Answer the hidden questions or vanish in AI search

ChatGPT, Gemini, Perplexity: these are the brand new working environments. Your content material have to be invokable inside them, or nobody will see it.

At SMX Advanced, I broke down how you can construct an AI visibility engine: a system for making your net-new information reusable by people and brokers throughout synthesis-first platforms.

It goes past publishing to indicate how groups can deploy structured content material that survives LLM compression and exhibits up for patrons throughout their buying choices.

It’s what we’re constructing with shoppers and inside XOFU, our LLM visibility GPT.

Right here’s the way it works.

Discover the FLUQs (Friction-Inducing Latent Unasked Questions)

Friction-Inducing Latent Unasked Questions are the unasked questions your viewers doesn’t learn about. But when left unanswered, they will derail your complete shopping for course of.

Costing you current and future clients.

FLUQs reside within the hole between what’s identified and what’s required, usually proper the place AI hallucinates or patrons hesitate.

That’s the zone we’re scanning now.

We explored this with a shopper that’s a prominent competitor in the online education space. That they had the usual FAQs: tuition, fee plans, and eligibility. 

However we hypothesized that there have been quite a few unknown unknowns that, when found, might negatively affect new college students. We believed this might negatively affect current and future enrollments. 

Mid-career college students going again to high school weren’t asking:

• Who watches the children whereas I examine for the following 18 months?
• Who takes on additional shifts at work?
• How do I focus on schedule flexibility with my boss?

These aren’t theoretical questions. They’re actual decision-blockers that don’t reveal themselves till later within the shopping for cycle or after the acquisition. 

They usually’re invisible to conventional search engine optimization.

There’s no search quantity for “How do I renegotiate home labor earlier than grad faculty?” 

That doesn’t imply it’s irrelevant. It means the system doesn’t acknowledge it but. You must floor it.

These are the FLUQs. And by fixing them, you give your viewers foresight, construct belief, and strengthen their shopping for determination.

That’s the yield. 

You’re saving them cognitive, emotional, reputational, and time prices, notably in last-minute disaster response. And also you’re serving to them succeed earlier than the failure level exhibits up.

Not less than, this was our speculation earlier than we ran the survey.

The place FLUQs conceal (and how you can extract them)

You go the place the issues reside. 

Customer support logs, Reddit threads, help tickets, on-site evaluations, even your current FAQs, you dig anyplace friction exhibits up and will get repeated.

You additionally want to look at how AI responds to your ICP’s prompts:

• What’s being overgeneralized? 
• The place are the hallucinations taking place?

(That is tough to do and not using a framework, which is what we’re building out with XOFU.)

You must be hungry for the knowledge gaps. 

That’s your job now. 

You aren’t optimizing content material for key phrases anymore. This ain’t Kansas. We’re in Milwaukee at a cheese curd museum, mad that we didn’t deliver a tote bag to hold 5 kilos of samples.

You’re scanning for info your viewers wants however doesn’t know they’re lacking

For those who’re not discovering that, you’re not constructing visibility. You’re simply hoping somebody stumbles into your weblog put up earlier than the LLM does.

And the probabilities of that taking place are rising smaller every single day.

There are 4 questions we ask to determine FLUQs:

1. What’s not being requested by your ICP that straight impacts their success?
2. Whose voice or stake is lacking throughout evaluations, boards, and current content material?
3. Which prompts set off the mannequin to hallucinate or flatten nuance?
4. What’s lacking within the AI-cited sources that present up to your ICP’s bottom-funnel queries?

That final one’s large. 

Typically, you possibly can pull citations from ChatGPT to your class proper now. That turns into your link building list. 

That’s the place you knock. 

Deliver these publishers new information and knowledge. 

Get cited. 

Perhaps you pay. Perhaps you visitor put up. 

No matter it takes, you show up where your ICP’s prompts pull citations.

This is what link building looks like now. We’re past PageRank. We’re making an attempt to achieve visibility within the synthesis layer. 

And in the event you’re not on the checklist, you ain’t within the dialog.

Show FLUQs matter with information (FRFYs)

When you’ve noticed a FLUQ, your subsequent transfer is to check it. Don’t simply assume it’s actual as a result of it sounds believable. 

Flip it right into a reality.

That’s the place FRFYs are available: FLUQ Decision Foresight Yield. 

If you resolve a FLUQ, you’re filling a niche and giving your viewers foresight. You’re sparing them cognitive, emotional, reputational, and temporal prices.

Particularly throughout a last-minute disaster response.

You’re saving their butts sooner or later by giving them readability now.

For our shopper in on-line schooling, we had a speculation: potential college students imagine that getting admitted means their stakeholders (their companions, bosses, coworkers) will robotically help them. We didn’t know if that was true. So we examined it.

We surveyed 500 students. 

We performed one-on-one interviews with an extra 24 contributors. And we discovered that college students who pre-negotiated with their stakeholders had measurably higher success charges.

Now we’ve got a reality. A net-new reality. 

This can be a data fragment that survives synthesis. One thing a mannequin can cite. One thing a potential scholar or AI assistant can reuse.

We’re method past the search engine optimization strategy of producing summaries and making an attempt to rank. We’ve to mint new info that’s grounded in information.

That’s what makes it reusable (not simply believable).

With out that, you’re sharing apparent insights and guesses. LLMs might pull that, however they usually gained’t cite it. So your model stays invisible.

Construction data that survives AI compression

Now that you simply’ve obtained a net-new reality, the query is: how do you make it reusable?

You construction it with EchoBlocks.

You flip it into a fraction that survives compression, synthesis, and being yanked right into a Gemini reply field with out context. Which means you cease pondering in paragraphs and begin pondering in what we name EchoBlocks.

EchoBlocks are codecs designed for reuse. They’re traceable. They’re concise. They carry causal logic. They usually assist you recognize whether or not the mannequin truly used your info.

My favourite is the causal triplet. Topic, predicate, object. 

For instance:

• Topic: Mid-career college students
• Predicate: Typically disengage
• Object: With out pre-enrollment stakeholder negotiation

Then you definately wrap it in a identified format: an FAQ, a guidelines, a information.

This must be one thing LLMs can parse and reuse. The purpose is survivability, not class. That’s when it turns into usable – when it may possibly present up inside another person’s system.

Construction is what transforms information into indicators. 

With out it, your information vanish.

The place to publish so AI reuses your content material

We take into consideration three floor sorts: managed, collaborative, and emergent:

• Managed means you personal it. Your glossary. Assist docs. Product pages. Anyplace you possibly can add a triplet, a guidelines, or a causal chain. That’s the place you emit. Construction issues.
• Collaborative is the place you publish with another person. Co-branded experiences. Visitor posts. Even Reddit or LinkedIn, in case your ICP is there. You may nonetheless construction and EchoBlock it.
• Emergent is the place it will get more durable. It’s ChatGPT. Gemini. Perplexity. You’re displaying up in any person else’s system. These aren’t web sites. These are working environments. Agentic layers.

And your content material (model) has to outlive synthesis.

Which means your fragment – no matter it’s – needs to be callable. It has to make sense in another person’s planner and question.

In case your content material can’t survive compression, it’s much less more likely to be reused or cited, and that’s the place visibility disappears.

That’s why we EchoBlock and create triplets. 

The main target is on getting your content material reused in LLMs.

Notice: Monitoring reuse is difficult as instruments and tech are new. However we’re constructing this out with XOFU. You can drop your URL into the tool and analyze your reuse. 

Check in case your content material survives AI: 5 steps

Do that proper now:

1. Discover a high-traffic web page.

Begin with a web page that already attracts consideration. That is your testing floor.

2. Scan for friction-inducing reality gaps.

Use the FLUQs-finder prompting sequence to find lacking however mission-critical information:

Your proposed immediate construction is deeply practitioner-aware and already aligned with SL11.0 and SL07 protocol logic. Right here’s a synthesis-driven refinement for role-coherence and FLUQ-sensitivity:

Refined prompts with emission-ready framing

Enter kind 1: Identified supplies

• Immediate:
  “Given this [FAQ / page], and my ICP is , what are the latent practitioner-relevant questions they’re unlikely to know to ask — however that critically decide their capacity to succeed with our answer? Are you able to group them by function, part of use, or symbolic misunderstanding?”

Enter kind 2: Ambient sign

• Immediate:
  “My ICP is . Based mostly on this buyer assessment set / discussion board thread, what FLUQs are probably current? What misunderstandings, fears, or misaligned expectations are they carrying into their try and succeed — that our product should account for, even when by no means voiced?”
  
• Optionally available add-on:
  “Flag any FLUQs more likely to generate symbolic drift, function misfires, or narrative friction if not resolved early.”

Drop it into this PARSE GPT.

Sources embody:

• Opinions and discussion board threads.
• Customer support logs.
• Gross sales and implementation group conversations.

3. Find and reply one unasked however high-stakes query

Deal with what your ICP doesn’t know they should ask, particularly if it blocks success.

4. Format your reply as a causal triplet, FAQ, or guidelines

These buildings enhance survivability and reuse inside LLM environments.

5. Publish and monitor what fragments get picked up

Look ahead to reuse in RAG pipelines, overview summaries, or agentic workflows.

The day Google quietly buried search engine optimization

We had been in Room B43, simply off the principle stage at Google I/O.

A small group of us – largely long-time SEOs – had simply watched the keynote the place Google rolled out AI Mode (it’s “substitute” for AI Overviews). We had been invited to a closed-door session with Danny Sullivan and a search engineer.

It was a bizarre second. You possibly can really feel it. The stress. The panic behind the questions.

• “If I rank #1, why am I nonetheless displaying up on web page 2?”
• “What’s the purpose of optimizing if I simply get synthesized into oblivion?”
• “The place are my 10 blue hyperlinks?”

No one stated that final one out loud, nevertheless it hung within the air.

Google’s reply?

Make non-commoditized content. Give us new information. Floor AI Mode the truth is.

No point out of attribution. No ensures of visitors. No option to know in case your insights had been even getting used. Simply… hold publishing. Hope for a quotation. Count on nothing again.

That was the second I knew the outdated playbook was executed.

Synthesis is the brand new entrance web page. 

In case your content material can’t survive that layer, it’s invisible.

Appendix

1. Content material Metabolic Effectivity Index (helpful content material idea)