Instant Press Co.

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Approval rate for new Wikipedia pages: under 10%.

Most founders who try fail. Here's why, and what works.

Why Wikipedia matters in 2026

Primary LLM training source. Pages = AI citation.

Google Knowledge Panel trigger.

Permanent authority. Press ages out, Wikipedia stays.

Value in 2026 is 3-5x what it was in 2022.

The 5 failure reasons

Insufficient notability. Need 3-5 Tier 1 press features. No features = no page.

Promotional tone. Reads like marketing. Editors decline.

Primary sourcing. Own website, interviews, press releases don't count.

COI not disclosed. Fast route to deletion.

AfC queue neglect. Ignoring reviewer feedback kills drafts.

Sources that count

Forbes (staff), WSJ, NYT, Business Insider, Fast Company, Bloomberg, Reuters, TechCrunch (staff), Inc., academic journals.

Sources that don't count

Forbes Business Council, press releases, podcasts, LinkedIn, company blogs, YouTube, subject-sourced interviews.

The three paths

Articles for Creation (AfC). Volunteer review queue. 2-8 months. 30-40% success.

Direct creation. Autoconfirmed editors skip queue. Variable success.

Disclosed paid editing. Ethical paid editors. 40-60% success with strong sources.

Realistic timeline

Source audit + draft: 2-4 weeks

AfC submission to first review: 2-8 months

Total from zero: 3-14 months

Anyone promising 30 days is selling a page that gets deleted.

Cost reality

Press foundation: $15K-$60K

Drafting + submission: $3K-$10K

Year 1 monitoring: $1K-$3K

Total: $20K-$75K for a real page.

Services under $2K = scam or deleted page.

The pull-quote

Wikipedia pages aren't bought.

They're earned through press foundation, drafted with encyclopedic discipline, and defended through post-approval monitoring.

Starts with press.

Want the full framework? Read the full Wikipedia approval playbook.

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SaaS AEO playbooks don't work for ecommerce.

Different channels. Different content. Different measurement.

The 5 channels for DTC AEO

Review aggregators. Amazon + Reddit + YouTube + Wirecutter. Threshold: 100+ reviews at 4.3+ stars.

Comparison pages. "X vs Y" content. Balanced, not self-praising.

Category thought leadership. Buyer's guides. Material deep-dives. Industry analysis.

Category press. Apartment Therapy > Forbes for home goods. Outside > WSJ for outdoor.

Structured product data. Schema markup + specification tables + use case descriptions.

Miss one = citation rate drops.

Content priority

Buyer's guides ("Best X for Y")

Competitor comparison pages

Use case content

Material/tech deep-dives

Review roundups

What doesn't work

General SaaS AEO playbooks

Paid review manipulation

Homepage-focused SEO

Business press without category coverage

Case pattern

2025 DTC home goods client.

Starting: 3,200 monthly organic, 0.4% AI referral, 0 category citations.

6 months in: 11,800 monthly organic (3.7x), 8.2% AI referral, 31% category citation share, $340K attributed revenue.

4 category features (Apartment Therapy, Dwell, Domino) beat any Forbes feature for category AI citation.

The inflection

30-40% of under-35 consumers use AI for product research

Category citation patterns capture click share fast

Brands not cited = invisible in increasing purchase decisions

The pull-quote

Forbes helps your brand.

Apartment Therapy helps your products get cited when buyers ask AI for recommendations.

For DTC, category press beats business press.

Want the full framework? Read the full ecommerce AEO playbook.

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HARO shut down in 2024. The infrastructure fragmented.

Single-platform strategy = 30-40% of HARO-era volume. Stacked strategy matches it.

The must-use stack

Qwoted ($99-$299/mo). Forbes, Inc., BI, Fast Company use heavily.

Featured.com (free + paid). Question-based, high publication rate.

#journorequest on X (free). Real-time, UK-heavy.

Help A B2B Writer (free). B2B-specific niche.

Total cost: $99-$399/month. Expected: 20-30 useful queries per week.

Skip these

Cision Connect (low post-shutdown engagement)

Muck Rack (enterprise-priced, overkill)

Most "AI-powered" HARO alternatives launched 2025 (thin journalist bases)

The response that gets published

First 1-2 sentences: direct answer

Middle: specific data or case

Last sentence: memorable closing

150-250 words. Journalists quote first 30-60 words most.

What doesn't work

Generic commentary

Slow response (queries close in 24-48 hours)

Single-platform strategy

Responding to irrelevant queries

Case pattern

Founder. HARO-era baseline: 12 publications/year.

Switched to Qwoted + Featured + X + Help A B2B Writer.

2025 result: 34 publications, 8 Tier 1 placements.

Same time investment. Stacked platforms. Better outcome.

The pull-quote

HARO taught press strategy.

The post-HARO era tests execution.

Founders running stacked platforms compound authority. Founders waiting for HARO to return lose visibility.

Want the full roundup? Read the full HARO alternatives playbook.

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Business Insider compounds harder than most Tier 1 publications.

DA 94. Heavy AI citation. Reader intent converts 3-5x vs general business press.

Why BI features compound

Readers research companies and executives

Articles get cited heavily in ChatGPT, Claude, Gemini

One feature = 6-18 months of inbound lift plus permanent AI visibility

The three paths

Exclusive story. Data no one else has. Contrarian take with evidence. 1-3 features/year.

Source relationship. 3-5 reporter relationships over 6-18 months. 4-12 quotes/year.

Owned content lift. Publish proprietary research, reporter cites it. 2-6 citations/year.

What doesn't work

HARO/Qwoted (BI rarely uses)

PR distribution services

Generic cold pitches

Agencies without BI relationships

The pitch structure

Subject: specific claim, not expertise description.

"Data: 73% of Series A SaaS dropped ICP in 2025"

Not: "Executive available for SaaS commentary"

150 words max. Reporters scan in 15 seconds.

Case pattern

2025 SaaS founder. Zero prior Tier 1.

6 months: one BI feature. Next 6 months: 47 inbound inquiries, 8 podcasts, 3 speaking invites, AI citation share 4% → 28%.

Single feature. Compound authority.

The foundation mistake

Most founders pitch BI before they have:

Proprietary data

Contrarian position with evidence

Tier 2 coverage for baseline

Newsworthy moment

Build Tier 2 first. Forbes Business Council, Inc., Entrepreneur. Then BI.

The pull-quote

One Business Insider feature beats fifty Tier 3 mentions.

Volume is noise.

Placement in the publications buyers read is signal.

Want the full framework? Read the full Business Insider playbook.

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AI Overviews now appear on 40-60% of informational queries.

Cited pages own the top of SERP. Uncited pages lose visibility even when they rank #1 organic.

Why citations matter

Sit above #1 organic result

1.5-2x qualified traffic of traditional rankings

Appear for broader query sets (2-3x impression volume)

The five citation factors

Domain Authority 60+. Below DA 50 = rare citations. DA 60-80 = regular. DA 80+ = dominant.

Direct answer in first 200 words. Question heading, answer, then depth.

Structured data. FAQPage, HowTo, Article schema.

Topical depth. 30 pieces beat 3 at same DA.

Recency. Updated within 6-12 months.

Missing one = citation rare.

Content patterns that cite

Comparison pages (X vs Y)

Definition pages (What is X)

How-to pages

Statistical pages

Framework pages

What doesn't work

Keyword stuffing (Gemini uses semantics)

Thin volume (20 deep pages beat 100 shallow)

Homepage focus (citations come from topic pages)

90-day execution

Days 1-30: Audit, map 20 queries, document current citations

Days 31-60: 5-10 cluster pages, schema, refreshes

Days 61-90: Backlinks, internal linking, monitor

First citation: 60-120 days.

Case pattern

Phoenix B2B SaaS. Starting: DA 52, 8,500 organic, zero citations.

6 months in: 18 citations, 22,000 monthly organic (2.6x), $1.8M pipeline.

Same domain. Structured work. Compound outcome.

Why 2026 raised stakes

Gartner: 25-50% Google traffic decline by 2028. AI Overviews are the cause.

Brands optimizing now capture the shift. Brands running traditional SEO lose SERP top.

The pull-quote

Traditional SEO optimized for position #1.

AI Overviews moved position #1 above the organic results.

Rank for the new top or lose visibility at the old one.

Want the full framework? Read the full AI Overview playbook.

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Most executive ghostwriting produces archives, not authority.

The default model treats ghostwriting as content production. Real ghostwriting is position externalization.

The default model

30-minute intake call

8-15 posts per month

Quarterly review

$3K-$15K/month

Output looks fine. Equity: minimal.

What real ghostwriting is

Writer extracts executive's specific views, sharpens them into claims, renders them in the executive's voice.

Writer as forcing function. Not content producer.

Three requirements

Position before production. 4-6 hours documenting 3-5 proprietary positions before any content is written.

Voice capture. 60-90 min unedited audio, prior writing samples, multiple feedback rounds.

External amplification integration. LinkedIn post + Forbes article + podcast pitch + speaking proposal, same position, multiple channels.

Skip any = content mill output.

The intake

Week 1: Discovery. Probing for positions, not topics.

Week 2: Position documentation. 3-5 position docs.

Week 3: Voice calibration. Sample posts, iterate.

Week 4: Production begins.

Content-mill agencies skip weeks 1-3.

Pricing reality

Below $8K/mo: content mill

$8K-$20K/mo: real programs

Above $20K: diminishing returns

Can't profitably do position development at $3K-$6K.

Three signs your writer isn't good enough

Doesn't push back on vague positions

Skips position documents

Can't adapt positions across formats

Case pattern

Tech CEO. 14 months with prior ghostwriter. 180 posts. Zero inbound.

Switched to position-first model.

12 months later: 6 Tier 1 placements, 22 podcasts, 3 conference keynotes, 42% AI citation share.

Same person. Different model.

The pull-quote

Writers who produce filler fill calendars.

Writers who develop positions build equity.

One is marketing spend. The other is career compound.

Want the full framework? Read the full executive ghostwriting playbook.

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Most personal branding services sell content volume. Compound equity requires something different.

Ghostwriters fill calendars. Positions build authority.

What most agencies sell

Logo and visual identity

LinkedIn profile optimization

5-15 ghostwritten posts per month

Brand guidelines doc

Quarterly strategy calls

Price: $2K-$10K/month. Equity produced: minimal.

Why calendars fail

Readers detect ghostwriting. Engagement drops.

Generic content doesn't differentiate. No position.

Compound requires positions, not volume.

One client: 200+ posts, 18 months, $120K, zero inbound.

What builds equity

Three components:

Proprietary positions. Specific, defensible takes grounded in experience.

Owned distribution. Audience built on owned content.

External amplification. Tier 1 press, podcasts, speaking.

All three. Missing one = content marketing at premium price.

The 80/20 rule

20% of equity = owned content

80% = external amplification

Most agencies invert this. Ratio backwards.

Three signs of a content mill

Deliverables heavy on content volume

External amplification not a core package element

Promises results in 90 days

Real programs produce signal at 6 months, compound at 12-24.

Case pattern

2025 tech founder. 2K LinkedIn followers, no press, no speaking.

Months 1-2: 4 proprietary positions

Months 3-4: Weekly LinkedIn + newsletter

Months 5-6: 4 Tier 1 placements

Months 7-9: 18 podcasts

Months 10-12: 15K followers, 4 speaking invites, 35% AI citation share

Budget reality

Below $8K/mo: scope too small

$8K-$25K/mo: real programs

Above $25K: diminishing returns

Price reflects work.

The pull-quote

Content calendars produce filler.

Positions produce compound equity.

One is marketing spend. The other is career equity.

The starting test

Hold specific positions your industry disagrees with?

Can commit 3-5 hours/week for 12+ months?

Willing to allocate 80% to external amplification?

All three yes = real program. Any no = don't start.

Want the full framework? Read the full personal branding playbook.

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Producers book experts every day. Most slots go unfilled.

Most experts fail on the booker's criteria, not on subject expertise.

What bookers need

Timely hook. Story breaking this week.

Clean 45-60s soundbites. Ramble, get cut.

Visual credibility. Camera-ready in 15 minutes.

All three or no booking.

The expert pyramid

Tier 1: National regulars. 5-10 years to build.

Tier 2: Frequent contributors. 1-4 bookings/month.

Tier 3: Topical experts. Start here.

Tier 4: Local experts. Also start here.

Three paths to first booking

Cold pitch. 2-5% success. Scales.

HARO/Qwoted. TV producers scout here.

Print/podcast leverage. Highest probability. Build print first.

Five booking killers

Generic expertise, no hook

Camera setup not ready

Zero prior media samples

Talking points don't fit format

No post-appearance follow-up

90-day readiness

Days 1-30: Setup + samples + reel. Days 31-60: Podcasts + HARO + local pitches. Days 61-90: National pitches. 1-3 TV bookings if tight.

Case pattern

2025 client. Zero TV. Strong print.

Built studio. Pitched 15. Landed 2 local. Leveraged local into CNBC. 12 months: 14 appearances across CNBC, Fox Business, Bloomberg.

Why TV still matters in 2026

AI cites TV transcripts

Authority signaling for sales, investors

One clip = 12-15 derivative content pieces

The pull-quote

Experts don't get booked because they're smart.

Experts get booked because they're ready.

Readiness is the difference between pitching and landing.

Want the full playbook? Read the full TV expert framework.

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SaaS is the highest-ROI category for AEO in 2026.

Three reasons:

B2B buyers research vendors in AI search first

Category query patterns are stable for years

Content assets (docs, case studies) already exist

Five channels SaaS must run

G2, Capterra, TrustRadius. Reviews cited in AI comparison queries.

Technical documentation. Docs extracted by AI coding assistants.

Reddit in technical subreddits. r/SaaS, r/startups, category-specific.

YouTube with transcripts. Demos, interviews, case studies.

Trade publications. TechCrunch, SaaStr, VentureBeat.

Additive to general AEO (Wikipedia, Tier 1 press).

The buyer journey shift

Old funnel: Google search → SERP click → vendor site → demo.

New funnel: ChatGPT query → AI shortlist → vendor site → demo.

Brands not in the AI response never enter the consideration set.

Case pattern

Phoenix SaaS. 2/40 queries cited baseline.

Month 1: G2 profile + 25 reviews. Month 2: Wikidata + Wikipedia. Month 3: 3 Tier 1/trade placements. Month 4: Reddit. Month 5: YouTube + transcripts.

Day 180: 24/40 cited. 12x lift.

G2 disproportionate driver.

Budget by stage

Early (<$1M ARR): $8K-$12K/mo

Growth ($1M-$10M): $12K-$20K/mo

Scale ($10M+): $20K-$30K/mo

Tier 1 placement cited in 4 models for commercial query = 50-200 qualified leads over 12 months.

The pull-quote

The AI shortlist is the new B2B SaaS SERP.

Brands cited own the category.

Brands absent don't enter consideration.

Want the full framework? Read the full SaaS AEO playbook.

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Your B2B prospects ask ChatGPT for vendor recommendations before hitting your site.

If your brand isn't in those responses, you're not losing deals. You're invisible.

What it measures

Citation frequency (% of queries citing you)

Citation sentiment (positive / neutral / negative)

Citation specificity (top rec / example / passing reference)

Baseline: 0-5%. Strong programs: 30-50% in 12 months.

Six inputs that drive citation

Wikipedia + Wikidata. Single highest correlation. 4-5x lift.

Tier 1 mentions. Forbes, Inc., Business Insider compound.

Reddit discussion. Strong ChatGPT signal.

YouTube transcripts. Training data.

Owned content depth. Extraction surface.

Brand consistency. Entity naming across platforms.

Additive. 4+ inputs beat 1-2.

Why it's different from SEO

Ahrefs 75,000 brands: mentions correlate 0.664 with AI visibility. Backlinks 0.218.

Authority over optimization.

SERPs show 10 results. LLMs mention 1-5 brands. Long tail disappears.

Case pattern

Phoenix SaaS. Baseline: 2/40 cited.

Month 2: Wikidata. Month 3: 4 Tier 1 placements. Month 4: Reddit + YouTube. Month 6: 24/40 cited. 12x lift.

Budget rule

80% authority work

20% on-page SEO

Single Tier 1 placement cited in 4 AI models = 4x compound vs. single Google SERP result.

Why 2026

ChatGPT: ~2B searches/day. B2B buyers: 30-40% start research in AI search.

Brands starting now own category queries by 2027.

The pull-quote

Citation frequency is the new market share for AI search.

Brands that measure, optimize, and compound over 12 months own their categories.

Brands that ignore it hand share to competitors cited in every vendor recommendation.

Want the full framework? Read the full LLM visibility playbook.

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Inc. Magazine gets 200-300 pitches per day.

5-10 turn into features per week. Individual pitch success rate: under 2%.

Single-path persistence fails. Multi-path wins.

The three paths

Path 1: Inc. 5000 list. Qualify for fastest-growing list. Use placement as leverage.

Path 2: Contributor track. Your byline on inc.com.

Path 3: Editorial feature. Writer profiles you. Highest authority. Hardest to land.

Path 1 criteria

US-based for-profit, independent

3+ years revenue

$100K base year, $2M+ current

250%+ growth

Real value: follow-on coverage within 90 days of list publication.

Path 2 criteria

5-10 prior business publication placements

Specific column angle, not vague

Clear contributor pitch

Rejected: "Leadership column" Accepted: "Monthly column on operational leverage for founders under $10M"

Path 3 criteria

Specific newsworthy angle

Data or case study

Founder on-record

Near-exclusive to Inc.

Under 200-word pitch. One follow-up at 7 days. Done.

Case pattern

2025 client. All three paths.

Path 1: Listed August. 2 follow-ons by October.

Path 2: Applied May. First column October. 8 pieces by year-end.

Path 3: 22 pitches. 2 landed.

Total: 12 Inc. pieces in 12 months. Single-path founders get 0-2.

How to choose

Growing fast → Path 1

Good writer, patient → Path 2

Data-backed angle → Path 3

All three parallel beats any one alone.

Inc. vs Forbes voice

Different editorial lenses. - Inc.: operational specifics, growth, founder lessons - Forbes: industry impact, thought leadership, wealth

Read 5 recent pieces before pitching. Match voice.

The pull-quote

Founders who run all three Inc. paths in parallel land 8-12 pieces in 12 months.

Founders who pitch one angle 50 times land 0-2.

Strategy beats volume.

Want the full framework? Read the full Inc. placement playbook.

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Perplexity is the AI search engine most brands underestimate.

Small user base. Disproportionately high-value users. Researchers, executives, journalists, B2B buyers.

How it works

Live web search. LLM synthesis. Cites 3-10 sources per answer.

Freshness matters more than in ChatGPT.

Five ranking factors

Domain authority. DA 60+ wins. Below DA 40, rare.

Topical relevance. 15-article clusters beat generalist content.

Direct answers. First 100 words = citation snippet.

Schema. FAQPage, Article, HowTo increase extraction.

External authority. Tier 1 mentions, Wikipedia, Reddit, academic citations.

All five, additive. One or two alone won't crack.

Content blueprint

Top 100 words: direct answer

Middle: depth + data

Bottom: FAQ with 5-8 Qs

Schema: Article + FAQPage + Organization

Perplexity-specific wins

Quarterly refresh of evergreen pages

Cite academic sources (.edu, .gov links)

Author bylines with credentials

Long-tail queries first

Comparison content ("X vs Y")

Case pattern

Phoenix SaaS. 0 citations at baseline across 40 queries.

Month 1: Topic cluster. Month 2: Tier 1 amplification. Month 3: Reddit + YouTube transcripts.

Day 90: 12/40 cited. Day 180: 24/40 cited.

Why it matters beyond Perplexity

Perplexity citation correlates with ChatGPT/Claude/Gemini citation.

Live retrieval = fastest AI search to crack.

Leading indicator for broader AI visibility.

The pull-quote

Brands that rank in Perplexity rank across AI search.

Brands that ignore Perplexity ignore the highest-intent AI audience.

Want the full model? Read the full Perplexity ranking framework.

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Ninety percent of "thought leadership" is content marketing at a markup.

The test that separates real from fake.

The three components

Miss any one, the program is fake.

A non-obvious take. Specific position your industry would disagree with. Backed by evidence.

Evidence that holds up. Data, case studies, patterns others can verify.

A platform you own. Audience built on your own channel. Not rented.

What it is not

Ghostwritten op-eds nobody remembers

Five generic LinkedIn posts a week

Sponsored speaking slots

Paid industry awards

Agencies selling these at premium rates are selling content marketing dressed in a suit.

The taxonomy

Three categories that work:

Specialist. Deep expertise in narrow domain.

Category creator. Names the frame before competitors.

Founder-narrator. Shares specific operational lessons.

Pick one. Build for it.

Why 2026 changed the math

Two forces:

AI citation. Real positions get cited in ChatGPT. Generic content doesn't.

Content commoditization. AI floods every channel. Differentiator is what only you could write.

Thought leadership is what's left when AI can produce everything else.

The failure mode

Executive hires agency. Expects 90-day results. Agency produces content marketing dressed as thought leadership. Calendar fills with placements. Metrics don't move. After 9 months, executive cancels and concludes thought leadership doesn't work.

Mutual failure. Agency didn't set timeline. Executive didn't own positions. Doomed before month one.

Three conditions for real programs

Executive owns the positions. Can defend them cold.

3-5 hours per week committed to the work.

12 months minimum. 24-36 preferred.

All three = category-defining voice. Miss any = content calendar nobody remembers.

The starting test

If you can't answer this in one sentence, don't start:

What is the specific, non-obvious position you hold that most of your industry would disagree with, backed by evidence most of your industry doesn't have?

Most executives can't. That's fine. Developing the answer is the program.

The pull-quote

Content marketing produces calendars.

Thought leadership produces authority.

Agencies that conflate them waste budgets. Executives who invest in real positions build moats.

Want the full framework? Read the full thought leadership framework.

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AI SEO and AEO are not the same.

Getting this wrong is the single most expensive allocation mistake I see brands make in 2026.

The distinction

AI SEO: on-page technical optimization so AI models find and cite your owned content.

AEO: the broader strategy, AI SEO + external authority (Wikipedia, Wikidata, press, community).

AI SEO is the foundation. AEO is the building.

The 20/80 rule

20% of AI visibility impact: AI SEO (owned)

80%: AEO external authority

Ahrefs, 2025: mentions correlate 0.664 with AI visibility. Backlinks 0.218.

Six AI SEO components

Schema.org (Organization, Person, FAQPage, Article)

Content structure (H2/H3, definitions, TL;DRs)

Semantic depth (topic clusters, entity links)

Technical performance (<2s mobile)

Entity anchoring (Wikidata, sameAs, naming)

Freshness signals (dates, versions)

The 30-minute audit

View homepage source → find Organization schema

About page entity links (Wikipedia, Wikidata, LinkedIn)

Top 5 content pages for definitions, H2/H3, schema

PageSpeed Insights on top 5 pages

2-click rule for internal linking

Most brands score 30-50/100 first pass.

Case study

Phoenix SaaS client. 6 months of AI SEO only → citations 2→4. Added AEO external layer 90 days → citations 4→21. Foundation was necessary, external authority was multiplier.

The five gaps

No FAQPage schema

Sparse Organization schema

Thin priority pages

Orphaned content

Inconsistent entity naming

The pull-quote

AI SEO alone plateaus.

AEO without AI SEO leaks value on live retrieval.

Both or neither.

Want the full framework? Read the full AI SEO and AEO breakdown.

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Most pre-seed founders should run their own PR for 12 months.

Not what agencies want to hear. True for founders at specific stages.

When DIY works

Pre-product-market-fit startup (narrative still changing)

Bootstrapped founder with 10 hours/week and no budget

Founder who wants to learn the craft

When it doesn't

Time-poor founder with funding

Regulated or highly technical category

Crisis situation

The five-component stack

Narrative. One page, three proof points.

Target list. 30 journalists/editors/hosts in your category.

Pitch library. Three angles, 120-150 words each.

Content foundation. Long-form, Schema.org, Wikidata.

Cadence. 10 tailored pitches/week, never mass.

The pitch rule

Generic agency templates = sub-3% response rate.

Tailored pitches that reference the journalist's recent work = 12-22% response rate.

The 90-day program

Weeks 1-2: Foundation

Weeks 3-4: Podcast tour (20 targets → 3-5 yeses)

Weeks 5-6: Contributor bylines (10 pitches → 2-4)

Weeks 7-8: Journalist pitching (10/week)

Weeks 9-10: Follow-up and close

Weeks 11-12: Measurement

Outcome: 3-5 podcasts + 2-3 bylines + 1-2 features.

$9K-$21K equivalent agency value.

The DIY tool stack

Google Sheets

Hunter.io or Apollo ($49-$99/mo)

Grammarly

ChatGPT or Claude

Calendly

Under $100/month total.

The mistakes

Mass pitching (burns response rates)

Pitching before narrative is tight (burns relationships)

Giving up at 30 days (placements come weeks 6-12)

Ignoring follow-up (doubles response rate)

Vanity metrics only

The pull-quote

Founders who run their own PR for a year before hiring an agency are the best agency clients.

They know what good looks like. They know what to demand.

Want the full playbook? Read the full DIY PR guide.

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Commit to 12 months of PR or don't start.

That's the rule I give every founder. If budget or runway can't support 12 months, skip PR and spend on paid acquisition or product. PR compounds. It punishes short attention spans.

What startup PR works for

Hiring velocity

Fundraising signal

DTC / prosumer / high-trust B2B acquisition

Category creation and defensive positioning

What it doesn't work for

Raw demand gen in non-trust categories

Product-led SaaS flywheels

Enterprise sales driven by RFPs

Stage-by-stage budget

Pre-seed / early seed. $0-$3K/month. Founder-led podcasts, bylines, community.

Late seed / Series A. $3K-$7K/month. Trade publications, customer stories, analyst briefings.

Series B+. $7K-$20K+/month. Tier 1 placements, Wikipedia, category creation.

The mistakes at each stage

Pre-seed: manufacturing a funding story before funding exists. Press wires and fake awards = credibility debt.

Late seed: over-investing in Tier 1 before trade publications. Three trade features > one Forbes piece for most B2B.

Series A: treating PR as attribution-driven growth channel. PR compounds brand equity, doesn't produce linear MQLs.

Series B: hiring in-house comms before the function is defined.

The minimum viable pre-seed program

Founder narrative (one page)

Content foundation (long-form + Schema + Wikidata)

Podcast tour (20 targets, 3-6 yeses)

Contributor bylines (Startup Grind, Hackernoon, The Muse)

Community participation (2-3 communities, weekly)

8-12 hours/week founder + 10-15 hours/week contractor.

90-day outcome: 3-6 podcast appearances, 2-3 bylines, 500-1500 qualified followers.

The compounding principle

First placement builds credential for second. By tenth, gravity inverts. Journalists start coming to you.

Most startups quit at placement three.

The pull-quote

Founders who commit to 12 months of PR build brand equity that cannot be replicated later by throwing money at it.

The window closes when competitors start.

Want the full playbook? Read the full startup PR guide.

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AI visibility = how often your brand appears in AI-generated answers.

Not keyword rankings. Not backlinks. Citations in ChatGPT, Claude, Gemini, Perplexity, Grok.

It is the metric that most directly predicts pipeline in 2026.

The data

Ahrefs, 2025: Mentions correlate 0.664 with AI visibility. Backlinks correlate 0.218.

Gartner: Google organic traffic expected to decline 25-50% by 2028.

Similarweb: ChatGPT handles 2B searches/day, up from 100M two years ago.

What AI models read

Training data (long-term, cutoff-dependent)

Live retrieval (real-time search)

Structured data (Schema.org, OpenGraph)

The minimum viable measurement

Spreadsheet + 2 hours = better than 95% of competitors.

40-80 prompt bank across all intent types

Run manually in ChatGPT, Claude, Gemini, Perplexity, Grok

Log citations, competitors, sources

Re-run monthly

The 80/20

20% = owned content optimization

80% = external authority foundation

Most brands invert this. Don't.

The five channels

Wikipedia + Wikidata

Tier 1 publications

Industry trade publications

Reddit

YouTube with transcripts

The lift

Typical 90-day program: 2-4x baseline citations. Top performers hit 5x+.

The mistakes

Measuring through branded search (lagging indicator)

Treating it as SEO 2.0 (different methodology)

Chasing low-authority mentions (AI downweights)

Skipping entity foundation (Wikidata is free)

The pull-quote

AI visibility is the new Google ranking.

Brands that measure it can improve it. Brands that don't are invisible in the conversations that decide their quarter.

Want the full framework? Read the full AI visibility strategy.