Microsoft Clarity Now Shows Grounding Queries Behind AI Citations via @sejournal, @TaylorDanRW

When Microsoft Clarity made AI citations available to all users, it opened up a new playground for SEOs to harvest AI visibility data. Finally, we can see the exact “grounding queries” an AI engine uses to pull our content.

It raises a massive question because this is a Microsoft tool: Are the insights useless if your audience doesn’t touch the Bing ecosystem?

Microsoft Clarity Grounding Queries

When you ask Copilot a question, it translates your words into simple search terms called grounding queries to find facts on the web before it answers. You can use this data to improve your own website and content.

• Finding gaps where your content does not match what the AI searches for.
• Simplifying pages that the AI reads but does not link to.
• Using these simple layouts to help your Google search results.

Copilot Vs. Gemini

Both Copilot and Gemini use retrieval-augmented approaches. Instead of generating answers using only pre-trained parameters, they dynamically query external search indexes to retrieve real-time data, which they then use as context to ground their final responses.

Does Ranking In Bing Matter?

Yes (Correlation).

One of my websites was doing extremely well in Copilot, with over 36,000 citations across all queries. Now, Clarity doesn’t give you the prompts/queries themselves, but it does give you the Grounding queries (grounding queries and key phrases used to retrieve your site’s content).

My website has a history, running for years with a previous domain merged in 2019, and boasts over 1,000 articles. Given that Google barely sends traffic, and third-party SEO tools often label it as spam due to non-English backlinks (it covers search engines like Baidu, CocCoc, SwissCows, attracting an international audience), I never expected 36,000 citations.

So, why the Copilot love? I took the 147 grounding queries and tracked their rank in Google and Bing.

Of the 147 queries, Bing ranked all but 6, with the majority in traffic-driving positions (top 20). Google didn’t rank a single one.

So, If This Is Heavily Dependent On Bing Indexing, Is Clarity’s Data Useful Outside Of The Bing/Microsoft Ecosystem?

Because this is a Microsoft tool, the backend data feeding this dashboard is primarily capturing how your site is cited across Microsoft’s AI surfaces (like Copilot and Bing generative search).

It is not giving you a direct window into how OpenAI’s ChatGPT (using its own search), Google Gemini, or Perplexity are citing your links, because those platforms do not share their internal grounding logs with Microsoft.

And historically, we as an industry have been neglectful of Bing.

Even though the data collection source is skewed toward Microsoft’s AI engine, the insights themselves are highly transferable to your broader, platform-agnostic AI optimization strategies.

Can We Assume Other LLMs Retrieve Data In The Same Way?

AI engines, whether Google Gemini or Microsoft Copilot, use similar RAG frameworks to fetch data.

If the Bing ecosystem flags that a specific page on your site has a high “Share of Authority” for a complex query, it means that page is structured perfectly for AI consumption (clear tables, bullet points, direct answers). Data suggests that you can replicate that formatting across your site to appeal to Google Gemini as well.

However, this can be argued against as other research suggests that the similarity between LLMs is dependent on positional biases, and some may use the SDSR method rather than RAG.

Researchers in SEO have also found that ChatGPT has started to use Google Search as a fallback, when it was initially Bing.

In Summary

If your audience doesn’t touch the Microsoft ecosystem, this dashboard won’t give you a perfect 1-to-1 reflection of your total AI traffic, but it doesn’t make the data useless.

What grounding queries reveal is how AI systems distill user intent into retrievable search terms. That process is broadly consistent across platforms, even when the underlying indexes differ. A page earning citations in Copilot is doing something right structurally with clear answers, well-scoped topics, content aligned with how AI engines translate questions into queries. The Bing dependency tells you where the data comes from. The structural patterns tell you something more transferable.

The gap data is equally instructive. Pages your site ranks for in Bing that never appear as grounding queries signal a mismatch. Either the content isn’t structured for AI retrieval, or the topic isn’t one AI engines are actively grounding answers around.

Treat Clarity’s Citations dashboard as a useful proxy or “lab environment” and window into how LLMs read, slice/chunk, and credit your website’s content. Even if Copilot isn’t your primary AI traffic source, the patterns it surfaces are worth paying attention to.

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Mt. Stupid Has A Pricing Page via @sejournal, @pedrodias

“There is now ample evidence, collected over the last few years, that AI systems are unpredictable and difficult to control.” That’s Dario Amodei in January, writing about the technology his company sells.

Compare with what’s on your LinkedIn timeline this week. Here’s the script: Schema markup ensures AI engines parse your content. The first sentence of every section must be the answer. Optimize for chunk-level retrieval. There’s a 13% citation lift available if you do X, a 2.8x conversion improvement if you do Y.

It’s one of the cleanest patterns going right now, and the industry has elected not to notice. The people closest to these systems are increasingly cautious about claims of control. The people furthest from it are increasingly certain they know how it works … they’ve cracked it. That gradient runs the wrong way.

What The People Who Built It Actually Say

Anthropic published its main interpretability research post in May 2024. It opens:

“We mostly treat AI models as a black box: something goes in and a response comes out, and it’s not clear why the model gave that particular response instead of another.”

Anthropic, writing about its own model, two years ago.

Things haven’t gotten more confident since. Neel Nanda, who runs Google DeepMind’s mechanistic interpretability team, gave an interview to 80,000 Hours in September 2025 in which the headline finding was that the most ambitious version of mech interp is probably dead. He doesn’t see a realistic world where the discipline delivers “the kind of robust guarantees that some people want from interpretability.” Worth re-reading.

The person whose job is to read AI minds is publicly conceding that the project, as originally conceived, won’t get there.

At NeurIPS 2024, Ilya Sutskever, co-founder of Safe Superintelligence and formerly chief scientist at OpenAI, accepted his Test of Time award and used the platform to say something the room wasn’t expecting from him:

“The more it reasons, the more unpredictable it becomes.”

Sutskever’s career is essentially the scaling hypothesis with a face on it. Hearing him say the next phase produces less predictable outputs is itself an admission.

Now scroll back to your timeline. The gradient is Dunning-Kruger redrawn at an industry scale: Mt. Stupid with a pricing page, and the valley of calibration where the actual work happens.

What The People Selling It Actually Say

A practitioner posts a four-pillar framework for “Technical GEO.” A consultant guarantees inclusion in AI Overviews. An agency markets a 13% lift in citation likelihood, derived from data the agency itself produced about the agency’s own prescriptions. A widely shared post promises that maintaining a 300-character paragraph limit dictates how a vector database chunks your content. A vendor claims a 78% “share of model.” A senior figure in your inbox describes a 2.8x improvement in conversion from being cited in SGE.

The vocabulary is deterministic: “ensures,” “guarantees,” “dictates,” percentages precise to the decimal, frameworks confidently named. None of it sounds anything like the language the people who built these systems use when describing how the systems behave.

This is the part I keep getting stuck on. The consultants are confident about the tactics they’ve measured against themselves. Run the same playbook on a few clients, watch some metric move, call it evidence. No control groups, no pre-registered hypotheses, no measurement of what the tactic is actually claimed to change. That’s the bar a real test has to clear; everything else has been confirmation in costume. The problem is the confidence level, which is wrong by an order of magnitude regardless of whether the underlying tactic does anything. The same model that Anthropic publicly says it cannot fully account for is being optimized against by people who confidently claim to know exactly what they’re doing.

Either Anthropic has been suspiciously modest in public, or somebody else is suspiciously certain.

When Somebody Tests

On Monday, last week, Ahrefs published a study by Louise Linehan and Xibeijia Guan with a title that should ideally be impossible: We Tracked 1,885 Pages Adding Schema. AI Citations Barely Moved.

The methodology is the kind of work you would expect to be standard, if the discipline cared about standards. 1,885 pages that added JSON-LD schema between August 2025 and March 2026. 4,000 matched control pages. Citation changes measured 30 days before and 30 days after the schema was added, across Google AI Overviews, Google AI Mode, and ChatGPT. Difference-in-differences on the matched groups.

The finding: No meaningful uplift in citations on any platform. AI Overviews actually showed a small but statistically significant decline. The report notes the odds of a gap that large being chance are roughly 1 in 2,500. The schema-makes-LLMs-understand-your-content thesis, tested at scale against a controlled baseline, did not survive the test.

This is the empirical confirmation of the technical case I made a week ago in The Whole Point Was the Mess: that LLMs read unstructured language, and that schema-and-chunking prescriptions are reasoning about an architecture that doesn’t exist. From first principles, two weeks ago. From controlled measurement, last Monday.

It is worth sitting with that. The dominant prescriptive category in the entire GEO playbook has been empirically falsified under controlled conditions, by a vendor with a substantial audience, in the open. And the frameworks keep selling.

Then Google Itself Answered

On May 15, 2026, Google published official documentation on optimizing for generative AI features in search. The page mythbusts the GEO prescriptions in writing: llms.txt files aren’t needed; chunking content isn’t required; rewriting content for AI systems isn’t necessary; special schema markup isn’t required; pursuing inauthentic mentions doesn’t help. The framing is unusually direct for a Google developer page:

“Many suggested ‘hacks’ aren’t effective or supported by how Google Search actually works.”

Google names Answer Engine Optimization and Generative Engine Optimization by their full terms and rejects the playbook outright.

That is the search engine the consultants claim to be optimizing for, telling its own developer audience that the optimizations don’t work. From first principles, two weeks ago. From controlled measurement, last Monday. From Google itself, last Friday. Three independent sources of the same answer, all within a fortnight. All ignored by the people selling the opposite.

The Cost Of Asking

This is where the diagnosis stops being polite.

Confident claims compound on these platforms in a way that skeptical corrections don’t. The difference is in who pays. Posting a confident claim costs you nothing. It gets engagement, builds an audience, generates inbound, makes the slide deck look forward-looking. If it turns out to be wrong, nothing happens. By the time anyone notices, everyone’s moved on to the next acronym.

Posting the correction costs you. It picks a fight. It marks you as a contrarian, or worse, as somebody who doesn’t get it. On LinkedIn, where most of this happens, it works against your professional brand. The algorithm will not reward it. The original poster owns the comment section and can ignore your methodology question while engaging with the congratulatory replies. Your reply lives in a collapsed thread.

There’s a specific move worth naming here. Ask a GEO consultant to explain, in plain terms, what their methodology actually does, what mechanism it acts on, what would count as evidence, what would falsify it. The response escalates into jargon. “Vector-space alignment.” “T1 query optimisation.” “Chunk-level semantic retrieval.” Real terms from machine-learning research, glued into combinations that sound rigorous and resist plain-language verification. The pattern works because it can. Asking “what does that actually mean” looks naive, and observers without the specific technical knowledge can’t tell which combinations are real and which are improvised on the spot.

Read the comments on any high-engagement GEO post. Fifteen replies in, 12 are agreements or “here’s another skill to add to your list.” Two or three offer diplomatically-framed skepticisms: “I would love to see more data,” or “the list is right, but…” The author engages substantively with the philosophical objection because pushing back against “this is too technical” is easy. The methodological objection, that the prescribed skills produce confident speculation without a measurement layer underneath, gets the politest burial.

What this adds up to is gaslighting at industry scale. The people reading the technology correctly get positioned as the ones who haven’t caught up; the prescriptions that controlled tests just falsified get sold as forward-looking. GEO has worked out how to make calibration look like the deficiency.

A recent X experiment captured the dynamic outside SEO. Someone posted a Monet painting and claimed it was AI-generated, asking the replies to explain its inferiority to a real Monet. Hundreds responded, confidently cataloging the “AI tells.” Flat brushwork, soulless composition, no cohesion, no soul. They were analyzing a Monet. The frame determined what they saw.

The original post, where a lot of the initial replies have now been deleted.

It’s the same trick. Vocabulary substitutes for substance; framing activates confirmation bias before any examination begins; the performance of analysis becomes what’s purchased rather than the analysis itself; “this is X” arrives before anyone checks whether it is. Once the frame is set, the analysis follows.

So the people most equipped to push back, the practitioners who’ve actually tried to test things, the technical SEOs who know what schema does and doesn’t do, the ones who can spot a fabricated lift number from across the room, stay quiet.

The result, on the timelines the C-suite reads, is a one-sided market.

The cost falls on the people who buy the claim. Clients pay for schema audits the Ahrefs study just falsified. Junior practitioners build careers on methodologies that won’t survive a controlled test. And the discipline burns credibility it will need later, when traditional search displaces further, and SEOs are expected to sit in rooms with engineering teams who’ve just spent two years watching the field confidently mis-call the technology.

Knowledge advances by trying to disprove your hypothesis, not confirm it. GEO does the opposite, runs studies designed to validate what it’s already selling. If the professionals claiming this expertise won’t even try to falsify themselves, who do we expect to believe us?

The Absence Is The Data

Strip the discourse, and what remains is the absence.

A serious technical field watches a controlled test contradict its dominant prescriptions, and the prescriptions keep selling. At that point, asking whether the prescriptions are wrong stops being the interesting question. That has been answered. The harder question is what’s wrong with a field that watches and doesn’t correct.

Same with the gradient. When the people who built the systems hedge and the people optimizing for those systems guarantee, asking who’s right stops being interesting. The researches and builders are right. Nobody who has worked on inference attribution thinks otherwise. The harder question is why the field lets the guarantees travel unchallenged.

The honest answer is that the incentives don’t pull toward correction. Confidence sells in ways caution can’t. The reportable framework wins the budget; the sensible assessment loses. And hedged language doesn’t fit on a pricing page where a guarantee fits perfectly.

None of this needs villains. The market for attention rewards confidence over calibration, every time.

You can keep watching the gradient run the wrong way. Or you can read what it actually is: an industry standing on Mt. Stupid, charging for the view.

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This post was originally published on The Inference.

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Anthropic’s Infrastructure Crisis – What It Means for Marketers & SEO Pros via @sejournal, @gregjarboe

On May 6, 2026, Anthropic CEO Dario Amodei walked out onto a stage at his company’s developer conference in San Francisco and said something you almost never hear from a tech CEO: Growth is the problem.

Anthropic had planned for a 10-fold expansion. What it got was 80-fold growth in Q1, on an annualized basis. Revenue has crossed $30 billion, up from $9 billion at the end of 2025. The company is weighing a funding round at a reported $900 billion valuation – which, if it closes at those terms, would likely surpass OpenAI’s most recent post-money valuation of $852 billion. And yet, as Amodei told the audience that day, “I hope that 80-times growth doesn’t continue because that’s just crazy and it’s too hard to handle.”

He wasn’t being falsely modest. Demand for Claude has already created what Anthropic described as “inevitable strain on our infrastructure,” hitting reliability and performance during peak hours. Hours before Amodei took the stage, the company announced a deal with SpaceX – which, earlier this year, merged with xAI, the company behind the Grok AI models, now rebranded SpaceXAI – to take over the entire compute capacity at the Colossus 1 data center in Memphis, giving it access to more than 300 megawatts of capacity and 220,000 Nvidia GPUs.

The detail worth noting: xAI and Anthropic are direct competitors at the model layer. The fact that Grok’s infrastructure is now running Claude’s workloads is the clearest signal yet of how constrained high-end compute capacity has become. That’s a bridge built under emergency conditions, not a planned expansion.

So, why should SEO professionals, content marketers, and entrepreneurs care about Anthropic’s infrastructure problems? Because this story is actually about something much bigger than one company scrambling for server capacity.

This Has Happened Before

In 2011, I read I’m Feeling Lucky: The Confessions of Google Employee Number 59 by Douglas Edwards, who was Google’s first director of marketing and brand management. That’s when I learned how close Google came to buckling under its own success in the early days.

In late 1999, Edwards wrote, “Google began accelerating its climb to market domination. The media started whispering about the first search engine that actually worked, and users began telling their friends to give Google a try. More users meant more queries, and that meant more machines.” Then the machines became impossible to get. A global shortage of RAM hit at the worst possible moment, and Google’s system, as Edwards put it, “started wheezing asthmatically.”

That infrastructure crisis drove decisions that shaped the web for the next two decades. Google started filtering duplicate content – even non-malicious versions like printer-friendly pages – because every redundant page required adding hardware without improving user experience. The constraint shaped the product. The product shaped SEO.

Anthropic’s compute crisis is the same dynamic, playing out 25 years later at a different scale. The question isn’t whether they’ll solve it. They will. The question is what decisions they’ll make under pressure, and how those decisions will reshape the products that millions of marketers depend on.

What The Data Actually Shows

When I went looking for what this growth moment means for practitioners, I found the headlines and the data pointing in surprisingly different directions.

Rand Fishkin recently shared findings from the Datos State of Search Q1 2026 report, which draws on clickstream data from tens of millions of real devices. His summary was pointed: AI is disrupting traditional search – no, the data doesn’t show that. AI tools are growing faster than traditional search in absolute terms – no, traditional search is still outpacing AI tool growth on an absolute basis. AI Mode in Google is huge – no, it’s still under 0.2% share, growing but still small. ChatGPT is pulling away from Claude – actually, no. Claude is closing the gap, Gemini holds the number two spot and is growing, and ChatGPT has plateaued since September 2025.

These are not the narratives that get clicks. They are, however, what the data says.

At the same time, I went to Think with Google and worked through its report, “The Rise of the Super-Empowered Consumer,” which tells a different part of the same story. Some of what’s in there deserves more attention than it’s getting. AI Overviews is used by over 2 billion people, and users report making decisions faster and with more confidence. AI Mode now has over 75 million daily active users, with nearly 1 in 6 queries using voice or images. Queries in AI Mode run three times longer than traditional searches, and sessions are becoming more conversational. Google Lens handles over 25 billion visual searches every month. Shoppers are 2.3 times more likely to use Google Search than ChatGPT for purchase decisions, and 40% of consumers who use Google AI Mode for shopping say they’re using ChatGPT less as a result.

Two different pictures of the same moment. Both accurate. Neither is complete on its own.

The Takeaway For Practitioners

The AI industry is generating a firehose of information, and most of it gets consumed at the headline level. A company announces 80-fold growth, and people read it as a story about AI winning. Fishkin publishes data showing traditional search still outpacing AI tools in absolute volume, and people read it as a story about AI losing. Google publishes a consumer report showing AI Overviews reaching 2 billion users, and people read it as confirmation that SEO is dead.

None of those readings are wrong. All of them are incomplete.

The strategic value isn’t in reading the news. It’s in following the thread further – downloading the Datos report, working through the Google consumer study, checking the CNBC article against the Cryptopolitan analysis of what the Anthropic-SpaceX deal actually signals about the infrastructure war playing out between the major AI companies.

Google’s early infrastructure crisis produced lasting decisions about duplicate content that practitioners are still navigating. Anthropic’s current one will produce decisions about rate limits, model availability, enterprise pricing, and compute allocation that will shape how Claude-powered tools perform for the marketers and developers using them. Those decisions are already being made.

The practitioners who understand the context those decisions come from will be better positioned than those who only read the headline.

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