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The creativity mirage
There’s a persistent fantasy floating around that AI is about to revolutionize creative work. Marketing copy, blog posts, ad campaigns, product descriptions—all supposedly ripe for automation. And yes, AI can produce these things. It can produce them quickly, in volume, and with a veneer of competence that passes muster on first glance.
But here’s the problem: it all sounds the same.
Not just similar. The same. The same cadences, the same transitions, the same rhetorical flourishes. If you’ve read enough AI-generated content, you start recognizing the tells. The em-dashes. The “Here’s the thing:” openers. The “In other words,” pivots. And my personal favorite red flag: the “This isn’t just X—it’s Y” construction, where some mundane thing gets elevated into a profound revelation.
This uniformity isn’t a bug that will get patched in the next release. It’s a fundamental consequence of how these systems work. Large language models learn patterns from their training data and reproduce those patterns. They’re extraordinarily good at producing text that looks like the text they’ve seen. What they can’t do is produce something genuinely novel, something that doesn’t already exist in the statistical distribution of human writing they’ve absorbed.
A recent Harvard Business Review piece by Rebecca Hinds and Robert Sutton highlights research showing that students who used AI to draft essays initially saw a spike in “creativity”—but those who started with AI-generated ideas showed reduced alpha-wave activity, a marker of creative flow. Their output converged on common words and ideas, becoming “very, very similar” to one another’s work. The tool that was supposed to unlock creativity was actually homogenizing it.
Where AI actually delivers
So if AI struggles with genuine creativity, where does it shine? The answer is almost embarrassingly mundane: structured transformation.
Take some input, apply well-defined rules, produce structured output. That’s the sweet spot. It’s not glamorous, but it’s where AI can genuinely save hours of tedious human labor without degrading the quality of the result.
Think about the tasks that make developers groan. Parsing CSV files and mapping fields to database columns. Transforming API responses from one format to another. Generating boilerplate code from specifications. Writing unit tests for straightforward functions. Converting documentation between formats. Extracting structured data from semi-structured text.
These tasks share common characteristics: the input is well-defined, the transformation rules are clear (even if tedious to implement), and the output needs to conform to a specific structure. There’s no creative judgment required, no subjective quality to evaluate—just mechanical translation from one representation to another.
This is work that humans can do, but probably shouldn’t. It’s error-prone because it’s boring. It’s time-consuming because the volume is high. And it adds no value beyond the transformation itself. When I spend three hours writing a script to parse vendor data into our internal format, I haven’t created anything meaningful. I’ve just bridged a gap that shouldn’t exist in the first place.
AI handles this brilliantly. Feed it a sample of the input, describe the desired output structure, and it will generate the transformation logic faster than you can finish your coffee. More importantly, it won’t get frustrated, won’t make typos from boredom, and won’t resent the task. It just… does it.
The input→output→consume pattern
There’s a specific pattern where AI really earns its keep: input → structured output → application layer consumption → input to other systems.
In plain terms: AI is fantastic at being a translator between systems that don’t speak the same language.
Consider a practical example. Your company receives purchase orders via email in various formats—some as PDF attachments, some as inline text, some as Excel files from vendors who apparently think it’s still 1997. A human currently spends hours extracting order details, normalizing them, and entering them into your order management system.
AI can sit in the middle of this flow. It takes the messy, inconsistent input (the emails and attachments), extracts the relevant data, structures it according to your internal schema, and passes it along for the next system to consume. The AI doesn’t need to be creative. It needs to be accurate and consistent at a task that’s well-defined but annoying. (This is exactly the kind of workflow that businesses like Magic Ingredient are built to handle.)
This pattern scales. Integration points between systems, data normalization pipelines, format conversions, validation and enrichment of incoming data—anywhere you have structured input that needs to become differently-structured output, AI can slot in and handle the transformation.
The key insight is that these tasks don’t benefit from human creativity. There’s no value in a creative interpretation of a purchase order. You want the exact quantities, the correct product codes, the right shipping address. The transformation is mechanical, even if implementing it would require a human to understand context and handle edge cases.
Why creativity requires a human in the loop
Here’s what bothers me about the “AI will handle creative work” narrative: it fundamentally misunderstands what creativity is for.
Creative work—real creative work—exists to produce something that surprises, that challenges expectations, that introduces ideas or perspectives that didn’t exist before. A blog post that sounds like every other blog post isn’t creative; it’s content. An ad campaign that follows all the standard patterns isn’t creative; it’s filler.
When you ask AI to write a blog post, you’re essentially asking it: “Look at all the blog posts that exist and produce something that fits that pattern.” The result will be competent, perhaps even polished. But it will never be distinctive. It will never carry a genuine voice. It will never say something in a way that makes the reader stop and think, “I’ve never thought about it that way before.”
This doesn’t mean AI has no role in creative work. It can be a useful collaborator—generating rough drafts to react against, suggesting alternatives when you’re stuck, handling the mechanical parts of creative production while you focus on the parts that require human judgment. But the moment you hand over the creative decisions to the AI, you’ve accepted convergence toward the mean. You’ve chosen to sound like everyone else.
For some purposes, that’s fine. SEO content, product descriptions, routine communications—these don’t need to be distinctive. They need to be clear, accurate, and optimized for their purpose. AI handles them well. But if you’re trying to build a brand voice, establish thought leadership, or create something that actually connects with people, you can’t outsource the creative judgment to a statistical pattern matcher.
Practical implications
So what does this mean for how you should think about deploying AI in your work?
First, audit your tasks for structure. Look at where you spend your time and ask: is this work primarily about transforming inputs into outputs according to known rules? If yes, it’s a candidate for AI. If the work requires judgment calls, creative decisions, or synthesizing information in novel ways, keep a human in the loop.
Second, be honest about quality requirements. AI-generated content can be “good enough” for many purposes. Internal documentation, first-draft communications, routine reports—these don’t need to sparkle. They need to convey information accurately. But customer-facing creative work, strategic communications, anything that represents your brand or builds relationships—these deserve human attention.
Third, think about AI as a translator rather than a creator. The most reliable value comes from using AI to bridge gaps between systems, formats, and representations. It’s middleware for information. That’s not as exciting as “AI writes your marketing copy,” but it’s where the ROI is most predictable.
Finally, remember that tools shape outcomes. If everyone uses the same AI tools for the same purposes, everyone’s output starts looking the same. The Hinds and Sutton research on student essays isn’t just an academic curiosity—it’s a preview of what happens to any domain where AI-generated content becomes the norm. Differentiation becomes harder, not easier.
The boring revolution
The real AI revolution won’t be televised because it’s not photogenic. It’s happening in data pipelines, integration layers, and back-office automation. It’s the slow replacement of tedious transformation tasks that nobody wanted to do anyway.
That’s fine. That’s valuable. That’s worth pursuing.
But let’s stop pretending that AI is about to make human creativity obsolete. What it’s actually doing is making the boring parts of work obsolete, freeing humans to focus on the parts that actually require a human perspective. The catch is that you have to correctly identify which parts are which.
The developers and businesses who get the most value from AI will be the ones who resist the temptation to automate everything and instead ask a simple question: does this task benefit from human judgment and creativity, or is it fundamentally about transforming structured inputs into structured outputs?
Answer that honestly, and you’ll know exactly where AI belongs in your workflow—and where it doesn’t.
Magic Ingredient helps small businesses work smarter with AI-powered workflows
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