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AI and society

Does the market buy AI slop? What it pays for, if not hours of work

Short answerIt does, but not everywhere and not for everything. A cartoon with a cappuccino for a head the viewer happily accepts from a neural network. News and product recommendations are harder. The market really does not pay for hours of work, but that does not mean it is indifferent to process. It pays for the consequences of process: a repeatable result, a truthful image, someone answerable when things break, and the provenance of a thing. And the line where 'nobody cares' begins is often drawn not by the reader but by the owner of the channel through which the content reaches them.

Among people who make content with neural networks, a simple and fairly harsh position has taken hold: nobody cares who made the material, a human or a machine. The market looks only at the result. In its extreme form, process is judged by those who cannot get results, while the strong care only about the outcome.

From this logic it is easy to assemble a ready-made business scheme: every two days take a problem from Reddit, generate an article about it and flood the internet with such texts. Sooner or later you will sell something.

Two different ideas are joined in this position. The first is strong: the buyer is not obliged to pay for the hours spent on the work. No work ethic will change that. The second is weak: if the buyer does not pay for hours, then how the result was obtained does not matter to them at all.

But hours and process are not the same thing. Hours end the moment the work is delivered. The consequences of process stay with the buyer after delivery, and those are what the buyer quite often pays for.

The first idea I accept. The second is the one to take apart.

Hours really are not paid for

The right place to start is agreement. If one person with a model does in a day what used to take a team a month, and the result is the same, the buyer is not obliged to pay for that month. They are buying a solution to their problem, not the biography of the solution.

Yes, the label “made by AI” can still devalue an answer in the reader’s eyes, even when the answer itself has not got worse. Why that happens, I have covered separately. For today’s conversation one conclusion matters: this is a perception error on the reader’s side, not a property of the answer. You cannot build a price on such an error.

The entertainment market confirms this nicely.

The character “Ballerina Cappuccina”, a ballerina with a cup of cappuccino for a head, appeared on TikTok in March 2025. By summer, according to an Associated Press count, videos featuring her had gathered more than 55 million views and 4 million likes. And a video by the Italian animator Fabian Mosele, in which the same characters dance at an underground party, passed 70 million views.

Mosele explains the success simply: the viewer does not care whether the picture has anything to do with what is being sung in the video. The genre’s core audience is children of ten to thirteen. They do not ask who drew the character, because they do not need to. The only decision the viewer of such a video makes is whether to keep watching or scroll on.

For that decision, the origin of the picture does not matter. Here the phrase “nobody cares” is true almost without qualification.

But the key word is here.

On an entertainment video the viewer usually decides nothing except whether to spend a few more seconds on it. With a news item or a product recommendation it is different. On their basis a person may decide what to buy, whom to believe and whom to vote for. And the more serious that decision, the more important the question of where the content came from.

That gives us a first criterion: the thesis “nobody cares who made it” works where the person decides nothing important on the basis of that content. In all other cases it has to be tested.

Where the origin of content matters

In 2026 two consumer surveys came out, of consumers, not content makers and not AI specialists. Both tested whether the way material is made affects the audience’s attitude.

In August 2026 Clutch surveyed 601 people. The results were fairly sharp:

  • 53 percent of respondents are less likely to buy from a brand if they know it creates its social media content with AI;
  • nearly 90 percent believe the use of AI should be disclosed;
  • 71 percent would doubt a recommendation from a living influencer if they learned that a neural network wrote the text for them;
  • 64 percent would trust a brand less if AI showed a product as something other than what it is.

The Meltwater and YouGov survey, conducted in April 2026 among nearly 10 thousand people in seven countries, gives a more complex, and therefore more useful, picture. On learning that a brand uses generated content, 32 percent of respondents would trust it less and 15 percent more. For the rest, roughly half, nothing changes. At the same time, 86 percent still expect disclosure of AI use.

The main difference showed up between genres:

Willing to accept content from AI
  1. Entertainment53%
  2. Advertising47%
  3. News21%
  4. Politics18%

Share of respondents by genre. Meltwater and YouGov, nearly 10 thousand people in seven countries, April 2026.

Both studies measure stated intentions, not real purchases. So these figures cannot be translated directly into lost sales. But for our argument they are enough.

Roughly half the audience genuinely does not care where content comes from. For them the “nobody cares” position works. But the other half does look at origin. And the sequence 53, 47, 21, 18 shows an understandable pattern: the closer the content is to a decision the person will make on its basis, the lower the willingness to entrust that content to a neural network.

At one end of the scale is entertainment. At the other, news and politics. The phrase “nobody cares” describes one end of the scale and then passes it off as the whole scale.

Between the content and the reader stands the channel owner

Suppose we do aim at the half of the audience that does not care where a text comes from. The scheme “every two days take a problem from Reddit and publish an article” could work, if the article found its reader by itself.

But by itself it goes nowhere. Between the author and the audience stands a search engine, a social network or a marketplace. Each such channel has an owner. And it is the owner who can stop the scheme, even if the reader is willing to put up with an endless stream of generated texts.

The industrial experiment has already been run.

In March 2024 Google introduced a policy called scaled content abuse. It penalises content produced at scale for the sake of search ranking, regardless of who produces it: automation, humans, or both together.

The wording matters. Google did not ban neural networks. It banned the scheme itself. A person with a copywriting marketplace breaks this rule just as much as a person with a text generator.

Before the update the company promised to remove 40 percent of low-quality, unoriginal content from its results. After the April update Google reported 45 percent. For sites that lived off a huge volume of search articles, this meant losing their main asset: traffic. And such a site cannot be rebuilt to fit the new rule, because the rule targets not individual bad texts but the business model itself.

A second fact from the same year shows what the channel owner is, on the contrary, willing to pay for.

In February 2024 Reuters reported that Google pays Reddit about 60 million dollars a year for access to its discussions for training models. That makes a curious pair. For yet another generated retelling of Reddit, Google pays nothing and may remove it from search. For the conversations themselves, in which living people ask questions, argue and answer each other, it pays tens of millions.

The difference between these two things is what forms the price. The value lies not in one more text, which is easy to produce, but in the human questions, context and phrasing from which that text can be made. The mass-production scheme proposes taking the questions for free and selling the articles made from them. With its contract, Google showed which of the pair is the expensive good: the questions.

That is the economy the thesis “the market does not pay for process” fails to notice.

While a text demanded many hours of work, the high cost of production limited supply by itself: writing too much did not pay. Now production costs cents, supply has become almost infinite, and the reader’s attention has not grown. So the price shifted to where scarcity remained: from producing the text to the filter that decides which text reaches the reader.

That filter belongs to the platforms. They also define what counts as abuse. And the scheme “an article from Reddit every two days” makes money exactly until the day Google decides there are too many such articles.

“Result” often means just one sale

Hours really are not bought. But then what counts as a result at all?

Usually a result means a sale that has already happened. The post brought leads, so the post is good. For the buyer that is enough: they got what they paid for.

For whoever published the post, everything is only beginning. They need to understand whether the sale will repeat, what one run of the scheme cost, and whether today’s result was paid for with trust that cannot be recovered tomorrow.

A channel can build up trust for years and then squeeze it out in one aggressive run. Today that brings sales, but in six months the audience stops believing. For the same reason 10 million random views are sometimes worse than 5,000 views from people who actually need the product.

Both situations force you to judge the result not in the moment but over distance. One sale says nothing about whether there will be a next one. The only thing connecting today’s sale to a future one is the way it was obtained. Was it repeatable or accidental? Can it be used again? What does it destroy along the way?

That way is the process.

So the argument “process or result” is largely false. Over the long run, process is the future result, only seen in advance.

The brainrot from the start of this article shows how success ends without a repeatable way of turning attention into money. The videos by Mosele and by nameless authors gathered tens of millions of views. But when serious money came into the genre, it did not go to the video makers.

One of the most popular games on Roblox in the summer of 2025 was Steal a Brainrot, where children are sold the same characters for in-game currency. Then came plastic figures and stage shows. The video makers had a result: 70 million views. But they had no process to turn that result into the next one. Roblox had such a process. The money went to Roblox.

Manual labour on its own earns no premium for repeatability. The label “made by a human” does not turn into a mark of quality. But the phrase “this brought sales” does not prove quality either, as long as it is unclear what exactly produced those sales and whether the method can be repeated without losses.

The price of process becomes visible when everything breaks

Repeatability can be checked on the next run. Another consequence of process is usually noticed only at the moment of failure.

Take SaaS as the clearest example. The user may not care whether the service was built by one person with Claude Code or by a team of a hundred programmers over two years. While the service works, that is largely true.

In July 2025 SaaStr founder Jason Lemkin spent twelve days building a service with the Replit agent. On day nine the agent deleted the production database holding data on more than 1,200 executives and 1,190 companies. This happened despite a declared “code freeze”. After the deletion the agent told Lemkin the database could not be restored.

In fact the data was recovered. Replit’s chief Amjad Masad called what happened unacceptable and said it should never have been possible at all. Within a week the company separated development and production databases; before that, the agent had no such separation.

Before the failure the user really could not care how the service was built. On the day of the failure the service’s price was set by quite different things: whether a backup exists, whether production and test environments are separated, and whether there is a person capable of restoring the data.

All three decisions are made in advance and are almost invisible from outside. All three are process. The user was paying for them all along; they only learned about it when the decisions were needed.

A corporate customer does not want to discover the price of process on the day of failure. So before buying it asks about the bus factor, security audits, SLAs and documentation. Who will fix a critical bug in three years if the author of the code leaves? Who keeps the access? Where are the backups?

Answers to these questions are paid for not out of respect for manual labour. The buyer is purchasing in advance the consequences of a properly built process. Who should answer for a system’s decision, I have covered separately. Connected to it is another text, on the six questions without answers to which delegating to AI turns into handing over the stop button. On day nine Lemkin had no answer to the fourth question: what happens to the backups and the access.

Sometimes provenance is the product

Repeatability and someone answerable when things break the buyer pays for even without calling them process. But there is a third consequence they pay for directly: the provenance of the thing.

A painting and its flawless copy can be physically almost identical and yet cost completely different money. The buyer of the original is not mistaken. They are acquiring not just the image but the history, the authorship and the social signal. The copy has none of that by definition.

Jewellery, ceramics, photography and the author’s book work the same way.

Etsy earns precisely on this difference. In July 2024 the platform changed its rules. Instead of the general label handmade, the seller must now specify their role more precisely: for example, “made by seller”, “designed by seller” or “handpicked by seller”. A product created with AI can fall only into the “designed by seller” category, and the use of AI must be disclosed in the description.

Etsy did not ban neural networks. It added a separate piece of information about process to the product listing. And it did so not out of romantic respect for craft. If the buyer pays for a thing’s provenance, any substitution of provenance hits the platform’s revenue. The new field protects Etsy’s money.

Five questions before publishing

How do you tell whether the phrase “nobody cares” works in your particular case? I would test the material or service with five questions.

  1. What problem does the material solve, and what number measures the result? Views, leads and trust a year from now are three different problems. Material useful for the first may turn out to be a loss for the third.

  2. What happens to trust if the reader learns how the material was made? The question is not whether they will find out, but what happens afterwards. If the answer is “nothing”, the use of AI can be disclosed calmly. If after disclosure people stop believing you, the problem is not the disclosure but the material itself.

  3. Does the material show the product as it is? How the advertising was produced may interest no one. Truthfulness of the product image, judging by the Clutch survey, matters to 64 percent of buyers.

  4. Who answers if the service breaks, and can the result be repeated? Where is the backup? Who holds the access? What does one run of the scheme cost? How many runs will it survive?

  5. Who controls the distribution channel, and what does it consider abuse? Google and Etsy have already given their written answers. A scheme built against the platform’s rules works only until the next update of those rules.

So does the market buy AI slop?

The position “nobody cares who made it” is right in the main and wrong in its loudest part.

The market really does not pay for hours. “Made by a human” is not a mark of quality, and “made by a neural network” is not a mark of defect. Roughly half of buyers do not change their attitude to a brand on learning where the content came from. In entertainment even fewer people care.

But from the phrase “the market does not pay for hours” it does not follow that the market does not pay for process. Still less does it follow that only losers judge process.

The market pays for at least four consequences of process: the probability of repeating the result, the match between the product in the picture and the product in the box, a person who will answer when things break, and the provenance of the thing, if that is part of its price. That is process, just not in the sense of hours worked.

And the final line is often drawn not by the reader and not by the author. It is drawn by whoever controls the distribution channel. Cheap production made the filter expensive. The filter belongs to the platforms. And the platforms have already called the content conveyor abuse, while at the same time putting a price of 60 million dollars a year on human discussions.

Taking a problem from Reddit every two days is possible. The only question is how many days will pass before that conveyor is noticed by those able to switch it off.

Sources

Sources checked on 18 September 2026 at two levels: link and author match, the specific claim found in the text.

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