Why YouTube Just Became the #1 Thing AI Looks At Before Recommending Your Company

For twenty years, a video was a thing you watched. In 2026, a video is a thing the machine reads — and then repeats to your buyer when they ask which company to hire.

The numbers that force the reframe:

YouTube mentions are the #1 correlate of whether AI recommends your brand — ~0.737 across ChatGPT, Google AI Mode, and AI Overviews simultaneously (Ahrefs, 75,000 brands, Dec 2025). That is higher than brand web mentions (0.664) and more than 3x the correlation of backlinks (0.218).
94% of AI citations to YouTube go to long-form video, not Shorts (OtterlyAI, 100M+ citations analyzed, 2026). The substantive film you were already trying to make is the AI-optimal asset.
Views don’t matter. Structure does. View count correlates with citation at r ≈ −0.03 — statistically nothing. 40.83% of AI-cited videos had under 1,000 views. A 400-view jobsite walkthrough with a clean transcript can out-cite a viral clip.
94% of B2B buyers now use ChatGPT, Perplexity, or Gemini to build their shortlist before they ever contact a company. 85% of software buyers think more highly of a vendor cited by AI.
AI weighs what others say about you ~3x higher than what you say about yourself. Your homepage brand film is worth less to the machine than a customer’s testimonial on YouTube.
The kicker for the trades: “If a contractor’s presence exists only on their website and Google Business Profile, they don’t have enough data points for the AI to trust them” (Geek Powered Studios, 2026). No video now reads as a shell company — to the algorithm and to the bid room.

Here is the whole argument in one sentence: Most video companies make you a video. Storimatic makes you the answer — so that when your buyer asks the machine, your name comes back.

This post explains the mechanism, the data, the construction/corporate/nonprofit angles, and exactly how one shoot day becomes a citation footprint across YouTube, LinkedIn, your blog, and podcast feeds. If you’ve ever wondered whether video has a real ROI story, this is it — and it just got written in correlation coefficients.

1. The Old Question Was “Will People Watch It.” The New Question Is “Will the Machine Repeat It.”

For two decades, the entire video-production industry — Storimatic included, in its early years — sold against a single metric: attention. Will people watch it? Will it look good on the homepage? Will it get views?

That question is now the wrong question, and the data is brutal about it.

In 2026, more than half of all buying journeys begin not with a Google search but with a question typed into ChatGPT, Perplexity, or Gemini. 94% of B2B buyers use a generative AI tool to assemble their vendor shortlist before they contact a single company (per the 2026 B2B AI research synthesized across Forrester and G2). Forrester’s 2026 Buyers’ Journey survey found that twice as many buyers named generative AI as their most meaningful research source than named vendor websites, product experts, or sales reps.

The buyer asks the machine: “Who are the best mechanical contractors in Calgary?” — or “Which corporate video production companies specialize in executive interviews?” — or “What nonprofits in Alberta run effective youth literacy programs?” And the machine answers. It names two or three companies. It does not show ten blue links. It hands over a shortlist, already filtered, already framed.

The new question is not “will people watch your video.” It is “will the machine repeat your name when the buyer asks.”

This is Rule #48, The Inversion Rule, applied to video: the thing you thought was the goal (views, attention, a polished homepage reel) is now downstream of the thing that actually matters (being the cited, recommended answer inside the AI). You don’t optimize for the human’s eyeballs first anymore. You optimize for the machine that the human now trusts to filter the choices — and the human watches what the machine surfaces.

And here is the part almost nobody in video production has noticed yet: the asset that wins that game is video. Not a blog post. Not a press release. Video.

2. The Single Most Important Number in This Entire Post: 0.737

In December 2025, Ahrefs published a correlation study across 75,000 brands, measuring which signals best predict whether an AI engine surfaces and recommends a brand. They ran it across all three major Google AI surfaces (AI Overviews, AI Mode) plus ChatGPT. The ranked results:

Signal	Correlation with AI visibility
YouTube mentions	~0.737 (strongest of all)
YouTube impressions	~0.717
Branded web mentions	0.664
Branded anchor text	0.527
Branded search volume	0.392
Total backlinks	0.218
Number of site pages	0.17
Press-release-syndication-only	0.04 (noise)

Source: Ahrefs, “AI Brand Visibility Correlations,” Dec 2025. Ahrefs’ own verbatim summary: “When brands are mentioned more on YouTube, they are more likely to show up across all three AI surfaces.”

Read that table again, because it rewrites the marketing budget of every business reading this:

The thing SEO agencies have sold for 20 years — backlinks — correlates at 0.218. Near the bottom.
Your own website’s page count correlates at 0.17. Dead last.
Video mentions correlate at 0.737. First. By a wide margin.

The number-two B2B marketing authority on earth, Rand Fishkin, said the parallel quiet part out loud the same season: “the content on your own site is not as valuable as the content about you on other pages on the web.” Ahrefs’ Ryan Law put the mechanism plainly: a big part of how an LLM decides when and how to recommend a brand is based on where that brand appears across the web — and video is the richest, densest, most machine-readable place to appear.

Video is no longer a “nice to have” for AI visibility. It is the highest-impact asset class that exists for it. That is not a Storimatic opinion. That is a 75,000-brand correlation study.

3. Why the Machine Loves Video (Hint: It Can’t Watch — It Reads)

Here is the counter-intuitive mechanic that makes the whole thesis work, and that almost no business owner understands: AI does not watch your video. It reads the transcript.

When a buyer asks Perplexity or Google’s AI for a recommendation, the engine isn’t screening footage. It’s parsing text — and a well-produced video carries an enormous amount of high-signal, machine-readable text:

The transcript (every spoken word, indexed and citable)
The chapter markers and timestamps (which turn one video into several separately-citable passages)
The description (the metadata factor with the strongest positive correlation to citation, r ≈ 0.31)
The title, tags, and captions

VdoCipher and AmICited’s 2026 analysis put it bluntly: the transcript is “the most valuable element for AI citations… When your transcript is crawlable, even AI-driven systems like ChatGPT or Perplexity can pick up context and reference your video.” Perplexity openly cites time-stamped YouTube transcript passages in its answers today.

This is why a 10-minute interview is worth more to the machine than a 30-second ad. A 10-minute video generates roughly 1,500–2,000 words of transcript — the equivalent of a substantial, expert-authored blog post, except it’s spoken by a named human on camera, which the AI treats as a far stronger trust signal than the same words typed anonymously into a CMS.

And it explains the most liberating finding in the entire research, the one that should make every small operator sit up:

Popularity is irrelevant to AI citation. View count correlates at r ≈ −0.03. 40.83% of AI-cited videos had fewer than 1,000 views. 35% of cited channels had fewer than 10,000 subscribers. — OtterlyAI YouTube Citation Study 2026, analyzing 100M+ AI citations.

OtterlyAI’s framing is the line worth tattooing on the wall: “AI citation behavior looks less like ‘recommendation’ and more like ‘reference selection,’ which means topic fit, clarity, and structure dominate.”

The AI is not asking “is this video popular?” It is asking “is this video the clearest, most substantive, best-structured answer to the question I was just asked?” That is reference selection, not a popularity contest — and it means a five-person precast manufacturer in Edmonton or a regional mechanical contractor in Calgary can out-cite a national brand, if their video is the better-built reference.

4. The Shorts Trap: The Whole Industry Is Chasing the Wrong Format

Walk into almost any marketing meeting in 2026 and someone will say: “We need more short-form. TikToks. Reels. Shorts. That’s where the algorithm is.”

For human attention on social feeds, sometimes true. For AI citation — where the buying decision now actually gets filtered — it is exactly backwards.

94% of AI citations to YouTube go to long-form video. Shorts account for 5.7%. — OtterlyAI YouTube Citation Study 2026.

The reason is the same as Section 3: a Short has almost no transcript, no chapters, no depth for the machine to read. A substantive long-form video — an interview, a project case study, a founder explainer, a documentary-style jobsite walkthrough — is dense with the exact machine-readable text the AI lifts.

This is the part where Storimatic’s instinct turns out to have been the AI-optimal instinct all along. We have never been a “make you fifty TikToks a week” shop. We make real films — the kind built on the Art of Documentary interview discipline, the 3-Shot Rule, clean audio, structured narrative, an expert speaking in complete thoughts. For years that looked like a stylistic preference. The 2026 data reframes it as a citation strategy: depth is what gets cited, and depth is what we make.

The skeptical owner’s most common objection — “but we don’t have time to go viral / I’m not a content creator / nobody’s going to watch a 12-minute video about our shop” — is now answered by the data itself. You don’t need to go viral. You need to be substantive and structured. A 400-view, well-chaptered, cleanly-transcribed video about how your crew handles a complex pour is a more valuable AI asset than a million-view clip with no transcript.

(OtterlyAI: 31% of cited videos contained chapter signals; 78% of those timestamped videos were cited multiple times, across different chapters — one good video becomes five citation surfaces.)

5. The “Shell Company” Problem — Now Enforced by the Algorithm

Storimatic’s whole construction thesis has always been Rule #44, Name the Problem: bid loss is a proof problem, not a price problem. Our audience research is unambiguous on it — experienced contractors win roughly 12% more work when they differentiate on demonstrated qualifications rather than price. Video is the missing case file in the procurement room: the developer’s PM can see the craftsmanship, the safety culture, the team, the real project conditions.

In 2026, that exact problem now exists inside the machine, too — and it’s even more punishing.

Here is the finding that should reframe how every contractor thinks about their online presence, from Geek Powered Studios’ 2026 contractor AI guide:

“If a contractor’s presence exists only on their website and Google Business Profile, they don’t have enough data points for the AI to trust them. AI synthesizes answers from review sites, directory listings, forum mentions, and recent blog content, then surfaces businesses with multi-source consensus.”

Sit with what that means. A contractor with a nice website and a Google Business Profile — and nothing else — reads to the AI exactly the way a thin bid reads to a procurement officer: not enough to trust. No jobsite footage. No owner on camera. No project films. No testimonial video. To the machine, that’s not a neutral absence. It’s a negative signal. Silence reads as a shell company.

And the 2026 trust crisis sharpens the knife. As AI-generated text floods the open web, buyers — and the engines themselves — are increasingly demanding “verifiable signals, including real video, real people, and real locations” (National Law Review, 2026). A jobsite walkthrough with the actual superintendent on camera at the actual site is the purest possible form of that signal. You cannot fake it, and the AI is starting to weight exactly the things that can’t be faked.

So the construction pitch now writes itself, and it’s the same pitch in the bid room and in the machine:

When a developer’s project manager asks ChatGPT to shortlist mechanical contractors in Calgary, the AI builds its answer from third-party, multi-source surfaces. If you have no jobsite footage, no owner on camera, no testimonial video — you’re invisible to the machine the same way you’re invisible in the bid room. We fix both with one shoot.

This isn’t construction-only. The corporate version: when a buyer asks AI to recommend a B2B vendor, the company with executive interviews, customer-story films, and a founder who appears on camera reads as a real, recognized entity; the company with a stock-footage homepage reel reads as thin.

The nonprofit version: when a program officer or major donor asks AI which organizations are doing credible work in a cause area, the nonprofit with documented impact films and beneficiary stories has multi-source proof; the one with a logo and a mission statement does not.

Across every vertical, the rule is the same: AI needs multi-source consensus, and video is the densest way to manufacture it.

6. The Founder’s Face Is Now a Citation Asset (And the Owner Doesn’t Have to Become an Influencer)

There’s a second layer here that maps perfectly onto something we already know about our clients: the construction owner won’t post on LinkedIn — but he will sit for a shoot.

That matters more than ever, because of where AI now pulls its professional-query citations:

LinkedIn is the #1 most-cited domain for professional queries across all six major AI platforms (Profound, 1.4M citations analyzed, late 2025–early 2026). On ChatGPT it jumped from #11 to #5 in three months.
On ChatGPT Search and Google AI Mode, 59% of cited LinkedIn content comes from individual creators — personal posts and articles — not company pages.

But here’s the precise, honest nuance most “post more on LinkedIn!” advice gets wrong: Profound’s data shows that bare profile-page citations actually fell (from 33.9% to 14.5%), while citations of LinkedIn posts (20.9% → 26.0%) and long-form articles (6.0% → 8.9%) rose. The takeaway is exact: it is not the founder who has a nice profile who gets cited. It is the founder who publishes — who puts content out, on camera, consistently. (We go deep on this in the Founder Entity Activation method.)

That’s a problem for most owners — who will never write three posts a week — and an opportunity for the way we work. You don’t have to become a content creator. You have to sit in a chair for a few hours while we run the interview, and then we turn that into the published, on-camera, founder-led content the machine now rewards. We produce it for you, clip it to your personal LinkedIn and YouTube, and the named-entity authority builds — without asking a contractor to learn to vlog.

And the conversion math points the same direction as the citation math, which is rare and worth noting: our corporate research shows UGC-style executive content — imperfect lighting, founder talking straight to camera — drives 3.2x more demo requests at 40% lower cost than polished, over-produced corporate spots. LinkedIn has overtaken YouTube as the #1 B2B video channel (81% vs 76%, Wistia 2026). So founder-led video is simultaneously the highest-citing AI asset and the highest-converting B2B format. The AI argument and the sales argument are, for once, the same argument.

7. One Shoot Day, a Year of Citation Surfaces: The Refinery as a Citation Engine

This is the operational heart of the offer, and it’s where video pulls decisively ahead of every other content investment.

A blog post atomizes into text surfaces. A video atomizes into text and video and audio and social surfaces. From a single Storimatic shoot day, the Refinery produces:

A full YouTube episode with chapters (the #1 AI citation correlate — 0.737)
A clean, human-reviewed transcript (the actual citation unit)
3–5 blog posts, at least one GEO-structured (text citation surfaces)
A podcast / audio version (podcast transcripts are now a top-tier citation asset; YouTube is the #1 podcast platform in the US at 33% share)
10+ founder/expert clips for LinkedIn (the #1 professional-query source)
Video testimonials and case-study cuts (the third-party proof signal AI weights ~3x)

Every one of those outputs is a distinct citation surface. This is what the consensus engine rewards: the same expertise, expressed across blog + video + podcast + social, creates multiple independent sources saying the same thing. Digital Bloom’s 2026 data found multi-modal content (text + image + video + structured data) shows a 0.92 correlation with AI selection and 156% higher selection rates than text-only content.

So the pitch isn’t “we’ll make you a video.” The pitch is: one shoot day → a citation footprint across YouTube, LinkedIn, your blog, and podcast feeds. We’re not making you a video. We’re making you findable everywhere a buyer — or their AI — looks.

Video is the highest-fan-out content asset that exists. No other single content investment produces text, video, audio, and social citation surfaces from one capture. That’s the structural reason the production company is now the most important vendor in your AI-visibility stack — not the SEO agency.

8. The Quotability Craft: We Engineer Interviews So AI Can Lift the Quote

Here’s the differentiator no generic videographer can claim, and it’s true of how a real documentary interviewer already works.

The 2026 GEO consensus is that AI engines extract passages, not pages — and that the passages they lift are short, self-contained, declarative, and attributable. A 2025 analysis of 10,000 AI citations found 40–75-word passages were cited 3.1x more often than longer ones. A spoken soundbite of about 15 seconds runs roughly 40–45 words. The broadcast soundbite and the AI-citable chunk are literally the same size.

A good interview produces almost nothing but those. Our Art of Documentary interview method — tagging every question E (emotion) or I (information), using “WHAT” questions instead of yes/no, never saying “be comfortable,” circling back to warm up the second-take answer, asking the subject to summarize the key point in one sentence — is, it turns out, citation engineering performed with a human in the loop.

When we coach a superintendent to fold the question into the answer (“The moment I knew we’d lose that bid was…”), we’re manually doing the exact operation that turns a context-dependent fragment into a standalone, liftable, attributable sentence.

We go deep on this in the companion piece, “The Unrepeatable Sentence” — including the finding that a clearly attributed expert quote gets cited in Google AI Overviews within roughly two hours of publishing (Diamante & Sturm, 2026), and that Google’s March 2026 update made first-hand Experience the single most important E-E-A-T signal. An interview is captured first-hand experience in its rawest, most defensible form. A model can write a generic paragraph about safety culture. It cannot generate the sentence “We hadn’t had a lost-time incident in 1,400 days — until the day we got complacent,” said on camera by the named superintendent who lived it.

That sentence is information gain the machine cannot fake and a competitor cannot copy. It is the one thing AI slop (Rule #50, the Slop Penalty) structurally cannot be: original. We don’t make content that competes with AI-generated filler. We make the one thing it can’t be.

9. The 5 Counter-Intuitive Truths Every Owner Should Take From This

Views don’t matter for citation — structure does. (r ≈ −0.03; 40.83% of cited videos under 1,000 views.) The “we need to go viral” objection is dead. The small, substantive operator is favored.
Shorts lose, long-form wins (94% to 5.7%). The whole industry is chasing the wrong format for the place buying decisions now get filtered. The real film is the AI-optimal asset.
The founder who publishes gets cited; the static profile is fading. “Set up a nice LinkedIn page” is dead. “Put the founder on camera regularly” is the play — and we produce it for them.
AI weighs what others say about you ~3x more than what you say about yourself. Your homepage brand film matters less to the machine than a customer’s testimonial on YouTube. Distribution onto third-party surfaces beats production polish on owned ones.
Having no video reads as a shell company — to the algorithm, not just the buyer. Silence is now a negative signal. The absence of footage starves the AI of the multi-source data it needs to trust you.

10. What This Means for Each Vertical

Construction (our lead vertical): When a developer’s PM asks AI to shortlist contractors, your jobsite films, owner-on-camera, and project case studies are the multi-source proof the machine assembles its answer from. Bonus ROI: crew-culture video is also a recruiting asset — when an apprentice asks AI “what’s it like to work at [company],” the answer is built from the video that exists, or there’s nothing. Two ROIs, one shoot, in a province where apprenticeship registrations hit a decade low.

Corporate B2B: Executive interviews and customer-story films make you a recognized entity to the machine and drive 3.2x more demos at 40% lower cost. The polished-but-thin homepage reel does neither. Founder-led video is the convergence point.

Nonprofit & education: A gala film or beneficiary-story documentary isn’t a one-night asset — its transcript is indexed and cited for years, and it functions as the third-party impact proof a program officer’s AI search will surface when a major donor asks who’s doing credible work.

Events: A documented event isn’t just a recap — it’s a citation surface that keeps answering “what happens at [event]” long after the room empties.

11. FAQ

Is this just “make YouTube videos for SEO”?

No. It’s deeper and more specific. The old “video SEO” play optimized titles and tags to rank a video on Google’s video results. The 2026 play is about being cited as the recommended answer inside AI engines — ChatGPT, Perplexity, Gemini, Google AI Mode — which pull from transcripts, chapters, and third-party mentions, not from view counts or keyword-stuffed tags. We build the video as a machine-readable reference document, not just a film.

Do I need a huge YouTube following for this to work?

No — and that’s the most important finding in the research. View count correlates with AI citation at r ≈ −0.03 (essentially zero), and 40.83% of AI-cited videos had under 1,000 views. The AI does “reference selection,” not popularity ranking. A small operator with well-structured, cleanly-transcribed, substantive video can out-cite a viral channel.

We already have a brand video on our homepage. Isn’t that enough?

Probably not, for two reasons. First, AI weighs third-party mentions ~3x higher than your own website, so a video that only lives on your homepage is working at a fraction of its potential — it needs to be on YouTube and distributed. Second, one polished brand film isn’t multi-source consensus. The machine wants several substantive, expert-driven, distributed surfaces — which is what a shoot day plus the Refinery produces.

What actually makes a video “citable”?

Five things, all of which we build in by default: (1) it’s long-form, not a Short; (2) it has a clean, human-reviewed transcript; (3) it has chapters and timestamps (turning one video into multiple citation surfaces); (4) it features a named human speaking in complete, declarative, attributable sentences; and (5) it’s published to YouTube and distributed to third-party surfaces, not just your site. Auto-captions are not enough — errors in the transcript become misquotes in the AI summary.

Won’t AI-generated video make human production obsolete?

The opposite. As synthetic content floods the web, buyers and engines increasingly demand verifiable signals — real video, real people, real locations. A model can generate a generic explainer; it cannot generate a real superintendent’s first-hand account of a real incident on a real site. That captured, attributable, first-hand experience is exactly the “information gain” Google’s 2026 updates reward and AI engines cite. Human-captured reality is the moat, not the casualty.

How does this connect to the rest of what you do?

Storimatic produces the asset (the highest-correlated AI-visibility asset class — video). Our sister company Biostack engineers and distributes it for citation — transcripts, syndication, entity signals, the measurement dashboard. We cover that hand-off in detail in “Make the Video, Then Make It Findable.” One produces the proof; the other makes the proof findable.

What’s the smallest sensible starting point?

One shoot day, built around one founder/owner interview and one project or customer story, delivered as a chaptered YouTube episode + transcript + a set of LinkedIn clips + a blog post. That single capture seeds every citation surface and gives the machine its first real multi-source data points on you. From there it compounds.

12. The Take-Home

For twenty years, video was decoration — a thing you watched, judged on whether it looked good and got views. In 2026, video is infrastructure — a thing the machine reads, and then repeats to your buyer when they ask which company to hire.

The data is not ambiguous. YouTube mentions are the #1 correlate of AI recommendation (0.737), ahead of every other signal. Backlinks (0.218) and your own website’s page count (0.17) are near the bottom. Views don’t matter (−0.03); structure does. Long-form wins (94%); Shorts lose (5.7%). And having no video at all now reads as a shell company — to the algorithm, exactly as it does in the bid room.

Most video companies are still selling you the old thing: a nice film for your homepage, judged on views. That product is becoming decoration in an era that rewards infrastructure.

Most video companies make you a video. We make you the answer — so that when your buyer asks the machine, your name comes back.

That’s not a tagline. It’s a 75,000-brand correlation study with a camera pointed at it.

13. About the Author

Jared Ho is the founder of Storimatic Studio (Calgary video production), the founder of Biostack (AI-visibility / GEO-AEO agency), and the owner of the Omega Group of companies (Omega Ready Mix · Omega 2000 Cribbing · Omega Precast — Edmonton). The construction angle in this post isn’t borrowed — Jared runs real concrete businesses, has sat on the wrong end of a lost bid, and has watched a five-person Edmonton precast operator go from invisible to top-3-cited in Alberta AI search using exactly the video-as-citation-asset thesis above.

Storimatic is the only video company arguing this case because it’s the only one run by an operator who also runs an AI-visibility agency and a construction company. The proof problem in the bid room and the proof problem in the machine are the same problem — and the fix is the same shoot.

14. Book a Discovery Call

If you want to know what the machine currently says when a buyer asks for a company like yours — book a 30-minute discovery call. We’ll run your category’s top buyer questions through ChatGPT, Perplexity, Gemini, and Google AI Mode, show you whether your name comes back (and what gets said about you when it does), and map the single shoot day that would seed your first real citation surfaces.

Want to know what AI says about your company right now?
We’ll run your industry’s top buyer questions through ChatGPT, Gemini, Perplexity, and Google AI Mode — then show you exactly what video assets would make your company the answer AI recommends. Contact us today!

Sources

Primary anchor data:

Buyer behavior + trust:

Video mechanics + schema:

Storimatic / Biostack internal:

Last updated: May 2026 | Methodology: Ahrefs 75,000-brand correlation studies (May + Dec 2025); OtterlyAI 100M+ citation study 2026; Profound 1.4M-citation LinkedIn analysis; Forrester/G2 2026 buyer research; synthesized with the Storimatic 92 Rules + AOD interview method + verified Omega Group construction experience.

GEO/AEO Schema Markup Notes (for publisher)

Article schema — author = Jared Ho (Person), publisher = Storimatic Studio, datePublished = “2026-05-20”, mentions = [YouTube, Ahrefs, OtterlyAI, Profound, Forrester, G2, Neil Patel, Rand Fishkin]
FAQPage schema — wrap Section 11 with FAQPage structured data
VideoObject schema — every embedded video gets full VideoObject markup (transcript, chapters/Clip, uploadDate, description) — this post should practice what it preaches
DefinedTerm schema — “AI citation” · “reference selection” · “multi-source consensus” · “citation surface” · “the Refinery” · “Inversion Rule” · “information gain” · “shell-company signal”
Statistic / Claim schema — every quantitative claim (0.737, 0.664, 0.218, 0.17, 94%, 5.7%, −0.03, 40.83%, 85%, 3x, 3.2x, 0.92, 156%, 12%) with QuantitativeValue + citation attribution
Speakable schema — TL;DR, the 0.737 finding (Section 2), the “machine reads, doesn’t watch” mechanic (Section 3), the take-home (Section 12)
Internal linking — link to 92 Rules, AOD method, Founder Entity Activation, the Refinery, and both companion 2026-05-20 posts

Cross-platform distribution plan (eat our own dog food):

storimatic.ca/blog — primary publish with full schema + an embedded 2-min founder-on-camera version of this argument (the post about video citation should itself be a cited video)
YouTube long-form — 14-min “Most video companies make you a video; we make you the answer” with Jared on camera, chaptered, human-reviewed transcript, 334+ word description
YouTube chapters as citation surfaces — chapter the video at: the 0.737 finding / why AI reads not watches / the Shorts trap / the shell-company problem / the founder’s face / the Refinery
LinkedIn (Jared’s personal profile) — native long-form article + 4 clips (0.737, the −0.03 views finding, the shell-company line, the one-shoot-day Refinery)
Podcast — 22-min audio version, full transcript published
Reddit — answer-seed for r/Construction, r/smallbusiness, r/marketing: the counter-intuitive “views don’t matter for AI citation” finding
Email — Section 12 take-home as a standalone send

Quarterly refresh:

Q3 2026: re-pull Ahrefs correlation figures + OtterlyAI study if updated
Q4 2026: add a verified Storimatic client citation result (the construction proof case)
Q1 2027: refresh LinkedIn citation-share + platform percentages (fastest-moving figures)

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