AI production promises fast and affordable results. But behind the shiny promises lie technological limits, risks, and a lot of hidden work. Let’s examine the most common myths that prevent businesses from accurately evaluating the capabilities of neural networks.
My name is Ilya Zmienko, and I’m the founder of the communications agency «Svyazi», which focuses on AI production. Every month, we receive over 50 requests for AI-generated videos. Almost 90% of clients come with two extreme expectations: either that AI is a «magic button for making millions», or that it’s a «Pandora’s box» best avoided in favor of traditional methods. As usual, reality is somewhere in between. That’s why we decided to explain the key misconceptions in the form of myths: it’s the easiest way to explain where expectations don’t match reality, and what businesses need to keep in mind if they want to use AI for content.
Myth 1: «Anyone can make AI content»
It may look like anyone can quickly figure out how to generate text or social media posts with AI. But in practice, AI production — is an extension of traditional production.
The same rule applies here as in design or filmmaking: everyone can have the tool, but the result depends on the expert using it. The machine can answer «what», but never «why» or «how». The last two are always handled by a human.
The same with text. Many think: if GPT can write an article, the job is done. But without understanding how AI generates text and how to direct it toward a specific goal, the result is often an empty string of phrases. True quality only appears when specialists know the mechanics and can manage the process.
Myth 2: «The AI process is fully controllable»
Traditional production is predictable: if you’re a photographer and need a portrait, you go to a studio, set up the lights, and pick a camera. With AI, things are different. Even with perfect prompts, the system can produce unexpected results.
That’s why we run tests: first trial renders, then show the client a general idea of the potential outcome, and only after that decide — either accept this version or continue the search. It’s part of the process, and there’s no way to «turn it off».
That’s exactly why we choose short formats — videos of 30 – 40 seconds. In real production, the character looks the same in the morning and in the evening. In AI, the same character can drastically change in just a few hours. Short videos let us experiment faster, reduce risks, and keep the process under partial control. Five-minute AI videos simply don’t make sense yet; the chances of the visuals «breaking» mid-story are too high.
Myth 3: «AI content costs next to nothing»
Many assume that generating text or video with AI help is a matter of a couple of clicks and a few dollars. In reality, it’s much more expensive.
Every generation requires computing power. A single request to a neural network can cost about $1 in electricity alone. Multiply that by hundreds of tests and refinements — and you get the real price of a product.
A 30-second video, depending on complexity, can burn through $1,000 to $2,000 just in computation. Many companies intentionally work at a loss to build a client base, but that’s a road to nowhere. Quality AI production always balances between three factors — price, speed, and quality. You can’t have all three at once.
Myth 4: «AI content is unique and belongs only to the client»
Clients often assume: if AI made the image, it must be unique. The reality is more complicated.
Prompts that we enter remain in the system and can be used for further training. It cannot be guaranteed that a similar result will not appear with a competitor. Moreover, a prompt can be copied and reused.
The real value — isn’t the content itself, but the ability to control how it’s created. The same goes for text: it’s important to understand not just how to generate it through GPT, but also how to adapt it for your audience and goals. This is a new skill that is expensive, unlike the mere «picture» or «string of words» that anyone can produce.
Myth 5: «AI is ready to replace traditional production»
We’re still at the very beginning. Just think back to 2020 — remember those viral videos of Will Smith eating spaghetti? Compare them to today’s results, and the progress is obvious. But in terms of development, the technology is still at the stage of a one-year-old child.
Neural networks need more time and investment before they can become full-fledged tools for big film or advertising projects. And here it is important to understand: it’s not that specialists work «badly», the issue is that the software is currently at this level of AI development, production is not a magic button. Yes, anyone can try it and see how AI generates text or images. But real results only come to those who know how to manage the tool.
We’re at the very start of the AI content era. You can turn away and say, «this isn’t for us», or you can accept reality and adapt. After all, the internet works the same way: you can use it to play in online casinos or to study Harvard’s top courses. The tool is the same — the difference lies in who uses it and how.
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