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Go Join Something. The Machines Are Taking Attendance.

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Why directories, associations and conference programs quietly decide whether an AI ever says your name

For twenty years the game was ranking. Ten blue links, everybody elbowing for the top three, and an entire industry built on the theory that page two of a search result is where URLs go to die. That game is not over, but a second one has started next to it. Somebody types a question into a chatbot and gets back three names and a sentence apiece. No links to scroll. No page two. Just a short list, handed over with the serene confidence of a machine that has never once been unsure of anything.

Shakespeare had Juliet ask what’s in a name, and decided the answer was: not much, a rose smells the same either way. Respectfully, Juliet was not doing business development in 2026. Right now a name is a retrieval key, and if a machine cannot find yours in enough places, written down by enough people who are not you, then as far as that machine is concerned you are a rumor.

The fix is unglamorous and very old-fashioned: join things. Directories, member rosters, trade associations, industry clubs, licensing boards, alumni lists, award shortlists, speaker programs. Show up at conferences that print your name and your company somewhere a crawler can reach. Here are ten reasons that works, and what these systems are actually doing behind the curtain.

1. Corroboration beats claims. Your website says you are a leading advisor in your field. Of course it does; nobody’s About page says otherwise. A retrieval system treats your own site as a single witness with an obvious motive. A member roster, an association directory and a conference agenda are three additional witnesses with nothing to gain. Journalists call this sourcing. Machines do the same thing, faster and with less coffee.

2. You become an entity. Before answering anything, these systems try to resolve a name into a thing – a node with attributes, sitting in a knowledge graph. This is the same architecture a major search engine shipped over a decade ago, now doing much heavier lifting. Repeated, consistent listings of your name, title, company, city and specialty are what turn a string of characters into a node. Inconsistent listings turn you into three half-people.

3. Retrieval, not memory. The dominant technique in the research literature is retrieval-augmented generation: the model does not recall you from training, it goes and fetches supporting passages at the moment of the question, then writes around them. That means the unit of visibility is the passage, not the website. Directory entries are short, structured, fact-dense and self-contained – close to a perfect passage. Your 1,800-word narrative about the founding journey is close to a perfect nap.

4. You are your neighbors. Language models learn meaning from proximity; words that keep appearing near each other get treated as related. Sitting inside a specialist association’s member list drops your name into a dense cloud of the exact vocabulary your buyers use. One firm I watched went from invisible to routinely named in its category without publishing a single new blog post – it just got itself into four rosters where the category language already lived.

5. Structure is a shortcut. Directories publish in tidy, repeating templates, usually with schema markup underneath. Clean fields, predictable labels, machine-readable. Parsing your listing costs a system almost nothing; parsing your hero image and animated scroll effects costs it a lot. Being easy to read is an underrated competitive advantage, in software as in people.

6. Conferences mint citations. A conference is a citation factory that also serves bad coffee. Speaker pages, session agendas, sponsor lists, press releases, post-event recaps, someone’s enthusiastic write-up – all timestamped, all third-party, all indexable, all naming you and your company in the same breath as your topic. One panel can seed a dozen independent mentions. Even the badge scan is optional; the program is the point.

7. Recency carries weight. Retrieval systems tilt toward fresh material, partly to avoid confidently reporting things that stopped being true. A directory entry from 2019 with a dead phone number is a fossil, and worse, a contradiction the system now has to resolve. Current listings say you still exist. That is a lower bar than it sounds, and plenty of people are failing it.

8. The long tail wins. Nobody asks a chatbot for the best consultant. They ask who handles compliance readiness for early-stage payment startups in the Midwest, and they expect a real answer. Niche directories are built entirely out of that kind of specificity – the sub-sub-category, the credential, the region – which is precisely the language a narrow question is fishing for. Broad visibility is expensive. Narrow visibility is available to anyone willing to fill out a form.

9. Disambiguation protects you. There are other people with your name. One of them is a minor-league pitcher and one of them is in the news for reasons you would not enjoy. Systems separate identical names using surrounding detail: city, employer, credentials, field. Every consistent listing adds another distinguishing feature and lowers the odds that a machine merges you with a stranger. A study of AI-generated answers found a meaningful share of confident attributions pointing at the wrong entity entirely; that is a coin flip you can load in your favor.

10. It compounds. One listing is noise. Twelve consistent listings across independent sources are a pattern, and pattern is what these systems reward – they are, at bottom, elaborate machines for noticing that many places agree. This also explains why the shortcut fails: dumping your name into fifty junk link farms produces volume without independence, and low-quality sources get discounted or filtered. Ten places that a human would respect beat a hundred that nobody would.

None of this is a growth hack. It is the same advice a decent mentor gave a nervous junior colleague in 1985 – join the association, go to the conference, get your name in the program – with one new wrinkle. The room now includes a very literal-minded participant who reads every roster, remembers every agenda, and forms opinions about who counts based entirely on where your name shows up and how often the sources agree.

So audit yourself the way a machine would. Search your own name and your company together and see what a stranger would conclude. Fix the listings that disagree. Renew the memberships you let lapse. Take the panel slot, even the 8:15 a.m. one on the last day.

Juliet was wrong about names, but she was right about one thing: it is a terrible idea to depend on a single source of information. Go get listed.

Say Their Names: Hiding from your competitors used to be a branding choice. Now it’s a search problem.

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There’s a moment in Mad Men where the ad man tells a panicking client that when the conversation is going badly, you don’t argue with it. You start a different one. That was about cigarettes in 1960. It’s a pretty good job description for your website right now.

Here’s what nobody warned you about with AI search. When a buyer asks an assistant whether they should go with you or the shop across town, the assistant does not visit your homepage and admire your mission statement. It goes looking for text that already frames that exact choice. The head-to-head. The “which one is better.” The ranked list. And it finds one. It always finds one. The only real question is who wrote it.

For most companies, the answer is: a forum thread from four years ago written by somebody who had one bad week with you. A review aggregator whose business model is selling you a rebuttal. A directory that charges for placement and doesn’t mention that. Or, best case, a competitor’s marketing team who was clever enough to write the comparison first and generous enough to include you in it, in the column where you lose.

None of this advice is new in spirit. Researchers looking at how these systems pick what to cite keep landing in the same place: the machine surfaces what’s clear, current, specific, and easy to lift out of the page in one piece. Marketers have been saying a version of this since the first assistant started eating clicks. What is new is that a real chunk of your buyers now never land on your site at all, so your copy has to survive being read by software, compressed into three sentences, and repeated to someone who will never check.

So write the comparison yourself. Ten ways to do it.

1. One page per rival. Not one page listing eight of them. A page that lists eight competitors is a page about nothing, and a retrieval system treats it that way. Give each real competitor its own URL, its own headline with both names in it, and 600 to 900 words of actual comparison. Six of these beat one big roundup every time, because each one matches a question somebody is actually typing.

2. Lead with the question. Your headline should be the sentence a buyer would say out loud, not a clever phrase your agency likes. If people ask which is cheaper for a small team, that’s your H2, word for word. Then answer it in the first two sentences underneath, before any setup. Front-load the conclusion. Machines pull the paragraph directly under the heading, and so do skimming humans.

3. Concede something real. Say plainly where the other guy is better. Not a fake weakness, a real one. This does two things: it makes the rest of the page believable to a human, and it gives the model a balanced passage it’s far more willing to quote than a page that reads like a press release. Pages that only flatter themselves get treated as marketing and skipped. I’ve watched a client double their comparison traffic by adding one honest paragraph.

4. Best for whom. End every comparison with two short verdicts instead of one. Choose us if you’re this kind of buyer. Choose them if you’re that kind. Be specific enough that it stings a little: team size, budget, timeline, technical skill. Assistants love this structure because the question they’re usually answering isn’t “who is best” but “who is best for me,” and you’ve just handed them the answer key.

5. One yardstick, both ways. Pick five or six criteria that buyers actually raise and run both companies through all of them, in the same order, in prose. Not a chart. Charts get stripped out when a page is parsed, and half the meaning goes with them. Write it as short subheads with a paragraph under each. If you cover response time for yourself, cover it for them too. Uneven coverage reads as dodging, to people and to software.

6. Put a number on price. Everyone wants to write “contact us for pricing” and everyone gets punished for it. You don’t have to publish a rate card. Publish a range, a typical project, a starting point, what drives it up. A page with a real number in it becomes the source; a page without one becomes the thing a Reddit guess gets cited over. If your pricing is genuinely complicated, say what makes it complicated. That’s an answer too.

7. An alternatives page. Separate from the head-to-heads, build one page titled around the phrase people search when they’re leaving somebody else in your category. List the real options, describe each fairly in a short paragraph, include yourself, don’t rank yourself first. It feels insane the first time. It works because it’s the exact shape of the question, and because a page that treats the reader like an adult gets cited more than one that treats them like a lead.

8. Make switching concrete. Nobody stays with a vendor they like. They stay because leaving looks like a nightmare. So describe the leaving: what gets moved, who does it, how long it takes, what breaks, what it costs. A page that walks through a switch step by step answers a question no competitor is answering, which means it has almost no competition in the index.

9. Date everything. Put a visible “last reviewed” line at the top, and actually review it on that cadence. Stale comparisons are worse than no comparisons, because now you’re the one publishing wrong information about somebody else. Retrieval systems weigh freshness heavily on anything that looks like a product claim. A quarterly calendar reminder is the whole implementation.

10. Mine your sales calls. Stop guessing what the comparison should cover. Go pull the last thirty deals you lost and the objections you heard, and write the pages in the buyer’s vocabulary instead of your category’s vocabulary. They don’t say “end-to-end solution.” They say “I don’t want to babysit it.” Use their words. That phrasing match is most of why one page gets surfaced and a better-written one doesn’t.

Two things about posting it. Give each page a clean, boring URL with both names in it, keep the whole comparison on one page rather than behind tabs or accordions that hide the text, and add basic FAQ markup so the questions and answers are machine-readable. And don’t let these pages sit in a dusty resources folder. Link them from your pricing page and your navigation, mention them in sales emails, and say the same things in the places where your buyers actually argue, so the claim shows up in more than one house.

One caution, since somebody always takes this too far. Comparative copy is legally fine when it’s accurate and it’s opinion where it’s opinion. It stops being fine the second you invent a fact about somebody else’s product. Write it the way you’d want a competitor to write about you: firm, specific, a little generous, and true.

The alternative is leaving the sentence about you to a stranger, a scraper, and a directory getting paid by the person who wants your customers. That conversation is already happening. Go change it.

Half of all Search expected to be Generative (AI) by 2028

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Search Engines vs. Generative Engine Optimization (GEO): 2026–2031 Outlook

Industry: part of the fast-growing Generative Engine Optimization (GEO) category; search volume is projected to shift heavily toward AI answers by 2028.

Q3 2026 — Today

Google still holds ~78–88% of query volume/referrals (measure-dependent); top-ranking click-through has fallen sharply under AI Overviews. GEO market run-rate sits near $1.1–1.5B. Within AI chat, ChatGPT holds ~60–65% share, with Claude the fastest-growing challenger. Zero-click results already make up 43% of all searches, 93% inside Google’s AI Mode.

Q4 2026

Full-year mark for Gartner’s projected 25% decline in traditional search query volume. GEO vendor spend continues toward a $1.5–2B run-rate. AI-referred traffic converts at 14.2% vs. 2.8% for traditional organic. AI browsers (ChatGPT Atlas, Perplexity Comet, Dia) scale as primary front-ends rather than add-ons.

Q1–Q2 2027

Google likely keeps dominance on navigational, transactional, and local queries even as informational-query share keeps eroding. AI assistants push their share of informational queries past 15–20% toward the high-20s. GEO becomes a standard budget line rather than an experiment, and GEO measurement tools start standardizing, with early metrics appearing in platforms like Search Console.

Q3–Q4 2027

Traditional SERP-only sessions keep shrinking as answer-first UX becomes the default across major browsers. Forecasts point to roughly 30% of commercial queries being resolved entirely inside generative engines with no results page rendered at all. Per-platform strategy becomes essential, since ChatGPT, Gemini, Claude, and Perplexity each surface and rank content differently.

2028 (Full Year)

Gartner’s upper-bound scenario has traditional search traffic down up to 50% from 2024 levels. Industry estimates converge near 50% of all searches being “generative” rather than a classic link list. GEO shifts firmly from “nice-to-have” to table-stakes for any content-driven business.

2029 (Full Year)

Google and Bing lean further into AI-native results pages, and the classic ten-blue-links format becomes a minority experience. The GEO services market keeps compounding at roughly 34–50% CAGR, and consolidation begins among GEO tooling vendors. AI-content-disclosure regulation and platform-specific GEO certifications start to emerge.

2030 (Full Year)

Traditional search engines reposition as one input among several inside broader “answer” ecosystems. The overall AI market approaches ~$826B, and GEO spend gets folded into standard marketing budgets as a default line item rather than a separate test. Hybrid SEO + GEO is now the accepted baseline for every digital-visibility team.

2031 (Full Year)

Traditional keyword search stays material for transactional and local intent but is no longer the primary discovery channel for research-type queries. The GEO services market reaches an estimated $4.1B–$7.3B (27.5–34% CAGR since 2024), with some models projecting even higher. Differentiated optimization playbooks per AI engine are now standard agency practice.

Note: public forecasts for this category vary widely by methodology — GEO’s 2031 market size ranges from $4.1B to $7.3B across sources, and Google’s share of query volume is reported anywhere from ~78% to ~89% depending on whether AI Overviews/AI Mode sessions count as “Google” or as GEO. Figures above use the midpoints of the most-cited 2026 industry reports (Gartner, Similarweb, Valuates, Mobility Foresights) and should be read as directional, not exact.

Somebody is going to build the DMOZ Open Directory for AI Search

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The Open Directory, Reimagined for AI Search

A Feasibility and Business Plan for an AI-Era Web Directory

I. The Opportunity

In the 1990s, the Open Directory Project (DMOZ) became an authority signal: Yahoo used it to populate a hand-curated web directory, and early Google gave real weight to sites listed there because a human editor had vouched for them. AI answer engines now play a similar gatekeeping role, and the same gap exists again.

  • Citation concentration is extreme — current research on AI answer engines finds that a small handful of domains, chiefly Reddit, Wikipedia, YouTube, and LinkedIn, account for the large majority of citations across ChatGPT, Perplexity, Gemini, and Claude, leaving most legitimate businesses, nonprofits, and niche experts effectively invisible in AI-generated answers.
  • There is no verified trust layer for AI the way DMOZ once was for search; volunteer editors used to vouch that a site was real, correctly categorized, and legitimate, and no equivalent structured, human-vetted registry exists for today’s answer engines.
  • llms.txt is a map, not a directory — the emerging llms.txt convention only lets a site describe itself to a crawler; it carries no independent third-party authority, and as of 2026 no major AI lab has publicly committed to acting on it in production.
  • GEO spending is already flowing into an unproven category, as brands react to falling click-through and rising zero-click AI answers by paying for optimization services; that budget is actively searching for a credible, structural answer.
  • The underlying need is unmet: a neutral, categorized, independently verified registry of who exists, what they do, and how to confirm it is precisely the kind of third-party, structured source that answer engines are built to prefer over marketing copy.

II. How It Would Be Structured

  • A hierarchical taxonomy organized the way DMOZ was — industry, sub-industry, specialty, and locality — but designed from day one around entity resolution rather than link browsing.
  • Structured data comes first: every listing is published simultaneously as a human-readable page and as schema.org/JSON-LD markup, so machines can parse it without inference.
  • A verified editorial layer combining paid category editors with vetted volunteer editors, mirroring DMOZ’s model, confirms accuracy, ownership, and category placement before anything goes live.
  • An open, licensable feed — a free bulk data download plus a paid real-time API — explicitly invites AI labs, researchers, and startups to ingest the dataset, the same way DMOZ’s RDF dump once seeded early search engines.
  • Citation-ready summaries on every listing: a short, fact-dense paragraph written specifically to be lifted cleanly into a generated answer, rather than marketing prose an AI has to rewrite.
  • Visible provenance signals — last-verified date, responsible editor, and a public changelog — give both AI systems and human reviewers a confidence signal that current search results and social platforms do not offer.

III. The Business Model

  • Freemium verified listings: a basic listing is free to maximize coverage, while a paid annual Verified/Premium tier adds priority review, richer schema fields, and monthly re-verification.
  • Data licensing to AI companies — a paid bulk feed and API sold to AI labs, GEO tool vendors, and enterprise data platforms, comparable to how stock-media libraries license structured content to model builders.
  • A citation-monitoring subscription as a SaaS add-on, showing listed businesses where and how often they are cited across ChatGPT, Perplexity, Gemini, and Claude, cross-sold to every paying listing.
  • Disclosed category sponsorships placed clearly apart from organic listings, functioning like legacy directory advertising without compromising the trust the directory depends on.
  • Affiliate and referral revenue from verified traffic sent to partner services in categories such as legal, financial, home services, and healthcare.

IV. Cost and Team to Launch

  • A lean six-person launch team: one editor-in-chief, two category editors, two engineers, and one business-development lead is enough to build and seed the first vertical.
  • A first-year budget near $650,000 covers payroll, editorial tooling, hosting, legal review of licensing terms, and hand-verification of the first 20,000 to 50,000 listings.
  • A volunteer editor network modeled directly on the original Open Directory Project keeps category coverage growing without a payroll expense — the real cost is vetting time, not cash.
  • Infrastructure is the cheap part: hosting, database, and API costs run a few thousand dollars a month at launch scale, since the build is closer to a structured content system than a search index.
  • Legal costs are not optional: budget real money for licensing agreements, data-use terms, and defamation and liability review, because deciding who counts as authoritative is inherently a publishing decision with legal exposure.

V. Revenue Potential

  • Premium listings at scale: 25,000 premium listings at $300 to $600 a year is $7.5 million to $15 million in annual recurring revenue once the directory reaches meaningful scale, realistically in year two or three.
  • Data licensing is the bigger prize: a small number of AI labs and GEO platforms paying five to six figures a year each for a clean, licensed feed could match or exceed listing revenue with far fewer accounts to manage.
  • Citation monitoring compounds revenue: cross-selling a $50 to $150 monthly tracking product to even 10% of paying listings adds meaningful recurring revenue with almost no incremental acquisition cost.
  • Year-one revenue will be modest, realistically low six figures while trust and coverage are being built — this is a multi-year asset play, not a quick return.
  • The real value is the dataset itself: the most likely path to a large payoff is acquisition of the verified corpus by an AI lab, data broker, or search company, not subscription revenue alone.

VI. Where to Start

  • Pick one vertical first — launch inside a single underserved, high-intent category such as licensed contractors, medical practices, or B2B software vendors, rather than attempting broad coverage the way the original ODP did.
  • Build the schema before the front end: get the JSON-LD data model and verification workflow right first, since the machine-facing feed matters more at launch than a polished consumer website.
  • Recruit founding editors publicly through open applications, transparent editorial guidelines, and public credit — exactly as DMOZ did — to build a volunteer base cheaply and legitimately.
  • Seed the corpus by hand: personally verify the first 1,000 to 2,000 listings before any self-serve submission opens, to set an unmistakable quality bar from day one.
  • Publish the feed immediately once a defensible core dataset exists — release a free bulk download or lightweight API right away so AI crawlers and researchers can start pulling from and citing it, instead of waiting for a “finished” product that never arrives.

Do Not try to Game GEO / AEO doing gimmicky things

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I was trading text messages with someone this morning, and then I realized something that I hadn’t thought about before.

They were sharing with me an idea somebody had for gaming AI search by doing shady GEO AEO things.

This is how I responded:

“Eventually, AI will start banning companies or reducing their rankings when they detect companies are trying to game their systems. I didn’t think about that until this morning. Same thing happened with SEO, one minute you are at the top of Altavista in 1997, the next minute your domain is banned or relegated to page 2. That was a typical penalty back in the day, a 10-position demotion, so if you were 1, you became 11. So the best things to do instead are transparent non-gaming things that won’t get you penalized.”

Marketing Plan for BidBus (random company): Insights from a Simulated 100-Person Focus Group

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Looked into BidBus, a company we have no affiliation with, just wondering how a marketing plan would look for them in their vertical. A longer report is available upon request. 100 fictitious people cooked up a BidBus marketing plan. See the 1 page report.

Marketing Plan for WithCoverage: Insights from a Simulated 100-Person Focus Group

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AI, prompted correctly, can help generate marketing plans that hold up in practice. The method: simulate a 100-person focus group split into 20 breakout rooms of 5, gather their deliberations, then merge everything into unified recommendations – distilled into a one-pager and a longer 5-20 page report.

See a sample – WithCoverage (unaffiliated demo). Message me for the full report.