In the age of Generative AI, is advertising facing a battle between creation & generation?

In the past two years, generative AI has quietly moved from experimentation to everyday use across the advertising ecosystem. Tools that can generate visuals, scripts, voiceovers and edits within seconds are increasingly finding their way into agency workflows and brand marketing teams. The promise is simple. Faster content production, lower costs and the ability to produce multiple variations at scale.

But alongside this technological leap has emerged a growing concern within the industry. As generative tools make it easier to produce content in large volumes, the internet is witnessing the rise of what many now call AI slop. The phrase refers to low-effort, mass-produced AI-generated content designed primarily to capture algorithmic attention rather than deliver meaningful storytelling.

The phenomenon is no longer confined to obscure corners of the internet. Research indicates that between 21 percent and 33 percent of YouTube feeds may now consist of AI-generated or “brainrot” videos. In a simulation of a new YouTube account, researchers found that 21 percent of the first 500 Shorts recommended were AI-generated while 33 percent were brainrot videos designed to maximise addictive engagement rather than provide genuine value.

The rapid spread of such content highlights a deeper shift in the attention economy. When algorithms reward frequency, engagement and discoverability, the economics of generative AI makes it possible to produce content at unprecedented scale. The cost of production falls dramatically while the incentive to flood platforms with content increases.

Yet the advertising industry finds itself at an inflection point. While generative AI offers powerful new capabilities, the risk of diluting brand equity through low-quality automation is beginning to spark debate across agencies and marketing departments.

The economics of scale in the attention economy

The scale of the AI slop ecosystem reveals why it has become difficult for platforms and advertisers to ignore. Despite their low production quality, AI-generated content farms are generating enormous viewership and revenue. The top ten AI slop channels globally are estimated to generate more than $33.6 million annually through advertising revenue alone. Algorithmic amplification and Shorts discovery features ensure that content designed for quick engagement can travel widely across platforms.

Different regions have emerged as major consumption hubs. Spain leads in total subscribers for AI slop channels with 20.22 million subscribers, followed by Egypt with 17.91 million and the United States with 14.47 million. Brazil, Pakistan and South Korea follow closely behind.

India also features prominently in this ecosystem. The country’s AI slop channels collectively generate 2.32 billion views, placing India sixth globally in total views. The scale of the domestic audience reflects the country’s mobile-first internet culture and the massive consumption of short-form video. One of the most striking examples comes from India itself. The YouTube channel “Bandar Apna Dost” has emerged as the world’s most viewed AI slop channel with more than 2.07 billion views across over 500 videos. The channel features an AI-generated monkey placed in dramatic human-like situations ranging from family conflicts to emotional rescue narratives.

The monetisation potential is equally significant. The same channel is estimated to generate around $4.25 million annually through advertising revenue. For creators, the economics are compelling. Generative tools allow them to produce hundreds of videos quickly, turning automated storytelling into a profitable content engine. But the very incentives that make AI slop successful are also raising questions about the long-term impact on content quality and brand safety.

The tension between automation and brand equity

Within the advertising industry, opinions remain divided on how seriously the threat of AI slop should be taken.

For some industry veterans, technological disruption is nothing new. Sandeep Goyal, Managing Director of Rediffusion, believes that fears around generative AI are being overstated. “If your advertising is not speaking to the algorithm in the way it wants to process it, then your advertising almost becomes meaningless. Today it is often less about the strength of the creative idea and more about brute media power. You can make almost anything famous if you run it during the IPL and spend ₹200 crore on media,” Goyal says.

He argues that the advertising industry has historically reacted with similar skepticism to every new technology. “The concerns around AI are exaggerated. Every technological shift has faced similar skepticism. When digital arrived, people asked the same questions. When email replaced letters, people said it was impersonal. Eventually every technology becomes normal. AI will follow the same curve.” For Goyal, the more productive response is adaptation rather than resistance. “You can actually do some really exciting things with AI. Instead of resisting it, people should move with the times and explore what technology makes possible.”

Yet marketers themselves appear more cautious about how AI is deployed in brand communication.

Pawan Jagnik, Marketing Head India at McVitie’s maker Pladis India, draws a clear distinction between purposeful use of AI and what he describes as the rise of meaningless content. “We are firmly against what is commonly referred to as AI slop. Content that is created quickly with little thought and adds no real value to the brand or the audience,” he says. However, Jagnik acknowledges that generative AI can play a meaningful role when used strategically. “We do see merit in using AI to create contextual content. Initiatives like the ‘McVitie’s Premiere League’ after every IPL match use AI to capture match highlights in a way that is relevant and engaging for our consumers.”

The concern arises when brands chase short-term visibility at the expense of long-term identity. “The real concern is when brands flood platforms with random or disconnected content. Associating a brand like McVitie’s with something unrelated like a meme such as ‘Ganji Chudail’ may generate momentary attention but it ultimately dilutes long-term brand equity,” he adds.

The tension between instant visibility and sustained brand value lies at the heart of the AI slop debate.

Creation versus generation

Agencies are also grappling with a deeper philosophical question. Where does the line lie between creating advertising and simply generating it?

Manish Sharma, President of Arena Media at Havas Media Network India, believes the distinction is becoming increasingly important as clients themselves become more familiar with AI tools. “AI for us is an enabler. It is not something which is to be looked at as a replacement of anything because we work with a lot of data sets and consumer insights that keep evolving,” Sharma says.

He describes AI primarily as a research tool that helps agencies process large amounts of information rather than as a predictive creative engine. “AI works on past data and understanding that it has as a part of our research. It is an advanced research tool so to say. Prediction happens on the past journey.”

At the same time, Sharma notes that clients are beginning to ask a new question when reviewing campaign assets. “The first question that comes up is whether it is created or whether it is generated. Generation is something the client can do themselves because they know their brand better than anyone else. But creation is the domain of an agency.” The issue extends beyond creativity to data governance. Agencies routinely handle sensitive client information, and Sharma warns that feeding such data into open AI systems raises serious concerns around confidentiality and privacy.

“There are a lot of data points that clients share because we are an agency on record. We cannot make that data public by using AI as an open platform. Data privacy becomes a big chunk,” he says.

The rise of generative content also raises strategic questions about short-term engagement tactics. Sharma observes that certain AI-driven trends have captured attention among younger audiences, particularly Gen Z users who consume vast volumes of short-form content. Yet he believes the impact may be temporary. “If you are looking at short term gains then all this will work. But everything will have a shorter span of attention. When you look at the long term approach which is where the brand plays a very important role, you remember the brand meaningfully. Generating brands is not going to happen,” he says.

Platforms face a quality dilemma

For platforms such as YouTube and other social media networks, the rise of AI slop presents a complex balancing act. Generative AI represents one of the most powerful technological innovations in content creation. At the same time, an unchecked flood of automated videos risks overwhelming the ecosystem.

Researchers warn that excessive volumes of automated content can create an information noise problem. Algorithms may struggle to distinguish between meaningful storytelling and repetitive automated templates. As more creators adopt AI production pipelines, genuine creators risk being drowned out by automated uploads designed purely to maximise engagement metrics.

Another concern is the potential amplification of misinformation. Experts point to the illusory truth effect where repeated exposure to AI-generated visuals or narratives can lead audiences to believe something simply because they encounter it frequently. The implications extend beyond entertainment. For advertisers, the possibility that brand messages might appear alongside low-quality or misleading content raises questions about brand safety and trust.

The next phase of the AI content economy

The debate around AI slop reflects a broader transformation taking place across the digital economy. Generative tools have dramatically lowered the barriers to content production. What once required teams of editors, animators and voice artists can now be created by a single operator with a prompt. In this new ecosystem, visibility is increasingly shaped by algorithms that reward speed, volume and engagement. Creators and brands alike are learning how to optimise for these signals.

Yet the advertising industry has historically thrived on something harder to automate. The ability to translate cultural insight into memorable storytelling. As agencies and marketers navigate the next phase of AI adoption, the challenge will not simply be about whether to use generative tools. It will be about how to ensure that automation enhances creativity rather than replacing it.

The rise of AI slop may be a byproduct of technological progress. But the long-term future of brand building will likely depend on whether the industry can strike the right balance between algorithmic efficiency and human imagination.