If You Can’t Tell the Difference Between AI-Generated and Human-Generated Content, There Is No Difference

By Shelly Palmer

The debate over AI and its role in creative industries often centers on one question: Can AI ever be as creative as humans? While it’s tempting to philosophize about inspiration and ingenuity, this line of inquiry misses a crucial point for anyone tasked with making practical decisions about content creation: If the audience can’t tell the difference between AI-generated and human-generated content—or if they don’t care—then, for all practical purposes, there is no difference.

In advertising and marketing, content isn’t created to hang in a museum or win a Pulitzer. It’s created to achieve specific goals: attract attention, sell products, or raise awareness. This distinction—between production content and inspired content—is where much of the confusion begins. Production content is functional, created under tight deadlines to accomplish a clear objective. Inspired content, on the other hand, stems from the human need to express emotion, it is mostly unconstrained by time or utility. Comparing these two types of creativity is like comparing a business letter to a love poem: both involve language, but their purposes—and expectations—are worlds apart.

For a wide range of production tasks, AI is already “good enough”

I started my career as a composer and producer of commercial music. I’ve worked on literally hundreds of radio and television show themes, and thousands of jingles and underscores. So, I’ve got a pretty good idea of what clients will accept as finished work product. I can also say (without hesitation) that creative services and production companies are run like factories and efficiency and productivity are absolutely key to economic success. Clients have budgets – that’s what you can charge. Your profit is based on your ability to produce an acceptable work product at the highest possible margin.

Then, There’s The Audience

It probably won’t surprise you to learn that most people cannot reliably sing a simple tune in key or detect subtle off-key notes. Tone deafness has an actual name, “congenital amusia.” Add to this the reduction of music and art education in our K-12 system and you have a recipe for a generation (or two) of audiences that truly cannot discern the difference between best-in-class AI-generated music and journeyman-created production music. Our clients’ tests confirm this, if the audience isn’t told what it’s listening to, they have no idea how it was created, don’t notice, and don’t care.

Steve Jobs Proved The Point

The world of recorded music was irrevocably changed in October 2001 when Apple introduced the iPod. While it is well remembered as a stepping stone to the greatest comeback in American corporate history, the iPod is less well remembered for dealing the final, almost fatal, blow to sonic quality. First, audio files were compressed to fit on the iPod. Called “lossy compression” it reduced sonic quality by about 29X. Next, were the 29-cent earbuds (which Apple sold for $29 if purchased separately). Put all that together and you get the world of recorded music as mass marketed by Steve Jobs. You also get the death of sonic quality. The funny thing is, almost nobody noticed. (See: Hi-Res Audio: A Solution in Search of a Problem for more details).

This Has Nothing To Do With Enjoyment

Importantly, this has nothing to do with the enjoyment or emotional connection people feel to music. Everyone should sing like no one is listening, and everyone is the world’s foremost expert on the music they like. But production music isn’t about personal expression—it’s a tool. It’s designed to support a story, sell a product, or evoke a mood. AI is capable of doing this now and it’s getting better every day.

AI May Always Have Limitations

Will AI work for every case? No. Neither will every professional composer. Humans are magical and they do magical things – like create hit songs or timeless artwork or stories that capture our imaginations. That’s not what we’re talking about here.

In advertising, AI tools like ChatGPT and MidJourney are already crafting high-performing ad copy and visuals. This past year, A/B tests performed by our clients have shown AI-generated content performing on par with, if not better than, human-created alternatives. Background music for commercials, once the domain of human composers, can now be generated by AI in minutes, meeting technical and emotional requirements at a fraction of the cost. Why would a business pay a premium for human creators if AI can deliver similar or better metrics? You may cite ethical reasons, but in practice, every production function that can be automated, will be automated.

Generative AI is Not Artificial Creativity

Current generative AI platforms are not capable of originality. They lack the ability to originate truly novel ideas, struggle with emotional depth, and frequently require human oversight to ensure quality and ethical standards. But these shortcomings don’t negate its utility. Production content is not about originality or depth—it’s about efficiency, consistency, and scalability. In this context, AI fits seamlessly into the content factory model, offering significantly faster turnaround times and lower costs than traditional workflows.

The Right Tool For The Right Job

Which brings me back to where I started. Artificial creativity is not required to fulfill a growing range of production requirements. Generative AI is already doing a pretty good job of mimicking what we can fairly label, “average human creative output,” which is neither inspired or magical or truly creative. This is extremely bad news for people who do rule-based work in creative fields (such as setting type, sizing images, color grading, mixing, proofing, rudimentary editing, banging out an underscore for a chase scene in xyz musical style, etc). Said differently, no one should confuse creativity with execution. Most workers in the creative arts do not create, they technically execute someone else’s creative vision.

To that end, generative AI systems are improving their execution capabilities exponentially. Which means, deploying AI content production workflows isn’t just a matter of staying competitive—it’s an imperative. After all, if the audience can’t tell the difference, for all practical purposes, there is none.