Chapter 3: Learning in Public, Discovering by Accident
I built my first attempt at solving the disambiguation problem in 2024.
It wasn't structured data. It wasn't schema markup. It wasn't anything technical at all.
It was a page on my website called "Kenny Kane on the Internet."
The idea was simple: collect every credible mention of me in one place. Every article I'd written. Every company profile. Every podcast appearance, press mention, and author page. Forbes. HuffPost. Crunchbase. BigCommerce. Practical Ecommerce. Amazon. Muck Rack. The Org. Speaker Booking Agency. BookBub.
I had over twenty-five links on that page. Each one pointing to a different corner of the internet where my name appeared alongside real work I'd actually done.
The logic made sense to me at the time. If someone wanted to know which Kenny Kane I was, they could visit this page and see the full picture. Not the comedian. Not the rapper. Not the fictional character. The CEO and author in Austin who'd built companies, written books, and led national organizations.
It was thorough. It was accurate. It was completely organized.
And it was doing almost nothing for search engines.
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Here's what I didn't understand yet: that page was built for humans.
A person visiting kenny-kane.com/links would scroll through those twenty-five links and think, "Okay, this guy has done a lot. He's legitimate." They'd click through to Forbes or Crunchbase or Amazon and verify the claims. They'd form an impression. They'd understand who I was.
But Google doesn't visit a page and scroll through links the way you do. Google crawls a page, looks at the HTML, and tries to extract meaning from the structure. And a list of outbound links, no matter how impressive, doesn't tell Google anything structured about who I am or how those links relate to my identity.
My links page was a business card for humans. Search engines needed something else entirely.
I just didn't know what yet.
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A few months later, I built another page.
This one was more targeted. I called it "Kenny Kane Name Disambiguation," and I put it right in my site's footer where both visitors and crawlers would find it.
The page did something I hadn't seen on anyone else's personal website. It explicitly identified the other people who shared my name and clarified which one was me.
I wrote it in plain English:
This is the official site of Kenny Kane, entrepreneur, author, and executive based in Austin, Texas. I serve as CEO of Firmspace, CEO of the Testicular Cancer Foundation, and CTO at Gryt Health.
Then, underneath, I listed the others:
Kenny Kane (Comedian & CrossFit Coach) --- Los Angeles-based stand-up comic. Kenny Kane (Musician / Rapper) --- Memphis-based hip-hop artist.
I even linked to their websites and profiles. Not to compete with them. Just to make it clear: if you're looking for the comedian, go here. If you're looking for the rapper, go here. If you're looking for the CEO and author, you're in the right place.
I was proud of that page. It felt strategic. It felt like I was finally taking control of the narrative.
And in a way, I was. I just didn't realize I was writing a disambiguating description in prose without knowing that an actual schema property called disambiguatingDescription already existed for exactly this purpose.
I was solving the right problem. I was using the wrong language.
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Let me pause here and tell you something that would have saved me a lot of time.
The infrastructure I was trying to build manually has existed since 2011.
That's when Google, Microsoft, Yahoo, and Yandex launched Schema.org, a shared vocabulary for structured data on the web. The whole point was to give website owners a way to communicate with search engines in a language machines could actually process.
Not paragraphs. Not links. Not well-organized pages written in conversational English.
Structured data. Machine-readable code that says, explicitly: this person's name is Kenny Kane. He's a CEO. He works at Firmspace. He's based in Austin, Texas. He's not the comedian in Los Angeles or the rapper in Memphis.
Schema.org launched with 297 types and 187 properties. By 2025, it had grown to over 800 types. As of 2024, more than 45 million web domains were using schema markup on their pages.
Forty-five million.
And I'd never heard of it.
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I don't say that to be self-deprecating. I say it because if you're reading this book, there's a good chance you haven't heard of it either.
And that's the real story of this chapter. Not that I was ignorant. That the awareness gap is enormous.
Structured data has been the backbone of how search engines understand identity for over a decade. It powers Knowledge Panels. It feeds Google's Knowledge Graph. It determines how AI systems like ChatGPT and Claude describe people when they're asked "Who is [name]?"
And almost nobody outside of professional SEO circles knows it applies to them personally.
The 45 million domains using schema markup are overwhelmingly businesses. Restaurants marking up their hours and menus. Ecommerce sites marking up product prices and reviews. Event venues marking up dates and locations.
Individual professionals using structured data to disambiguate their identity and control how machines understand who they are? That's still the frontier. That's what nobody is talking about.
I spent twenty years building websites, writing content, and establishing a professional presence online. And it wasn't until 2025 that I discovered the language machines actually speak.
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Here's how I found it.
I'd been working with Claude for months. Initially on my books. The Accidental Nonprofiteer had been stuck in manuscript form for years. Mission-Driven Ecommerce was an idea that needed structure. Working with AI to write, organize, and publish those books changed how I thought about content. It forced me to articulate who I was and what I'd done in a way that was structured, specific, and verifiable.
That process led to blogging more intentionally. Which led to thinking about how people discover content. Which led to asking questions about how search engines actually process identity.
I don't remember the exact moment it clicked. It might have been a YouTube video I stumbled across late one night. It might have been a search result I followed while researching something else. That's how most real learning happens. Not through a curriculum or a consultant. Through a thread you pull because something made you curious.
But at some point in the fall of 2025, I encountered the concept of schema markup as it applied to people. Not businesses. Not products. Not recipes. People.
And I realized: everything I'd been doing manually on my links page and disambiguation page, there was already a machine-readable vocabulary for it. There had been for fourteen years.
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Once I understood the concept, everything accelerated.
In the fall of 2025, I published a blog post called "Why I'm Investing in Structured Data." I wrote about taking a hard look at the schema markup on my website and realizing it wasn't doing enough. My site had basic markup, but it wasn't emphasizing the things that made me distinct. My location. My career timeline. My specific affiliations with organizations that had their own authority and recognition.
Weeks later, I'd completed a full Knowledge Panel optimization project. Created a Wikidata entry. Connected my Google Knowledge Graph ID. Built a validation loop across three platforms. Updated my website's schema with a proper disambiguating description, sameAs links, and connections to every authoritative entity I could verify.
One week from awareness to implementation.
That's not because I'm exceptionally fast or technical. It's because once you understand what structured data is and why it matters, the actual implementation isn't that complicated. The hard part isn't doing it. The hard part is knowing it exists.
That's the awareness gap this book is trying to close.
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Here's what I want you to take from my stumbling, iterative path to this discovery:
You don't need to follow a perfect roadmap. I didn't.
I built a links page that was useful for humans but invisible to machines. That taught me that consolidation matters.
I built a disambiguation page that explicitly stated who I was and who I wasn't. That taught me that clarity matters.
I wrote books and blog posts that forced me to articulate my expertise in structured ways. That taught me that content is infrastructure.
And eventually, I stumbled into the realization that machines had their own language for everything I'd been doing in English.
Each step was incomplete on its own. But each step led to the next question. And the next question eventually led me to structured data.
That's how learning in public works. You don't wait until you have the complete picture. You build with what you know, you document what you're doing, and you stay open to discovering that a better tool has been available the whole time.
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The links page is still on my website. So is the disambiguation page. I haven't taken them down.
They still serve a purpose for human visitors who want to see the full picture of my online presence. But now they're supported by something those visitors never see: structured data in the <head data-preserve-html-node="true"> section of my site that tells every search engine and AI system exactly who I am, what I do, and how I'm connected to the organizations and work that define my career.
The human-readable layer and the machine-readable layer. Both matter. But if you only have one, you're leaving half the internet unable to understand you.
I spent 2024 building the human layer. In the fall of 2025, I discovered the machine layer had been waiting for me since 2011.
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In the next chapter, we'll get into exactly what structured data is, how it works, and why your website needs it. Not the theory. The actual infrastructure.
But before we go there, I want to leave you with this:
If you've been building your online presence and wondering why search engines still can't figure out who you are, the problem probably isn't your content. It's probably not your website design or your posting frequency or your social media strategy.
It's probably that you've been speaking to humans and forgetting that machines are listening too.
And machines have their own language.
It's time to learn it.
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