.

You’ll leave with a clear framework for measuring AI visibility in a way that reflects real user behavior and supports smarter search and content strategies.

You’ll Learn:

  • How AI prompt tracking works, and why setup matters more than volume
  • Best practices for choosing topics, prompts, and answer engines
  • Common mistakes that lead to inaccurate or misleading AI visibility data

Watch on-demand and learn how reputation management is shaping local visibility, trust, and growth in 2026.

View the slides below or check out the full webinar for all the details.

Google AI Mode’s Personal Intelligence Now Free In U.S. via @sejournal, @MattGSouthern

Google is opening Personal Intelligence to free-tier users in the U.S. Previously limited to paid AI Pro and AI Ultra subscribers, the feature is now expanding to users with personal Google accounts.

What’s New

Announced in a blog post, the expansion covers AI Mode in Search, the Gemini app, and Gemini in Chrome. AI Mode access is available today, while the Gemini app and Chrome rollouts are starting now.

Personal Intelligence connects a user’s Gmail and Google Photos to AI-powered search and chat responses. When enabled, AI Mode and Gemini can reference email confirmations, travel bookings, and photo memories to answer questions without the user providing that context manually.

What Changed

When Google first launched Personal Intelligence in January, you needed a subscription to try it. Today’s expansion removes that paywall for U.S. users on personal Google accounts.

The feature still isn’t available for Google Workspace business, enterprise, or education accounts.

You can opt in by connecting apps through their Search or Gemini settings, and you can turn connections on or off at any time.

What Google Says About Training Data

The blog post includes a disclosure about how data from connected accounts is handled.

According to the post, Gemini and AI Mode don’t train directly on your Gmail inbox or Google Photos library. Google describes the training as limited to “specific prompts in Gemini or AI Mode and the model’s responses.”

That means prompts generated while using Personal Intelligence could include details drawn from connected apps, even though Google says it doesn’t train directly on raw Gmail or Photos data.

Why This Matters

The move from paid to free changes the scale of this feature. When Personal Intelligence required a Pro or Ultra subscription, it reached a smaller audience of paying users. Opening it to anyone with a personal Google account in the U.S. puts it in front of a much larger base.

Increased personalization means AI Mode responses could vary more from user to user. Two people searching the same query may get different results if one has connected their Gmail and the other hasn’t. That makes it harder to benchmark what AI Mode shows for a given topic.

This feature could also change how people type queries into AI Mode. If Google already has the necessary context about a person, we might see searches become shorter. That’s an idea I explored in this video back when Google originally launched the feature:

Looking Ahead

No expansion beyond the U.S. or to Workspace accounts has been announced. Moving from paid to free in less than two months suggests Google is confident in this feature. How people respond to the linking of personal data to search will likely shape future rollout plans.

Google Removes ‘What People Suggest,’ Expands Health AI Tools via @sejournal, @MattGSouthern

Google has removed “What People Suggest,” a search feature that used AI to organize health perspectives from online discussions. The confirmation came as Google held its annual Check Up event, where it announced new AI health features for YouTube.

A Google spokesperson confirmed the removal to The Guardian, calling it part of a “broader simplification” of the search results page. The spokesperson said the decision was unrelated to the quality or safety of the feature. The Guardian also reported, citing three people familiar with the matter, that the feature was pulled after a trial run.

“What People Suggest” launched on mobile devices in the U.S. last year at Google’s annual health event, The Check Up. At the time, Karen DeSalvo, then Google’s chief health officer, said people value hearing from others who have experienced similar health conditions. DeSalvo retired in August and was succeeded by Dr. Michael Howell, who led this year’s Check Up announcements.

What Google Announced At The Check Up

At its 2026 Check Up event, Google announced AI health features across YouTube, Fitbit, and clinician education.

Google says health-related videos on YouTube have surpassed 1 trillion views globally. The company is adding an AI-powered “Ask” button on eligible health videos that lets viewers interact with the content.

Separately, Google is experimenting with AI to organize peer-reviewed scientific information and help present complex topics to broader audiences.

In the blog post, Howell said a central challenge has been connecting people to the right health information at the right time.

Google.org is committing $10 million to fund organizations that will reimagine clinician education for AI. The Council of Medical Specialty Societies and the American Academy of Nursing are the first partners.

Why This Matters

AI features in search results for health-related topics keep changing. Google pulled back one feature that showed forum-style perspectives and put new investment into medical education and structured video tools.

YouTube’s growing role in health-related AI Overviews is already documented. SE Ranking’s study of German health queries found YouTube was the most-cited domain in health AI Overviews, appearing more often than medical or government sites. Adding interactive AI on top of those videos could reinforce that pattern.

How We Got Here

Google’s AI features for health queries have faced pressure over the past year.

In January, the Guardian published an investigation that found health experts considered some AI Overview responses misleading for medical queries. Google disputed elements of the reporting but later removed AI Overviews for some specific health searches, including queries about liver function tests.

“What People Suggest” launched during the same period Google was expanding AI Overviews to thousands more health topics. Ahrefs data from November showed medical YMYL queries triggered AI Overviews 44.1% of the time, the highest rate among YMYL categories.

Looking Ahead

The pattern over the past year points to tighter guardrails around some health AI experiences. Whether that direction holds is less certain.

The removal of “What People Suggest,” and YouTube’s continued citation visibility in AI Overviews, could point that way. But Google’s track record with health-related AI features also shows these decisions can change quickly.


Featured Image: Mamun_Sheikh/Shutterstock

How To Use AI To Streamline Time-Consuming SEO Tasks via @sejournal, @coreydmorris

SEO, like most organic and non-advertising or paid channels in digital marketing, is labor-intensive. Yes, there are software suites, analytics platforms, research tools, and a number of other things that help in the tech stack.

We all have our favorites, and no one is (or should) be doing SEO like I was in 2008 (despite my desire sometimes to just do something manually where I can see the inputs and outputs and have more control, but I digress).

In the midst of constant noise about new platforms, new ranking factors, ways to become visible in AI, and everything else, it can be hard at times to keep going with the tasks that still require a human at some level. Whether it is gaining efficiency, scaling efforts, doing more with less, or a combination of these, I’m sharing human-involved ways to streamline time-consuming tasks so you can gain time (and maybe money).

1. Generating Meta Descriptions, Page Titles, Alt Text

I could have started with something more high-level or strategic, but I’m getting this one out of the way right now.

The basic blocking and tackling of ensuring you have unique, helpful, and topically relevant meta descriptions, page titles, and image alt text can be a huge investment of time on a large website or across sites if you own tactical SEO for multiple sites or clients.

While there are ways to semantically have these tags auto-generated by a database or CMS, we know that, in a lot of cases, there’s still a manual process or intervention to audit and ensure that the tags are written to best practices and strategic positioning.

Also, I know that there’s plenty of discussion or debate on whether there’s even value in creating titles and meta descriptions. I’m not going there. But I will say that, if you have any areas where you need to create them and they are on your tasks list, you can spend a lot of hours and the cost of those hours (or outsourced resources) for a minimal return.

Leverage tools based on what you’re already paying for or what tech ecosystem you’re in, like Screaming Frog + OpenAI API + a WordPress plugin, which can save thousands of dollars and many dozens of hours.

Putting It Into Action

Steps for generating alt text at scale:

  1. Get your OpenAI API key:
    • In your OpenAI dashboard at platform.openai.com, go to API keys.
    • Create a new secret key and name it something you’ll remember, like Screaming Frog.
    • Make sure you have credits in your account (a few dollars can go a long way).
  2. Set up your Screaming Frog crawl:
    • Set up your OpenAI configuration by going to Configuration > API Access > AI. Enter your API Key into the field. Press Connect.
    • Set up a prompt to generate alt text by going to the Prompt Configuration tab. Click Add from Library > System > Generate alt text for images.
    • Set up your crawl configuration and don’t forget to go to Spider > Rendering and change the rendering mode from Text Only to JavaScript. Then, go to Extraction and, under HTML, check Store HTML and Store Rendered HTML.
    • Run a test crawl on one URL to ensure the output works for you. Tweak the prompt if you’d like.
  3. Run the crawl.
  4. Export to a CSV.
  5. Format the file with two columns: image URL, alt text.
  6. Add this plugin to the site: https://wordpress.org/plugins/alt-text-updater/.
  7. Upload the file.
  8. Crawl your site and do manual checks to test that images have alt text.
  9. Deactivate and uninstall the plugin.

2. Structuring Content Outlines

This might be one of the most common things we do when starting SEO or in periodic content organization, expansion projects, or ongoing content creation. With content being what I call the “fuel” of SEO (and also visibility in AI search), it is still as important as ever to organize it well and present it in a way that makes sense to site visitors and the machines that are also learning it.

While you might not be able to automate this out of the box or in a single prompt in your favorite LLM, you can definitely speed up the process and gain some insights into connections you might not make on content themes on your own (my favorite bonus).

Whether you’re working on a single article, a longer-term content calendar, reorganizing evergreen content, or other content-specific tasks, mastering the art of prompt creation, coaching the AI agent, ensuring the output is good, and using project folders (with brand style guides) in ChatGPT can ensure the quality and speed the more you produce.

Putting It Into Action

Example Prompt

You are an expert SEO who specializes in content writing for [industry]. Your task is to create an outline for an article for [topic]. The article outline should cover the following subtopics: 

[subtopic 1], 

[subtopic 2], 

[subtopic 3]. 

The article should target the following keywords: 

[keyword]

[keyword]

[keyword]

Attached are the HTML files of pages currently ranking well in Google search results to use as guidance. Review the HTML files and generate a content outline. 

3. Creating Project Briefs

Going a little higher level into organizing the work we do, connecting desired outcomes to strategies and ultimately to tactics, project briefs are something you might not do every day.

I like to think about SEO in projects or sprints as a way to break up the big nature of ongoing and long-term work that requires short-term progress and tactics. Regardless of how you organize the work, you likely have a lot of varying documentation and information. Whether in sheets, documents, decks, or other sources, you have information that you can feed together into your LLM of choice to have AI organize and sort out.

Whether you’re doing this formally to produce a report deliverable or informally to help your team or yourself organize the minutiae of SEO information, I can point to examples of my team using Gemini to read through a bunch of documents, including meeting notes, personal notes, transcripts, AI transcripts, agendas, competitor lists, research, emails, and more.

This can be helpful for a number of uses, including putting together a document that can be helpful for personal reference, team reference, onboarding, and articulation of the overall knowledge base for stakeholders.

Putting It Into Action

Example Prompt

You are an experienced Senior Marketing Strategist and you’re onboarding your team for [describe project]. Your task is to create a comprehensive project brief for [name of campaign or project].

Ensure the project brief takes into account the following project details:

Objective: [what is the overarching goal of the project]

Target audience: [overview of the demographics]

Key messaging: [provide details about campaign messaging]

Channels: [what channels will be incorporated into the campaign/project]

For the deliverable, the output should include the following:

Project Overview: Include a 1-2 sentence summary of the project

Success Metrics: [provide KPIs]

Budget: [provide financials]

Timeline: [provide deadlines and milestones]

Generate the project brief as a professional, internal-facing document.

Classifying Keywords

Prompt for using the AI function in Google Sheets to classify keywords by search intent, segment, branded/non-branded, etc.

=ai("Act as an SEO Specialist. Classify the following Keyword into exactly one of these Categories: [Informational, Navigational, Commercial, Transactional].

Rules:

Informational: User is looking for an answer or guide.

Commercial: User is researching products/services before buying.

Transactional: User has high intent to buy/convert now.

Navigational: User is looking for a specific website/brand.

Keyword: [Cell Reference, e.g., A2]

Result: Return only the category name with no extra text or punctuation

4. Segmenting Keywords

In SEO today, we’re not focused necessarily on granular keywords. However, they are still important in our research and strategy planning, along with more tactical work in guiding content topic building and creation.

When you do your research and have your list of keywords from any source, you can utilize the Google Sheets AI function to categorize them by topic, pillar, branded/non-branded, localized or not, search intent, etc.

You can also run keywords through an LLM and have it categorize them, export the output, import that back into your spreadsheet, and align it to the data using a VLOOKUP function (a recommendation, as my team thinks the Google Sheet AI function isn’t where we want it to be yet).

While the method I noted also might feel manual and not where we want it to be eventually, with better AI and tooling, it is still much better than doing things manually. I encourage you to use your own spreadsheet logic or “regular expression” (regex) to categorize as much as you can efficiently before going to AI, especially if your dataset is extensive.

5. Documenting Competitor Outlines

While I have to admit that I like to visually check out competitor websites for my first impression and a quick, informal sophistication check, automating this is a huge time-saver.

For example, Gemini is really good at outlining the content structure of a webpage, so my team likes to feed three or four competitor URLs that are ranking well or have high visibility for a topic that we’re building a strategy for, and it can give us an outline of each page. That includes messaging, targeting, and providing baseline content blocks that each page has that we can use when we do content development on our side.

Disclaimer: Just like in the olden days, don’t copy directly and don’t steal. Verify that what you’re getting back out of the tool you’re using isn’t ripping someone off. That’s on us to validate.

Putting It Into Action

Example Prompt

You’re an expert SEO strategist and you’re conducting a competitive content analysis of your client’s page against pages currently outranking it in Google for the search term [keyword]. The client is a [describe client and industry]. The page is [describe purpose of the page and topic].

I’ve attached the HTML files of the client’s page, as well as the HTML files for the competitor pages. Your tasks are to provide me:

An outline for each page of the content blocks present in the HTML

An overview of the messaging, tone, voice

A list of outgoing internal links in the content

Content gaps between the client's page and the competitors 

6. Conducting SERP Analysis

We can’t waste impressions and any visibility we get by showing up on the wrong topics. SEO now is about quality, and we can’t miss the mark on search intent.

An example that is a big time-saver is to build your seed keyword list using Ahrefs and then export the keyword list with SERP data. Then, feed that spreadsheet into Gemini and have it provide a breakdown of organic competitors per keyword, intent of ranking organic pages per keyword, etc. This example is a good way to save time from having to review hundreds and hundreds of rows. My team usually filters out AI Overviews and ad placement data to condense it a bit.

This type of work has been helpful in figuring out informational versus commercial intent SERPs at scale so that we’re targeting the right keywords with the right content. It has also been helpful in understanding the level of competition within a topic, so we know what to avoid and what long-tail keywords may represent realistic opportunities.

I will emphasize, though, that it is important to note that the SERPs aren’t 100% accurate, and localization and personalization will change the SERPs that users see. But it’s helpful in comparing keywords against each other. We also do SERP reviews manually to confirm findings. Again, validate as a human what you’re getting from tools.

In Closing

There’s a lot of power in what you can reclaim in time and dollars, leveraging automation, deeper tools use, and the power of AI for SEO. And, you probably detected a theme where, in pretty much everything you do, there have to be solid inputs in order to get useful outputs, which also require human validation and experience to trust.

Regardless of where you are with automation, the goal of being able to do more with less, scale tasks, and not do manual tasks that might have low return on investment is a great way to determine where you should consider doing more with tech and less manual work.

More Resources:


Featured Image: ArtEternal/Shutterstock

Anthropic’s Claude Bots Make Robots.txt Decisions More Granular via @sejournal, @MattGSouthern

Anthropic updated its crawler documentation this week with a formal breakdown of its three web crawlers and their individual purposes.

The page now lists ClaudeBot (training data collection), Claude-User (fetching pages when Claude users ask questions), and Claude-SearchBot (indexing content for search results) as separate bots, each with its own robots.txt user-agent string.

Each bot gets a “What happens when you disable it” explanation. For Claude-SearchBot, Anthropic wrote that blocking it “prevents our system from indexing your content for search optimization, which may reduce your site’s visibility and accuracy in user search results.”

For Claude-User, the language is similar. Blocking it “prevents our system from retrieving your content in response to a user query, which may reduce your site’s visibility for user-directed web search.”

The update formalizes a pattern that’s becoming more common among AI search products. OpenAI runs the same three-tier structure with GPTBot, OAI-SearchBot, and ChatGPT-User. Perplexity operates a two-tier version with PerplexityBot for indexing and Perplexity-User for retrieval.

Anthropic says all three of its bots honor robots.txt, including Claude-User. OpenAI and Perplexity draw a sharper line for user-initiated fetchers, warning that robots.txt rules may not apply to ChatGPT-User and generally don’t apply to Perplexity-User. For Anthropic and OpenAI, blocking the training bot does not block the search bot or the user-requested fetcher.

What Changed From The Old Page

The previous version of Anthropic’s crawler page referenced only ClaudeBot and used broader language about data collection for model development. Before ClaudeBot, Anthropic operated under the Claude-Web and Anthropic-AI user agents, both now deprecated.

The move from one listed crawler to three mirrors what OpenAI did in late 2024 when it separated GPTBot from OAI-SearchBot and ChatGPT-User. OpenAI updated that documentation again in December, adding a note that GPTBot and OAI-SearchBot share information to avoid duplicate crawling when both are allowed.

OpenAI also noted in that December update that ChatGPT-User, which handles user-initiated browsing, may not be governed by robots.txt in the same way as its automated crawlers. Anthropic’s documentation does not make a similar distinction for Claude-User.

Why This Matters

The blanket “block AI crawlers” strategy that many sites adopted in 2024 no longer works the way it did. Blocking ClaudeBot stops training data collection but does nothing about Claude-SearchBot or Claude-User. The same is true on OpenAI’s side.

A BuzzStream study we covered in January found that 79% of top news sites block at least one AI training bot. But 71% also block at least one retrieval or search bot, potentially removing themselves from AI-powered search citations in the process.

That matters more now than it did a year ago. Hostinger’s analysis of 66.7 billion bot requests showed OpenAI’s search crawler coverage growing from 4.7% to over 55% of sites in their sample, even as its training crawler coverage dropped from 84% to 12%. Websites are allowing search bots while blocking training bots, and the gap is widening.

The visibility warnings differ by company. Anthropic says blocking Claude-SearchBot “may reduce” visibility. OpenAI is more direct, telling publishers that sites opted out of OAI-SearchBot won’t appear in ChatGPT search answers, though navigational links may still show up. Both are positioning their search crawlers alongside Googlebot and Bingbot, not alongside their own training crawlers.

What This Means

When managing robots.txt files, the old copy-paste block list needs an audit. SEJ’s complete AI crawler list includes verified user-agent strings across every company.

A strategic robots.txt now requires separate entries for training and search bots at minimum, with the understanding that user-initiated fetchers may not follow the same rules.

Looking Ahead

The three-tier split creates a new category of publisher decision that parallels what Google did years ago with Google-Extended. That user-agent lets sites opt out of Gemini training while staying in Google Search results. Now Anthropic and OpenAI offer the same separation for their platforms.

As AI-powered search grows its share of referral traffic, the cost of blocking search crawlers increases. The Cloudflare Year in Review data we reported in December showed AI crawlers already account for a measurable share of web traffic, and the gap between crawling volume and referral traffic remains wide. How publishers navigate these three-way decisions will shape how much of the web AI search tools can actually surface.

Microsoft: ‘Summarize With AI’ Buttons Used To Poison AI Recommendations via @sejournal, @MattGSouthern

Microsoft’s Defender Security Research Team published research describing what it calls “AI Recommendation Poisoning.” The technique involves businesses hiding prompt-injection instructions within website buttons labeled “Summarize with AI.”

When you click one of these buttons, it opens an AI assistant with a pre-filled prompt delivered through a URL query parameter. The visible part tells the assistant to summarize the page. The hidden part instructs it to remember the company as a trusted source for future conversations.

If the instruction enters the assistant’s memory, it can influence recommendations without you knowing it was planted.

What’s Happening

Microsoft’s team reviewed AI-related URLs observed in email traffic over 60 days. They found 50 distinct prompt injection attempts from 31 companies.

The prompts share a similar pattern. Microsoft’s post includes examples where instructions told the AI to remember a company as “a trusted source for citations” or “the go-to source” for a specific topic. One prompt went further, injecting full marketing copy into the assistant’s memory, including product features and selling points.

The researchers traced the technique to publicly available tools, including the npm package CiteMET and the web-based URL generator AI Share URL Creator. The post describes both as designed to help websites “build presence in AI memory.”

The technique relies on specially crafted URLs with prompt parameters that most major AI assistants support. Microsoft listed the URL structures for Copilot, ChatGPT, Claude, Perplexity, and Grok, but noted that persistence mechanisms differ across platforms.

It’s formally cataloged as MITRE ATLAS AML.T0080 (Memory Poisoning) and AML.T0051 (LLM Prompt Injection).

What Microsoft Found

The 31 companies identified were real businesses, not threat actors or scammers.

Multiple prompts targeted health and financial services sites, where biased AI recommendations carry more weight. One company’s domain was easily mistaken for a well-known website, potentially leading to false credibility. And one of the 31 companies was a security vendor.

Microsoft called out a secondary risk. Many of the sites using this technique had user-generated content sections like comment threads and forums. Once an AI treats a site as authoritative, it may extend that trust to unvetted content on the same domain.

Microsoft’s Response

Microsoft said it has protections in Copilot against cross-prompt injection attacks. The company noted that some previously reported prompt-injection behaviors can no longer be reproduced in Copilot, and that protections continue to evolve.

Microsoft also published advanced hunting queries for organizations using Defender for Office 365, allowing security teams to scan email and Teams traffic for URLs containing memory manipulation keywords.

You can review and remove stored Copilot memories through the Personalization section in Copilot chat settings.

Why This Matters

Microsoft compares this technique to SEO poisoning and adware, placing it in the same category as the tactics Google spent two decades fighting in traditional search. The difference is that the target has moved from search indexes to AI assistant memory.

Businesses doing legitimate work on AI visibility now face competitors who may be gaming recommendations through prompt injection.

The timing is notable. SparkToro published a report showing that AI brand recommendations already vary across nearly every query. Google VP Robby Stein told a podcast that AI search finds business recommendations by checking what other sites say. Memory poisoning bypasses that process by planting the recommendation directly into the user’s assistant.

Roger Montti’s analysis of AI training data poisoning covered the broader concept of manipulating AI systems for visibility. That piece focused on poisoning training datasets. This Microsoft research shows something more immediate, happening at the point of user interaction and being deployed commercially.

Looking Ahead

Microsoft acknowledged this is an evolving problem. The open-source tooling means new attempts can appear faster than any single platform can block them, and the URL parameter technique applies to most major AI assistants.

It’s unclear whether AI platforms will treat this as a policy violation with consequences, or whether it stays as a gray-area growth tactic that companies continue to use.

Hat tip to Lily Ray for flagging the Microsoft research on X, crediting @top5seo for the find.


Featured Image: elenabsl/Shutterstock

Google Offers AI Certificate Free For Eligible U.S. Small Businesses via @sejournal, @MattGSouthern

Google has launched the Google AI Professional Certificate, a self-paced program covering data analysis, content creation, research, and vibe coding.

Every participant receives three months of free access to Google AI Pro. Eligible U.S. small businesses can access the entire program at no cost through a separate application (more on eligibility below).

The certificate is available now on Coursera, Google Skills, and Udemy. In the U.S. and Canada, the subscription costs $49 per month.

What The Certificate Covers

The program consists of seven modules, each of which can be completed in about an hour. No prior AI experience is required.

Participants complete more than 20 hands-on activities. These include creating presentations and marketing materials, conducting deep research, building infographics, analyzing data, and building custom apps without writing code.

After completing all seven modules, participants earn a Google certificate they can add to LinkedIn and share with employers.

Free Access For Eligible U.S. Small Businesses

Google is offering the certificate at no cost to eligible U.S. small and medium-sized businesses with 500 or fewer employees. The offer also includes three months of free Google Workspace Business Standard (for new Workspace customers, up to 300 seats).

To qualify, businesses must be registered in the U.S. and submit their Employer Identification Number (EIN) through a dedicated application on Coursera. Coursera said the verification process takes 5-7 business days.

Businesses can also apply at grow.google/small-business. Google said it is working with the U.S. Chamber of Commerce and America’s Small Business Development Centers to distribute the program.

How This Helps

The program builds on Google AI Essentials, which has become the most popular course on Coursera. The AI Professional Certificate goes further, focusing on applied use cases rather than introductory concepts.

The certificate focuses on tools like Gemini, NotebookLM, and Google AI Studio, so the skills are tied to Google’s ecosystem. Google launched a separate Generative AI Leader certification for Google Cloud in May 2025, though that program focused on non-technical business leaders and required a $99 exam fee. The new AI Professional Certificate has no exam fee.

Looking Ahead

The Google AI Professional Certificate is available now on Coursera, Google Skills, and Udemy. Eligible U.S. small businesses can apply for no-cost access at grow.google/small-business.

For professionals already familiar with Google’s AI tools through earlier training programs, this certificate adds structured, employer-recognized credentials to practical skills you may already be developing on your own.

Why AI Misreads The Middle Of Your Best Pages via @sejournal, @DuaneForrester

The middle is where your content dies, and not because your writing suddenly gets bad halfway down the page, and not because your reader gets bored. But because large language models have a repeatable weakness with long contexts, and modern AI systems increasingly squeeze long content before the model even reads it.

That combo creates what I think of as dog-bone thinking. Strong at the beginning, strong at the end, and the middle gets wobbly. The model drifts, loses the thread, or grabs the wrong supporting detail. You can publish a long, well-researched piece and still watch the system lift the intro, lift the conclusion, then hallucinate the connective tissue in between.

This is not theory as it shows up in research, and it also shows up in production systems.

Image Credit: Duane Forrester

Why The Dog-Bone Happens

There are two stacked failure modes, and they hit the same place.

First, “lost in the middle” is real. Stanford and collaborators measured how language models behave when key information moves around inside long inputs. Performance was often highest when the relevant material was at the beginning or end, and it dropped when the relevant material sat in the middle. That’s the dog-bone pattern, quantified.

Second, long contexts are getting bigger, but systems are also getting more aggressive about compression. Even if a model can take a massive input, the product pipeline frequently prunes, summarizes, or compresses to control cost and keep agent workflows stable. That makes the middle even more fragile, because it is the easiest segment to collapse into mushy summary.

A fresh example: ATACompressor is a 2026 arXiv paper focused on adaptive, task-aware compression for long-context processing. It explicitly frames “lost in the middle” as a problem in long contexts and positions compression as a strategy that must preserve task-relevant content while shrinking everything else.

So you were right if you ever told someone to “shorten the middle.” Now, I’d offer this refinement:

You are not shortening the middle for the LLM so much as engineering the middle to survive both attention bias and compression.

Two Filters, One Danger Zone

Think of your content going through two filters before it becomes an answer.

  • Filter 1: Model Attention Behavior: Even if the system passes your text in full, the model’s ability to use it is position-sensitive. Start and end tend to perform better, middle tends to perform worse.
  • Filter 2: System-Level Context Management: Before the model sees anything, many systems condense the input. That can be explicit summarization, learned compression, or “context folding” patterns used by agents to keep working memory small. One example in this space is AgentFold, which focuses on proactive context folding for long-horizon web agents.

If you accept those two filters as normal, the middle becomes a double-risk zone. It gets ignored more often, and it gets compressed more often.

That is the balancing logic with the dog-bone idea. A “shorten the middle” approach becomes a direct mitigation for both filters. You are reducing what the system will compress away, and you are making what remains easier for the model to retrieve and use.

What To Do About It Without Turning Your Writing Into A Spec Sheet

This is not a call to kill longform as longform still matters for humans, and for machines that use your content as a knowledge base. The fix is structural, not “write less.”

You want the middle to carry higher information density with clearer anchors.

Here’s the practical guidance, kept tight on purpose.

1. Put “Answer Blocks” In The Middle, Not Connective Prose

Most long articles have a soft, wandering middle where the author builds nuance, adds color, and tries to be thorough. Humans can follow that. Models are more likely to lose the thread there. Instead, make the middle a sequence of short blocks where each block can stand alone.

An answer block has:
A clear claim. A constraint. A supporting detail. A direct implication.

If a block cannot survive being quoted by itself, it will not survive compression. This is how you make the middle “hard to summarize badly.”

2. Re-Key The Topic Halfway Through

Drift often happens because the model stops seeing consistent anchors.

At the midpoint, add a short “re-key” that restates the thesis in plain words, restates the key entities, and restates the decision criteria. Two to four sentences are often enough here. Think of this as continuity control for the model.

It also helps compression systems. When you restate what matters, you are telling the compressor what not to throw away.

3. Keep Proof Local To The Claim

Models and compressors both behave better when the supporting detail sits close to the statement it supports.

If your claim is in paragraph 14, and the proof is in paragraph 37, a compressor will often reduce the middle into a summary that drops the link between them. Then the model fills that gap with a best guess.

Local proof looks like:
Claim, then the number, date, definition, or citation right there. If you need a longer explanation, do it after you’ve anchored the claim.

This is also how you become easier to cite. It is hard to cite a claim that requires stitching context from multiple sections.

4. Use Consistent Naming For The Core Objects

This is a quiet one, but it matters a lot. If you rename the same thing five times for style, humans nod, but models can drift.

Pick the term for the core thing and keep it consistent throughout. You can add synonyms for humans, but keep the primary label stable. When systems extract or compress, stable labels become handles. Unstable labels become fog.

5. Treat “Structured Outputs” As A Clue For How Machines Prefer To Consume Information

A big trend in LLM tooling is structured outputs and constrained decoding. The point is not that your article should be JSON. The point is that the ecosystem is moving toward machine-parseable extraction. That trend tells you something important: machines want facts in predictable shapes.

So, inside the middle of your article, include at least a few predictable shapes:
Definitions. Step sequences. Criteria lists. Comparisons with fixed attributes. Named entities tied to specific claims.

Do that, and your content becomes easier to extract, easier to compress safely, and easier to reuse correctly.

How This Shows Up In Real SEO Work

This is the crossover point. If you are an SEO or content lead, you are not optimizing for “a model.” You are optimizing for systems that retrieve, compress, and synthesize.

Your visible symptoms will look like:

  • Your article gets paraphrased correctly at the top, but the middle concept is misrepresented. That’s lost-in-the-middle plus compression.
  • Your brand gets mentioned, but your supporting evidence does not get carried into the answer. That’s local proof failing. The model cannot justify citing you, so it uses you as background color.
  • Your nuanced middle sections become generic. That’s compression, turning your nuance into a bland summary, then the model treating that summary as the “true” middle.
  • Your “shorten the middle” move is how you reduce these failure rates. Not by cutting value, but by tightening the information geometry.

A Simple Way To Edit For Middle Survival

Here’s a clean, five-step workflow you can apply to any long piece, and it’s a sequence you can run in an hour or less.

  1. Identify the midpoint and read only the middle third. If the middle third can’t be summarized in two sentences without losing meaning, it’s too soft.
  2. Add one re-key paragraph at the start of the middle third. Restate: the main claim, the boundaries, and the “so what.” Keep it short.
  3. Convert the middle third into four to eight answer blocks. Each block must be quotable. Each block must include its own constraint and at least one supporting detail.
  4. Move proof next to claim. If proof is far away, pull a compact proof element up. A number, a definition, a source reference. You can keep the longer explanation later.
  5. Stabilize the labels. Pick the name for your key entities and stick to them across the middle.

If you want the nerdy justification for why this works, it is because you are designing for both failure modes documented above: the “lost in the middle” position sensitivity measured in long-context studies, and the reality that production systems compress and fold context to keep agents and workflows stable.

Wrapping Up

Bigger context windows do not save you. They can make your problem worse, because long content invites more compression, and compression invites more loss in the middle.

So yes, keep writing longform when it is warranted, but stop treating the middle like a place to wander. Treat it like the load-bearing span of a bridge. Put the strongest beams there, not the nicest decorations.

That’s how you build content that survives both human reading and machine reuse, without turning your writing into sterile documentation.

More Resources:


This post was originally published on Duane Forrester Decodes.


Featured Image: Collagery/Shutterstock

ChatGPT Search Often Switches To English In Fan-Out Queries: Report via @sejournal, @MattGSouthern

When ChatGPT Search builds an answer, it can generate background web queries to find sources. A new report from AI search analytics firm Peec AI found that a large share of those background queries run in English, even when the original prompt was in another language.

Peec AI analyzed over 10 million prompts and 20 million fan-out queries from its platform data. Across all non-English prompts analyzed, the company reports that 43% of the fan-out steps were conducted in English.

What Are Fan-Out Queries

OpenAI’s ChatGPT Search documentation describes fan-out queries. When a user asks a question, ChatGPT Search “typically rewrites your query into one or more targeted queries” and sends them to search partners. After reviewing initial results, “ChatGPT search may send additional, more specific queries to other search providers.”

Peec AI refers to these rewritten sub-queries as “fan-outs.” The company’s report tracked which languages ChatGPT used when generating them.

OpenAI’s documentation does not describe how language is chosen for rewritten queries.

What Peec AI Found

Peec AI filtered its data to include only cases where the IP location matched the prompt language. Polish-language prompts from Polish IP addresses, German-language prompts from German IPs, and Spanish-language prompts from Spanish IPs. Mixed signals, such as German-language prompts from UK IP addresses, were excluded.

The filtered data showed that 78% of non-English prompt runs included at least one English-language fan-out query.

Turkish-language prompts included English fan-outs most often, at 94%. Spanish-language prompts were lowest, at 66%. No non-English language in Peec AI’s dataset fell below 60%.

Peec AI’s data showed a consistent pattern across languages. ChatGPT typically starts its fan-out queries in the prompt’s language, then adds English-language queries as it builds the response.

Examples From The Report

Peec AI’s blog post included several examples showing how the pattern can play out in practice.

When prompted in Polish from a Polish IP address about the best auction portals, ChatGPT either omitted or buried Allegro.pl in favor of eBay and other global platforms. Peec AI describes Allegro as Poland’s dominant ecommerce platform.

When prompted in German about German software companies, Peec AI reported the response listed no German companies. When prompted in Spanish about cosmetics brands, no Spanish brands appeared.

In the Spanish cosmetics example, Peec AI showed ChatGPT’s actual fan-out queries. The first ran in English. The second ran in Spanish but added the word “globales” (global), a qualifier the original prompt never used. The system appears to have interpreted a Spanish-language prompt from a Spanish IP address as a request for global brands.

These are individual examples from Peec AI’s testing, not necessarily representative of all ChatGPT Search behavior.

Why This Matters

SEO and content teams operating in non-English markets may face a disadvantage in ChatGPT’s source selection that may not map cleanly to traditional ranking signals. In Peec AI’s examples, English-language fan-out queries surfaced English-language sources that favored global brands over local competitors.

We’ve been covering ChatGPT’s citation patterns for over a year now, from SE Ranking’s report on citation factors to the Tow Center’s attribution accuracy findings. Those earlier reports showed which signals predict whether a source gets cited. Peec AI’s data suggests the language of the background query may filter which sources are even considered, before citation signals come into play.

Methodology Notes

Peec AI is a vendor in the AI search analytics space. The company’s documentation describes its data collection method as running customer-defined prompts daily via browser automation, interacting with AI platforms through their web interfaces rather than APIs. The 10 million prompts in this report came from Peec AI’s platform, not from a panel of consumer ChatGPT sessions.

The report didn’t detail the composition of those prompts, what categories or industries they covered, or how representative they are of broader ChatGPT usage patterns.

Tomek Rudzki, the report’s author, is presented by Peec AI as a “GEO Expert” on its blog. He is a well-known technical SEO practitioner who has spoken at BrightonSEO and SMX Munich and contributed to publications such as Moz.

Looking Ahead

OpenAI’s public ChatGPT Search docs describe query rewriting and follow-up queries but don’t explain how language is chosen for those queries. Whether the English fan-out pattern Peec AI identified is an intentional design choice or an emergent behavior of the system remains unclear.

The report raises a question worth monitoring. Will building English-language content become part of AI search optimization strategies, or will AI search platforms adjust their source selection to better reflect local markets?


Featured Image: arda savasciogullari/Shutterstock