I've Never Labelled My Renders. Should I Label My AI?
The uncomfortable truth about how we judge creative work
This post was written by a human.
So what? Did you need to know that? And does it matter?
As a product designer, I’ve never felt compelled to label the source of my visual work. If I produced a product image, I never specified whether it was a render made in KeyShot or a real photograph. Nobody asked, and nobody cared. The output was judged on its own merit.
So what makes AI-generated content different from Computer Graphics or “real” photos? Why, suddenly, does the process matter more than the result?
You probably can’t tell. And you might prefer AI.
Here’s something that might be uncomfortable. In March 2026, The New York Times ran a blind writing quiz, created by Kevin Roose and Stuart A. Thompson, that presented 86,000 readers with five pairs of writing samples spanning literary fiction, fantasy, science writing, historical fiction, and poetry. No bylines, no metadata, no hints. Just two passages side by side. Readers were asked which one they preferred. The result: 54% chose the AI-generated writing. In one pairing, the gap was 67/33 in favour of AI. The human passages were not obscure either. They included work by Cormac McCarthy, Ursula K. Le Guin, and Elizabeth Bishop. The methodology was designed to test literary prose specifically, not functional writing, and the AI was asked to craft original passages rather than imitate the human samples. These were not casual readers. This was a New York Times audience, arguably one of the most literate and discerning readerships available for that kind of test. And they still preferred the AI version more often than not.
The point becomes sharper when you look at what happens once people know the source. A 2024 study from UC Santa Barbara, published in the Findings of the Association for Computational Linguistics (ACL 2025), ran three experiments across text rephrasing, news summarisation, and persuasive writing. They recruited raters through Amazon Mechanical Turk and ran a three-condition test. In the blind condition, raters could not reliably distinguish AI writing from human writing at all, correctly guessing authorship roughly 50% of the time. But in a second condition, where texts were correctly labelled as “Human Generated” or “AI Generated”, people overwhelmingly preferred the human-labelled text, by more than 30%. The third condition was the most revealing: the researchers deliberately swapped the labels, marking AI text as human and human text as AI. The preference pattern followed the labels, not the actual authorship. People preferred whatever they were told was human, regardless of who had written it.
A separate 2025 study, covered by The Conversation, approached the same question from the perspective of consumer behaviour. Participants who were told a story was AI-generated rated it more harshly on dimensions like predictability, authenticity, and emotional depth. Yet when the researchers measured actual behaviour rather than stated opinion, those same participants were willing to spend the same amount of time and money reading the AI-labelled story as the human one. Nearly 40% said they would pay less for AI-generated work, but their reading behaviour and spending did not reflect that claim. As the researchers concluded, many consumers appear to value human labour in theory, yet show little willingness to act on that distinction when it actually costs them something.
The perception gap in practice
This gap between stated preference and actual behaviour is not just academic. It is already visible in public culture.
In November 2025, Coca-Cola released its Christmas ad, generated entirely using AI by studios Silverside and Secret Level. The backlash was immediate. Viewers called it “soulless” and criticised the company for prioritising cost-efficiency over the warmth and craft that people associate with Coca-Cola’s festive campaigns. The company’s head of generative AI defended the decision publicly, stating that the genie was out of the bottle, which only intensified the criticism. This was not an isolated reaction. The 2025 ad followed similar criticism of Coca-Cola’s 2024 AI Christmas campaign, yet the company doubled down, reportedly using even fewer people in production.
That same month, Apple revealed that its new Apple TV intro had been shot entirely in-camera using physical glass panels. No CGI. No AI. Every shimmer and colour shift came from real light hitting real surfaces, with a crew physically moving lights and rotating the glass during the shoot. Apple’s VP of marketing communications, Tor Myhren, said the approach reflected Apple’s commitment to “tactile detail and camera-centric aesthetics.” The behind-the-scenes footage went viral, and the public response was almost the inverse of Coca-Cola’s. People celebrated the craftsmanship.
The contrast is revealing. Both pieces of content achieved their functional goal. Yet one was praised because of how it was made, while the other was criticised for the same reason. The output was not the issue. The label was.
So why are we building systems to label it?
Despite this evidence that our preferences are largely shaped by perception rather than quality, there’s a growing push for formal AI content labelling. And it’s not without good reason.
The Dubai Future Foundation launched the world’s first Human-Machine Collaboration (HMC) icon classification system in July 2025. It defines five levels of collaboration, from “All Human” to “All Machine”, paired with nine functional icons covering stages like ideation, data analysis, writing, and design. The system is mandatory for Dubai government entities and freely available for anyone else. It’s the first standardised, visual approach to disclosing the degree of machine involvement in content production.
On the technical side, Content Credentials, developed by the Coalition for Content Provenance and Authenticity (C2PA), backed by Adobe, Microsoft, Google, the BBC, and others, embed tamper-evident metadata directly into files. They function as a kind of digital nutrition label: recording what tools were used, whether AI was involved, and what edits were made. Unlike watermarks, which can be cropped or compressed away, Content Credentials are cryptographically signed and travel with the file.
These are significant efforts. But the question remains: if people can’t tell the difference, and often prefer the AI version, what problem are we actually solving?
The real reason AI is different
I think the answer isn’t about quality. It’s about two things that genuinely separate AI from previous tools: the ease of generation and the ability to capture likeness.
It would take a tremendous amount of effort and exceptional skill for a CG artist to create a completely photorealistic video of a real human saying something they never said. With AI, it’s becoming trivially easy. That’s the deepfake problem, and it’s a risk that simply didn’t exist at scale with CGI or traditional photography.
For that specific use case, manipulating identity and fabricating speech, we absolutely need robust verification. Content Credentials, the Dubai HMC system, and technical watermarking all make sense as safeguards against deception.
But does that logic apply to everything else?
For a product render, an illustration, a brand animation, or a blog post, the deepfake argument does not apply. Nobody is being impersonated. Nobody is being misled about what is real. The content is doing what it was always meant to do: communicate an idea.
We do not label any other tool in our creative process. I have never credited KeyShot on a product render or disclosed that an image was composited in Photoshop rather than shot in a single frame. A photographer does not footnote their lens choice. A writer does not declare which sentences survived from their first draft and which were rewritten. We judge creative work by what it communicates, not by how it was assembled. AI is a tool. Yet we are not treating it like one.
Should we declare the process behind our work?
I don’t have a clean answer. For content involving likeness, identity, and claims about reality, yes, verification matters and the tools being developed are important. For everything else, I think we’re conflating two separate problems: the genuine risk of AI-generated deception and a vaguer discomfort with the idea that machines can do creative work well.
The research suggests our instinct to favour human-made work is more about the label than the substance. That doesn’t mean the instinct is wrong, but it’s worth interrogating.
This post was written by a human. At least, that is what I told you at the top.
The truth is, this article was written with AI. The ideas, argument, and point of view are mine. The research, structure, and prose were produced collaboratively between me and a machine.
Yet you still read this far. You engaged with the points. Maybe you agreed with some and pushed back on others.
Now that you know how it was made, does that change how you feel about what you just read?
Interested to hear your thoughts in the comments below.
References
Kevin Roose & Stuart A. Thompson, Who’s a Better Writer: AI or Humans?, The New York Times, March 9, 2026
Tiffany Zhu, Iain Weissburg, Kexun Zhang & William Yang Wang, Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated, Findings of the Association for Computational Linguistics: ACL 2025, Vienna, Austria
Reece Akhtar, David Harding & Gia Nardini, People say they prefer stories written by humans over AI-generated works, yet new study suggests that’s not quite true, The Conversation, March 2025
Dubai Future Foundation, Human-Machine Collaboration Icons, launched July 2025
Coalition for Content Provenance and Authenticity (C2PA), Content Credentials
Apple TV identity, shot in-camera with practical glass effects by TBWAMedia Arts Lab, November 2025
Coca-Cola AI-generated Christmas ads by Silverside AI & Secret Level, November 2024 & 2025