What to Keep Out
The Art and Business of Photography in the Era of AI
The last three issues were an argument for building something. A voice guide. Project knowledge. A feedback loop that compounds over time. The methodology is real, and I stand behind it.
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.