I had no plan for it. I opened Gemini, typed give me something fun, which is less a prompt than an abdication, and handed over the photo.

What came back was a character sheet. Birdy as the protagonist of a fantasy role-playing game, stats and all. Charisma boosted by the forehead wrinkles. Perception maxed out, on the reasoning that she can hear a cheese wrapper open from the next room. Stealth close to zero, because she is too striking to blend into anything.

We passed the phone back and forth from there. One of us would read the output, laugh, and prompt the next turn. The sheet asked for Birdy’s name and her real-life superpower, so we told it: Birdy, and mind control, since there is no version of events in which the hand does not eventually produce the treat. The model took that and kept building. Birdy became a sorcerer whose subclass is Absolute Ruler of the Living Room. It asked for her highest-value target. We said cheese. It returned a doctrine, the Cheese Tax, under which the sound of a wrapper travels on a frequency that bypasses ordinary physics.

I installed a phone update that night, and that is how I learned the phone now makes music. Lyria 3, Google’s music model, builds a track from a text prompt. I told Robin I could not wait to send her my first song. She was skeptical there was much use in a phone that writes music. I already knew what the song would be.

I left the update alone until late the next morning before I made anything with it. The play is real. It does not get to cut in front of the work.

The result is called The Cheese Tithe. It runs two minutes and thirty-eight seconds and came with its own cover image. It is a rock anthem about a dog holding a household hostage for dairy.

The lyrics describe the actual room.

One blue, one brown, they’re locking in the static,
Birdy’s on the blanket, keeping it emphatic.
The wire crate is open, the brick wall is a blur,
There is no escaping the gravity of her.

I never told it what the room looked like.

The chorus is the part that gets stuck in your head, which is the job of a chorus:

You feel the voltage, the master command!
There is no stopping the reaching of the hand!
The tithe must be paid, the willpower is gone!

I sent it to Robin. She laughed. She had doubted there was any use in a phone that writes songs, and the use turned out to be this: two and a half minutes of arena rock about our dog’s dairy racket, useful in no sense a spreadsheet would recognize.

None of it was for anything. That is the whole point, and it took me years to trust it.

In graduate school I started making photographs that had no assignment behind them. Macro-close work on train cars parked near a mall, paint and scratches and weather shot so tightly the train stopped reading as a train and became color and line. Ink dropped into water and caught as it fell. Glass lit until it looked like deep space (okay, that one came later). I didn’t think of it as a body of work. I thought of it as play, and I hadn’t yet learned that play could justify itself.

I understand it now. That work became a series I still print and sell, seventeen years on, and one of the glass photographs spent a season on a gallery wall in Los Angeles. I only gave the series a name recently, while rebuilding my website, when I noticed I had been filing the images by how I made them instead of by what they were.

The Birdy session and those grad-school photographs are the same move, seventeen years between them. Each one starts with something in front of me that has no particular use, a gratified train car or a dog waiting on cheese, and each one follows the play without settling the destination first. Sometimes it arrives at a pigment print on cotton rag. Sometimes at a rock anthem about a tithe. The destination does not need to justify the beginning. It doesn’t need to justify the final product either.

That does not make the song an outreach strategy. It was not made for that. But a song about the Cheese Tithe travels in a way a workflow essay never will. Someone forwards it to a person who had decided the whole subject was either frightening or beneath them, and for two and a half minutes it is neither. It is just funny.

Birdy has no idea any of this happened. She is on the blanket by my feet while I write this, snoring gently away.

The cheese will wait.

P.S. I finished this, and then Robin sent me hers.

The work begins before the draft. I start with a problem I have encountered in my own business, form a hypothesis, run tests, collect evidence, and work through what the results mean. That often includes long written exchanges, dictated notes, original tools I have built, outside research, and repeated attempts to clarify the argument before I ask for a full draft. The models work from a detailed writing guide, established business facts, documented terminology, audience context, and lessons carried forward from previous articles.

Once a draft exists, I edit it, question it, add examples, remove language that weakens the argument, check externally verifiable claims, and decide what remains. I add the photographs, captions, artwork, structure, and final context. I also review what the process taught me and update the writing system so the next article reflects those decisions more accurately.

AI usually writes most of the first complete draft because it can turn a large body of directed material into prose faster than I can type it myself. It does not decide what I believe, what the evidence supports, what the article argues, or what I publish.

Pangram may describe the surface of that workflow as “AI 100%, Human 0%.” I do not. That score can identify the tool that produced much of the sentence-level wording. It cannot measure the research, judgment, experience, direction, revision, or responsibility behind the finished work. Treating those two things as equivalent mistakes a production method for an account of authorship.

I do not think my own score proves how well or badly Pangram works across Substack. It does expose a question worth testing. I am considering a small study built around writers with documented workflows, comparing fully hand-written work, corrective AI use, directed AI drafting, and unattended generation. The useful question is not simply whether Pangram detects machine-produced wording. It is whether the label it gives readers accurately describes how the work was made.

If you publish on Substack and can document your process candidly, I would be interested in hearing from you.

The tools are part of how I make this. They are not the reason it exists.

Ask five AI engines whether anyone in Tucson brings a mobile photography studio to a client’s home or office, and my business appears in five out of five answers, ranked first in four.

Ask those same engines who creates fine art pet portraits in Arizona, and I appear in zero. Several recommend painters instead.

Same brand. Same credentials. Same afternoon.

In the Note, I called the hard half “discovery.” That word covers less of the problem than I thought. Discovery is whether the engines can resolve you: your name goes in, correct facts come out.

Distinguishability is whether the engines choose you: a buyer’s question goes in, your name comes out.

They are different problems. The second does not follow automatically from the first, no matter how much of the first you build. I started calling this condition in June: known but not recommended.

That still describes part of the problem, but not all of it. The more disappointing failure happens after the engine has found you, verified you, and assembled the facts correctly. It still has to decide what those facts mean.

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What came back looked right. Business name, correct. Categories, correct. Price range, correct. Address: 6440 N Campbell Ave, Suite 210, Tucson, AZ 85718.

That’s not my address. It’s Vivace, a Northern Italian restaurant in the Catalina Foothills, established in 1993, and to the best of my knowledge has no relationship whatsoever to my photography business.

I caught it in about four seconds because I know where my mail goes, and this wasn’t it. Gemini apologized and corrected it, the way it apologizes for anything once you catch it, which isn’t the same as being trustworthy. I’d handed it a task that sat just past what I could verify myself on sight: structured data, schema fields, the layer I understand in principle but can’t proofread the way I proofread a sentence. That’s the bot-sitting territory from the last issue. The tools go furthest wrong precisely where you’re least equipped to catch them.

I only caught this one because the address happened to fall inside what I know cold.

I’ve been running this business through AI tools for more than a year now, and I still don’t trust Gemini with anything I can’t verify myself. I don’t know that changes.

Nothing published, no real damage. But I kept thinking about the version that doesn’t get caught in four seconds. I recognized my own address wasn’t the one in front of me. What happens with a fact I’d recognize a little less certainly? A credential date that’s off by a year. A service category that used to be right and quietly stopped being right somewhere along the way?

I built two documents for future work. Then I asked an AI tool to hold both of them against pages I’d already published.

What the Card Actually Is

The first document is what I’ve started calling my entity card. It gives an AI tool one place to check who I am instead of reconstructing the answer every time.

Legal name, founding date, the two cities I moved through before Tucson, the credentials with the years they were actually earned, the service categories in the language I’ve decided wins over the language that doesn’t. None of the information is secret. Most of it is already sitting in my ORCID record for anyone who wants to look.

None of it is new, either. Putting it in one dated document gives me something concrete to check everything else against, rather than asking an AI tool to reconstruct me from whatever it half-remembers every time I need something written.

A model without a document like this doesn’t lie to you, exactly. It encounters a gap and supplies something plausible enough to fit, and a business address in a business’s own city is plausible right up until it turns out to belong to someone else’s restaurant.

Give it a dated source of truth, and at least the address no longer depends on a guess.

Who It’s For, Not What I Sell

The second document took longer to admit I needed because it’s less obviously about me. It’s a set of profiles, one for each real kind of client or organization I work with, built in detail and in writing before I ask an AI tool to help with anything that touches marketing.

The shape is worth describing because the shape is the useful part.

One profile starts with the obvious markers: age range, income bracket, whether they rent or own. None of that does the actual work. What matters is three lines down: what they’re worried about before they ever email me, and which detail in my first response would tell them I already understand that worry without my having to say so out loud.

Demographics tell an AI tool who might buy something. Decision drivers tell it what might move that specific person from considering the service to contacting me. Most client profiles stop after the first part.

The same principle applies across the rest of my practice. A corporate team needs different language than an individual replacing a ten-year-old LinkedIn photo. A private pet client needs something different from a rescue trying to get an overlooked dog adopted. The subject might still be a dog, but the audience, purpose, emotional stakes, and useful next step are not the same.

Whatever I ask an AI tool to write, from a corporate service page to a caption for a shelter dog’s Instagram post, it’s supposed to know which situation it’s writing for and who needs to read it. Most of the time I don’t have to rebuild that context from scratch. It’s already in the reference material the tool is working from.

The Part I’d Already Solved

There’s a third piece of this that I wrote about a few issues back, the writing guide that tells whatever tool I’m using how I actually sound, not how a generic photographer sounds.

I won’t re-walk that ground here. By the time I built the other two documents, I already had proof this approach worked because the writing guide had been quietly doing its job for months.

The newer documents answer two different questions: is this aimed at the right person, and is it even true?

Checking the Work That Already Existed

I assumed the main benefit would be retrieval. Having the facts and the audience written down meant I could stop re-explaining myself every session. That part’s real, and it’s most of what I wrote about a few issues back.

Then I stopped treating the documents only as background material living in a knowledge library. I asked an AI tool to check my existing work against them.

I picked three older pages on my site, ones I hadn’t touched in a while, and asked whether they still matched what the entity card and the writing guide actually say. They didn’t, in two different ways.

One page, built for corporate clients, still sells something called an Executive Session. That’s the word I ruled out months ago, in writing, after my search data showed that people find me for “corporate team headshots,” not “executive headshots.” I didn’t deliberately put it back. The page was built before I’d made that decision, and I hadn’t gone back to check it against a decision that came later.

The tool caught it in one pass because for the first time it had something concrete to check against instead of an instinct that something read a little off.

The other page, a fine art series from my years in Kentucky, gives two different date ranges for when the work was made, one in the metadata and a different one in the paragraph underneath it. Read past that, and it describes the choice behind the work as something I made “before relocating west in 2011,” which skips the fact that I’d already left Kentucky for Washington three years earlier.

Claude helped me build that page. It was created before I had any of these persistent identity documents in place, which meant the tool was working from whatever context I supplied during that session and whatever it could infer around the edges.

I didn’t set out to invent anything. I’m sure Claude didn’t make that choice either. A compressed sentence had turned into an error in my biography, sitting on my own website, waiting for whatever eventually reads it and decides it’s true.

Both pages are getting rewritten this week. That’s not really the point.

Neither error would have surfaced from a normal proofread because a normal proofread checks whether the sentence works. What caught these was checking the sentence against something written down and dated, the same discipline that caught the restaurant’s address before it ever went live.

I got lucky with Gemini because I happened to know my own address cold. I don’t know every fact about my own business that well, and neither does any AI tool I hand a task to unless I’ve already told it what to check against.

The address was obvious. The next error might not be.

Over the past year I’ve gotten faster at my work than I’ve ever been. I’ve also been more tired at the end of the day than the work alone explains. For a while I filed that under getting older, running a one person shop, the general weather of the year. I started paying attention in March, when Julie Bedard of Boston Consulting Group went on the podcast Hard Fork to talk about a study her team had just put in Harvard Business Review. They surveyed about fifteen hundred full-time workers, gave the exhaustion a name, AI brain fry, and tied it to a number: self-reported productivity climbed as people added AI tools, up to around three, then fell off a cliff past four. I run five pretty regularly. By their count, I’m the cautionary tale, and I’ll come back to that.

A separate institute survey has been sitting with me more recently. This one comes from Glean’s Work AI Index, six thousand workers rather than fifteen hundred, and it isn’t asking how many tools fry you. It’s asking where the time goes. Eighty seven percent using AI. Seventy three percent saying it makes them more productive. On average they report saving about thirteen hours a week, close to a third of a workweek. And only thirteen percent say their organization is actually performing significantly better because of any of it. The gains are real. The gap is real. The report exists to ask where the thirteen hours went, since they clearly didn’t all land where they were supposed to.

One answer is bot sitting: the tracked and untracked labor of making the tool usable. Feeding it context. Watching its output. Debugging the parts that come back broken. Cleaning up after it. The report puts that work at roughly six and a half hours a week, about half of everything the AI just saved you. Not all of that is wasted time, some of it is iterative and healthy, but a lot of it is tedious, invisible, and tiring.

The single most draining piece is feeding the tool context, the part it feels like it should already know. Which documents matter. Where they live. Which ones are authoritative. Second is debugging, because the thing is probabilistic rather than predictable. You fix something and you can’t quite tell what you fixed or whether it will stay fixed. It adds up to a quiet tax that shows up as fatigue long before it shows up as a performance problem.

Then comes the finding underneath the finding. Forty percent of workers admit to shipping AI work they couldn’t explain if someone asked them to. The report gives that its own cruder name. The path from bot sitting to that failure mode is short, and it runs straight through exhaustion. You sit the bot until you can’t anymore, and somewhere in there “good enough” quietly becomes permission to ship.

Researchers have a polite term for settling at good enough: satisficing. Creatives have heard the slogans that make that feel reasonable. Done is better than perfect. If you’re not embarrassed by the first version of your product, you’ve launched too late. Document, don’t create. All of them were originally answers to a real problem, perfectionism that kept people from shipping anything at all. They work when the rough thing is still yours, when you can explain what you tried, what you learned, and what you’ll change. They stop working the moment the rough thing is something the tool wrote and you can’t defend it. That’s the edge where iteration turns into surrender and where “good enough” stops being a stepping stone and starts being a ceiling.

What the report describes is satisficing scaled into a workplace norm, work going out the door that the person who sent it could not defend. That’s the surrender of agency written as a survey statistic. It’s the generic photographer seen from the inside: competent, smooth, and indistinguishable from anyone else. It doesn’t get fixed by a better prompt or a faster model, because the variable was never the tool. The variable is whether you’re still in the loop or just clearing the machine’s throat for it until you stop checking.

Rebecca Hinds, who runs the Work AI Institute, said the line that I’ve been pondering since. Most people are too exhausted to be curious. I believe her. I also know that’s not the only way this goes, because it isn’t the way it went for me, and the difference between the two is where this issue earns its keep.

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I checked it against something. The same grader gave a friend’s business eight percent too. And another. The number wasn’t a measurement. It was a default.

This is the story of what I found when I went looking for where that eight percent came from, and what it took to build a tool that would stop handing me numbers like it. I rebuilt the thing twice. The first rebuild lied to me in exactly the way the original had, which turned out to be the most useful thing it could have done. The second one stopped lying, and the price of that was watching most of my scores fall.

The grader that started the journey

The tool I started with came out of HubSpot, demonstrated on a podcast, the kind of free instrument a company builds to show you a problem it would like to sell you the solution to. You gave it a brand and it gave you back a composite score out of a hundred, built from five dimensions: brand recognition, market position, presence, sentiment, and share of voice. It ran across three AI engines and averaged them.

It looked like measurement. It had the furniture of measurement, the weighted dimensions and the provider columns and the tidy final number. What it didn’t have, in the one dimension I went looking inside, was data. The share-of-voice figure was an estimate the language model was asked to produce, a guess at what percentage of the market each competitor held, and when the model couldn’t produce one for the subject brand, the code filled the hole with a literal fallback value. Eight percent. Written into the source as a default for when the real answer was absent.

That is worth sitting with, because it is the whole problem in one line of code. When the tool had nothing, it did not say it had nothing. It said eight percent.

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No spread. The engines agreed.

Where Michael Kloth Photography pulled fifty points of disagreement out of five systems, the newsletter pulled near-consensus. Perplexity, OpenAI, Gemini, and Grok landed within a few points of each other, low. Claude went lowest, 5 to 8, noting that a search for the exact title “returned zero direct results.” One engine read the Substack profile closely enough to report the subscriber count: seven, the day I ran it. The number was correct, and two months in, it wasn’t the part that stung.

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I’ve been running AI through my photography business since June of last year, which is long enough to have formed opinions about where the convenience stories hold up and where they leave something out. This is one that leaves something out.

The old memory was passive. It wrote down what you explicitly told it to remember, and not much else. The new system is active. In OpenAI’s own description, it synthesizes context from your chat history on its own, including information that comes up naturally in conversation rather than through a “remember this” instruction. It also acts on what they call implicit preferences, the things you never stated but that it inferred from how you work.

Read the announcement and the example they lead with is a vacation. You plan a trip, ChatGPT remembers your travel style, future trips get easier. That’s the version of the worry most people land on, too: is my vacation going to end up somewhere it shouldn’t.

It’s the wrong example. Not wrong exactly, just not the business example.

Here is the one that should concern anyone with clients. You write a proposal for Client X in ChatGPT. Next week you write one for Client Y, in the same account, because that’s simply how a person uses one login. The system isn’t storing a note that says “this pricing is confidential to X.” It’s building a working model of your projects and surfacing what’s relevant. And the moment relevance peaks, the moment the model is most ready to volunteer what it knows about your proposal work, is the moment you sit down to write the next proposal.

What crosses over necessarily isn’t a name you’d catch on a read. It’s a number. A scope assumption. A piece of positioning you developed for X, arriving fluent and on-topic in the draft for Y.

This is a different failure than the one I usually write about. Fabrication gets caught eventually, because some part of you stays alert to the invented detail that was never real. I’ve called that plausible fiction. This isn’t that. The detail here is real. It’s just real about the wrong client. Your accuracy radar never fires, because nothing is inaccurate. The content is correct. The context is the breach.

That’s what makes it slip. Bleed that reads as competent is the kind that clears review.

And the stakes clear a real bar. If X and Y compete, and in a narrow market they often do, two firms in the same city, two organizations chasing the same grant, this stops being a privacy curiosity and becomes a confidentiality exposure with NDA-shaped edges. The embarrassing version is your dinner preferences showing up in a work email. The serious version is one client’s terms shaping another client’s deal.

The defense isn’t clever, and that’s the point. Keep client work in separate, governed spaces. Use Temporary Chat, which doesn’t read or write memory, for anything you don’t want synthesized into the general model of you. Read the memory summary page now and then, so you actually know what the tool thinks it knows. None of that is hard. It’s just deliberate, and deliberate is exactly what the convenience pitch invites you to stop being.

That’s the whole tension in a line. The casual user hears Dreaming and thinks, good, now I don’t have to bother. Run a business on these tools and the conclusion runs the other way. The bother was never overhead you could finally shed. The bother is the discipline that keeps one client’s work out of another client’s draft. It’s the moat.

The setting worth reviewing isn’t in the menu. It’s the assumption that convenience comes free.

I got a spread from 28 to 78.

A control isn’t supposed to do that. And the reason it does is the whole point of this issue, because once you understand why a brand with this much real signal scatters across fifty points, you stop reading the headline number as “your AI visibility” and start reading the thing underneath it, which is where the actual information lives.

One note before the numbers. These runs come from a version of the grader I’d already adjusted since last week’s issue. I was in its guts before I’d decided to write about what’s in there. More on why next week.

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The tool lets clients upload a photo of their wall, select a print from my gallery, choose a mat color and frame style, and see roughly what the finished piece would look like in their actual space. The visualization part worked from early on. The “Send to Michael” button was a different story. Clients could build something and then it disappeared. I debugged it, made partial progress, and then set it aside when other things needed attention.

I came back to it recently using the Fable 5 preview of Claude Mythos, which Anthropic has made available to all account holders through June 22. The difference was immediate. Mythos found the problem, explained exactly what was wrong, and fixed it in one session. Two months of intermittent frustration, one session. I’m not going to oversell it, but the model matters, and that’s worth saying plainly.

The send problem turned out to be two issues compounded. The endpoint wasn’t registered correctly, and a pre-existing bug was re-triggering the export panel and hiding the contact form every time a step completed. Both fixed.

Once the email was actually arriving, I noticed it was reporting pixel dimensions of the art file rather than the physical print size on the wall. The pixel count of the source image tells me nothing useful during a client conversation. The print size in inches does. Fixed that. Then I reworked the resize behavior: the original tool locked art to its native aspect ratio, which isn’t how print ordering works. The corner handle now moves freely, and when the dimensions come within about half an inch of a standard print size (8x10, 11x14, 16x20, 20x30, and others, either orientation), it snaps to that size. Anywhere else it stays custom and labels itself as such. The email I receive now includes each print’s image area, whether it landed on a standard size or something custom, and the full framed dimensions.

The tool is live at visualizer.michaelklothphotography.com/visualizer/ if you want to see it. All that’s left is to finish populating the gallery with my artwork. Pet clients will get a customized version that shows portraits from their session.

I have ideas about where it could go from here. As it stands, it does what I built it to do: gives clients a concrete way to think about print size and wall placement before we get to the viewing appointment. That’s enough.

Thread closed.

On June 5th, the argument landed in Google’s formal developer documentation. Not one page. Three, coordinated. That’s a different kind of signal.

The first is the “Do You Need an SEO?” guidance page, which lists the services worth paying a professional for. The updated list now ends with this: “Optimizing for generative AI.” Lower on the same page, in a new section on evaluating an SEO’s tools, Google names the discipline directly: “advice on optimizing for AI experiences (also known as ‘AEO’ ‘GEO’ services).”

Not a conference talk. Not a blog post from a marketing agency. Google’s official hiring guidance.

The second is a new page dedicated to evaluating third-party SEO tools and advice. It covers the range of services claiming to help with AI search visibility, and on the tooling specifically, it says: “Third-party tools don’t have access to our internal ranking data. They can’t guarantee performance. Any predictions are their own and like predictions generally, may not happen.”

The third is the one that completes the move: a dedicated official guide on optimizing for generative AI features, which both of the other pages now link to. So the picture as of June 5th is three coordinated parts. The service is worth paying for. Here is how to evaluate the tools claiming to do it. Here is our own guidance on how to do it.

Read together, the updates make the same argument Sullivan made in Toronto, but now it’s in the documentation: AEO is real enough to pay for, and the tooling claiming to measure it is unreliable enough to scrutinize carefully.

I’ve been sitting with that second point for a while.

After the May issue I started running my own brands through the HubSpot AEO Grader, which is the tool Kipp Bodnar used for his live demo in the Marketing Against the Grain episode I referenced. The grader produces scores across multiple dimensions: brand recognition, share of voice, presence quality, sentiment. It generates competitor comparisons.

It has problems. I found them. And then I built something else. It’s not pretty, but it is effective.

The next two issues cover what I found and what I built. They’re specific, they’re mine, and they’re behind the paywall, which is where the methodology work in this newsletter lives.

The arc over the next three weeks:

Issue 2 (June 17, paid): What the tool found on a mature photography brand.

I ran seven subspecialty queries against Michael Kloth Photography, the business I’ve operated since 2006, with 20 years of credentials, two published books, and a Wikidata entity. The signal web behind it is real: PPA, ASMP, the TMC Foundation collection, Getty Images.

The scores ranged from 28 to 78 depending on the AI provider and the subspecialty. That spread isn’t a calibration error. It’s structural, and understanding why Grok and Perplexity score the same brand 50 points apart on the same query is the first thing you have to understand before any of this is actionable.

Issue 3 (June 24, paid): What the tool found on a three-month-old newsletter brand, and why I stopped using the HubSpot grader.

I also ran the tool on artofphotographyai.com, this newsletter, three months old, scoring in the 40s across the board. A newer brand has a cleaner failure profile. The gaps are diagnosable and the remediation path is explicit.

That issue also covers the HubSpot grader’s architecture problem, what I found when I looked inside it, and why a client acquisition funnel dressed as a diagnostic tool produces a specific kind of unreliable output that Google’s June 5th documentation is, whether intentionally or not, describing exactly.

The May issue ended with this: the strategy question isn’t whether to build non-commodity content. It’s recognizing which decisions have been optimizing for the wrong thing.

The June documentation adds a layer: the measurement question isn’t whether to evaluate your AI visibility. It’s recognizing which tools are built to answer your question versus which ones are built to answer a different question entirely, and produce your score as a byproduct.

Both problems have the same shape. That’s not a coincidence.

Issues 2 and 3 are for paid subscribers. If you’ve been reading this newsletter on the free tier and the direction this is going sounds useful, this is a reasonable moment to consider upgrading.

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In early May, I ran four queries through Gemini the way a potential client might. Not my name. The kind of questions someone types into an AI assistant before they know whose name to type.

“I’m looking for a pet photographer in Tucson that is experienced with shy pets.” “I’m looking for a headshot photographer to come photograph my team in my Tucson office.” Two others in the same range.

All four came back with accurate information about my business.

The shy pet query was the strongest. Gemini named Pima Animal Care Center and the Humane Society of Southern Arizona, got the volunteer history right, described the mobile studio as something that eliminates the stress of unfamiliar environments for anxious animals. The team headshot query came back with the correct pricing, the ten-person minimum, the full-day format. Someone who had read my site carefully could have written both responses. In a real sense, someone did. The synthesis came from content that exists, documented specifically enough to be cited accurately.

This is what a site doing the content work looks like from the outside. Accurate synthesis doesn’t come entirely from your own site, though. Directory listings, published features, institutional affiliations, and other third-party sources carry significant weight in what gets cited. The queries don’t always tell you which lever is being pulled. Both matter.

I didn’t run these queries to audit my site precisely. I ran them because I was looking for what chatbots know about my site. But you already know this.

I also look for the actual questions my ideal clients ask when they’re talking to an AI assistant, phrased the way they phrase them in conversation, not the way a keyword tool formats them. Those are different things, and the gap between them is getting wider. That second kind of query will be what I talk about next week.

A search bar gets keywords. “Pet photographer Tucson anxious dog.” An AI assistant gets a sentence. “I’m looking for a pet photographer in Tucson who has experience with shy and anxious dogs, ideally someone who comes to your home.” The intent is identical. The language is completely different. And increasingly, the AI assistant is where the research goes when a surface answer isn’t enough, or where it starts altogether. That’s how I use these tools as often as not: a Google search that surfaces an AI Overview, then a deeper follow-up in Perplexity or Claude rather than clicking through to individual results. I know others who work the same way. The search bar and the chatbot aren’t separate behaviors anymore. They’re sequential steps in the same research session. They’ve also drastically lowered click through rates.

The People Also Ask research habit some of you have built is still useful. Run the questions people are asking, build content around the answers, show up when someone searches. The limitation is that PAA captures search bar language, and search bar language is only part of the picture now. Clients talking to Gemini or ChatGPT before they call you are using sentences. Run the queries yourself, in client language, and you get two things back: the actual vocabulary your clients use when they describe their problems, and a live measure of whether your content is producing accurate synthesis.

The same ten minutes of work. Two different kinds of useful information.

The queries worth running fall into a few types.

Discovery queries are what someone runs before they know who to call: “pet photographer in Tucson experienced with anxious dogs,” “headshot photographer who comes to offices in Tucson.” Intent-driven queries are specific service requests, phrased as a client would phrase them to an assistant: “I need someone to photograph my team at our location in Tucson, headshots, facility, some action shots.” Specialty match queries surface your niche credentials: “photographer in Tucson experienced with photographing veterinary practices.”

Run them across multiple tools. Gemini, ChatGPT, Perplexity at minimum. What one synthesizes about you and what another synthesizes may not match, and the variance tells you something about which sources those tools are drawing from and where your content is actually landing.

Screenshot what comes back, with dates. This changes. What Gemini said about me in early May is not what it said six months ago, and won’t be what it says in six months. The only way to track drift is to document it. I run this at least monthly, usually in a gap between other things. About ten minutes once you have the query set built.

What to do with what you find, how to use the client-language data to inform content, how to influence what gets synthesized, is a longer conversation. That’s a future issue.

What I can say now is that the queries your clients are running through AI tools are the most direct signal available for what they actually need answered before they call you. The PAA equivalent for conversational search isn’t a formal tool yet. The queries are free. The methodology takes ten minutes a month. The information is real.

This issue complicates it slightly.

Brian Halligan, the co-founder of HubSpot, posted something on X on May 4th that I’ve been thinking about since I read it. He’s been one of the louder voices arguing that companies need to make themselves “legible” to AI — meaning structured, documented, queryable, so agents can actually navigate them. He’s been right about this. I’ve been making a version of the same argument at the scale of a solo photography practice.

But the May 4th post introduced a counterweight he’s calling strategic illegibility. The idea: as you race to make your operation readable to AI tools, you may be translating proprietary knowledge into a format those tools can learn from, pattern-match against, and eventually surface to everyone else using the same platform. Your edge becomes their feature.

He offered three tests for deciding what to keep out of the legible layer. The vendor test: if a vendor sold an agent tomorrow that did this specific thing, would my competitive position weaken? The departure test: if the person carrying this knowledge left, would the company lose a capability that takes months to rebuild? The replication test: could a smart competitor read the legible version and rebuild the underlying capability?

These are useful questions. They’re also aimed at companies with teams, trade secrets, and enterprise AI contracts. Applying them to a solo photography practice requires some translation.

Run the vendor test against a voice guide. If every photographer in your market had access to your voice guide, would your competitive position weaken?

Probably not much. A voice guide captures how you write, what you won’t say, how your credentials translate into client language. It’s specific to you, which means it doesn’t become a generic tool in someone else’s hands. The generic photographer problem runs in the other direction: a photographer without voice documentation produces output that could belong to anyone. Your voice guide is the solution to that problem, not a new version of it.

Now run the vendor test against your pricing intuition. Against the specific read you’ve developed for when a client is genuinely interested versus being polite. Against the sequence you use in a viewing appointment when you sense hesitation about the larger collections. Against the thing you notice in the first five minutes of a session that tells you how the next hour is going to go.

Different answer.

There’s a body of work in philosophy and cognitive science on why certain forms of expertise resist documentation. The foundational version is Michael Polanyi’s observation, made in 1966, that we know more than we can tell. He wasn’t making a mystical argument. He was making a practical one: a significant portion of what makes an expert an expert is knowledge that lives in pattern recognition, in physical disposition, in the gap between what a rule says and what a specific situation requires. When you try to articulate that knowledge fully, something changes. The articulation isn’t the thing itself.

This argument has been updated for the AI context. A scoping review published in 2025 in AI & Society, covering practitioners across creative industries, described it this way: embodied practices resist technological substitution because they encompass contextual judgments, sensory discriminations, and improvisational responses. The Photographic History Research Centre has an entire 2026 conference organized around this question for photographers specifically, framing it as the unspoken, embodied, and often invisible forms of expertise that shape photographic practice.

There’s also a counter-argument worth taking seriously. Subbarao Kambhampati, an AI researcher at Arizona State, has argued that the history of AI progress is largely a history of conquering domains that were once considered tacit and therefore AI-proof. Face recognition. Motor control. Pattern detection in medical imaging. Each of these was supposed to be beyond systematization. Each yielded.

So the honest version of the argument is this: right now, in 2026, the judgment that comes from twenty years of sessions is yours in a way that documentation can’t fully capture and AI can’t replicate. How long that remains true is an open question. Methodology built for today, with eyes open about tomorrow, is the most defensible posture available.

That’s not a guarantee. It’s a hypothesis with good supporting evidence and an important dissenting voice. Science operates that way too: a hypothesis becomes a working theory through accumulated evidence, and the accumulated evidence usually surfaces complications along the way. This is where we are.

So what belongs in the documented layer, and what belongs in the other one?

The documented layer is the infrastructure for making AI useful: your voice, your prohibitions, your credentials and what they mean in plain language, your process as it exists on paper, the context that lets a tool operate in your register rather than the generic one. This is what the last three issues covered. It’s worth building and worth maintaining.

The other layer is harder to describe precisely, which is part of the point. It includes the judgment calls that don’t have rules: which images to show first in a viewing appointment and which to hold back, what you notice about a client in the first exchange of emails that changes how you prepare, when to stop pushing for the frame you planned and make a different one, how you read an animal that’s been in a kennel for three weeks and hasn’t had meaningful human contact since intake. These aren’t secrets you’re protecting from competitors. They’re not things you’ve decided to keep proprietary. They’re knowledge that exists in the doing, and writing them down in any form usable to an AI tool would require simplifying them past the point where the simplification is accurate.

The test Halligan offers is whether a competitor could read the legible version and rebuild the capability. I’d reframe it slightly for this context: could you write it down without making it smaller? If the answer is no, it belongs in the other layer. Not because documentation is dangerous, but because the documented version wouldn’t be the thing you’re trying to protect.

This distinction between the two layers isn’t unique to photography, or to AI. Any craft practice has knowledge that lives in the practitioner and knowledge that can be transmitted through instruction. What’s changed is that the documented layer now has a powerful collaborator who can work from it across sessions, maintain consistency at scale, and handle the output that doesn’t require judgment. That’s genuinely useful.

What hasn’t changed is that the undocumented layer is still where the work comes from.

The voice guide doesn’t make the photograph. It helps the writing that surrounds the photograph sound like the person who made it. That’s the right job for it. Knowing what job you’re asking it to do, and what jobs it can’t do, is what keeps the whole system calibrated.

What the shelter taught me, though, is that being patient and appearing patient to an animal are not the same thing.

You can be waiting in a way that transmits urgency. The posture is right, the pace is right, but something underneath is impatient, and animals receive that signal with a directness that humans are too polite to acknowledge. The dog doesn’t ignore it. The dog goes further into the corner.

I’ve been volunteering my photography services to animal welfare organizations since 2005. That’s not a credential the way MFA is a credential or CPP is a credential. It’s closer to what happens to your eyes when you spend enough mornings photographing animals who have no stake in cooperating with you. They can’t be coached before the session. They can’t be reasoned with. “We’re just going to do a few quick shots for the website” is not a phrase that lands with a dog who arrived three days ago and has spent most of those days unsure about the nature and intentions of every person who walks through the kennel door.

What you learn, over enough of those mornings, is that comfort isn’t something you can explain into existence. You build it through behavior, through specific behavior, over specific time. The animal watches what you do, not what you intend. The shutter sound either becomes routine or it doesn’t, based on how many times you’ve fired it with nothing bad following, not based on whether you told them it was going to be okay.

This is one of the places where mirrorless cameras changed something real. An electronic shutter fires in complete silence. No mechanical sound, no auditory signal, nothing. The first time you photograph a cat using silent mode and the cat doesn’t move because, from the cat’s perspective, nothing happened, you understand immediately why that matters.

At some point, I started noticing something in executive portrait sessions that I had only previously encountered in shelter work.

The senior partner who sits down in front of the camera with the expression of someone waiting for a moderately unpleasant medical procedure. The real estate agent who has been photographed for fifteen years and has learned, over those fifteen years, to produce a version of her face that reads as friendly without actually being present. The physician who is technically sitting still and technically looking at the camera and is, in every frame, obviously somewhere else.

You might be able to fool some people with that. After enough hours studying faces on a screen, you don’t fool me. And in an era when everyone has been photographed more, through video calls and social media and LinkedIn profiles and every other surface that requires a face, the performance has gotten more practiced. More practiced performance is still performance, and a portrait needs a person, not a performance. That problem has gotten, if anything, harder to solve.

I don’t know exactly when I made the connection between the two. It wasn’t a revelation so much as a gradual recognition, the kind that arrives on the drive home after a session when you realize you’ve been solving the same problem in a different species. The nervous executive is not nervous in the way a frightened dog is nervous. But the underlying fact is similar: they’ve learned to produce a performance of being photographed rather than to actually be photographed.

The tools for addressing it are, I’ve come to understand, essentially the same.

Work slowly, not to seem patient but because working slowly is patience. Move the camera before it’s technically ready, because waiting for perfect stillness transmits your own tension back. Let silence do work. Don’t fill every quiet moment with direction. Ask questions that require actual thought, not the performance of thoughtfulness. Watch for the moment when the person in front of you forgets, briefly, that a camera is present.

That moment exists in most sessions. It doesn’t always arrive. It arrives more often when you’ve built the conditions for it than when you’ve tried to manufacture it by asking someone to relax, which is an instruction no one in the history of photography has ever successfully followed.

People occasionally ask whether I use treats in executive portrait sessions.

No, but the squeaker works great.

This lands differently in different contexts. To someone who doesn’t know the shelter work, it reads as a photographer being glib. To someone who does, it describes something real: the skill set I’ve built over twenty years photographing animals who can’t be coached translates, more directly than I initially expected, to photographing professionals who are performing being comfortable rather than actually being comfortable. The squeaker is shorthand for a longer argument about where the expertise lives and what it was built from. And it’s unexpected. Sometimes unexpected is the right tool in the camera bag.

This is the penultimate issue of what I’ve been calling the AI foundational arc. Over the past several months we’ve covered voice infrastructure, the generic photographer problem, persistent identity, recognizable output. The argument underneath all of it has been that AI tools work differently when there’s something real for them to work from.

What the patience transfer has to do with that is this.

The voice guide doesn’t capture my writing by analyzing my sentence patterns. It captures my writing because I know what I believe about photography and why, and most of that knowing came from mornings at shelters, from sessions that went nowhere useful, from dogs who spent ten minutes watching me before they decided I was worth approaching. The methodology, the voice infrastructure, the project knowledge, the feedback loop, those are a system for preserving something. The something came first.

I’ve been building that something since 2005. Some of it came from the MFA. A different part came from the shelter. And a specific part of it, the part that shows up most clearly in executive portrait sessions, came from the realization that the skills I was building by photographing animals who couldn’t be reasoned with were exactly the skills I needed for photographing professionals who had learned to reason themselves into a performance.

The squeaker is a joke about a real thing.

That’s usually where the useful insights live.

Schema markup is confirmed to help Google and Bing’s AI systems understand and represent your business more accurately. Both companies have said so directly. Google’s Ryan Levering described structured data as playing a “critical role” in grounding their generative AI systems. Microsoft’s Fabrice Canel stated explicitly that schema helps their LLMs understand content. A controlled experiment published by Search Engine Land found that only the page with well-implemented schema appeared in a Google AI Overview, while the page without it wasn’t surfaced at all.

What’s not confirmed: whether schema has any direct influence on ChatGPT, Claude, or Perplexity. Those systems haven’t said. The likely mechanism is indirect — schema feeds Google and Bing’s knowledge graphs, which then feed retrieval pipelines that AI generators draw from. Your JSON-LD doesn’t have a direct line to ChatGPT. It works by making your business more legible to the systems that supply the AI, not by talking to the AI itself.

That distinction matters because it changes what you’re building. Schema isn’t a shortcut to AI recommendations. It’s infrastructure that makes everything else you’re doing more interpretable to the systems that matter.

One more honest thing before the implementation details: what others write about you matters more than what you write about yourself. Research analyzing AI brand citations found that roughly 85% come from third-party sources — press mentions, directory listings, venue blogs, review sites — not from the brand’s own content. Schema makes your first-party content more legible. No amount of it replaces the third-party signal you earn by doing visible work in the world.

With that context established, here’s what I built and why.

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This isn’t a new idea. It’s a newly official one.

Sullivan’s framing at the event was commodity versus non-commodity content. Commodity content is factual, widely available, offers no unique perspective, and requires no expertise to produce. Non-commodity content carries your original voice, your particular take, your firsthand experience. The test he offered: only you could have written it.

Kipp Bodnar at HubSpot picked up the framework almost immediately in a Marketing Against the Grain episode, extending it into a six-checkpoint version with a grader tool. That’s where I encountered it, and it’s what I’ll use for the audit below.

If that framing sounds familiar, it should. This newsletter has been calling the same thing the generic photographer problem since March. The argument I’ve been making is that AI tools without voice infrastructure produce output indistinguishable from what any model would generate for any photographer anywhere. Sullivan is making the same argument from the search side. The language is different. The problem is identical.

I find it useful when Google confirms something I’ve been worried about for a while. Less useful when I realize I need to go audit my own site.

The framework

He built a grader around the six checkpoints that scored the top-ranking Google result for countertop material comparisons at 9 out of 100 during a live demo in the episode. A page sitting at number one on Google, scored against this framework, nearly bottom of the scale.

That’s the gap between traditional SEO ranking and AI-era discoverability in one number.

The six checkpoints:

Proprietary evidence. Data or documentation only you could have. Not “professional photographers recommend” but a specific customer’s shoes at 402 miles with 4mm of foam compression on the right heel, photographed in your shop.

First-hand experience. I did, I saw, I built. The word “I” followed by a verb describing something that actually happened.

Specificity. Names, numbers, dates, places. The test Bodnar offers: for every adjective, ask whether you could replace it with a number, name, or date. Fast becomes 11 seconds. Popular becomes 4,200 orders last month. Recent becomes March 14th.

Point of view. A line you’re willing to lose business over. The example from the episode is a countertop installer who won’t put marble in a house with kids, and will walk away from the job. The test: can you start a sentence with “I believe” or “I won’t”? If not, you don’t have a point of view, you have a position statement.

LLM test. Could ChatGPT write this without your input? If yes, it’s commodity. If the piece depends on your being there, on something you documented, on something only you could have observed or decided, it passes.

Information gain. Does the reader learn something not in the top three Google results? Before publishing, search your own headline and read what comes up. If you can’t circle three facts in your piece that aren’t already out there, your information gain is zero.

What happens when you run it against a real site

I ran it against michaelklothphotography.com using Bodnar’s grader, because if I’m going to argue that photographers need to think about this, I should be willing to show what it looks like on my own pages.

The first result stopped me. I submitted the blog URL and got a score of 21 out of 100. Mostly commodity. The grader’s verdict: “Teaser-only excerpt with a compelling hook but zero payoff — no data, no visuals, no firsthand detail delivered.”

Which is accurate. The blog index page surfaces teasers, not full posts. The grader evaluated what it could access, which was a premise and a “Read More” button. That’s not a failing grade on the blog. It’s a correct evaluation of what a blog index page actually is — and a useful illustration of how algorithms encounter your content when they can’t get past the surface.

I ran two actual posts next.

The parking lot/OR post — a session at Friends of PACC that started with a dog escaping in the parking lot and ended in a veterinary surgical suite — scored 69. The grader’s summary: “Strong firsthand storytelling with specific clinical detail, but undersells its own proprietary evidence.” Its evidence for the non-commodity rating: the runaway dog, the disconnected power adapter, the Valley fever radiograph series. Details, it noted, that no LLM or generic photography blog could fabricate. The score is held back by missing images in the text and a few passages that slip into explanatory mode.

Even Odds, the shelter photography retrospective published in March, scored 74. The grader’s headline: “Twenty years of dated metadata, grant-impact footage, and euthanasia statistics the author personally witnessed make this genuinely non-commodity.” It correctly identified the grant-impact footage and the direct causal link between the photography and a funded behavior program as the load-bearing elements. Points lost for passages that drift into reflective generality rather than scene-level specificity.

Three scores: 21, 69, 74. The 21 is a grader limitation — it evaluated a teaser, not a post. The 69 and 74 have a limitation of their own worth understanding: the grader couldn’t read the images. Squarespace serves images through its CDN with captions as separate elements, and the grader treated them as absent from the content. Both posts have images with captions. The grader scored neither of them. The 69 and 74 were earned on text alone. The actual posts are stronger than the scores reflect.

That’s the finding worth sitting with: the content doing non-commodity work scored in the non-commodity range without the grader being able to see roughly half its evidence.

The first thing the audit clarified is that the framework doesn’t apply uniformly across a site. Service pages and blog posts are solving different problems, and conflating them produces a misleading result. That doesn’t mean there isn’t something to learn though.

A service page is navigational and transactional. Someone searching “Tucson headshot photographer” is trying to answer three questions: do you do the thing I need, what does it cost, and how do I book you. That’s a commodity search intent, and the right answer to it is a page that’s clear, credentialed, and easy to navigate. Weaving in non-commodity storytelling where someone is trying to check a box is friction, not value. The service pages on the site are doing exactly what service pages should do.

The framework applies where someone is researching rather than ready to book. Blog posts. Informational queries. “How much do professional photos of a dog cost.” “Do headshots matter for LinkedIn.” That’s where the non-commodity requirement is real and consequential, because that’s where Google and LLMs are deciding whether your site demonstrates actual expertise or just answers the question adequately.

So the audit finding isn’t “the service pages fail the framework.” It’s that the framework applies differently depending on what a page is trying to do, and the blog is where it matters most.

What the blog is doing right.

The “Even Odds” post has the specifics the framework calls for: the 2015 training pilot, the 14,000 fewer euthanasias figure, the rainstorm session where what the shelter actually needed wasn’t photography. First-hand experience documented at the level Sullivan is describing.

The dog photography pricing post passes the information gain test because it’s honest market data from someone who’s worked with thousands of dogs, positioned to help readers decide whether to book me or someone else. That’s a perspective no aggregator site can replicate.

The Friends of PACC post — the one that scored 69 — wasn’t written with this framework in mind. It was written because the session happened and was worth documenting. The score reflects that. When the underlying experience is real and documented at the right level of specificity, the framework takes care of itself.

Where the gap actually is.

The commodity content on the site didn’t happen because the writing got lazy. It happened because some posts were written to answer search queries rather than to document experience. That’s a structural decision, not a writing failure. “Do headshots matter on LinkedIn” has search volume. “What I learned photographing anxious lawyers” does not. Most photographers make the same call.

The dentist who writes “How to Choose a Dentist in Atlanta” gets buried in AI Overviews. The dentist who writes “Why I Refused to Place an Implant on This 67-Year-Old Smoker” gets cited. The search-volume version of the headshot post answers the question adequately. The experience-documented version is the one that only I could have written.

That’s the tension the framework surfaces. Not a writing problem. A strategy problem. And the corrective isn’t to rewrite every informational post — it’s to understand which content is earning discovery and which is just answering the question everyone else is also answering.

The distinction the framework keeps returning to

Bodnar’s test for point of view is worth sitting with: can you start a sentence with “I believe” or “I won’t”?

The countertop installer says: I won’t put marble in a house with kids. I’ve done too many etched surfaces for families who wanted the look. Quartzite or nothing.

What’s the photography equivalent?

I have positions. I don’t deliver same-day digital galleries because I’ve seen what rushed editing does to a portrait, and I’m not willing to put my name on work that hasn’t had time to breathe. I won’t retouch out features that define someone’s face. I’ve walked away from conversations where what the client wanted and what the portraits needed were too far apart to close.

Those positions exist. Most of them aren’t on the site yet.

That’s the gap the framework identifies, and it’s useful because it’s specific. It’s not “be more authentic” or “show your personality.” It’s: name the line, state it plainly, accept that some readers will disagree with it.

Why this matters more now than it did six months ago

Sullivan’s observation at the Toronto event is worth taking seriously: AI features speed up a reality publishers have already been facing for years. Commodity content was always replaceable. What’s changed is the speed of replacement and the scale of the competition.

A blog post that could have been written by any photographer, about any market, for any reader, competes with every other post written the same way. In an LLM’s training data, those posts blur together. In an AI Overview, the one that gets cited is the one that contains something the others don’t.

The photographers building non-commodity content now aren’t doing something exotic. They’re documenting what they actually do, with the specificity of people who were actually there, from a point of view they’re actually willing to defend. The strategy question isn’t whether to do this. It’s recognizing which content decisions have been optimizing for search volume when they should have been optimizing for irreplaceability.

That’s the work. The framework just makes it easier to see where it’s missing.

The connection between this framework and the persistent identity methodology I write about in the paid tier is direct: what Sullivan is describing from the search side is the same problem the generic photographer concept describes from the AI output side. The mechanism underneath both is the same. The conversation will continue to evolve.

Kristen is the founder of Outcomes Consulting and a former executive director at Pima Animal Care Center. She runs a five-week course called AI for Animal Welfare Professionals - instructor-led, peer-to-peer learning, continuing education credits through CAWA and NACA. The people in her class are shelter directors, communications managers, foster coordinators. People running organizations on tight margins who need AI to actually work, not just theoretically work.

I replied the same day. I’ve been volunteering at shelters since 2005. This was not a cold audience.

A few emails back and forth and I had a 15-minute guest slot in her Tuesday class the following week. The topic was AI and photos - specifically, how I’m integrating AI into shelter photography work and what’s actually worth the time of people who need results, not experiments.

Fifteen minutes is enough to establish whether the overlap is real. It was. Kristen reached out afterward to set up a one-on-one Zoom.

That conversation was something I hadn’t quite expected. I went in thinking it would be a speaker briefing. What I got was closer to a peer exchange, and it went in both directions. She’d been working with Gemini Nano specifically because it edits photos without changing the appearance of the animals - which turns out to be exactly the right instinct, and something I’d been thinking about from the photography side of the table. She’s also vibe coding using Claude, which put us in a very similar place methodologically.

The Venn diagram between teaching photographers to use AI without losing their voice and teaching shelter organizations to use AI without losing theirs turned out to be almost a circle. Voice guides, prompt specificity, knowing when to stop iterating, the difference between output that sounds like someone and output that sounds like no one in particular - these are not photography problems. They’re organizational communication problems that happen to show up in the same form whether you’re writing a headshot inquiry response or an adoption bio.

I sent her a follow-up email with a few things I’d promised during the call. She invited me to speak at her First Fridays collaboration call.

First Fridays is a monthly open session for animal welfare professionals navigating AI adoption - broader than the class, open registration, people at different stages of figuring this out.

The topic Kristen wanted covered was AI and shelter photography: what tools actually help, where they create problems, and why the line between enhancement and misrepresentation matters for adoption outcomes. I had a clear point of view on the argument. What I didn’t have was hands-on experience with the specific tools her audience would be using. I work in Adobe Lightroom Classic and Photoshop for my professional editing - not what a shelter volunteer with a phone reaches for between kennel rounds. So the night before the call I ran a series of Gemini editing experiments on real shelter photos, specifically to understand what the tools could and couldn’t do for that workflow. Some worked cleanly. Some hit hard ceilings. One produced something alarming from a two-word prompt that felt completely innocent.

More on that in the paid section below.

For the deck I used NotebookLM. I fed it my outline and it generated a 15-slide presentation I exported as a PowerPoint. The design was genuinely impressive - the kind of visual polish that would have taken hours to build from scratch. The tradeoff: individual elements in the exported file weren’t editable, and there were a few errors I caught during review that needed correcting before the call. The PDF of the deck is attached to this issue. It carries the visual argument better than a description would.

The script and speaker reference card came out of a working session with Claude. By the time I sat down for the call I had a full script, a one-page bullet point reference card mapped to each slide, and five documented experiments ready if the audience wanted to go deeper.

The short version of the argument: two reactions to AI in photography and communications are common right now, and neither is useful. Uncritical adoption says AI can do everything, use it for all your imagery. Reflexive opposition says AI is fake and undermines authenticity. Both skip the actual question - does this help the animal get adopted? Operational pragmatism is the frame that actually produces useful decisions.

What happens next with Kristen I don’t know yet. My assumption is that she’s building a rotating roster of guest speakers so her monthly audience gets a range of perspectives over time. If there’s a May conversation I’d be glad to be part of it. Twenty years of shelter work, the animal welfare community, and a methodology built around authentic communication don’t often find each other in the same room. When they do it’s worth staying.

If you work in animal welfare or know someone who does, Kristen’s monthly calls are open registration. I’ve linked her organization above.

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The question I kept coming back to wasn’t whether AI tools are useful for photographers. That argument is settled enough to be boring. The question worth asking is more specific: what does the AI actually know about you right now, and how does it decide who gets cited as an expert when someone asks?

I asked four AI systems - Claude, ChatGPT, Gemini, and Perplexity - the same question: when someone asks you about professional photography, what sources do you treat as authoritative, and why?

The methodology

I used the same prompt across all four. I asked about source categories, specific organizations, how individual photographers’ published content gets weighted against major publications, what signals make a photographer’s website more likely to be treated as authoritative, and whether they had any visibility into how their sourcing behavior might change as AI-mediated search becomes more common.

I also asked each of them to be honest about what they don’t know about their own source weighting.

That last part turned out to matter.

I’m describing this methodology because it’s replicable. You can run the same prompt across the same systems and compare the answers yourself. The spread of responses - from Claude’s frank acknowledgment of its own limits to Perplexity’s thin genericism - tells its own story about what these systems actually know about themselves. The comparison is the research.

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The marketing conversation around this has mostly focused on lead generation tactics. I want to take it somewhere more useful for photographers, because I think the implications go deeper than the tactics discussion suggests.

It starts before the conversation does

The “invisible prompt” framing focuses on context built within a single chat session - the back-and-forth before someone finally asks for a recommendation. That’s real, but it’s only part of the picture.

People who use these tools regularly carry something with them that persists across sessions. ChatGPT, for instance, has built something close to a persistent user profile - enabled by default since mid-2025, pulling from conversation history to build an understanding of your interests, tastes, skill level, and personal context. Claude’s memory architecture works differently, opt-in by design and scoped to specific projects rather than building a unified personal portrait. The tools aren’t identical in how they do this. But the broader reality is that someone who has spent months asking about mid-century modern furniture, donating to animal shelters, and the paintings of David Hockney arrives at the question “who does pet photography in Tucson” already known, in ways that no intake form or pre-session consultation could replicate.

The AI isn’t just filtering recommendations against what someone said in the last half hour. It’s personalizing against an accumulated portrait of a person.

What this actually means for the match

Think about what that portrait contains. Someone with contemporary design sensibilities and a strong preference for punchy color in their art collection is probably not a great fit for a photographer whose work runs toward traditional Rembrandt lighting and muted tones. The AI, if it has the signal to work with, knows that. It’s going to make a different recommendation for that person than it would for someone whose searches skew toward classical portraiture and traditional interiors.

This is new. We’ve always known that not every client is a good fit for every photographer. Experience teaches you to read the inquiry, ask the right questions, feel out whether your aesthetic sensibility and theirs are going to produce something both of you are proud of. Sometimes it’s obvious. Often it isn’t until you’re in the session, or worse, in the reveal.

What these tools are building toward is a matching layer that happens before the inquiry. The client who finds you through an AI recommendation may arrive already pre-filtered in ways you can’t see and didn’t engineer. They found you because what you put into the world aligned with who they already are.

The honest implication

Here’s where the marketing conversation around AI visibility goes sideways: it starts looking for the lever. The optimization play. The way to get recommended the same way photographers learned to game local search rankings.

Pay-to-play AI advertising has arrived, but not in any form that’s relevant to this conversation. ChatGPT launched sponsored placements in early 2026 with a $200,000 minimum commitment, access restricted to a handful of major agency holding companies running campaigns for Apple, McDonald’s, and American Express. Microsoft Copilot and Google’s AI search features have their own ad products at similar enterprise scale. Anthropic has explicitly committed to keeping Claude ad-free, and spent money on Super Bowl advertising making that point directly. Perplexity tested ads and abandoned them entirely, concluding they undermine trust in the responses.

So yes, pay-to-play exists. It exists at a scale that has nothing to do with working photographers, on platforms that a meaningful portion of your potential clients aren’t using for this kind of search. The playing field for the rest of us is still organic. And on the platforms most committed to keeping it that way, organic authority is the only game there is.

What the research actually shows

Here’s the part that the content optimization conversation tends to underemphasize: what others write about you matters more than what you write about yourself.

Research analyzing AI brand citations across major platforms found that roughly 85% come from third-party sources, not from the brand’s own content. Press mentions, directory listings, client reviews, venue blog posts that name you specifically, industry articles that reference your work - these are the dominant signal AI systems use when deciding who to recommend. Your About page matters. Your blog matters. But the mention in a local interior design blog, or the wedding venue that lists preferred photographers, or the review that describes your specific approach to anxious animals - those matter more.

The implication for photographers is practical: earning mentions in the right places is at least as important as publishing the right content on your own site. Probably more important. This doesn’t change what good content strategy looks like, but it adds a dimension that most photographers aren’t thinking about yet.

This is a call to put real work into the world

Which brings me to what I think is the most useful reframe, and the one that matters most for working photographers.

This isn’t primarily a marketing strategy. It’s a case for putting work and thinking into the world that honestly represents who you are as a photographer.

The photographer who has spent twenty years developing a specific approach to anxious animals, and who has written honestly about what that looks like in practice, and whose work reflects a genuine and identifiable aesthetic point of view, is building signal that AI tools can actually use. Not because they engineered it for discoverability. Because it’s true, and it’s specific, and it exists in enough places that the tools can find it and trust it.

The photographer who wants to be everything to every client, whose content is designed to appeal to the broadest possible audience, produces signal that either gets lost in the matching process or - worse - matches them to clients who aren’t actually a good fit. Which helps no one.

Experience teaches most photographers eventually that the clients who are right for your work are the ones worth finding. The invisible prompt, at its best, is a mechanism that accelerates that matching - and the foundation you build today, accurate representation of real work and genuine perspective, will matter more as these tools get better, not less.

When the pay-to-play layer eventually reaches the scale where working photographers need to think about it, the ones who built honest signal into their public presence over the years before that will have something to protect. The ones who waited for an optimization strategy will be starting from scratch.

Put real work out there. Write honestly about what you do and why. Earn the mentions that come from doing the work in public. Let the AI figure out who it’s for.

That part, it turns out, it’s already pretty good at.