Trustable
What happens when AI can find you, describe you — and still hesitates to recommend you.
Have you ever been an ARC reader? The step in the publishing process where you read an advance copy of a book in exchange for a review?
Usually, the request comes through a trusted source. Someone you’ve worked with in the past. And so, the expectation is that the book is good. After all, this is one of the last steps before publication.
I’ve been an ARC reader. Excited to get to read a book that sounded absolutely great.
Everything pointed to: This book is worth your time.
Only it wasn’t. There were plot holes, inconsistency in the information, unaccounted time skips. Everything that hiring a good editor would have fixed.
The book didn’t deliver what was promised. I finished it — because that’s the deal — and struggled to provide a review.
Somewhere along the way, I lost my trust in the ARC process.
Trust – the one element that is the foundation of all of the relationships we build. From picking a book to picking a friend – we interpret and build on those things which give us clues of what to expect. We rely on past experiences to paint for us what we can expect in the future.
When we are comfortable and confident with the picture, we trust.
Is this the way AI does it too?
The short answer – no. There’s no history. No memory of the last bad recommendation. No relationship to repair.
What it does instead is read the picture in front of it — right now — and ask one question: do the pieces agree?
Which means the question for you isn’t “have I earned trust over time?”
It’s “Does the picture AI finds today hold together?”
We’re at the third layer of the AI Visibility formula:
AI Visibility = Findable + Selectable + Trustable
Findable fixed the technical gap — Google can see you.
Selectable fixed the clarity gap — AI can describe what you do.
Trustable is different — it’s where AI decides whether to recommend you.
Findable and Selectable are things you can do in a morning, or a week. Set up Search Console. Tighten your tagline. Align your About paragraph.
Trustable isn’t a fix. It’s a layer you build over time.
Every post, every bio, every consistent description you’ve put out publicly — that’s what AI is reading when it asks “do the pieces agree?” The longer you’ve been consistent, the clearer the picture. The clearer the picture, the more confidently AI recommends you.
What AI calls trust
When a reader asks AI for a recommendation, it doesn’t consult a relationship or a reputation.
It scans everything it can see. Your Substack page. Your website. Your author bio on Amazon. Your LinkedIn profile. Your Goodreads page. Any interviews, guest posts, reader reviews, forum mentions, or public chatter that references you.
Then it asks, in effect: do these sources agree?
If your Substack says you help writers use AI in their systems, your website talks about your fantasy novel, your LinkedIn still lists a corporate job you left three years ago, and your Goodreads bio is empty — AI has four data points pointing in four different directions. It either makes its best guess or defaults to someone whose picture is cleaner.
Consistency across sources is what AI reads as trust. Not followers. Not polish. Not authority.
Just the question: “Can AI read the same person everywhere it looks?”
The Three Things that Build Trust
You can’t control what AI finds when it scans. You can’t control what readers say about you in places you’ll never see. But there are three things you can control — and they’re the ones that matter most.
Consistency
Think about the last time you met someone at a networking event. They told you one thing about what they do. Then you looked them up online and got a completely different story. Confusing, right?
That’s what AI runs into when your tagline says one thing, your About paragraph says another, and your LinkedIn bio is describing someone you were three years ago.
It doesn’t have to be word-for-word identical across every platform. But it should be recognizably the same person, the same work, the same reader you’re writing for.
When it isn’t, AI has to make its best guess. And its best guess is usually to pick someone whose picture is cleaner.
Footprint
Consistency is about what you say. Footprint is about where you say it.
AI builds confidence by cross-referencing. One source is a claim. Multiple sources that agree start to look like evidence. The more places that independently point to the same picture of you, the easier it is for AI to recommend you without hedging.
A single platform done well is a start. But if that’s the only place AI can find you, it’s working with one data point. That’s a thin foundation for a confident recommendation.
Compounding
This is the one most writers underestimate.
Trust doesn’t reset with each post. It accumulates.
Every week you show up with the same clear message, for the same reader, on the same platform, you add another week to the pattern AI is reading. The longer that pattern holds, the more durable it becomes. The more durable it becomes, the more confidently AI recommends you — not because you’ve done something extraordinary, but because you’ve been consistent long enough that there’s nothing ambiguous left to wonder about.
This is why established authors have an advantage. Not necessarily because they’re better, but because they’ve been doing this longer. Their footprint is wider and their message has had more time to compound. The gap is closeable. But it doesn’t close in a morning.
A Note for Substack-only Writers
If your whole presence lives on Substack, some of what I’ve described above may feel irrelevant — or worse, like a warning that you’ll never get recommended by AI.
That’s not true.
Trustable doesn’t require you to be on every platform. It requires you to be coherent on the platform you’re on.
A Substack writer who shows up consistently, serves a recognizable reader, and maintains a clear thread through their posts is building trust. Reader replies, restacks, and comments are your corroboration. Your archive is your footprint. Your tagline, About paragraph, and recent posts are your consistency layer.
By the way, you don’t have to write the same thing every week. What you have to do is write for the same reader most of the time.
A post about your cat, a hard week, a book you loved, or a weird thing you noticed — that reads as personality. It humanizes the newsletter and actually strengthens the reader’s connection to you. AI doesn’t penalize personality. It penalizes inconsistency.
What AI is reading isn’t whether every post covers the same topic. It’s whether, across your archive, a pattern is legible: the same writer, serving the same reader, with the same underlying promise.
If a stranger read your last ten posts, could they describe what this newsletter does in one sentence? If yes, you’re building trust. Occasional variety isn’t a problem. It’s the point.
Where I’m at with this
I won’t pretend I have this fully sorted out.
Findable took me a morning. Search Console, sitemap, manual indexing. A gap I discovered got fixed before lunch.
Selectable took a week. My tagline got rewritten a few times. The About paragraph tightened. A Start Here post pinned on my Substack. Language aligned across my posts. Including LinkedIn.
Trustable? I’m in it. I’ve updated most of my bios. I still have Goodreads and Amazon to do. I’m tracking down any others I let go stale. And I’m doing the thing that doesn’t have a single fix — showing up, week after week, with the same clear message, until there’s nothing ambiguous left for AI to wonder about.
Until I publish my fiction book – The Call To Witchery. At which point, the work becomes making sure AI can recommend me in both worlds — fiction and non-fiction — without getting confused about which Lorraine it’s recommending.
If you’re curious how AI sees you across all three layers - Findable, Selectable and Trustable - reply “audit” and we’ll take a look.
This is the final post in the AI Visibility trilogy. If you missed the earlier ones: Findable is here. Selectable for authors is here. Selectable for Substack writers is here.