There’s no such thing as ‘AI slop,’ only sloppy thinking about how genAI models work.
I know, controversial, right?
Most people reading that are probably waving their hands in frustration and clawing at their keyboards.
But that’s exactly how I feel when repeatedly watching the tired-ass meme unfold in LinkedIn comments and industry discussions: professionals dismissing AI-generated content as “slop” or “digital theft.” The frustrating part isn’t so much the skepticism as the blatant dismissal stemming from a fundamental misunderstanding of how these models actually work.
You may wonder why I care, and I’ll explain: These criticisms extend beyond academic distinction into a “slop mindset” that creates destructive blind spots. The blinders of others keep those of us who actually understand these tools from being recognized as having legitimate skill. When these tools are constantly being bashed, it paints all who use them with the same brush, discouraging others from building the competencies they’ll need to stay relevant in a changing job market.
So I thought I’d throw my hat in the ring to set the record straight.
How AI Models Actually Learn
Newsflash! They’re Not Copy-and-Pasters or Thieves.
I recently got into it with someone on LinkedIn over the claim that LLMs pilfer other people’s work:
“They’re a parasitic technology. An “original” prompt parasites on someone’s previous intellectual input. They hardly amplify your own originality. They just delude you.”
The ‘theft’ argument misunderstands how these models function. They aren’t digital collages stitching together chunks of existing text. LLMs are trained to understand the underlying patterns and structures of language, much as a human artist studies those who came before them as they develop their own style.
When a model generates text, it’s not retrieving stored sentences from a database. It’s learned statistical relationships between words, phrases, and concepts, then uses those patterns to generate new combinations. That’s why LLMs overuse em dashes: it’s a common pattern in written language, so models reproduce the structure rather than the specific source material.
This distinction matters because it changes how we think about the output. A model trained on thousands of mystery novels doesn’t steal plot points; it learns narrative structures, pacing patterns, and character development frameworks. The application of those patterns to new scenarios is synthesis, not theft.
Why “Slop” Thinking Hurts Your Career
Here’s what happens when you dismiss GenAI as low-effort copying: you miss the skill gap that’s actually opening up around you.
I recently used a content writing system I developed to tackle an idea I’d been sitting on for months but couldn’t fully articulate. The LLM didn’t generate the idea—that was 100% from my brain—or the prompt I embedded in the system I created—which was also a creation of mine—based on trial-and-error experience. But the LLM did help me structure and refine the concept until it became my most popular Simo’s Substack post: Over 3K reads in < 3 weeks, loads of engagement, landed me a paid ad placement in the post, got me a spot on a podcast, and brought in paid subscribers.
The difference wasn’t in the use of GenAI, but in understanding how to architect a process that amplifies human creativity rather than replacing it.
Most people see that explanation and think: “AI wrote your post; it's slop.” But that’s like saying a word processor “wrote” a novel because it handled the typing. The skill lies in knowing how to guide the tool toward meaningful output.
What Real AI Skill Actually Looks Like
Despite what skeptics assume, there’s genuine knowledge and skill in working effectively with GenAI. As someone who’s spent the past 18 months developing structured prompts for workflow automations, I can tell you that what I can get a system to produce is fundamentally different from what someone else generates.
The skill isn’t in typing “Hey ChatGPT, write me a post about X.”
The skill is in:
Understanding constraint design: Knowing how to structure inputs so the model focuses on the right patterns and ignores irrelevant ones.
Process architecture: Building workflows that use AI for specific cognitive tasks while keeping human judgment in control of decisions and quality evaluation.
Output governance: Developing systems to catch drift, verify accuracy, and maintain consistency across multiple iterations.
Embedding GenAI models: Coding API calls and custom prompts into bespoke tools and workflows that improve operations and business systems.
This is structured-logic work, not cognitive offloading or button-pushing; though, when you perfect a system, you can trigger exceptional outputs with the “flick of a switch.” And the people mastering these processes are building valuable intellectual property, not stealing other people’s.
The Rise of Procedural IP
We’re moving toward a world where “data as an ingredient” becomes the standard for innovation. While fair use battles continue in courts, the more interesting shift is happening in how we think about intellectual property itself.
Academic and legal theorists are exploring how creators might copyright the IP around their proprietary AI processes. Your unique prompting logic, your workflow architecture, and your quality control systems become defensible business assets.
I’ve seen this firsthand, building custom prompts over the past year and a half. Not only is there value in the model’s output, but even more so, there is value in the repeatable process that consistently generates useful results. That process is intellectual property in a way that’s just starting to be recognized.
How to Start Building Real AI Competency
If you’re ready to move past the “slop” dismissal and start building actual competency, here’s where I’d focus:
Start with your existing work: Take a task you already do well and figure out where an LLM could handle the mechanical parts while you focus on the strategic decisions.
Learn constraint-based thinking: Practice writing detailed instructions for tasks. If you can’t explain the process to a smart intern, you can’t prompt an AI effectively. Then focus on what the models should and—arguably more important—shouldn’t do.
Test and iterate systematically: Don’t just try prompts once; run them constantly, noting where they break or drift, and then iterate the constraints until the results are where you need them, consistently. It's a finicky, often annoying, but critical process.
Focus on workflows, not individual prompts-to-outputs: The real power lies in chaining AI tasks into repeatable processes within a set system, not just in generating one-off outputs via a prompt.
The professionals who understand this distinction—who can architect AI workflows rather than just consume AI outputs—will have a significant advantage as these tools become standard business infrastructure.
Always Iterating
In the not-so-distant future, many will look back and wish they’d spent this time mastering the seachange instead of fighting it. The choice isn’t between human creativity and AI replacement but between understanding how to amplify your unique thinking with powerful tools or getting left behind by people who do.
The models are learning patterns. The question is: are you?