A working weekly AEO program runs five activities, every week, sustained over time:

• Monday: review citation movement from the previous week
• Tuesday: publish one substantive piece of content
• Wednesday: one outreach for earned mention
• Thursday: refresh one existing page
• Friday: five-sentence internal report

That’s the entire operational cadence and the teams winning AEO in 2026 aren’t doing more than this. The teams losing AEO are either doing nothing, doing five things in a sprint and then disappearing for a month, or doing 20 things one week and zero the next.

Cadence beats intensity.

The compounding only happens when the rhythm is sustained.

Why is cadence the metric that matters?

Most marketing teams measure AEO programs by output.

Pieces published per quarter, Reddit threads commented on, and podcasts pitched. The output metrics produce a comfortable narrative. TLDR: more work equals more results.

The output metrics are wrong for AEO!

AEO citation share is a function of sustained presence in the source types that drive citations. A team that publishes one excellent piece per week for 52 weeks will outperform a team that publishes 200 pieces in Q1 and then disappears until Q4. The compounding effect requires the rhythm.

There ar three reasons cadence matters more than volume.

One: AI engines reward source consistency.

When an AI engine evaluates whether a brand is a defensible source on a topic, it looks at the consistency of the brand’s presence over time. Sporadic publication patterns signal inauthentic engagement with the topic. Consistent publication patterns signal genuine expertise. The engines weight the second pattern higher.

Two: maintenance work compounds.

Citations decay. The work to maintain inclusion is structural, not optional. A team running a weekly cadence does refresh work routinely. A team running on sprints does it only when they remember. The first team maintains citation share. The second team watches it decline.

Three: the team builds pattern recognition.

The Monday citation review, run weekly, produces something that quarterly reviews can’t: pattern recognition. The team learns what types of content earn citations in their category. They learn which competitors are gaining and losing share, and why. They learn how the engines respond to different content structures.

What does Monday citation review actually involve?

On Monday morning block 60-90 minutes on the senior AEO operator’s calendar. Open the previous week’s citation data. The activity has four parts.

Part one: read the citation share dashboard.

Look at the headline number. Citation share across your priority 20 buyer prompts, current week vs previous week. Note the direction and magnitude of change.

If your citation share is flat, the analysis stops here and the rest of the time goes to forward-looking work. If your citation share moved meaningfully (more than 2 percentage points in either direction), continue to part two.

Part two: identify the prompts driving the change.

Not all prompts move equally. Drill into the prompt-level view. Identify which specific prompts gained or lost citation share. Most weeks, the aggregate movement is concentrated in 2-4 specific prompts, not distributed evenly.

Part three: name the most likely cause.

For each prompt with meaningful movement, name one likely cause in one sentence. Examples:

• “Citation share dropped from 31% to 22% on prompt 7. A competitor (HubSpot) published a definitive comparison guide on April 14 that’s now appearing as the top citation across ChatGPT and Perplexity.”
• “Citation share increased from 12% to 18% on prompt 12. Our pricing page refresh on April 22 added structured data that’s getting picked up by Claude and Gemini.”

The discipline of naming a cause forces the team to develop hypotheses rather than just observe data.

Part four: identify the top action for the next week.

Based on the movements and hypotheses, identify the single highest-leverage action the team will take this week to improve citation share in the priority prompts. Not three actions. One action. Specific, owned, and dated.

The Monday review ends when the action is named, owned, and dated. The rest of the week executes against it.

What should Tuesday’s publish look like?

Tuesday is publish day. One piece. Not three. Not five. One.

The single most important AEO finding from Q2 2026 was that brands gaining citation share published less, not more. The eight B2B SaaS brands that gained meaningful citation share in Q2 each published one major piece in the quarter, not five.

Depth beats breadth.

The publish on Tuesday should be one of three types: a definitive piece (3,000-5,000 words, designed to be cited), a cluster support piece (800-1,500 words, supporting a definitive piece), or a maintenance refresh that’s substantial enough to count as new.

In aggregate: 4-6 definitive pieces, 12-20 cluster supports, 4-6 refreshes per year. That’s 20-32 pieces per year, or roughly one per Tuesday with appropriate gaps. This is dramatically less than most content teams publish. The reduction is the point…. I can see you smiling now!

What does Wednesday outreach for earned mention involve?

Wednesday is the day that breaks most AEO programs.

The Monday review is comfortable (it’s data work). The Tuesday publish is familiar (it’s content work).

The Wednesday outreach is uncomfortable for most marketing teams because it’s relationship work, and relationship work doesn’t fit cleanly into marketing function org charts.

The activity is one substantive outreach per week aimed at earned-media-light placements. Niche podcasts. Vertical publications. Industry conferences. Wikipedia contribution opportunities. Guest posts on established industry blogs.

The outreach is targeted, not spray-and-pray. The Monday review should have surfaced which earned-media targets matter most for your priority prompts.

Expected hit rate: 1 in 5 outreaches converts to a real placement. Expected timeline: 4-12 weeks from outreach to publication. At 52 outreaches per year, that’s roughly 10 earned-media placements per year. Across two years, 20 placements. Each one compounds… not to shabby now.

A senior person should do this work. A junior contractor running automated outreach will get a 1-in-50 response rate. A senior strategist who has actually engaged with the target’s content for three months will get a 2-in-5 response rate.

What does Thursday’s page refresh involve?

Thursday is maintenance day. Pick one existing page that’s losing citation share and refresh it.

A real refresh has six elements:
Updated data, strengthened answer block, improved schema, new examples, stronger internal linking, and re-publication signaling. Not just changing the date.

Maintenance is the most-underrated AEO activity. Most teams skip it entirely. It’s invisible work but it does keep your priority pages in the AI citation pool, which matters because citation half-life means pages drop out without intervention.

What does the Friday five-sentence report look like?

Five sentences:

Sentence 1: Citation share number, current week.
Sentence 2: Movement direction and most likely cause.
Sentence 3: Top action for next week.
Sentence 4: Owner and deadline for the top action.
Sentence 5: Confidence rating (high/medium/low) with one sentence of context.

The CMO reads it in 30 seconds. The format forces specificity, produces decisions, surfaces confidence, builds pattern recognition over 52 weeks, and makes AEO legible at executive level.

Where do most teams get stuck?

Typically we see three failure modes:

The cadence becomes intermittent.
A launch happens and they skip a week. Then a conference. By month four, the rhythm is broken.

The senior operator role is unfilled or junior.
Work gets delegated. Junior team produces good data and weak strategy. The strategy doesn’t ship.

The cadence runs but doesn’t connect to broader marketing strategy.
AEO runs parallel to content, PR, community. Nothing coordinates.

Fix: protect the cadence ruthlessly. Staff a senior owner. Make the cadence the central rhythm, not a parallel function.

How long until results show?

The honest timeline: 30-90 days for early signals, 6-9 months for meaningful citation share movement, 18-24 months for category-level pulling away.

Days 1-30: learning the cadence. Citation share doesn’t move yet.

Days 31-90: first content from the cadence starts being indexed. Early citation pickups appear.

Days 91-180: pattern recognition develops. Citation share starts moving measurably.

Days 181-365: compounding kicks in. The library of definitive pieces anchors citation share across multiple prompt clusters.

Months 13-24: the team pulls away from competitors who didn’t start a sustained cadence.

Teams that quit before day 180 never see the compounding. Teams that maintain the cadence past day 365 build moats that take years to dislodge.

Frequently asked questions

What if I’m a one-person marketing team?

The cadence is designed for a one-person operator. The five activities take 8-12 hours per week for a senior person, which is workable for a solo marketer with AEO as a primary responsibility.

Can the cadence be run by an agency?

Yes. Many agencies are starting to package it. The agency runs Monday review and Friday report, drafts Tuesday publish, identifies Wednesday outreach targets, executes Thursday refresh. The in-house team approves and ships.

What if my CMO doesn’t want a weekly report?

The five-sentence format is short enough that most CMOs will read it. If not, switch to bi-weekly. Don’t go monthly. Monthly loses too much signal.

What if I miss a week?

It happens. Run the cadence again next week as if nothing happened. The damage from a single missed week is small. The damage from quitting after a missed week is large.

The short answer

97.4% of citations in AI-generated answers come from non-Tier-1 sources. Reddit threads, YouTube transcripts, niche forums, vertical publications, long-tail blogs, LinkedIn long-form posts. The other 2.6% comes from the publications most marketing budgets are allocated against. These are your Forbes, Bloomberg, the New York Times, and the Wall Street Journal. The implication is that PR-led AEO strategies are optimizing for 2.6% of citations and missing the rest of the market.

Three things follow:

• The press release as an AEO tool is functionally dead in 2026
• The AEO organizational role needs to live across PR, content, and community functions
• Most marketing budgets are inverted, spending heavily on the 2.6% and ignoring the 97.4%

This piece walks through the data, the implications for marketing org structure, the budget reallocation that follows, and what an earned-media-light AEO program actually looks like in practice.

What is the 97.4% finding?

The 97.4% finding comes from Profound, the AEO platform that raised $58.5M in 2025. Profound analyzed a large sample of AI-generated answers from ChatGPT, Perplexity, and Google AI Overviews, then categorized the cited sources by publication type. The methodology is public and the finding has replicated across every independent test I’ve seen since.

The categorization split sources into two buckets:

Tier-1 publications include Forbes, Bloomberg, the New York Times, the Wall Street Journal, the Financial Times, Reuters, the Economist, the Washington Post, Wired, the Atlantic, and a small number of equivalent global publications. These are the publications most public-relations efforts are oriented toward securing coverage in.

Non-Tier-1 sources include everything else. Reddit threads. YouTube videos and their transcripts. Niche industry publications. Long-tail vertical blogs. LinkedIn long-form posts. Substack newsletters. Forum communities. Wikipedia. Vendor blogs. Comparison sites. Review platforms. GitHub repositories. Podcast transcripts.

The split is 2.6% Tier-1, 97.4% non-Tier-1.

This is a structural finding, not a noise pattern. It holds across query types (definitional, comparison, buying). It holds across categories (B2B SaaS, healthcare, e-commerce, professional services, financial services). It holds across the four primary AI engines tested. The replication consistency is what makes it worth building strategy around.

Why does this break the traditional PR-AEO assumption?

The traditional assumption among CMOs and PR teams is that Tier-1 placements drive AI visibility. The reasoning runs roughly like this: Tier-1 publications have the highest domain authority. AI engines preference high-authority sources during retrieval. Therefore Tier-1 placements should produce disproportionate AI citation share.

The data disagrees in three specific ways.

AI engines prefer passage relevance over domain authority during retrieval.

When an AI engine generates an answer, it doesn’t just rank sources by authority. It retrieves passages that directly answer the question. A 200-word Reddit comment that answers the question precisely will beat a 2,000-word New York Times article that addresses the question peripherally. The retrieval mechanics favor specificity. Tier-1 publications optimize for comprehensiveness, which is the wrong target.

AI engines weight conversation density as a quality signal.

Reddit threads in particular benefit from comment density. A thread with 200 substantive comments signals to the retrieval system that the topic has been examined from multiple angles. The engine reads this as triangulated truth and weights it higher than single-author sources. Tier-1 publications are structurally single-author and lose this signal.

AI engines have been deliberately tuned away from over-reliance on traditional media authority.

The major AI labs (OpenAI, Anthropic, Google, Perplexity) have all faced public scrutiny for reproducing media biases. The response has been to broaden citation source diversity. Internal retrieval mechanisms increasingly weight earned-media-light sources that traditional authority models would have under-cited. This is policy, not accident.

The combined effect is that Tier-1 placements still contribute to brand awareness, executive credibility, and capital-markets perception. They do not drive AI citation share. The two outcomes have decoupled, and most marketing teams have not noticed yet.

What sources actually drive AI citations?

Working from the Profound data and three months of independent replication on And Zeros client work, the citation source breakdown by category looks roughly like this:

For B2B SaaS:

• Reddit threads: 32% of citations
• Niche industry publications: 12%
• YouTube videos and transcripts: 9%
• Comparison pages from established SaaS companies: 7%
• Wikipedia entries: 5%
• Vendor blog posts with original data: 4%
• G2 and Capterra-style review platforms: 3%
• LinkedIn long-form posts: 3%
• Substack and other newsletter platforms: 2%
• GitHub repositories and documentation: 2%
• Founder podcasts and interviews: 1.5%
• Long tail: 19.5%

For healthcare:

• Regulated authorities (FDA, NIH, CDC): 41%
• Major medical reference sites (Mayo Clinic, WebMD): 28%
• Academic and peer-reviewed sources: 9%
• Patient experience forums and Reddit: 6%
• Professional medical publications: 5%
• Long tail: 11%

For e-commerce:

• YouTube product reviews and unboxings: 24%
• Reddit lifestyle and category subreddits: 18%
• Review platforms (Trustpilot, Sitejabber): 11%
• Comparison sites and shopping guides: 9%
• Brand blogs with original data: 6%
• Influencer blog content: 5%
• Long tail: 27%

For professional services:

• Vertical industry publications: 28%
• LinkedIn long-form posts (especially by named experts): 14%
• Industry conference content and slide decks: 9%
• Niche newsletters: 8%
• Reddit threads in industry-specific subreddits: 7%
• Long tail: 34%

The patterns differ by category but the structural finding holds: Tier-1 publications appear in single-digit percentages across all of them. The 97.4% non-Tier-1 finding is not a B2B SaaS quirk. It’s a property of how AI engines retrieve citations across the board.

What does this mean for PR teams in 2026?

The honest answer is uncomfortable. Most PR teams are working on the wrong problem if their KPIs include AI search visibility.

PR teams are structurally excellent at:

• Relationships with tier-1 publication editors
• Pitching newsworthy stories
• Managing executive interview opportunities
• Crisis communications
• Long-form thought leadership placements
• Brand perception in capital markets

None of these activities, executed well, materially move AI citation share in 2026. They move brand awareness, executive credibility, and analyst perception. Those are real outcomes, they are not AEO outcomes.

The PR functions that do move AEO citation share are different:

• Strategic appearances on niche podcasts (especially vertical-specific ones)
• Wikipedia notability work and entity injection
• LinkedIn thought leadership at the named-executive level (with sustained cadence)
• Long-form contributor relationships with niche vertical publications
• Reddit AMAs and substantive ongoing participation
• YouTube interview placements where the transcript will be indexed

These activities require different skill sets, different relationships, and different success metrics from traditional PR. They are closer to community management than to media relations.

Most PR teams are not staffed to do this work. Some PR leaders are aware of the gap, but few have the budget authority or organizational mandate to restructure their function around the new mechanics.

This is the central tension in the AEO-PR conversation. The gap between what PR teams are good at and what AEO requires is structural, not skill-based. Closing it requires reorganization, not retraining.

What should marketing leaders do this week?

Here are three concrete actions for the next seven days:

One: audit your current marketing budget against the source mix.

Pull the budget allocation. Map each line item to the AEO source mix. Identify the gap. Most teams will find they’re spending heavily on the 2.6% (Tier-1 PR, paid acquisition) and nothing on the categories that drive the 97.4% (Reddit participation, LinkedIn long-form by executives, niche podcast tour, Wikipedia work, community management).

The gap is the opportunity. Quantify it.

Two: identify whether the AEO operator role exists in your org.

Look at the org chart. Find the person who is explicitly accountable for AEO citation share. If no one is, the role is vacant. If someone is, ask whether they have authority across SEO, content, PR, and community. If not, the role is structurally weak.

Three: pick one earned-media-light workstream and pilot it for 90 days.

For most B2B SaaS teams, the right pilot is LinkedIn long-form by named executives. The skills exist internally. The platform doesn’t require external relationships. The compounding starts within 90 days.

Frequently asked questions

What if my company has no presence on Reddit at all?

Start with phase 1 of the Reddit AEO playbook: read every thread your category’s primary subreddit produces for 90 days before posting anything. This is research, not participation. The research phase doesn’t expose you to risk and builds the pattern recognition you’ll need before contributing.

Does the 97.4% finding hold for B2C brands?

Yes, with category-specific source mix variations. B2C brands see higher citation share from YouTube and lifestyle subreddits compared to B2B brands that see higher Reddit and LinkedIn citation share. The structural finding holds. The dominant source types differ.

How does this interact with traditional SEO?

Traditional SEO and AEO are increasingly different disciplines with different optimization targets, but they share infrastructure (your domain, your content management system, your editorial team). The right approach in 2026 is to run both as parallel programs with shared infrastructure but distinct strategies.

What if my PR team pushes back on this analysis?

Most PR teams will. The pushback is usually about Tier-1 brand value, which is real but separate from AEO. The honest framing is; Tier-1 PR delivers brand awareness, executive credibility, and capital-markets perception. It does not drive AEO citation share. Both outcomes matter. We need to fund both, but stop confusing one for the other.

For about 20 years, getting found online meant the same thing.

Show up in the ten blue links on page one and hope someone clicks. We built entire strategies around that concept. Keywords, page authority, backlinks, position tracking. The whole industry ran on it.

That mechanic is breaking down and the shift happened faster than most people realize.

Google, ChatGPT, Perplexity, and every other major search surface are increasingly answering questions directly instead of sending people somewhere to find the answer. One response at the top of the page with a handful of cited sources underneath it. While not yet obsolete, the click is becoming a secondary habit. The citation is what matters now.

If your content isn’t structured in a way these systems can extract from, you may never get either.

What actually changed

The old game was about popularity.

Domain authority, backlink count, how many people were pointing at your site. Those signals still definitely matter but they’re no longer the deciding factor for whether an AI system uses your content in a response.

What these systems are actually looking for is clarity and structure. Can they find a direct answer to the question in your content without having to read the whole page? Are you naming specific things like tools, frameworks, people, and data points instead of gesturing at categories? Is your site set up in a way the system can actually parse?

A well-structured page from a smaller site will get cited over a vague page from a high-authority domain because the model needs something it can use, not something that’s technically impressive.

The three things that actually help

The first is writing for extraction instead of reading.

Every section of your content should open with a direct answer to whatever the heading promises. Not a buildup, not context-setting, a straight answer in the first two or three sentences. Models chunk content and they pull from the clearest, most direct blocks they find.

The second is naming specific things.

If you’re writing about marketing strategy and you say “leading CRM platforms” instead of “HubSpot, Salesforce, and Pipedrive,” you’re giving the model nothing to work with. Specificity is what lets these systems build a picture of whether you actually know what you’re talking about.

The third is structured data.

This is the one most small teams skip because it sounds technical and optional. It’s neither. Schema markup is essentially the language AI systems use to read your site’s logic. If it’s messy or missing, you’re invisible to a layer of the system that’s becoming more important every quarter. It’s not complicated to implement but it has to be done right.

What this means for how you think about content

The brands that are going to stay visible as search continues to shift are the ones treating their content like a data asset instead of a publishing calendar. Every piece should be structured to answer a specific question clearly, reference specific entities, and make it easy for a system to understand what you’re an authority on.

That’s a different brief than “write a blog post about X.” Just remember it’s not harder, it’s just a different mental model.

The good news is most of your competitors haven’t made this shift yet. The window to build a meaningful advantage here is open but it won’t stay open forever.

This is a core part of what we do for clients at And Zeros. Auditing how your brand reads to AI systems and fixing what’s broken. Get in touch if you want to know where you stand.

The speed is real.

I want to say that upfront because this isn’t a post about why AI-assisted development is dangerous or overhyped. We use it and it’s changed how fast we can move and for certain things that’s been genuinely valuable.

I’m writing this because we’ve had a version of the same conversation three or four times in the last year with founders who built something fast, launched it, grew it, and then hit a wall that cost them significantly more to fix than it would have cost to avoid.

The pattern is always roughly the same.

It starts with the demo working.

Week one is great. The thing runs and it does what it’s supposed to do. The AI helped you move fast and the fast movement felt like the right call because you needed to validate the idea before investing heavily in the infrastructure.

That part is fine.

That part is actually correct.

Then you add one more feature.

And another. And a third-party integration because the native solution was going to take two weeks and the bolt-on took two hours. And a workaround that one developer understood completely but never wrote down because there wasn’t time.

None of these decisions are wrong in isolation. Each one made sense given the deadline, the budget, the priorities that week. The problem is that they compound. Every shortcut that worked in the demo becomes an assumption baked into the system. Every undocumented decision becomes a puzzle for whoever touches the code next. Every patch that fixed the immediate problem without addressing the underlying one sits there quietly until something forces the reckoning.

The reckoning usually arrives when you hire someone or when traffic spikes or when a client asks for something that should be simple and suddenly nothing is simple.

By that point the person who understood how everything fit together has mentally moved on. The system works until it doesn’t and when it stops working nobody knows where to start. What you have isn’t a product anymore. It’s a patchwork that requires institutional memory to operate.

That’s when you call someone like us and we look at it and have to tell you that the fix costs more than the original build.

Engineering discipline doesn’t mean slow.

It doesn’t mean months of planning before you write a line of code. It means someone on your team is thinking one level above the immediate problem.

What does this decision mean three months from now?

What would a new developer need to know to understand why this works the way it does?

What are we cutting corners on intentionally versus accidentally?

Those questions don’t take long and skipping them consistently is what gets expensive.

Build fast. Use the tools. Ship the damn thing. Just make sure what you’re building can carry the weight of what you keep adding to it.

We build and maintain web and platform systems for growing businesses. If your stack is starting to feel held together with good intentions, let’s talk.

Everyone is writing “What is GEO” guides right now.

Almost nobody is actually studying what ChatGPT cites, or why.

So we did. Across dozens of commercial queries in our clients’ industries, we pulled the sources ChatGPT returned, compared them against traditional Google rankings, and looked for the patterns. Here’s what showed up in almost every answer.

The #1 Predictor Isn’t What You Think

If you had to guess, you’d probably say domain authority, or backlinks, or some algorithmic edge case only Neil Patel understands.

It’s not.

The single strongest predictor of whether a page gets cited by ChatGPT is structural clarity. Simple explanation is whether the page is built in a way an LLM can actually extract from. A Princeton, Georgia Tech, and Allen Institute for AI study found that 32.5% of AI citations come from comparison articles, not because comparison articles are better written, but because they’re structurally easier for a model to chunk.

Domain authority helps, but a clean Reddit thread will get cited over a DR 85 marketing blog if the Reddit thread answers the question in 60 words and the marketing blog buries it in paragraph nine.

Five Patterns We Saw Repeatedly

01: The answer lives above the fold.
Every cited page we studied had a direct, definitional answer in the first 100 words. Not an intro or a hook. A straight-up declarative sentence that a model could lift verbatim. If your content opens with “In today’s rapidly evolving landscape…” you are already out of the running.

02: The entities are specific and named.
Cited pages named the tools, the people, the studies, the companies, the frameworks. Vague pages lost every time. “Enterprise marketing platforms” gets beaten by “HubSpot, Marketo, and Salesforce Marketing Cloud.” The model cites the one that lets it build a knowledge graph.

03: The structure is chunkable.
H2s that ask the question a user would ask. Short paragraphs (3–5 sentences). Bulleted lists where bullets actually stand alone. If you have to read 400 words to extract a 50-word answer, the model won’t bother. It’ll cite the page that already did the extraction for it.

04: Recency matters more than depth on fast-moving topics.
For anything time-sensitive (prices, policies, product releases, 2026 trends), ChatGPT and Perplexity heavily favor content updated in the last 90 days. A thin but fresh article will beat a deep but stale one. This isn’t fair, but it’s how the systems behave.

05: The page exists as a node, not an island.
Cited pages link out to authoritative sources (studies, official docs, named experts) and link internally to related content. They behave like nodes in a knowledge graph, which is exactly what models are modeling. Orphan pages get ignored no matter how good they are.

What This Means for Your Content

Stop writing for humans who might skim.

Start writing for models that will extract.

This doesn’t mean robotic content, it means content with enough structural integrity that both a reader and an LLM can find the answer they came for in under 10 seconds. The best cited pages we saw were genuinely useful to humans and easy to chunk. Those aren’t competing goals anymore.

The practical shift:

• Lead every section with a standalone answer:
  A 40-60 word block that works if pulled out of context.
  
• Name specific entities:
  No “leading CRM platforms.” Say HubSpot, Salesforce, Pipedrive.
  
• Update aggressively on fast-moving topics:
  If your post says “2024 trends” in April 2026, it’s not getting cited.
  
• Build clusters, not islands:
  Pillar + spoke structure. The system rewards topical density.
  
• Treat structured data as a required input, not an optional nice-to-have:
  Schema gives the model the map.

The Bigger Shift

The agencies that figure this out in the next 18 months will build the category.

The ones that don’t will keep sending ranking reports to clients whose traffic is getting quietly rerouted into AI answers they don’t show up in.

We track both for our clients. If you want to see what your brand looks like inside ChatGPT, Perplexity, and Google AI Overviews, before your competitors do, that’s what we do.

You’re not ranking anymore. You’re being cited… or you’re not.

If your SEO strategy still involves a spreadsheet of keywords and a density percentage, you are optimizing for a version of the internet that no longer exists.

In 2026, the gap between keyword-centric and entity-centric optimization has become a divide. Search engines don’t match strings anymore, they comprehend concepts. They don’t count how many times you say a word, they measure the salience of your entities.

What is Entity Salience?

Salience is a technical score (usually between 0 and 1) that an algorithm assigns to a specific person, place, or concept within your content. It’s a measure of how central that thing is to the meaning of your page.

Google and the LLMs (Perplexity, Gemini, etc.) aren’t just scanning for the phrase “GTM strategy.” They are looking for the surrounding entities that prove you actually know what a GTM strategy is. Here’s what they’re actually looking for.

• The Connective Tissue:
  Are you mentioning Customer Acquisition Cost, LTV, and Sales Velocity in the same breath?
  
• The Hierarchy:
  Is your primary entity in the H1, or is it buried in the 3rd paragraph?
  
• The Semantic Net:
  Are you providing enough attributes (founding dates, specific frameworks, proprietary data) for the machine to verify you aren’t just hallucinating?

The Logic of the Knowledge Graph

This is where the And Zeros philosophy hits the metal. Think of the internet as one giant knowledge graph… a web of nodes and relationships.

When you publish a page, the goal isn’t to rank. The goal is to be indexed as a definitive node.

If your content is vague or uses AI-slop adjectives, your salience score drops. The machine can’t figure out if you’re an authority or just a noise generator (but email me if you want to nerd out on noise generators.) If you use Structured Data to explicitly declare your entities, you are handing the machine a map. You’re telling it, “This node is the Founder, this node is the Framework, and they are connected by this Relationship.”

Engineering for Extraction

In the era of GEO (Generative Engine Optimization), you have to write for extraction. AI models don’t read your whole article; they chunk it.

• The Answer-First Block:
  Start every section with a 50-word direct answer to the heading. This increases the salience of the entity in that section and makes you 10x more likely to be cited in an AI Overview.
  
• Topical Integrity:
  Stop writing scattered posts my friend! If you want to own a topic, you have to build a cluster. 1 pillar page (the hub) and 10 supporting pages (the spokes). This tells the system that your domain isn’t just a site, it’s a topical authority.
  
• Entity Resolution:
  Use consistent naming. If you’re “Samantha Smith” on your blog but “S. Smith” on LinkedIn, you’re making the machine work too hard. Consistency is a trust signal.

Stop Counting, Start Connecting

The Keyword Era (RIP) was about volume. The Entity Era is about Density and Relationship.

If you provide the cleanest, most interconnected data, the gatekeepers will have no choice but to use you as their source. You aren’t just playing the game anymore, you’re providing the board.

In case you haven’t figured it out yet, SEO isn’t dead—it’s the future. And that future is built on entities, not strings.

I’ve been watching the Brand space dissolve and reform for a decade, and 2026 is officially the year the old map burned.

If you’re still trying to build a following, you’re playing a game that ended 3 years ago. Gary Vee hit the nail on the head: Social Media has evolved into Interest Media.

If your brand signal is weak, Interest Media will treat you like static.

The fundamental shift is this… the social graph is dead.

It used to be that if you were cool enough to get 10,000 people to hit follow, you owned their attention. You had a captive audience. In 2026, the algorithm doesn’t care who you follow. It only cares what you are interested in right this second.

Your brand isn’t competing with your competitors anymore. It’s competing with a viral cooking video, a niche AI-generated anime, and a cat playing a synthesizer (hell yeah tho, right?)

Branding as High-Frequency Interest

In an Interest Media world, branding is no longer a static PDF style guide. It’s a frequency.

Most agency founders are still obsessed with consistency; using the same font, the same hex codes, the same boring corporate voice. That’s not a brand, that’s a uniform.

A real brand in 2026 is an Interest Anchor.

You have to engineer your brand signal so that when the “Interest Engine” (fka the algorithm) scans your content, it knows exactly which slot to put you in. If you’re too broad, you’re invisible. If you’re too corporate, you’re skipped. You have to be weird enough to be recognizable, but structured enough to be indexable.

The Mechanics of the Vibe

This is where the And Zeros logic comes in. You think vibe is an accident? It’s math.

• The Attention Hook:
  Interest Media rewards the first 1.5 seconds. If your visual branding doesn’t have a pattern interruption built into its DNA, you’re toast.
  
• The Semantic Bridge:
  You need to talk about your niche in a way that connects to other high-interest nodes. If I’m talking about GTM strategy, I’m also talking about high-end audio gear, and the philosophy of minimalism. Why? Because the Interest Engine sees the crossover in those audiences.
  
• The Zero Crossing Pivot:
  This is my favorite part. You have to find the point where your expertise (the logic) crosses over into what people actually give a shit about (the interest).

Stop Being a Business and Start Being a Signal

In 2026, the most successful brands don’t look like companies. They look like channels.

They provide a specific type of energy that a specific type of person craves. If you try to appeal to everyone, the Interest Media engines will interpret your brand as neutral, and neutral is just another word for archived.

Go ahead and be a little too much. Use the bold colors. Lean into the technical jargon that only your favorite 1% of clients understand. Be the signal that cuts through the noise floor.

In audio engineering, there is a concept called a Zero Crossing.

It is the precise mathematical moment a waveform passes through the center line of a graph. It is the point of zero amplitude. In the studio, this is the only place you can make a cut or a transition without creating a pop, click, or a digital glitch.

It is the only place where a transition is perfectly clean.

And yes, I did go to school for audio engineering 😄 Arguably, the most lucrative Associates of Applied Science degree that exists.

The Algorithm Noise Floor

We are currently living in the loudest era of human history. Between the rise of generative AI and the shifting sands of platform algorithms, the noise floor has never been higher.

Most brands and creators respond to this by simply turning up the volume. They post more, shout louder, and chase every fleeting trend. It’s honestly equally overwhelming as a consumer. But in a world of infinite content, volume doesn’t equal visibility. Volume just adds to the distortion.

Engineering Visibility

I’m Doug. I run a digital agency called And Zeros, and I’ve spent my life as a musician, audio nerd, and marketer. I’ve realized that the logic of a clean audio edit is the same as the logic of a successful brand.

Visibility isn’t about shouting, it’s about signal.

To surface work today, whether it’s a software product, a creative project, or an agency service, you have to understand the mechanics of the platforms you inhabit. You have to build a brand signal that the system can actually interpret and prioritize.

What to Expect from The Zero Crossing

This journal is an exploration of that intersection… where creative frequency meets digital logic.

Every week, I’ll be breaking down:

• System Architecture:
  How to build brands that are readable by modern systems, from traditional SEO and PPC to the new frontiers of AEO and GEO.
  
• Platform Mechanics:
  A deep dive into strategies and development cycles that ensure your work actually surfaces in an algorithm-driven world.
  
• The Clean Cut:
  Moving past marketing fluff to find the tactical points of transition. Where branding, tech, and even the rhythm of music inform how we move the needle.

We’re moving away from the noise and toward the signal.

— Doug