Love At First Search

If AI isn’t recommending you, the common reaction is to do more marketing. Publish more. Post more. Pitch more podcasts. Build more backlinks. Show up on more platforms. But if the thing blocking you has nothing to do with reach, all that extra visibility may just amplify the wrong signal. In this episode, I’m breaking down the sequence I use to think about expert authority: how you move from simply being understandable to becoming distinctive, reinforced, trusted, and eventually difficult to separate from the ideas you’re known for. You’ll learn why: clearer expertise has to come before broader distribution being specific isn’t the same as being distinctive your website, podcast, LinkedIn, YouTube, and guest appearances should reinforce the same underlying associations rather than create five different versions of you trust is an inference created by accumulated evidence, not something you can declare attribution becomes more likely when your ideas are repeatedly connected to you across your own body of work and third-party sources authority building works better as a sequence than as a giant marketing checklist I call this sequence the Unmistakable Authority Method, but the point isn’t to memorize five stages. The point is to figure out what is actually blocking recommendation right now. Maybe you have a clarity problem. Maybe you’re interchangeable with everyone else in your category. Maybe your strongest ideas are buried in a fragmented archive. Maybe your public evidence is solid, but it isn’t reinforced or corroborated anywhere else. Different problems need different interventions. If you want help identifying yours, take the free AI Signal Check: https://loveatfirstsearch.com/signal-check If you prefer the written diagnostic to the Signal Check assessment, check out the Content Signals Diagnostic.

AI can know exactly who you are and still never recommend you. That’s the distinction Meg Casebolt explores in this episode of Love At First Search: the difference between being recognized by AI systems and being selected as a strong fit for a specific problem. Recognition answers questions like: Who are you? What do you do? What company are you connected to? What topics are you associated with? Recommendation asks something harder: Why you, for this specific need? That requires more than identity. It requires enough public evidence for a system to understand your expertise, trust your authority, distinguish your point of view, and connect you to the right problem. In this episode, Meg talks about why broad visibility is not the goal, why smaller specialists can outperform bigger generalists for specific recommendation queries, and why established experts often already have the expertise but lack a clear enough public record for AI systems to recognize the pattern. She also explains why “just publish more content” is often the wrong advice, how fragmented expertise creates ambiguity, and why recommendation emerges from patterns across a body of work rather than from one optimized page or one self-declared claim. The key idea: Recognition means the system knows who you are. Recommendation means it has enough evidence to choose you. And for service providers, consultants, coaches, and other experts, that second outcome is the one that actually matters. Go deeper in the AI Findability Library: https://loveatfirstsearch.com/ai-library/

Search isn’t dead, but Google isn't the only tool that peoplea re using anymore And the path between someone asking a question and finding the right expert has changed. For years, search worked through a fairly simple model: someone typed a query into Google, Google ranked a list of webpages, and the goal of SEO was to earn a high enough position to get the click. That still happens. But it’s no longer the whole system. Google AI Overviews, ChatGPT, Gemini, Perplexity, and other AI-mediated search tools can now interpret questions, retrieve information from multiple sources, compare what they find, and synthesize an answer before someone ever visits a website. That changes what experts need to make visible. In this episode, Meg Casebolt, founder of Love At First Search, explains how modern search is moving beyond page rankings and keyword-first content strategy toward a system that also has to understand the source behind the information: who you are, what you know, what you’re known for, and whether there is enough evidence to trust and recommend you. You’ll hear about why AI did not suddenly replace search when ChatGPT launched, how individual ideas can be retrieved from webpages and synthesized into larger answers, why Meg no longer starts content strategy with keyword volume, and why expert ideas need to be stated explicitly enough for both humans and machines to understand. Meg also explores how zero-click search changes measurement. Rankings and traffic still matter, but they no longer tell the entire story. Citations, mentions, branded searches, recognition, trust, and recommendation are becoming part of the discoverability picture too. The takeaway: modern search is no longer only about getting a webpage to rank. It’s about making your expertise understandable enough to retrieve, credible enough to trust, and clear enough to recommend. Go deeper For more on AI discoverability and how search systems evaluate information and expertise: How AI Chooses What to Cite https://loveatfirstsearch.com/how-ai-chooses-what-to-cite/ Google AI Search: How Search Is Changing https://loveatfirstsearch.com/google-ai-search/ Why I Returned to Blogging for AI Discoverability https://loveatfirstsearch.com/blogging-for-ai/ AI Findability Library https://loveatfirstsearch.com/ai-library/

After nearly three years of podcast silence, I’m reviving this feed — but not to pick up exactly where Social Slowdown left off. Search has changed. My work has changed with it. And the questions I’m most interested in now are less about ranking for keywords and more about how experts become easier to understand, trust, and recommend across Google, ChatGPT, Gemini, Perplexity, YouTube, and the rest of the public web. In this reintroduction episode, I explain why I decided to revive an old podcast feed instead of starting from scratch, what happened when dormant listeners immediately started downloading three-year-old episodes again, and how I’m thinking about the value of old content, existing audiences, and accumulated authority. I also lay out the experiment I’m running across the blog, YouTube, this podcast, and LinkedIn: taking the same core concepts, translating them into different formats, and tracking both how humans respond and how search and AI systems pick them up. Over the next several episodes, we’ll get into: recognition vs. recommendation the Unmistakable Authority Method entity clarity semantic territory authority architecture public evidence and reinforcement why you can’t build authority in private how omnichannel distribution can strengthen your signal positioning and narrative differentiation what to do when years of accumulated content start making you harder to understand, not easier If you’ve been here since Social Slowdown, thanks for sticking around. If you found me more recently, welcome. Love At First Search is now about expert authority, discoverability, and the future of search. Explore the AI Findability Library: loveatfirstsearch.com/ai-library

Jessica Abel's background as an author led her to assume that mass marketing and a large audience was the ONLY way to succeed as a business owner; Thankfully, she finally figured out that her business models needed to first pay her bills, and THEN she could make decisions about audience and marketing style. In this week's episode, Jessica (she/her) and I discuss: How to think about choosing a business model, and why/when you may choose one over another Choosing a business model that allows you to make more money in less time Working with fewer clients for more money versus working with more clients for less money Figuring out how social media fits into your marketing - what do you need it to do for you? Connect with Jessica: Learn more on Jessica's website The Autonomous Creative podcast A little bit about Jessica: Jessica Abel (she/her) is a graphic novelist, author, creative business coach, and the founder of Autonomous Creative, a coaching company that helps mid-career creative professionals build businesses that can pay their bills AND honor their creativity. Abel founded the company in response to the ever-greater struggle creatives face in making a living. Pushing back on the “starving artist” mythology, Abel focuses on business fundamentals, stripped of jargon and adapted to the unique needs of creative professionals, and puts the tools of making real money in less time—without creative compromise—into the hands of the artists, writers, and entrepreneurs who need them. Support the Show. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit loveatfirstsearch.substack.com