Clwyd Probert writes about digital marketing for small businesses, with a focus on improving visibility in AI-powered search and building communities around a brand.
Clwyd Probert writes about digital marketing for small businesses, especially Answer Engine Optimisation (AEO), AI-powered discovery and community-led marketing.
Across these posts, they argue that businesses should make their expertise easy for AI systems to find and use, while keeping strong search foundations.
The work also connects visibility to a broader strategy: name a useful methodology, share it openly and build a community around it.
Follow them for a mix of practical AEO advice and arguments about how smaller businesses can compete as discovery changes.
Probert pairs a clear case for adapting to AI-driven search with actionable frameworks.
The five-step AEO post moves from allowing AI discovery through robots.txt to Q&A content, strong answers, structured data and continuing SEO fundamentals.
Other posts add context and a counterweight: one frames AEO as a potential opening for smaller businesses, while another argues that community and shared ideas can build demand beyond product promotion.
These are concise, persuasive LinkedIn posts; ask for evidence of how the recommendations perform across industries if that matters to your decision.
Clwyd Probert writes about how small businesses can stay visible as search moves from ranked links to AI-written answers.
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Across the four posts, the through-line is a shift in what earns attention: instead of backlinks and domain authority, they argue, the deciding factor becomes whether your answer is the best available one.
From that premise come a tactical AEO playbook, statistics used to make the change feel urgent, and a wider argument that category creation and community — not product features or price — build durable demand.
Quoted figures and company examples (Google click-through rates, HubSpot's Inbound Marketing, Whitehat's London HubSpot User Group) support the argument rather than represent Probert's own research findings.
Optimise for answers, not links
Probert's central argument is that AI answer engines change the contest.
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In traditional search, clicks thin out fast down the page, and Google's rankings rewarded established players with backlinks and authority.
AI systems such as ChatGPT, Claude, and Perplexity surface only two or three mentions instead, with no page two.
Probert treats that as a leveller: AI discovery prioritises the quality and relevance of an answer over accumulated authority, opening room for smaller businesses.
The practical instruction is to optimise for answers rather than links.
Probert contrasts the two systems directly: click-through rates run from 28.5% at position 1 down to 2.5% at position 10 in Google-style results, while an AI answer offers two or three mentions and no second chance. They cite Hampton as a business that acquired customers through ChatGPT queries about leadership challenges — traffic they say never existed in traditional search.
Probert's method is a named sequence of tactics rather than a general call to 'do AEO'.
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Enable AI discovery by fixing robots.txt so AI crawlers can reach the content; reformat content into question-and-answer style, which AI systems extract; apply a 'deserve to rank' test — if the answer is not the best on the subject, it will not appear; add structured data for pricing, reviews, and inventory; and keep SEO foundations intact because AI systems still lean on search engines. Probert presents the playbook as grounded in Whitehat's analysis of more than 100 campaigns and warns that those who execute will win visibility while others disappear.
The most concrete tactic is the robots.txt fix: Probert states that 73% of small businesses miss this step, making it a cheap first move before any content rework. The post closes by asking readers which of the five steps they have already implemented, turning the framework into a self-audit.
A recurring habit across the posts is anchoring the argument in a few striking numbers repeated from piece to piece: falling click-through rates, a 20–40% drop in organic search traffic across industries, more than 800 million people using AI platforms weekly, and the 73% robots.txt gap.
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Probert uses these figures to argue that the shift is already underway rather than looming, and that delay carries a cost. Verify the statistics with Probert before repeating them; the posts do not name their sources.
The 2006 comparison works the same way: Probert recalls Dharmesh Shah's personal blog outperforming venture-funded companies and laying the foundations for HubSpot's £25B empire, then argues the same David-versus-Goliath opening exists now for early AEO adopters.
Community and category creation as the growth engine
The fourth post widens the frame beyond search.
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Probert argues community is the new competitive advantage, and that HubSpot did not build a £25B company by pushing software but by starting a movement — Inbound Marketing — which it never trademarked.
The framework has four moves: identify an inefficient market where something should happen but does not; codify a methodology with a name, principles, and frameworks; open-source the thinking, since sharing earns trust; and foster community rather than only customers.
This sits alongside the AEO advice as the demand-side complement to the visibility work.
Probert points to Whitehat's London HubSpot User Group as driving more qualified leads than paid ads, and closes by asking readers whether they are building their own movement. Ask for lead volume and conversion data before using this as a performance benchmark.
Although the headlines push toward answer engines, Probert does not declare search dead.
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Keeping SEO foundations is one of the five playbook steps, and a separate post is titled around the idea that SEO is evolving.
The position is that AI visibility depends on existing search infrastructure — crawlers, structured data, ranking signals — while adding answer-shaped content and testing on top of it.
That framing matters for anyone deciding whether AEO work is a replacement programme or an extension of existing SEO effort.
The evolution framing pairs the traffic-decline statistics with the 2006 blog analogy, treating the current moment as another turn in a long-running pattern of discovery shifting rather than a clean break with the past.
Answers the newcomer’s question: why should a business care about answer engines rather than Google rankings alone?
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Start here for the core shift and a concrete customer-acquisition example.
Clwyd Probert contrasts traditional search with AI answers.
In Google-style results, click-through rate falls from 28.5% at position 1 to 2.5% at position 10.
AI systems such as ChatGPT, Claude, and Perplexity give two to three mentions, with no page 2.
They argue that this can favour smaller businesses because AI discovery rewards the quality and relevance of an answer more than backlink authority.
For example, Hampton acquired customers through ChatGPT queries about leadership challenges, traffic that did not exist in traditional search.
For brands planning a campaign
For a B2B SaaS AI-visibility brief, use this framing to explain why AEO changes discovery. Test answer-first content and track AI-referred traffic, not just rankings.
Answers the practical question: what concrete steps improve AI visibility?
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Start here for a tactical framework to test against a site and campaign data.
Probert lays out five steps for AI visibility, based on Whitehat's analysis of more than 100 campaigns: enable AI discovery by updating robots.txt, which 73% of small businesses miss; reformat content into question-and-answer style; apply "Deserve to Rank," meaning if the answer is not the best, it will not appear; use structured data for pricing, reviews, and inventory; and maintain SEO foundations because AI still relies on search engines. The post presents the shift to Answer Engine Optimisation as real and says those who execute will win visibility while others disappear.
For brands planning a campaign
Supplies an AEO content framework for a B2B SaaS brief. Turn the five steps into an audit checklist and implementation plan, then validate each tactic against the brand’s crawl, content, and structured-data setup.
Answers the strategic question: is SEO dead, and why adapt now?
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Start here for the historical analogy and urgency argument to help win internal buy-in.
Probert notes that organic search traffic has dropped 20-40% across industries and that over 800 million people now use AI platforms weekly to get answers.
The post compares the current shift to 2006, when Dharmesh Shah’s personal blog outperformed venture-funded companies and laid the foundations for HubSpot’s £25B empire.
They argue the same David versus Goliath opportunity exists with Answer Engine Optimisation, and that those who adapt first will lead the next wave of discovery.
For brands planning a campaign
Useful for persuading stakeholders that early AEO adoption can create outsized advantage. Pair the analogy with the brand’s traffic trend data to make the urgency concrete.
Answers the broader question: how does community relate to search visibility and category leadership?
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Start here for the movement-building angle behind AEO and content strategy.
Probert argues that HubSpot did not build a £25B company by pushing software but by building the Inbound Marketing movement, a philosophy it did not trademark.
The framework: identify an inefficient market where something should happen but does not; codify a methodology with a name, principles, and frameworks; open-source the thinking to earn trust; and foster community rather than just customers.
The example is Whitehat’s London HubSpot User Group, which they say drives more qualified leads than paid ads.
For brands planning a campaign
For a B2B SaaS campaign, consider owned community and open-source methodology as distribution, not just product marketing. A HubSpot User Group-style programme or a named framework can create qualified demand alongside AEO content.
Answer engine optimisation for AI search visibility3
Data-driven urgency: benchmarks for AI search adoption3
Framing AI disruption as a David vs. Goliath opportunity2
Community and category creation as a competitive advantage1
Answer engine optimisation for AI search visibility
Across three posts, Clwyd Probert argues that search is shifting from traditional engines to AI answer systems like ChatGPT, Claude and Perplexity.
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They cite declines in organic search traffic (20–40% across industries) and click-through rates (28.5% at position 1 falling to 2.5% at position 10), and say over 800 million people now use AI platforms weekly. The core advice is to optimise for answers, not links.
The five-step AEO playbook: update robots.txt so AI can discover content; reformat content into Q&A style; apply a 'deserve to rank' test to ensure the answer is the best; use structured data for pricing, reviews and inventory; and maintain SEO foundations.
The customer example — Hampton acquiring customers through ChatGPT queries on leadership challenges — and the claim that the playbook is based on Whitehat's analysis of 100+ campaigns are self-reported. Ask for supporting campaign data.
For brands planning a campaign
For a B2B SaaS company seeking AI search visibility, these tactics offer a concrete starting framework. The emphasis on robots.txt, Q&A formatting and structured data can be applied directly to a content audit or brief.
Community and category creation as a competitive advantage
In a post on community strategy, Clwyd Probert argues that community is the new competitive advantage, pointing to HubSpot's Inbound Marketing as a movement that built a £25B company.
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They outline a framework for small businesses: identify an inefficient market, codify a methodology with a name and principles, open-source your thinking, and foster community rather than just customers.
The framework has four steps: identify the inefficient market, codify methodology, open-source thinking and foster community. They cite Whitehat's London HubSpot User Group as an example that drives more qualified leads than paid ads.
The HubSpot and Whitehat examples are illustrations, not independently verified case studies. Ask for lead volume and conversion data.
For brands planning a campaign
For brands considering community-led growth, this post offers a structure for category creation and a rationale for investing in user groups or movements rather than focusing only on product features and price.
Framing AI disruption as a David vs. Goliath opportunity
Clwyd Probert compares the current AI-driven shift to 2006, when Dharmesh Shah's personal blog outperformed venture-funded companies and laid the foundation for HubSpot's £25B empire.
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They argue the same opportunity exists now for small businesses that adopt Answer Engine Optimisation early.
The post uses a historical analogy to make the case that early adopters of AEO can lead the next wave of discovery.
The post uses a historical analogy to make the case that early adopters of AEO can lead the next wave of discovery.
For brands planning a campaign
This framing can support campaign messaging that positions early AEO adoption as a competitive advantage for smaller brands.
Data-driven urgency: benchmarks for AI search adoption
Statistics underpin the argument that the shift to AI search is urgent: click-through rates falling from 28.5% to 2.5%, organic search traffic dropping 20–40%, over 800 million weekly AI users, and 73% of small businesses missing a robots.txt update.
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These figures recur across posts.
Each argument is anchored in a specific benchmark or statistic, often from Whitehat's analysis.
The posts do not provide sources for these statistics; check them independently before reuse.
For brands planning a campaign
The benchmarks are ready-made for a pitch or content brief making the case for AEO investment, but brands should verify the numbers before citing them publicly.
The four LinkedIn posts repeatedly feature two brands: Whitehat, whose campaign data and London HubSpot User Group they cite, and HubSpot, the central case study in the category-building argument.
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Ask how Clwyd Probert is connected to either business, including through ownership, employment, sponsorship, client work, or partnership. Google, ChatGPT, Claude, Perplexity, and Hampton appear only as search platforms or one-off campaign examples, not as relationships.
Whitehat
Mention
Two posts cite Whitehat by name: one builds its five-step answer engine optimisation playbook on Whitehat's analysis of more than 100 campaigns; another names Whitehat's London HubSpot User Group as an example of a community that produces better-qualified leads than paid ads.
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Ask whether Clwyd Probert works for, owns, or is sponsored by Whitehat.
HubSpot appears as a case study rather than a disclosed commercial partner.
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One post recounts Dharmesh Shah's 2006 blog outperforming venture-funded competitors and HubSpot's growth to a £25B company as evidence that a small player can lead a new discovery channel; another treats Inbound Marketing and the Whitehat-run HubSpot User Group as a template for category creation.
The posts use HubSpot's hubs.la link shortener.
Ask whether there is a sponsorship, employment, or client relationship.
Clwyd Probert writes about how businesses can earn visibility as search shifts from ranked links to AI-generated answers.
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Their five-step AEO playbook turns that argument into practical checks: make content accessible, structure it as clear answers, use structured data, and maintain SEO fundamentals.
Ask for the source of the figures in that post before reusing them.
Probert also argues that visibility is not just a technical task: their community and category-creation framework recommends naming a distinctive methodology, sharing ideas openly, and building a community around it.
Read Hadi for conversion and trust arguments, Nisar for hands-on ecommerce optimization, and Marguglio for the connection between AI agents, CRM context, and brand visibility.
Both discuss AI search as a route to customers, not just a new place to rank.
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Hadi explains why AI recommendations might influence buying; Probert offers an AEO playbook for improving visibility. Check Hadi’s numerical claims before reuse.
Hadi emphasizes conversion-rate figures and research on trust in AI recommendations. Probert’s posts focus on preparing small businesses for AI discovery, alongside a separate argument for community and category creation.
Both argue that generic keyword-focused content is not enough: visibility depends on specific, useful answers and clear positioning.
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Nisar applies that idea to page-level ecommerce work; Probert frames it as an AI-search strategy for businesses.
Nisar concentrates on practical search optimization, especially ecommerce product pages, entities, schema, and site structure. Probert takes a broader small-business strategy view and also writes about community and category creation.
Both write about answer-engine optimization and how AI systems may affect research and buying decisions.
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Marguglio extends the discussion to AI agents evaluating companies; Probert’s posts offer more direct advice on visibility and community-led positioning.
Marguglio connects AI agents to CRM data and internal business context, alongside external brand visibility. Probert’s selected posts focus on discovery, content, and demand-building rather than CRM operations.
For a B2B SaaS campaign focused on AI search visibility, Clwyd Probert's LinkedIn audience is a plausible placement.
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The four posts are written for small businesses and marketing leaders trying to adapt to AI-driven search, and the account shows 28,795 LinkedIn followers as of 8 October 2026.
The posts provide no data on audience geography, age, gender, employer, occupation or seniority.
Before booking, request recent performance on comparable AEO topics—reach, engagement and any audience survey results by role or company size.
Clwyd Probert's posts do not document any partnership, sponsorship or paid-collaboration format.
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Across four LinkedIn posts published between 6 and 13 October 2025, they publish educational commentary on answer engine optimisation and community-led marketing.
Three posts end with a shortened link to an off-platform resource; none is a sponsorship or partnership enquiry route.
The posts include no rate card, deliverable list, disclosure of a paid collaboration or contact channel for partnership enquiries.
A brand looking to commission them would need a direct conversation to establish format and terms.
A B2B software or martech company selling to small and midsize businesses, with a campaign to own the conversation about AI search visibility and turn it into demand.
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All four posts focus on that subject.
Probert argues that discovery is moving from ten blue links to two or three AI mentions, that click-through falls from 28.5% at position 1 to 2.5% at position 10, and that this rewards the quality of an answer over accumulated backlink authority.
That is the argument a challenger software brand wants made on its behalf.
The second post offers a usable structure: enable AI discovery by updating robots.txt, reformatting content as questions and answers, applying a “deserve to rank” test, adding structured data for pricing, reviews and inventory, and keeping SEO foundations in place.
A brand can use those five steps as its content brief and have Probert write the explainer posts around them.
The audience section reports 28,795 LinkedIn followers as of 8 October 2026.
The posts are written for small-business owners and marketing leaders weighing an AI-search decision — the same people who shortlist B2B software.
Before booking, verify the benchmarks and Hampton customer example, which are self-reported and drawn from Whitehat's analysis of more than 100 campaigns.
The posts do not report audience composition, engagement or reach; ask for those figures before setting placement expectations.
When to choose a specialist
A consumer brand, or an enterprise vendor that needs a video-first, fully disclosed paid partnership with a rate card, guaranteed reach and measured conversion.
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None of those terms or results is documented in the posts.
Across the four posts, there is no sponsorship disclosure, deliverable list, rate or format specification, or enquiry route for partnership work; three close with a shortened link to an off-platform resource, not a booking channel.
All four are text posts on LinkedIn.
No video, podcast, newsletter, speaking or article formats appear, so a campaign that depends on short-form video or a live appearance has no demonstrated capability behind it.
The content speaks to small businesses and marketing leaders rather than general consumers or enterprise security buyers, and leans on category education rather than product demonstration or direct response.
The audience section does not report geography, seniority, occupation or employer data, so a brand cannot assess fit by role or company size.
Probert could still be right for this campaign, but a direct scoping conversation would need to establish the available offer, format and terms; their posts do not document them.
A worked campaign example
A worked angle for the good-fit brief: a six-week answer-visibility sprint in sponsored content, not a one-off post.
The company picks one category question it wants to own — for example, how a small business should structure pricing and review data so AI assistants surface it — and commissions Probert to write a three-part LinkedIn series adapting the five-step playbook to that question.
Post one makes the case for the shift, using the click-through and traffic-decline figures only after the company verifies them.
Post two walks through the technical steps in order: robots.txt, question-and-answer formatting, structured data, and the SEO foundations that still matter.
Post three applies the “deserve to rank” test to two competing answers, showing what an answer that deserves to appear looks like.
Each post links to one company resource, while the company runs the same five steps on its own site, so the series documents a real change rather than just describing one.
The topic matches what Probert already publishes, the framework is ready to use, and the audience of small-business owners and marketing leaders is the buying group.
Before starting, agree on the format and terms, since no rate or deliverable is documented, and the measurement plan, since no engagement or audience-demographic baseline exists.
Track AI-referred traffic and branded search during the run rather than relying on follower count.
If the verified numbers are weaker than the posts suggest, drop them and let the framework carry the series.
Choose this alternative when the job is to win an internal argument before publishing tactics.
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The two posts aim to persuade skeptical decision-makers with numbers.
One lays out a direct comparison of conversion rates: 1.8% from Google organic search against a claimed 14.2–16.0% from ChatGPT, 10.5–12.4% from Perplexity and 5.0–16.8% from Claude, with industry breakdowns of 18.7% for B2B SaaS and 21.3% for professional services.
It then explains the mechanism in three parts — trust transfer, intent precision, and reduced decision fatigue — and contrasts two-to-three-word Google queries with twelve-word AI queries.
The second post summarises published research that trust in an AI system links its recommendation to an actual purchase, and that AI tools reshape the set of brands a buyer considers.
For a B2B SaaS company whose budget holder still sees AI search as a novelty, this framing does work Probert's posts do not: it gives the buyer a quantified reason to act.
The tradeoff is execution.
Neither post offers a step to carry out.
Probert's posts supply the working framework — robots.txt for AI discovery, question-and-answer content formatting, the “deserve to rank” test, structured data for pricing, reviews and inventory, and the SEO foundations that still matter.
This alternative stops at making the case, so a campaign built on it would need a separate implementation plan.
The figures are also presented without links to their sources; ask for the sources before repeating them.
The persuasion relies on comparisons and asserted percentages rather than close readings of how a specific company solved a specific problem.
A campaign that needs a teachable, step-by-step angle is better served by Probert; this option is stronger when adoption, not knowledge, is the constraint.
Choose this alternative when the campaign is e-commerce or product-page specific, or needs a positioning diagnosis rather than a general AEO playbook.
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One post argues that the useful question is not “how do I optimize for ChatGPT” but “why would an AI recommend me over my competitors,” and makes the case that AI systems reward clear category positioning, credible social proof, specific outcomes and presence across multiple platforms.
It grounds that argument in named examples — merchants winning on detailed product data, forum threads surfacing because real buyers describe real use cases, and a signature-software company owning its category because its terminology is consistent across pages, content, mentions and citations — and cites a projection that LLM-driven traffic will pass traditional organic search by early 2028.
The second post is a product-page optimisation framework covering entity-attribute-value structure, query-intent mapping, schema markup, review and user-generated-content strategy, technical checks, variant handling and AI search readiness.
If the campaign targets a product-catalogue or e-commerce SaaS problem, this material maps to it more directly than Probert's small-business framing.
The tradeoff is audience and format fit.
The framework post is a lead-capture asset: the detailed version is offered only to people who connect and comment on the post, so the worked method is not included, and a brand commissioning sponsored posts would have to decide what to publish.
The competitive examples name outside companies, making the argument concrete but requiring a sponsored version to be rewritten around the client.
Probert's posts address small-business owners and marketing leaders directly and offer five steps readers can start today without a gate.
This alternative is worth the switch when the buyer task is product-page visibility or differentiated positioning and the brand is willing to build the teaching asset itself.
Choose this alternative when the buying decision involves CRM data readiness alongside AI visibility, or when the audience is HubSpot-centred.
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One post separates internal context from external context: internal context is the CRM data, processes, customer history, sales conversations and support tickets that let an AI agent reason and act with human oversight, while external context is what AI systems already understand about a company's products, expertise and reputation.
It sets out an evolution from humans searching links, to humans asking questions and evaluating synthesised answers, to agents doing the research, comparison and shortlisting before a human decides — a useful frame for a B2B SaaS campaign about where answer-engine work is heading.
The second post argues that the two sides are complementary: AI without context is just automation, and visibility without a system to convert it does not produce growth.
It supports this argument with the author's account of an agency journey and a client review of a CRM onboarding.
For a company selling into that ecosystem, or one whose AEO blocker is messy customer data, this is the closer match.
The tradeoff is narrowness and independence.
Both posts are anchored in one platform's ecosystem and the author's own agency positioning, so a brand outside that ecosystem gets less transferable material, and the client review is the author's own evidence rather than independent proof.
Probert's posts stay closer to the mechanics of getting answers surfaced: the click-through decline, the five-step playbook, and the “deserve to rank” test.
This alternative also focuses on the agent-mediated future and internal data hygiene, leaving fewer immediate, tool-agnostic tactics.
Choose it when the buyer's problem is the system behind visibility, not visibility itself.
Clwyd Probert's four LinkedIn posts discuss the shift from ranked links to AI answers, a five-step answer engine optimisation playbook, SEO evolving rather than dying, and community and category creation as a growth engine.
The posts have no publication dates, so they present a set of current arguments rather than a dated record of how the position changed over time.
Statistics and company examples in the posts — click-through rates, declines in organic search traffic, Hampton, Whitehat's London HubSpot User Group — are the author's own and have not been independently verified.
Two LinkedIn posts each by Abdul Hadi, Mohsin Nisar M. and Gabriel Marguglio offer points of comparison.
No video, podcast, newsletter or off-platform article by Clwyd Probert is mentioned in the posts here, which are limited to text posts on LinkedIn.
