Written By: Zachary Morin

Introduction

Growing up, my family and I would frequently visit this historical village about a half hour away from my hometown. It was a small area filled with specialty shops ranging from local jewelry stores to candy shops selling some of the best fudge I’ve had to this day. Seeing as these were small, locally owned businesses, you typically paid a premium, but that never turned out to be an issue given the quality of the product you’d receive in return.

Over time (especially post-COVID), I stopped visiting the village as much, but recently I went back to show a friend visiting the place. I was appalled at what I saw. All along the doors of each shop were these extremely vibrant, cluttered, and clearly AI-generated advertisements, with so much information on them that digesting the message was a challenge. The visit became even more foreign when I saw a furniture shop selling wooden wall decor completely printed with AI-generated artwork. The worst part? Each piece, no larger than a notebook, was $70. It felt as if the experience of being in a hub of small, family businesses was stripped away by the abundant presence of computer-generated content.

Why tell this story? This moment in my life serves as just one example of how AI-generated content (also known as AI slop) is simultaneously turning consumers away from brands and taking business away from the designers and advertisers who bring true creativity to their work.

The village is just one example of this growing loss of sentiment toward brands. From smaller entities like family shops, youth sports programs, and local restaurants to large corporations, the reliance on AI-generated creative is increasing at an alarming rate, causing consumers, especially younger ones, to disassociate from these brands.

Taking a Step Back: What Is AI Slop?

AI slop is loosely defined as low-effort, mass-produced AI content. This form of content has been made widely available in part to large language models (LLMs) and generative artificial intelligence (GAI) tools such as Claude and ChatGPT becoming more accessible and adopted by the general public. Companies have increasingly been utilizing these tools to automate their marketing and advertising efforts, largely increasing the quantity of their advertising output.

There are three core types of AI slop: text, videos, and images/art. Each type follows a similar pattern of often being repetitive, bizarre in design, and irrelevant in terms of the information in the outputs. All three have gained a significant share of the content shown in social media mediums, with SQ Magazine reporting Instagram recorded over 60 million user label actions on organic content with AI info labels.

In many cases, these labels might flag minor edits made with AI; however, the evident issue lies with the growing quantity of extreme edge-cases. In my experience, this has involved both smaller groups and entities posting unedited AI flyers on Meta mediums as well as larger brands, primarily prediction markets, running high volumes of AI video advertisements on platforms like YouTube. No matter the social media platforms you use, I’m sure you’ve seen your own set of examples of AI slop content. Let’s dive into its effects.

What’s the Benefit?

The benefit of utilizing this form of content lies primarily, if not entirely, with the “generator” of the content. By using AI to create large quantities of content in short timelines, these individuals can save costs by relying on generated content over hiring a professional to design content at a higher rate than an AI subscription would be. This proves to be effective in the present at raising profits, but time may alter this conclusion much faster than these individuals believe.

The Costs (By Party)

Consumers

Simply put, the dramatic increase of AI slop content online has made digesting content very difficult. This type of content is not only very common to fill up one’s feed, but it also contains lots of misinformation, causing the user to have to spend additional verifying material they see online. Over time, this added layer of verification fosters frustration and distrust toward the subject entities posting this content, ultimately leaving consumers feeling betrayed, angered, and lost when searching for engaging, accurate, and quality content.

Generators

Generators is a term I’ve chosen to encompass anyone posting this content. From a small business owner to someone on a big-brand marketing team, the term is used to describe those creating and publishing AI-generated content for public view, typically as adverts.

You might ask: How could those profiting from this tactic also have significant costs? The answer lies in generational behavior and purchasing power.

A recent study shows that Generation Z (Gen Z) has prominent negative reactions to AI-generated content. Rival Technologies reports 65% of Gen Z individuals reacting negatively to AI marketing and 38% completely avoiding buying from participating brands altogether. Rival goes on to say 72% of Gen Z consumers have acted against brands, ranging from public complaints to walking away from a purchase.

These facts don’t seem to hold up in the present, as firms are successfully delegating their marketing and advertising work to AI without a dip in sales; however, this is where purchasing power comes in. According to Statista, the Baby Boomer Generation holds over 50% (51.6%) of the entire United States wealth distribution as of Q1 of 2026. This wealth disparity drives home why profits have sustained in the short term: Gen Z doesn’t have the money yet. With cases sprouting frequently regarding senior individuals falling guilty to AI-driven scams, a claim can be made that Baby Boomers, on average, struggle to identify if content is AI-generated or authentic.

Piecing these components together, the threat to generators lies in long-term success. In the now, these individuals can get away with cutting costs on marketing because the populations that oppose these practices lack the assets to make a significant impact; however, as time passes and the wealth distribution moves toward Gen Z, generators will likely experience a wave of brand resentment that this time around, eats into the bottom line.

What Can Generators Do?

This question can prove to be very case dependent, but when it comes to using AI, a few best practices can come a long way in the eyes of a consumer.

Context

This is prompt engineering 101. An AI tool will never follow one’s brand guidelines exactly unless it is provided with the examples it needs to source from. Brand colors, fonts, and logos are a given, but providing previous examples of creative or mockups of future designs not only gives the AI a foundation to build off of, but creates constraints that ensure the output is more aligned to one’s brand.

Editing and Reviewing

Avoiding AI slop starts with review. Once a user creates a piece of content with AI, it is rarely publish-ready. Reviewing the generated output and ensuring it contains only relevant, accurate information is key to improving both the quality and digestibility of your messaging.

Reviewing goes hand in hand with editing as well. Using AI for creative work only avoids being labeled as slop if a core value is kept in mind: AI is a collaborator, not a replacement for human creativity. Having the capability and skill set to edit and polish the output AI provides is critical to using the tool effectively without cutting quality.

Honesty and Avoiding High-Risk Scenarios

Marta Zwierz writes a great piece in a Brand24 Blog that reinforces a number of arguments I’ve made in this post, with her key takeaways providing immense value.

First, labeling AI content creates a stronger mitigation of distrust than most other strategies. By disclosing the use of AI in brand content, consumers are often less likely to gain distrust with a brand, creating a more positive sentiment toward the messaging.

Secondly, and arguably more importantly, is knowing when generated content is a bad idea from the start. Zwierz reports AI-generated product photos are the most common trigger of negative action among consumers, especially in industries like gaming, food, sports, and fashion. Industries like finance, luxury, and general media also serve as high-risk venues for publishing AI-generated content, with consumers reporting boycotts and trust drops climbing to elevated levels.

Generators navigating these high-risk scenarios have a few options, but none safer than simply hiring a professional to create authentic content for their brand. Although this may result in a larger expense than buying AI tokens, the benefits of consumer sentiment and trust will generate much higher profits over time than publishing AI-generated content at scale.

Closing Thoughts

When discussing the ethics and effectiveness of AI generated content, many opinions arise. From AI-first advocates to those who shade an eye at anyone who’s ever used an LLM, everyone has their own stance on how our population should react to such an evolving technology. My beliefs lie in the realm of using AI as a tool for all workflows, but ensuring a professional eye has taken numerous passes on the content before it ever sees the feed of a consumer. Regardless of one’s opinions, when analyzing trends among current actions and consumer sentiment, it is imperative that quality is never sacrificed for efficiency, especially in the practice of creating content. AI can only provide value as an enhancement, not a substitute.