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The Coming Age of Extremely Average Content
There was a time when creating content required enough effort that the effort itself acted as a filter. Writing an article meant sitting down and actually writing it. Producing a video meant planning, filming, editing and, usually, involving several people. Designing a campaign required someone to come up with an idea, someone else to tu…
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That pressure is disappearing. Artificial intelligence has dramatically reduced the time, money and effort required to produce almost every common form of marketing content. A company can now generate social posts, blog articles, email campaigns, video scripts, advertising variations, headlines, product descriptions and dozens of creative concepts before the first coffee of the morning has gone cold. The technology is genuinely useful, and there is no point pretending otherwise. The problem is not that AI can produce content. The problem is that it can produce an enormous amount of content that is perfectly acceptable without being particularly interesting.
This is where I think marketing is heading: an age of extremely average content. Not terrible content, because terrible content is relatively easy to identify and reject. Not brilliant content, because genuinely distinctive work will always exist. The bigger problem will be the enormous middle ground filled with content that is grammatically correct, professionally presented, reasonably informative and almost completely forgettable.
The Content Problem Was Never Really Production
Marketing has spent years talking about the challenge of producing enough content. Companies have built content calendars, hired writers, created social media teams and established publishing schedules because there was a widespread belief that brands needed to maintain a constant stream of material. The question was often, "What should we post this week?" rather than the more difficult question, "Is there actually anything worth saying?"
AI changes the economics of that equation. If a marketer needs ten LinkedIn posts, an email newsletter, three blog articles and a handful of social captions, generating the first draft of all of them is no longer particularly difficult. The bottleneck has moved somewhere else. The difficult part is deciding whether those pieces deserve to exist in the first place.
Imagine a company that sells automotive batteries. It could now ask an AI system to produce twenty LinkedIn posts about battery maintenance, five articles about signs of battery failure, ten captions about summer driving and a series of educational posts about battery health. None of this is difficult. The resulting content might even be perfectly accurate. But there are thousands of companies producing automotive content that follows exactly the same formula, and customers have little reason to remember one generic battery post over another.
The problem is therefore not a lack of content. There is already more content than any person could consume. What is becoming scarce is content with a reason to exist.
When "Good Enough" Becomes the Default
The most interesting danger of AI-generated content is not that it will produce obviously bad work. It is that it will produce work that is good enough to get approved.
A marketing manager receives an article from an AI tool. It is 1,500 words long, contains the required keywords, has a logical structure and makes no obvious grammatical mistakes. The internal stakeholder reads it quickly and says, "Looks good." The article gets published. The next week, another one appears. Then another. Eventually the company has a library containing hundreds of articles that are technically competent but remarkably interchangeable.
This is what makes average content so difficult to fight. Nobody has made a catastrophic mistake. There is no embarrassing typo, bizarre sentence or obviously terrible idea that forces someone to stop the process. The content simply passes through every approval stage because there is nothing sufficiently wrong with it to reject.
The same thing happens visually. A company can generate a polished social media graphic containing a photograph of a smiling professional, a headline about innovation and a paragraph about customer-centric solutions. It looks like marketing. It resembles thousands of other pieces of marketing. It does its job just well enough to survive, but not well enough to become memorable.
That distinction matters because marketing is not ultimately about producing material that can be approved. It is about influencing what people notice, remember, believe and eventually do.
AI Didn't Invent Generic Marketing. It Industrialized It.
It would be unfair to blame AI for generic marketing. We were producing generic marketing long before generative AI existed.
Corporate language has been remarkably repetitive for decades. Companies describe themselves as innovative, customer-centric, future-focused, agile and committed to excellence. They talk about empowering customers, delivering value, building partnerships and driving transformation. A consulting company, a bank, a software business and an automotive company can all end up using almost exactly the same language to describe themselves.
AI has simply made it easier to reproduce this language at scale.
Ask an AI system to write a corporate announcement and there is a good chance you will encounter phrases such as "in today's rapidly evolving landscape" or "we remain committed to delivering innovative, customer-centric solutions." These sentences are not necessarily wrong. They are simply so broadly applicable that they communicate almost nothing about the company actually speaking.
One useful test for corporate content is to remove the logo and company name and read the material again. If the reader could easily imagine the same paragraph appearing on the website of five competitors, the company probably has a differentiation problem.
AI is exceptionally good at producing language that sounds appropriate for a category. That is precisely why marketers need to become better at producing language that sounds specific to a company, a person, a customer or an actual experience.
The LinkedIn Post That Could Have Been Written by Anyone
LinkedIn is likely to become one of the clearest examples of this problem.
There is already a familiar structure for executive content. Someone tells a short story about a meeting, a difficult decision or a morning conversation. The story leads to a lesson about leadership. The lesson becomes three or five takeaways. The post concludes with something about growth, resilience, listening or the importance of people.
Again, none of this is inherently bad. The problem begins when everyone starts using the same structure, the same vocabulary and the same emotional conclusions. Once AI can generate this format instantly, the internet can become filled with thousands of executives apparently discovering the same lessons every week.
The interesting executive post is usually not the one with the most polished writing. It is the one that contains something the person could only have learned by actually being there. Perhaps a major client rejected an idea they believed was excellent. Perhaps a campaign that looked successful in the dashboard produced disappointing business results. Perhaps an employee challenged a decision and turned out to be right. Perhaps a senior executive made a mistake and had to explain it to the team.
Those experiences contain something AI cannot manufacture simply by being asked to "write a thought leadership post." The technology can help articulate the experience, but the experience itself has to come from somewhere.
The SEO Article Nobody Needed
Search marketing provides another obvious example.
For years, businesses have produced articles because they wanted to rank for particular keywords. An article targeting "What is digital transformation?" might explain the definition, list several benefits, describe common technologies and finish with a paragraph encouraging the reader to contact the company.
There is nothing wrong with explaining digital transformation. The problem is that the internet already contains thousands of explanations. Publishing another generic version does not necessarily create value simply because it contains the correct keywords.
The same thing will happen much faster with AI. If every company can generate an SEO article about every conceivable topic, the number of articles available online will explode. Search engines and users will increasingly face an ocean of technically acceptable information that differs mainly in wording.
The response cannot simply be to publish more. If everyone can publish at enormous scale, volume stops being a meaningful competitive advantage.
A more useful article might begin with an actual observation: perhaps a marketing team spent six months implementing a new technology only to discover that the underlying process was broken. Perhaps a company invested heavily in automation and discovered that customers actually wanted more human interaction. Those experiences create a point of view, and a point of view gives an article a reason to be read.
Information Is Becoming Cheap. Experience Isn't.
AI is extremely good at explaining things that are already known.
It can explain the difference between CPC and CPA. It can describe the principles of search engine optimization. It can provide five tips for improving a LinkedIn profile. It can explain why customer segmentation matters. It can compare different advertising platforms and produce a reasonable summary of almost any established marketing concept.
What it cannot do is genuinely have your history.
It does not know what it felt like to sit in a meeting when a client rejected six weeks of work. It has not managed a campaign where the numbers looked fantastic but the actual business outcome was disappointing. It has not spent years watching different clients make similar mistakes for completely different reasons. It has not had to convince a skeptical executive to approve an idea with an uncertain outcome.
That is where experienced marketers have an advantage, provided they are willing to use it.
The value of experience is not simply knowing more information. It is knowing which information matters, understanding the exceptions, recognizing patterns and having enough scars to know when conventional advice does not apply.
The New Advantage Will Be Having Something to Say
For a long time, marketers competed partly on their ability to produce content. Increasingly, they will compete on their ability to have a perspective.
There is a huge difference between saying, "Here are five marketing trends businesses should watch in 2027," and arguing that marketers have become too dependent on attribution dashboards and are sometimes confusing measurable activity with meaningful business impact. The first statement is broadly safe. The second gives someone something to agree with, disagree with or think about.
That does not mean every piece of content needs to be controversial. It means it needs to contain a reason for existing beyond filling a publishing slot.
A marketer who has worked with many different companies might notice that businesses repeatedly confuse marketing activity with marketing progress. Another marketer might have learned that the best-performing creative is often the least polished because customers respond to familiarity rather than production value. Someone else might believe that many companies are collecting customer data they never actually use. These observations become interesting because they come from somewhere.
AI can help turn those observations into articles, scripts or posts. But it cannot replace the underlying observation.
AI Can Generate a Post. It Can't Generate Your History.
This is probably the most important distinction for people who want to use AI without becoming indistinguishable from everyone else using it.
If I ask an AI system to write an article about remote work, it can produce a competent article in seconds. It can discuss flexibility, productivity, communication, loneliness, meetings and work-life balance. It might even sound thoughtful. But thousands of other people can ask exactly the same question and receive something remarkably similar.
Now imagine someone writes about the specific experience of spending years working remotely across different countries, managing projects through time zones, discovering which meetings were genuinely necessary and which existed mainly because nobody knew how to communicate asynchronously. The subject is technically the same, but the material is different because the experience is different.
That is increasingly where the value of human content will live. Not in the ability to explain what everyone already knows, but in the ability to contribute something that could not have been produced without a particular person having lived a particular life.
The Marketer's Job Is Moving From Writer to Editor
As content generation becomes easier, the marketer's role will increasingly involve judgment.
The important question will not always be whether we can produce something. It will be whether we should. Marketers will need to decide which ideas deserve development, which claims are unsupported, which examples make an argument credible and which pieces should simply be deleted.
This makes editing much more important than many organizations currently understand. Editing is not merely correcting grammar. It is deciding what the work is trying to say and whether it says anything worth hearing.
A good editor might take an AI-generated article containing ten generic observations and discover that one paragraph contains an genuinely useful idea. Instead of publishing all ten observations, they might build the entire article around that one idea and add evidence, examples and experience around it.
That process may result in less content. It may also result in considerably better content.
The Corporate Content Factory
The temptation for companies will be to treat AI as a content factory.
Imagine a marketing department that previously published three social posts a week. With AI, it could easily produce thirty. The team could generate articles, newsletters, captions, video scripts and sales materials almost indefinitely. A dashboard could show an impressive increase in output, and someone could present this as a productivity success.
But production volume is not the same thing as marketing effectiveness.
If a company publishes thirty forgettable posts instead of three useful ones, it has not necessarily improved its marketing. It has simply increased the amount of material competing for people's attention.
There is also a hidden cost. Every additional piece of content needs to be reviewed, approved, distributed, monitored and eventually replaced or updated. An organization can create so much content that its own team struggles to distinguish the important material from the noise.
The future content operation that interests me is therefore not the one that produces the most. It is the one that has the strongest filter between an idea and publication.
The Death of the Content Calendar as a Productivity Metric
Content calendars are useful organizational tools, but they become dangerous when publishing frequency becomes a goal in itself.
A marketing team should be able to look at its calendar and ask why each important piece of content exists. Is it answering a real customer question? Is it demonstrating expertise? Is it addressing an objection? Is it telling a story competitors cannot tell? Is it explaining something customers genuinely struggle to understand? Is it reinforcing a position the company wants to own?
If the only answer is "because we needed something to post on Thursday," the calendar has become the objective instead of the tool.
AI makes this distinction more important because the cost of filling the calendar is approaching zero. The temptation will be enormous. There will always be another post to generate, another article to publish and another variation to test.
The better marketers will become comfortable leaving spaces empty when they have nothing worthwhile to say.
What Happens When Average Content Becomes Infinite?
The obvious consequence of this shift is that people's attention becomes even harder to earn.
We already ignore enormous amounts of content every day. Most social posts disappear into feeds without making an impression. Most marketing emails are deleted or ignored. Most corporate blogs are visited briefly, if they are visited at all. Increasing the quantity of this material does not necessarily make people more interested.
When average content becomes effectively infinite, people will become increasingly selective about what they pay attention to.
This may actually be good for marketing.
For years, marketers have been told that they need to produce more content because audiences supposedly expect constant communication. AI may finally expose the weakness in that logic. When everyone can produce endlessly, publishing more stops being a meaningful differentiator.
The scarce resources become attention, trust, credibility, taste, experience and judgment.
The Paradox: AI May Make Human Judgment More Valuable
There is an interesting paradox here. AI could make human judgment more important precisely because it makes production less important.
When writing was expensive, organizations had to decide what to write before investing significant resources. When writing becomes cheap, the decision becomes easier to postpone. A company can produce ten versions and worry about the consequences later.
But producing ten versions is not the same as knowing which version deserves to exist.
The same applies to creativity. AI can generate hundreds of concepts, but someone still needs to recognize the one idea that is actually interesting. It can create variations of an advertisement, but someone needs to understand why one might resonate with a particular audience. It can write ten headlines, but someone needs to know which headline reflects the brand rather than simply sounding persuasive.
The more abundant the output becomes, the more valuable selection becomes.
We Don't Need More Content
I suspect that one of the biggest marketing lessons of the next few years will be surprisingly simple: we do not need more content.
We need more reasons to care about the content we already produce.
The coming age of extremely average content will not mean that everything becomes mediocre. Quite the opposite. The flood of competent, generic material may make genuinely distinctive work easier to recognize. When every company can produce polished articles, a useful insight becomes more valuable. When every executive can produce a professional LinkedIn post, an honest experience becomes more interesting. When every brand can generate attractive creative, a strong idea becomes harder to ignore.
AI will make it easier than ever to create something that looks like marketing. That is not the same thing as creating marketing that matters.
The marketers who benefit most from AI will probably not be the ones who use it to replace every difficult part of thinking. They will use it to remove the tedious parts of production so they can spend more time doing the things that technology still struggles to do well: noticing what other people miss, asking better questions, understanding people, challenging assumptions, making judgments and developing a point of view.
The future may contain more content than any previous period in history. That does not mean we will have more things worth reading.
It means that the ability to recognize what is worth saying will matter more than ever.
