Art of Photography AI

Three Times More Likely to What?

Traces a citation-freshness statistic through several sources and finds that its meaning changed. A second source audit catches an AI footnote that attached the wrong evidence to a useful correction.

What I Give AI to Work With

Shows how saved research, project memories, authoritative documents, and a feedback loop give AI a more accurate account of Michael’s work. Explains how proposals, decisions, and published positions receive different weight.

The Machine Was Here. What About Me?

Examines why identifying an AI tool’s participation does not identify the person responsible for a work. Connects photographic authorship to creator assertions, private development records, and control over public provenance.

The Terms You Agreed To

A review of transcription tools reveals different choices about voiceprints, retention, model training, and consent. Michael follows the implications for client conversations in his own photography business.

I Was Asking for Painters

Revisits earlier visibility results after discovering that a category query was surveying painters. Explains why independent questions, documented assumptions, and a new baseline matter more than agreement between tools.

The Cheese Tithe

An unplanned experiment turns a photograph of Birdy into a character sheet and a song about cheese. Connects that play to the curiosity behind a long-running body of photographic work.

What “Human 0%” Gets Wrong

Documents the research, writing guide, directed drafting, editing, and feedback behind Michael’s newsletter. Questions whether an AI detector’s label can describe the human judgment and responsibility in that process.

Found, Not Chosen

Distinguishes named recognition from recommendation and examines how websites communicate the relationships within a photography practice. An audit also finds an invented URL in published schema; the later painter-query article revisits the measurement interpretation.

The Address Was Real. It Just Wasn’t Mine.

An invented restaurant address leads to an entity card and audience references that give AI verified facts to work from. Checking older website pages against those documents reveals outdated service language and biographical errors.

The Report Calls It Wasted Time. I Call It Tuition.

Reads AI fatigue research against the work of running a photography business with several AI tools. Explains how maintained context, voice documentation, and deliberate experimentation can turn repeated correction into a useful working system.

Eight Percent

Rebuilds an AEO grader after finding fabricated percentages and mixed live-search and training-memory scores. Describes observed recommendation inclusion, explicit uncertainty, and the need to preserve a trustworthy baseline.

The Grader I Started Using Instead

Tests a young newsletter’s thin public footprint and investigates fabricated competitor tables in AEO graders. Explains why a repeatable instrument must distinguish missing evidence from a confident estimate.

Memory That Doesn’t Ask

Examines the confidentiality risk when an AI’s memory carries real information from one client into another client’s work. Distinguishes that context failure from fabricated facts and argues for deliberate boundaries.

What the Tool Found on a Mature Brand

Compares recognition and recommendation across several photography queries and separates live-web evidence from training-data recall. The later painter-query review qualifies how one of those category results should be interpreted.

The Wall Visualizer Works Now

Closes the wall-visualizer build with repairs to the send function and a better account of physical print sizes. Shows what the working tool contributes to a client’s decisions about artwork on a wall.

It’s Now in the Documentation

Introduces a series examining AEO measurement after Google’s documentation addressed generative-search services and third-party tools. Sets up the mature-brand test, young-brand comparison, and grader rebuild.

The Queries, The Prompts, and What To Do With What Comes Back

Provides separate methods for audience research and client-simulation queries. Shows how client language, third-party evidence, and dated repeat runs can inform content and reveal recommendation gaps.

What Were You Doing?

Uses client-language queries to inspect how AI describes a photography business. Explains why both owned content and independent sources matter, and why answers should be recorded and compared over time.

What to Keep Out

Examines what belongs in voice documentation and what embodied professional judgment cannot be accurately reduced to instructions. Sets a boundary around the otherwise useful work of making a practice legible to AI.

The Patience Transfer

Connects years of shelter photography to the patience and observation needed in executive portraits. Explains why voice documentation needs something real, formed through experience, to preserve.

What Schema Actually Does (And What It Doesn’t)

Explains the relationships among business, person, credentials, and outside references in a photography site’s schema. Describes the implementation and its maintenance while separating structured declarations from evidence of improved recommendations.

Google Just Said the Quiet Part Out Loud

Applies a non-commodity content framework to real photography pages and connects it to the generic-photographer problem. Examines the different jobs of service pages and experience-based articles, along with the limits of a grader.

From LinkedIn Comment to Speaking Relationship

A speaking invitation leads to hands-on experiments with AI editing for shelter photographs. Tests where enhancement becomes invented animal appearance, when iteration reaches its limit, and where human judgment belongs.

Who Does the AI Think You Are?

Compares four AI systems’ explanations of authoritative sources for photography. Examines credentials, institutional links, third-party validation, and the limits of a model’s account of its own sourcing.

The Client the AI Already Knows

Explores how accumulated client context can shape a photographer recommendation. Explains the value of descriptive third-party mentions and an honest, specific public account of a practice.

Building the Guide That Makes Everything Else Work

Builds a voice guide from real writing, documents the instructions that matter, and puts it into persistent project context. Shows how corrections improve the guide and defines recognizable output as writing identifiable without a byline.

Vibe Coding a Wall Visualizer, Part Two

Continues the wall-visualizer build through API constraints and a confident CORS diagnosis that did not fit the evidence. Describes the actual iteration work and the judgment needed to stop following an unsupported explanation.

I Spent a Day Vibe Coding a Room Visualizer for My Photography Business. Here’s What I Learned Before Testing a Single Line of It.

Begins a room-visualizer build using AI builder and reviewer roles, with the photographer directing the client experience. Documents human handoffs, platform constraints, and what remains untested.

You Need to Hear About This AI Thing

Uses an exaggerated confirmation email to introduce the difference between generic AI writing and a recognizable professional voice. Explains the role of documentation, project context, iteration, factual checks, and privacy choices.

Issue 1: How I Got Here, and Why That Matters Now

Traces Michael’s path from cancer biology to photography, shelter work, publishing, teaching, and deliberate AI adoption. Establishes the professional judgment behind the newsletter.

Welcome to the Art & Business of Photography in the Era of AI

Introduces the newsletter through an experiment with photographs of Chica and Birdy. Frames the project as a working photographer’s account of where AI tools deliver practical value.

Coming March 10, 2026 @ 9:00 AM

Announces the March 10 launch of the newsletter.