The Three Giveaways of AI-Made Work: Four Pixels Off, an Invisible Stamp, and Nobody's Name

Start with the conclusion

Over the past two weeks there were three stories that look completely unrelated. They are actually about the same thing.

On October 7, Google opened its SynthID watermark detector to everyone. On October 9, Nikon stripped first prize from a microscopy competition after someone detected a watermark in the footage. Around the same time, an open-source project that generates diagrams for coding agents shot up the GitHub trending list, and its selling point was this: “no drop shadows, no Mermaid, and every spacing value must be divisible by 4.”

Detection, accountability, taste. But stack them together and “AI-ness” stops being a mystical word. It has three concrete sources: alignment, provenance, credit. The first two can be fixed. The third is very hard.

Giveaway one: four pixels off

The diagram project is called Diagram Design, and the author is Cathryn Lavery. It has a rule that sounds aggressively boring: every coordinate, width, and gap must be divisible by 4.

Her claim is that this is the key to output that doesn't “look like AI made it.” Sit with that for a second. You will never notice whether a box is 137 pixels wide or 136. But you will feel that something is off, and you won't be able to say what. The human eye is brutally sensitive to misalignment; it just reports that sensitivity as a vague discomfort rather than a clear verdict.

So the first layer of “fake” in AI images usually isn't in the content. It's in the rhythm. A human designer picks spacing values that form a system. A model hands you 13.5, 27, 54 — numbers that look close enough. Close enough is the whole problem.

The good news is that this layer is the easiest to fix. Set rules, use a grid, turn your spacing into variables. These are engineering problems.

Giveaway two: the invisible stamp

SynthID is an invisible watermark baked into a media file. You can't see it, and it usually survives compression and cropping. Since October 7, anyone can upload an image, a video, or an audio clip and check whether it carries that stamp. It covers more than Google's own models — OpenAI, Nvidia, and Kakao are in there too, and Apple is reportedly joining soon.

But what turned the stamp into a public story was Nikon.

Nikon's Small World in Motion competition gave first prize to a microscopy video of beating cilia by Xu Ning, showing airway tissue from a child with primary ciliary dyskinesia. After the results went public, scientists started picking at it. Some said purple structures appeared and vanished out of nowhere; others said the green cilia “came from God knows where.” A doctoral student at UT Southwestern Medical Center ran the footage through Gemini and found SynthID.

Xu's account: AI was used only in post, to separate and color structures of similar shape in reconstructed grayscale data; the original video and the cilia motion are real. Nikon's response was notably restrained — it said the entry “did not comply with this year's rules on generative AI,” framed it as a question of eligibility, and explicitly said this was not a judgment on the author's academic reputation or intent. The ranking was adjusted and the work pulled from the winners' gallery.

The hard part is this: microscopy imaging always involves processing. Denoising, deconvolution, false color — these are the steps that let you see anything at all. So the line Nikon had to draw wasn't “did you use AI.” It was “did your processing add something that wasn't there.” A watermark can't answer that. It only answers “some tool touched this file.”

The other case is more mundane. Fortnite listed a track called “Meow” whose cover art had a Gemini watermark in the bottom-right corner. Players spotted it, and the image was swapped. The distributor said the artist supplied the art; the artist said they had no idea. Nobody accused the platform of faking anything. The watermark did one small job: it told you who had touched the file. The social reaction did the rest on its own.

Giveaway three: nobody's name on it

The third layer is the hardest to fix, because it isn't a technical problem.

Wired reported this month that at least three of the five biggest US publishers — HarperCollins, Simon & Schuster, and Hachette — have used AI tools without public disclosure or telling authors. The uses went beyond covers: emails to literary agents, marketing copy, back-cover copy, promotional materials. Employees said much of it came as a management directive.

The three responses are telling. Hachette said it supports AI for “operational” work that helps authors' books reach more readers, but “does not support the use of AI for creative purposes, including communications with authors and partners.” The trouble is that emails to agents count as operations while back-cover copy counts as creative, and its own staff say the line has already been crossed. Simon & Schuster said employees can use a limited set of enterprise-grade tools, that use isn't mandatory, and that unapproved tools are prohibited — but “not mandatory” and “didn't happen” are two different things. HarperCollins did not respond.

Covers are the sensitive part because they're one of the few places in publishing where a name is explicitly attached. Illustrators have portfolios, rates, and agents. Replacing one commission with a generation doesn't just save money; it removes a doorway that new people use to get into the industry. And the author, whose name is printed on the cover, is usually the last to know where that cover came from.

This layer has no technical fix. A watermark can prove a tool touched a file. A grid can make an image look more human-made. But “who made this, and were they credited” can only be answered by policy: a line on the copyright page, a clause in the contract, a field in the metadata.

So how do you tell if an image “looks AI-made”?

Start with the skeleton. Is there rhythm in the alignment, the spacing, the negative space? This layer is about 80% of it, and it's almost entirely rules, not models.

Then look for traces. Is the invisible stamp there? Does the file have a traceable origin? This layer is being productized, and ordinary people are getting better at using it.

Finally, look for a name. Who made it, who approved it, who signed off. This layer has no tools — only habits.

The first two layers can be patched with technology. The third can't. It only gets fixed when someone is willing to put their name on it.