Writing Winning LinkedIn Content, Without AI

Writing Winning LinkedIn Content, Without AI ![rw-book-cover](https://i.ytimg.com/vi/rj3uH-dzLcg/sddefault.jpg) 🔗 **Source:** [Writing Winning LinkedIn Content, Without AI](https://www.youtube.com/watch?v=rj3uH-dzLcg) > [!note]+ Document Notes > tags: [linkedin-strategy, content-marketing, personal-branding, writing-craft, ai-assisted-writing] > author: Kasey Brown & Michael (Social Media Examiner host) > source: Social Media Examiner > reference: https://www.youtube.com/watch?v=rj3uH-dzLcg > date created: 2026-07-16 12:20 > > # 🏷️ Title: Writing Winning LinkedIn Content, Without AI >  ## 🧭 Table of Contents - [📝 Summary](#-summary) - [📖 Main Content](#-main-content) - [🗒️ Notes](#️-notes) - [📚 References](#-references) - [🗣️ Paraphrase](#️-paraphrase) - [📰 Newsletter](#-newsletter) - [✅ Action Items](#-action-items) - [🧪 Hypotheses](#-hypotheses) - [📊 Methodology](#-methodology) - [❓ Questions](#-questions) - [🔮 Future Research](#-future-research) - [💡 Implications](#-implications) - [🧠 Learning Styles Assessment](#-learning-styles-assessment) - [📊 Accessibility Assessment](#-accessibility-assessment) - [🌍 DEIB Assessment](#-deib-assessment) - [✅ Logical Validity](#-logical-validity) - [🛡️ DARVO Analysis](#-darvo-analysis) - [🎯 Conclusion](#-conclusion) ## 📝 Summary - LinkedIn growth strategist Kasey Brown (Digital Kairos, Brick by Brick cohort) walks Social Media Examiner host Michael through a "Stop, Stay, Share" framework for writing LinkedIn posts that grow a business without relying on AI to generate the content itself. - It matters because LinkedIn is described as a "greenfield opportunity" — only ~1% of users create content while the rest consume or critique, and most creators still write for themselves ("did I sound smart") instead of for the reader. - Key stakeholders: solo creators, coaches/consultants/speakers building authority (Brown's stated ICP), ghostwriters and agencies serving them, and platforms (LinkedIn, Hemingway App, Authored Up, Claude) used in the workflow. - Risks and tradeoffs: the framework leans on manufactured "pattern interrupt" photos (candid selfies over polished headshots) and manufactured narrative tension (withholding the story's resolution), which is persuasion-craft that borders on manipulation if applied without genuine substance behind it. Several claims (guaranteed "winning," a single-post lead-count comparison) rest on anecdote rather than controlled data. - Bottom line: written LinkedIn growth is treated as a repeatable craft — audience definition (ICP), pattern-interrupt visuals, curiosity-driving hooks, rhythm-varied pacing, mobile-first editing, and a share-optimized ending — with AI used only as an editing "sparring partner," never as the drafting engine. ## 📖 Main Content - The episode is a two-person interview (not a monologue): Michael (host) draws out Kasey Brown's applied framework through structured follow-up questions, and Brown answers with a named three-stage model: **Stop → Stay → Share**. - Brown's core operating belief is that content strategy is applied psychology: "humans are wired to be selfish," so successful creators must invert that by making at least 90% of content serve the reader's needs rather than the creator's ego or sales pitch. - **Stop** (get the scroll to halt): controlled almost entirely by the attached photo (vertical/portrait orientation outperforms landscape because it occupies more feed real estate; "pattern interrupt" images — candid selfies via the phone's wide-angle back camera, or screenshot-style infographics — outperform polished professional photography) and the first two lines of text, which should trigger 2–3 of Brown's "Five C's": curiosity, contrast, contradiction, controversy, confusion. - **Stay** (earn continued reading after the "See More" click): a third line must "confirm the click" by adding more narrative tension rather than resolving the story early; posts should vary sentence-length rhythm (mixing one-liners with 3–4-sentence blocks) rather than using AI's default of breaking every sentence onto its own line, which Brown says has become a visible "AI wrote this" tell; target reading level is fourth-to-seventh grade (checked via the Hemingway App, not AI); drafts should be previewed on the actual LinkedIn mobile app before publishing, since ~70% of consumption is on mobile and desktop-length paragraphs read as oversized blocks on a phone. - **Share** (the hardest and, per Brown, most meaningful metric): optimize for the shares-to-impressions ratio rather than for views, likes, or comments, because a share signals the reader found the content valuable enough to redistribute it. Brown's benchmarks: ~0.3% shares/impressions is solid; ~0.5%+ warrants reposting the identical text and photo every 60–90 days; near-zero means discard the post. The mechanism for earning shares is ending every post with a concrete, actionable takeaway (a "goodie bag" the reader can act on or pass along), not just a well-told story. - AI's explicit, bounded role: Brown feeds a rough first draft to Claude with two specific prompts — remove filler/sentences that don't move the story forward, and grade the post's likely "stay" rate out of 10 as if the reader were commuting and half-attentive. She frames AI as "a sparring partner, a thought partner, not the thought leader" — an editing/critique tool, not a generator of the underlying story or ideas. - Uncertainties/contested claims: the framework's evidentiary base is Brown's own agency case studies and anecdote (a single stage-selfie post reportedly generating 3,000 leads vs. 200 for a professional photo of the same event), not controlled testing; the 0.3%/0.5% share-ratio benchmarks are presented as her internal heuristics with no cited external validation; and the claim that consistent application guarantees "winning" is not falsifiable as stated. ### 🗒️ Notes #### ⏱️ [00:00:00](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=0) Cold open — LinkedIn's "1%" opportunity and the AI-slop misconception - Frames LinkedIn as under-supplied with creators despite high consumption: "Only 1% of people actually even create content on the platform." - Names the two competing misconceptions the episode will rebut: (1) LinkedIn is all AI slop and DM sales pitches; (2) if it is that low-effort, winning there must be easy. - Establishes the selfish-brain thesis as the throughline for the whole framework. #### ⏱️ [00:01:05](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=65) Guest introduction — Kasey Brown, Digital Kairos - Brown: Northwestern economics grad → management consulting → full-time agency (Digital Kairos) since 2021, originally built on short-form video (TikTok/Instagram) before pivoting focus to LinkedIn. - States her rationale for switching platforms explicitly: "my whole idea was if we can go on this platform and at least create mediocre content, we probably can win on this platform" — an arbitrage/supply-demand argument, not a claim that the content itself is easy to make well. - Credits short-form video training for forcing her team to "become better creators" — the LinkedIn framework is described as ported over from video-editing discipline, not built for text first. #### ⏱️ [00:09:56](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=596) Foundation — ICP and the 90-day transformation test - Before any framework tactics: define the Ideal Client/Customer Profile (ICP) and the transformation the content should produce, or the framework "doesn't even make sense." - Practical test she gives clients: "if somebody just followed you for 90 days, what transformation would you hope they had?" - Her own agency's ICP evolved through six iterations (V1: "help people build brand online" → V6: speakers/coaches/consultants already earning 20K/month who want systemized organic-content scaling) — illustrates ICP as a living, quarterly-revisited document, not a one-time exercise. #### ⏱️ [00:14:36](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=876) The psychology thesis — selfish brains and the 90% rule - Central claim: "Humans, we are designed and wired to be selfish, right? The selfish brain served us at one point." Content strategy follows from correcting for this default. - Diagnoses the common failure mode: "Most creators are clicking publish or post and only thinking about them. Did I sound smart here? Did I share enough client testimonials here?" - Prescribes the fix: at least 90% of content must be built around what the reader gets, not what the creator wants to project. #### ⏱️ [00:14:56](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=896) Stop — photo mechanics and the pattern-interrupt selfie - The two levers for stopping the scroll: the attached photo, and the first two lines of text. - Tactical detail: vertical (9:16) photos outperform landscape because LinkedIn doesn't crop them, so they occupy more vertical feed space. - Anecdote: a phone-selfie (shot with the back camera at 0.5x/wide-angle from a conference stage) generated roughly 3,000 lead-magnet comments, versus ~200 for a polished professional photo of the same talk — used to argue "we don't have to be better, we have to be different." - Infographic guidance: screenshots that resemble a leaked Google Doc or SOP outperform generic "Framework for X" title-only graphics; the graphic itself must be "packed with value," not just a teaser. #### ⏱️ [00:23:55](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=1435) Stop — the Five C's and the rewritten Bali post - Names the five emotional levers a hook should trigger at least 2–3 of: curiosity, contrast, contradiction, controversy, confusion. - Worked example: a generic "on vacation in Bali, having a great summer" caption is rewritten as "2021, I was at a job I hated. 2026, I just spent 30K to come here…" — same photo, restructured text — to manufacture contradiction and curiosity and force the "See More" click. #### ⏱️ [00:27:09](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=1629) Stay — the third-line re-hook ("confirm the click") - After "See More," the third line must add tension rather than resolve the story, borrowing the "first 60 seconds of a YouTube video" logic of confirming the viewer's decision to keep watching. - Contrasts a weak version (immediately explaining the Bali trip) against a stronger version that delays payoff with vulnerability ("I never thought I could actually leave my business… it felt like everything was going to fall apart. But somehow, I made it."). - Brown's framing device: "I always tell our folks that we work with, We don't have to be better, we have to be different" — reused here as the underlying design principle, and she separately compares good content to a personalized greeting card that "means something to you because of who it's from." #### ⏱️ [00:31:03](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=1863) Stay — AI as editing partner, rhythm, and reading level - States AI's role explicitly and narrowly: "I think of AI as like a sparring partner, a thought partner, not the thought leader." Two specific prompts given to Claude: strip filler/sentences that don't advance the story, and grade the "stay" likelihood out of 10 for a distracted mobile reader. - Flags an emerging tell: heavy line-breaking after every sentence now reads as "AI wrote this," so she deliberately varies rhythm — mixing one-liners with 3–4-sentence blocks. - Reading-level target: fourth-to-seventh grade, checked with the Hemingway App rather than AI ("I've had more success, for some reason, with Hemingway doing it"). - Mobile-first editing ritual: draft on desktop, paste as a LinkedIn draft on the phone, and re-scan — desktop-short sentences can render as oversized blocks on mobile, where ~70% of the audience reads. #### ⏱️ [00:39:26](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=2366) Format and length — the Adam Grant contrast, and overflow-to-comments - Argues creators should find one format/length and become "synonymous" with it, citing Adam Grant's ~5M-follower LinkedIn presence built mostly on short, tweet-style graphics (as few as 5–12 sentences) versus her own long, data-heavy posts. - Practical constraint: LinkedIn's character limit (~3,000 characters); her workaround for longer pieces is continuing the story in the comments section, which she says increases "stay" time further because users are already habituated to checking comments. #### ⏱️ [00:40:39](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=2439) Share — the shares-to-impressions ratio and the repost rule - Defines the metric: shares (LinkedIn "reposts") divided by impressions, explicitly chosen over views/likes/comments as the best proxy for impact rather than entertainment value. - Gives numeric benchmarks: ~0.3% is solid, ~0.5%+ is "great" and triggers a rule of reposting the identical post every 60–90 days; near-zero (".0, .02") means retire the post. - Mechanism for earning shares: end with a concrete, transferable takeaway ("here's how you can go do it") rather than just a resolved story — likened to "a goodie bag when you leave a party." - Explicit causal claim offered without evidence: "I believe income follows impact." #### ⏱️ [00:48:04](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=2884) Close — where to find Kasey Brown - Directs the audience to LinkedIn (as "Kasey Brown") and YouTube for more depth, framing YouTube as her lowest-pressure, most detailed channel; agency is Digital Kairos, cohort program Brick by Brick. ## 📚 References - [Social Media Examiner](https://www.socialmediaexaminer.com) — publisher/channel - Digital Kairos — Kasey Brown's agency, referenced by name in the episode - Hemingway App (hemingwayapp.com) — readability-grading tool named explicitly in the episode - Authored Up — LinkedIn post-preview tool named explicitly in the episode - Claude (Anthropic) — AI tool Brown names as her editing "sparring partner" - Adam Grant's LinkedIn profile — cited as a worked example of a tweet-style short-post format at scale ## 🗣️ Paraphrase - Paragraph A: Brown treats LinkedIn the way a video creator treats a scroll-heavy feed — the job isn't to inform first, it's to interrupt a scroll, then earn a "See More" click, then earn a full read, in that order, with each stage measured and iterated on separately. Human attention defaults to self-interest, so a post's first job is to promise something for the reader, delivered through a jarring, un-corporate photo and two lines of text engineered to produce curiosity, contradiction, or confusion. - Paragraph B: Once a reader is committed, the post has to keep proving its worth line by line — resisting the urge to resolve the story too fast, varying sentence rhythm so it doesn't read as AI-generated, writing at a grade-school reading level, and previewing everything on an actual phone. The final and hardest goal isn't likes or views but shares, measured as a ratio against impressions, because a share means the content did something useful enough for someone to pass along — and that ratio, not follower count, is what Brown reposts against every two to three months. ## 📰 Newsletter - If you write for LinkedIn (or any feed-based platform) and keep hearing "just use AI," this episode is a useful counter-argument: Kasey Brown's Stop-Stay-Share framework treats AI strictly as an editor ("a sparring partner, not the thought leader") and puts the actual craft — audience definition, pattern-interrupt visuals, narrative pacing, and a share-optimized ending — back on the human writer. - Handy, low-effort tools she names by brand: the free Hemingway App for reading-grade checks, and Authored Up for previewing how a LinkedIn draft actually renders on mobile before you post it. - Her headline metric is worth borrowing regardless of platform: shares ÷ impressions, not likes or views, as the signal that content was actually useful to someone. ## ✅ Action Items #### DevRel - Developer marketing: the Stop-Stay-Share structure (pattern-interrupt visual → curiosity hook → re-hook → concrete, shareable takeaway) is directly reusable for LinkedIn posts promoting talks, docs, or releases. - Education / community management / internal-external alignment: not applicable to this source — no developer-advocacy, workshop, or moderation content in this episode. #### OSPO - Not applicable to this source — the episode covers consumer/B2B content marketing, not open-source policy, licensing, or governance. #### Community - Community engagement: the "confirm the click" re-hook and reading-level/rhythm guidance are applicable to community newsletter or announcement copy meant to be skimmed on mobile. - Maintainers / contributors / sponsors / ecosystem: not applicable to this source. #### WordPress - Marketing: the shares-to-impressions ratio and the "repost winners every 60–90 days" rule are portable to a WordPress-project social calendar. - All other subteams: not applicable to this source — no WordPress-specific content in this episode. #### The WPCC - Not applicable to this source — no fellowship, funding, or governance content in this episode. ### FAIR - Not applicable to this source — no provenance, funding-transparency, or governance-artifact content in this episode. ## 🧪 Hypotheses - H1: Posts whose photo is vertical (9:16) rather than landscape achieve measurably higher stop rates (impressions-per-follower or scroll-stop rate), controlling for topic and posting time. - H2: Posts using a "candid/pattern-interrupt" image (phone selfie, screenshot-style graphic) outperform posts using polished professional photography on the same topic, measured by comment/lead-magnet conversion. - H3: Posts that delay narrative resolution past the third line (re-hook present) show higher average read-time or scroll-depth than posts that resolve the hook immediately. - H4: Posts written at a 4th–7th-grade Hemingway reading level achieve a higher shares-to-impressions ratio than posts above 8th-grade level, controlling for topic and length. - H5: Posts ending with an explicit, transferable takeaway ("here's how you do it") have a higher shares-to-impressions ratio than posts that end on story resolution alone. - H6: Reposting an identical post (text + photo) that scored ≥0.5% shares/impressions on first run will reproduce a comparable ratio on a second run 60–90 days later, net of audience-growth effects. ### 📊 Methodology - A/B or multivariate test of photo orientation and image type (candid vs. polished) across matched post pairs on the same account, tracking impressions, stop-rate proxies (dwell time if available), and shares/impressions. - Reading-level regression: sample a creator's post history, score each with Hemingway/Flesch-Kincaid, and regress against shares/impressions and comment count, controlling for length and topic. - Repost-decay study: track shares/impressions for the same reposted content across multiple 60–90-day cycles to test whether the ratio holds, decays, or requires audience-overlap adjustment. - Ending-type classification: hand-code a sample of high- and low-performing posts for "explicit takeaway present/absent" and test association with shares/impressions using a chi-square or logistic model. ### ❓ Questions - Does the shares-to-impressions ratio hold as a genuine "impact" proxy outside LinkedIn (e.g., X, Instagram), where resharing carries different social cost? - How much of the reported lead-count gap between the selfie and the professional photo is attributable to the image itself versus novelty/timing/algorithm variance on that specific post? - At what account size does the "0.3% is solid / 0.5%+ is great" heuristic stop applying — does the benchmark scale with follower count or impression volume? - Does deliberately varying sentence rhythm to avoid an "AI wrote this" signal actually change reader trust, or only pattern-match to what currently reads as "AI"? - What is the actual incremental lift from AI-assisted line-editing (filler removal, "stay" grading) versus a skilled human editor doing the same pass? ### 🔮 Future Research - Independent, cross-account replication of the 0.3%/0.5% shares-to-impressions benchmarks — currently sourced from one agency's internal client data. - Controlled comparison of AI-only-edited vs. human-only-edited vs. hybrid-edited posts on identical source material, scored blind on the "stay" and "share" metrics Brown defines. - Longitudinal tracking of whether the "candid pattern-interrupt" photo advantage persists as more creators adopt it (novelty-decay risk, since the tactic's power depends partly on standing out against still-common polished headshots). ### 💡 Implications - Practice: writers and marketers get a concrete, three-stage editing checklist (Stop/Stay/Share) that separates "get attention" tactics from "earn attention" tactics from "prove value" tactics, rather than treating a post as one undifferentiated block of copy. - Practice: using AI strictly as a critique/editing layer (not a drafting engine) is presented as a way to avoid both AI-detectable rhythm patterns and the "AI slop" reputation the episode opens by naming as a LinkedIn misconception. - Education: the ICP-and-transformation exercise ("if someone followed you for 90 days...") is a reusable, low-cost audience-definition tool for any content-training curriculum. - Funding/business models: Brown's framing that "income follows impact" (via shares) is an unverified causal claim worth flagging before it's repeated as settled fact in downstream content strategy. ### 🧠 Learning Styles Assessment - Reading level estimate: conversational interview transcript; informal syntax, contractions, and short clauses throughout. Estimated Flesch-Kincaid grade ≈ 7–8 (consistent with Brown's own stated target for LinkedIn writing, applied reflexively to her own speech). #### 👀 Visual learners - Present: none — this is an audio/video interview with no on-screen diagrams, charts, or labeled visuals in the transcript itself (the source video may show screen-shares of example LinkedIn posts, not captured in captions). - Improve: add annotated before/after screenshots of the Bali post rewrite and the selfie-vs-professional-photo comparison; a labeled diagram of the Stop-Stay-Share funnel. #### 🔊 Aural learners - Present: the source is itself a podcast/interview audio track — natively well-suited to this learning style. - Improve: add a 2–3 minute audio-only recap isolating the three-stage framework definition for quick re-listening. #### 🗣️ Verbal learners - Present: strong analogy use throughout (selfish brain, greeting card, goodie bag) that aids verbal/conceptual learners. - Improve: add a short glossary defining "ICP," "pattern interrupt," "the Five C's," and "shares-to-impressions ratio" for readers new to the jargon. #### 🧩 Kinesthetic learners - Present: the episode gives concrete, repeatable steps (take a 0.5x back-camera selfie, run text through Hemingway, paste as a mobile draft to preview). - Improve: package these as a literal checklist or template the reader can apply post-by-post. #### 🧠 Logical learners - Present: the shares/impressions formula and the 0.3%/0.5% benchmarks give logical learners a testable, numeric structure to hold onto. - Improve: a decision-tree ("if ratio < 0.1%, do X; if 0.3–0.5%, do Y; if >0.5%, repost") would make the implicit logic explicit. #### 🧑‍🤝‍🧑 Social learners - Present: the interview format itself models the back-and-forth Q&A structure social learners respond to. - Improve: a discussion prompt inviting readers to test the Stop-Stay-Share framework on their own recent posts and compare notes. #### 🧘‍♀️ Solitary learners - Present: none explicit. - Improve: add a self-audit worksheet — "score your last 5 posts against the Five C's and the shares/impressions ratio." #### 🌿 Naturalistic learners - Present: not applicable to this source — no ecosystem, sustainability, or environmental content. - Improve: not applicable. ### 📊 Accessibility Assessment #### Screen reader semantics - Present: source is a YouTube video with auto-generated/manual captions available, providing a text alternative. - Improve: a structured written companion post (headings for Stop/Stay/Share) would give screen-reader users faster navigation than scrubbing video/caption text. #### Keyboard navigation - Not applicable to this source — video-platform playback controls are YouTube's responsibility, not the content itself. #### Contrast and readability - Not applicable to this source — no on-screen text/UI to assess from the transcript; would require reviewing the actual video visuals (e.g., graphic overlays), which were not captured in captions. #### Text resizing and reflow - Not applicable to this source — video medium, not reflowable text. #### Media and captions - Present: manual English captions exist for this video (used as the source transcript for this analysis). - Improve: a downloadable full transcript (beyond auto-captions) and chapter markers at the Stop/Stay/Share transitions would aid navigation and comprehension. #### Forms and interactions - Not applicable to this source — no forms or interactive elements in the video itself. #### Standards reference - Gaps map to WCAG 2.2 AA §1.2.2 (Captions – present, satisfied) and §2.4.10 (Section Headings – absent; no chapter markers or structured navigation within the raw video). ### 🌍 DEIB Assessment #### Cultural breadth - Present: single-region (US-centric) framing throughout; examples (Bali vacation, Northwestern University, LinkedIn/TikTok market dynamics) assume a US/Western professional-services audience. - Improve: examples drawn from non-US LinkedIn markets or non-English-speaking creator communities would broaden applicability. #### Inclusive language - Present: gender-neutral throughout ("creators," "folks," "somebody"); no exclusionary language identified. - Improve: define jargon on first use ("ICP," "pattern interrupt") for readers outside marketing/growth circles. #### Neurodiversity support - Present: content is chunked into a clear three-part framework (Stop/Stay/Share) with consistent labeling, which aids predictability. - Improve: the interview format occasionally digresses (tangents into personal anecdotes) before returning to the throughline; a structured written recap would help readers who need consistent, linear structure. #### Representation - Present: the featured expert is a woman-led agency founder in a growth-marketing space; guest and host both speak from personal entrepreneurial experience. - Improve: the worked examples (Adam Grant, the Bali post) are all drawn from a narrow demographic (US business/thought-leadership creators); broader representation in examples would strengthen applicability. #### Psychological safety - Present: no content warnings needed; subject matter is professional/business, non-sensitive. - Improve: not applicable — no safety concerns identified. ### ✅ Logical Validity #### Strong reasoning - Inductive: the shares-to-impressions framework is grounded in a stated, falsifiable numeric structure (ratio benchmarks, repost trigger) rather than vague "engagement is good" advice — strong because it's operationalizable and testable by any reader with access to their own analytics. - Causal/mechanistic: the "confirm the click" re-hook logic (line three must add tension, not resolve) is explained with a plausible mechanism — readers decide to continue based on continued signal of payoff, mirroring documented short-video retention patterns (YouTube's "first 60 seconds" convention cited directly) — strengthening the analogy's credibility. #### Fallacies - **Hasty generalization / small-sample induction** — Location: ~00:19:41–00:20:07 ("We got 3,000 leads from that post... We got 200 leads" from the professional-photo repost). A single paired comparison (n=2 posts) is used to support a general claim about photo style. Fix: cite an aggregated sample across many posts/accounts, or explicitly flag it as an anecdote rather than a rule. - **Unfalsifiable claim** — Location: ~00:08:35–00:09:02 ("I do not see a world where you take what we're about to go through... and you don't win on LinkedIn... I do not believe that you can listen to this and be consistent on the platform and not win"). No defined failure condition or metric makes this claim testable; "win" is undefined, so no counterexample could disconfirm it. Fix: state a specific, measurable outcome and timeframe ("X% of clients who post consistently for 90 days see Y"). - **Correlation presented as causation** — Location: ~00:44:09 ("I believe income follows impact"). Shares/impressions (impact) and income are asserted to be causally linked with no data connecting the two metrics in the episode. Fix: label explicitly as a personal belief/heuristic, or cite revenue data tied to the shares metric. #### Tone of voice - Confident, coach-like, and enthusiastic throughout ("I love it," "let's dive in"), with frequent hedging softeners from the host ("I would imagine," "I'm curious") that keep the tone collaborative rather than adversarial — consistent with a promotional interview format on a marketing-education channel. ### 🛡️ DARVO Analysis - Not applicable to this source — this is a collaborative expert interview with no allegation, accusation, or conflict present; there is no denial, attack, or victim/offender reversal dynamic to analyze. ## 🎯 Conclusion - Bottom line: Kasey Brown's Stop-Stay-Share framework reframes LinkedIn growth as a three-stage attention economy — win the scroll-stop with a pattern-interrupt photo and a Five-C's hook, win the "See More" click with re-hooks and mobile-scannable rhythm, and win the share by ending on a transferable takeaway — with AI deliberately confined to an editing/critique role rather than idea generation. - Practical next steps for readers: audit five recent posts against the Five C's and the shares-to-impressions ratio; test a candid, wide-angle phone photo against a polished one on matched topics; run drafts through a reading-level checker before publishing; and treat the 0.3%/0.5% benchmarks as a starting heuristic to validate against your own account's data, not a universal law. ## 🔄 Refactor Appendix — Writing Winning LinkedIn Content, Without AI ### 📚 Literature Atoms (H3) #### AI edits LinkedIn drafts, never writes them > [!quote] ID:rj3uH-dzLcg_01 > I think of AI as like a sparring partner, a thought partner, not the thought leader. **Context & use**: - [00:31:03](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=1863) - Directly counters "AI slop" criticism of LinkedIn by naming AI's role as bounded editing (filler removal, "stay" grading), not drafting — reusable framing for Courtney's own AI-assisted content workflows. **Tags**: #xref #aphorisms **Highlight**: [view highlight](readwise://)  #### Vertical pattern-interrupt photos beat polished headshots > [!quote] ID:rj3uH-dzLcg_02 > We don't have to be better, we have to be different. **Context & use**: - [00:14:56](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=896) - Anchors the photo-selection tactic (candid selfie via 0.5x back camera, vertical orientation) in a broader differentiation-over-quality principle applicable beyond LinkedIn photos. **Tags**: #xref #aphorisms **Highlight**: [view highlight](readwise://)  #### Shares-to-impressions ratio measures content impact > [!quote] ID:rj3uH-dzLcg_03 > if somebody's reposting my work or sharing even an Instagram video with somebody, something in that piece of content must have been impactful, right, in some way. **Context & use**: - [00:40:39](https://www.youtube.com/watch?v=rj3uH-dzLcg&t=2439) - Defines the episode's core measurement heuristic (0.3% solid, 0.5%+ triggers a 60–90-day repost) — a portable metric for evaluating any platform's content performance, not just LinkedIn. **Tags**: #xref **Highlight**: [view highlight](readwise://)  ### 🧠 Permanent Candidates (H4) #### [P1] AI as editor, not author, preserves a writer's voice signal **Claim** (one sentence, Courtney's voice): Reserving AI strictly for critique and trimming — never first-draft generation — is what keeps prose from acquiring the rhythmic tells (uniform one-line breaks, over-smoothed cadence) that readers now pattern-match as "AI wrote this." **Evidence pointer**: [[#AI edits LinkedIn drafts, never writes them]] **Scope & conditions**: Holds for platforms where audiences actively scan for AI-generated content (LinkedIn, per this episode) and where voice/authenticity is part of the value proposition; may not hold for purely informational or reference content where voice is irrelevant. **Links** (≥1 with annotation): - [[AI-assisted writing workflow]] — because applies-in Courtney's own content pipeline (Readwise deep-reads, LinkedIn/blog drafting)  ### ✏️ Fleeting Queue - [F1] Test the "vertical pattern-interrupt photo vs. polished photo" claim against Courtney's own LinkedIn post history before adopting it as a rule — currently sourced from a single anecdote (n=2) in this episode. #xref - [F2] Cross-reference Brown's fourth-to-seventh-grade Hemingway reading-level target against Courtney's existing writing-style notes for consistency. #xref ### → Things 3 (max 2 actions) - [ ] Run 3–5 recent LinkedIn drafts through Hemingway App and check against the 4th–7th grade target from this episode — content-strategy #things  📄 **Full text:** [[Full Document Contents/Articles/Writing Winning LinkedIn Content, Without AI.md|Writing Winning LinkedIn Content, Without AI by Kasey Brown & Michael (Social Media Examiner host)]] ## Highlights - have to be better, we have to be different." And so there it is, some little tweaks that we can make to just stand out. Like, okay, I'll use this kind of crappy selfie to promote this speech that we just did, and we get a couple thousand leads off of that. You also mentioned something unusual, which is the idea of creating some sort of a infographic, for lack of better words, or a chart or something like that. I would imagine this is where AI can come in really handy for this kind of stuff. Is this, or Canva or something like that? Is that the kind of stuff you're recommending? ([View Highlight](https://read.readwise.io/read/01kxnv8my5nk1n3mjr9f25vdqj)) - Note: "We don't have to be better, we have to be different" — the differentiation-over-quality principle behind the pattern-interrupt photo tactic. - Tags: aphorisms, atom/literature/h2 - can consume the whole thing. This is where I will leverage AI as a tool to kind of help me here. I think of AI as like a sparring partner, a thought partner, not the thought leader. And so this is where I take my first draft, and I'll throw it into AI and say, "Hey, remove any words or sentences that don't move this story forward." Because I want to keep the person's attention, right? I want to earn their attention with each line. And so, just a little keyword there, remove any fillers or ([View Highlight](https://read.readwise.io/read/01kxnv8fwzsw9cnykkdeqqsxwq)) - Note: AI edits LinkedIn drafts, never writes them — bounded editor role, not idea generator. - Tags: aphorisms, atom/literature/h1 - On socials, I like to measure it through shares. Because if somebody is sharing, or on LinkedIn we call it a repost. That just didn't fit in my framework. Stop, stay, repost, so stop, stay, share. But if somebody's reposting my work or sharing even an Instagram video with somebody, something in that piece of content must have been impactful, right, in some way. If we're not on the entertainment side of the house, it must have been pretty impactful piece of content. And so what we like to analyze, ([View Highlight](https://read.readwise.io/read/01kxnv8qx7g0nc7k8b7kn7qmpz)) - Note: Shares-to-impressions ratio as the core content-impact measurement heuristic. - Tags: atom/literature/h3