You’ve all seen an image that looks like a painting in the style of Vincent van Gogh, yet it was never painted by human hands. Perhaps you’ve noticed that some articles on the internet appear almost immediately after a major event – and yet they have the right structure, language, and distinctive tone. Or you’ve listened to a song that played in your head, only to realize you’d never heard it before – and yet it sounded perfectly natural. It’s not a coincidence. These are the results of generative AI systems, which are no longer available only to research laboratories but are becoming part of everyday life.
And right now, in 2026, this phenomenon is extraordinarily important. Generative AI, capable of creating text, images, music, animations, and even code, has become a common tool – not only in technology corporations, but also in small businesses, schools, creative studios, and among independent artists. At the same time, it raises concerns: How high is the quality of the output? Who is the author of a work created by a machine? And what if this starts happening everywhere – too quickly?
It’s not just about whether AI can imitate humans. The more important question is: What impact does generative content creation have on our culture, economy, and the very idea of creative work?
How Does Generative Artificial Intelligence Work?
Imagine you had access to all the books, articles, images, music, and videos that humanity has ever created – all in digital form. Now imagine your mind could analyze each of these records, find patterns, relationships, style, sound properties, colors, rhythm – and then create something new on that basis, something that never existed before.
That’s essentially what generative models do.
Most current generative systems are based on so-called transformer architectures – a type of artificial intelligence introduced by a team of Google researchers in June 2017. Transformers enable models to understand context, text length, and language complexity much better than before. Transformers “read” text from the beginning and track how meaning changes within an article or sentence. Because of this, they can predict what comes next – and not just in English, but also in Czech, German, Japanese, or Arabic.
This capability is called generation – creating new content based on previous patterns. When you write “write a poem about sunset over lakes,” the model doesn’t just repeat something that already exists, but creates its own words, rhymes, and images that never existed before.
The result is, for example, the generative model GPT-4o, which can create text with very natural language, or Stable Diffusion, which produces images from a text description – for example, “portrait of a woman with blue eyes, sitting on a western shore and watching the sea in western light.”
How Will Creative Work Change Creators?
When someone asks: “Can AI be an artist?” the answer isn’t simple. But we can ask: What does it mean to be a creator?
In the past, creation was associated with a human’s unique performance – with emotion, personal experience, chance, inspiration. Today, however, more works are created with the help of AI than ever before – and often with human participation as well.
Let’s imagine an artist using a tool like Midjourney or DALL·E 3 to create a concept for an art exhibition. The artist enters: “a drawing in the style of Klimt with the promise of light coming from beneath the sea.” Within minutes, they have the result – detailed, dramatic, distinctly stylized. The artist then edits, filters, adds by hand, refines colors – and the result is a work presented at the exhibition.
So it’s not just about “AI does everything,” but the human is a significant creator, directing, criticizing, editing. This process is called co-creation – the human as a “director” or “curator” of content created in interaction with AI.
This also applies to music. Tools like AIVA, Boomy, or Suno AI can create music according to a description: “melancholic guitar song, tempo 60 bpm, significant chord progression, frightened tone.” After a few clicks, the entire song is created – some models can also generate vocal parts or accompaniment, but not all versions include singing automatically.
Don’t confuse such tools with “automatic creation” without human involvement. Most professional creators using them say AI is like a first draft, a “first sketch” – and they then add soul, style, emotional depth.
Quality of Content – How Good Is What AI Creates?
The answer is: Very good – and sometimes even better than human work.
In 2023, an image created with AI image generator took center stage, when it won the Sony World Photography Awards in the Creative category. The artist was German photographer Boris Eldagsen with an image titled “Pseudomnesia: The Electrician.” Ironically, Eldagsen later publicly refused to accept the award to highlight the fact that AI-generated images and photographs should not compete in competitions meant primarily for photography.
In music – in 2024, a song created with AI was broadcast on BBC Introducing, British radio dedicated to new artists. Artist Papi Lamour created the composition “Be Inspired” using generative AI tools with lyrics that he felt were connected to the theme of Black History Month.
In texts – generative models like Gemini 2.0, Claude 3 Opus, and others regularly prove they can achieve very high performance levels in tests evaluating logic, structure, grammar, and the ability to interpret instructions accurately.
However, this doesn’t mean everything is perfect.
AI often repeats patterns it saw during training – which can lead to “forbidden” styles that look unoriginal. Some generated images have, for example, “illegible” hands or expressions that don’t match human anatomy – a common problem with older model versions.
In music – some AI-created songs have a “too perfect” structure, which can be unnatural in some genres. In musical art, what is often valuable is what is “not perfect” – like an unintentional mistake that adds emotional weight.
At the same time, there’s a risk of false outputs that look completely realistic but are created from false sources or falsified data. This is why AI detection technology is developing, which makes it possible to identify AI-generated content.
Who Is the Author – Human, Machine, or Something In Between?
This is one of the most interesting questions of our time.
In Czech law, this question was clarified by the Prague Municipal Court in 2023. The court clearly ruled that works generated purely by AI without significant human creative contribution are not protected by copyright. According to Czech copyright law, only a natural person can be an author. In this specific dispute, the court also noted that the prompt itself may only constitute an idea or concept, which is not protected by copyright, and that an AI-generated image typically lacks protection without demonstrable human creative input.
In some countries, such as the USA, the decision is made based on whether the person was a “substantial creator” – that is, whether they had a significant influence on the final output.
In Europe, the situation is better defined. The EU adopted the AI Act (proposed by the European Commission), and it entered into force on 1 August 2024. Under the AI Act, transparency regarding AI-generated content is required — providers must ensure that users know the output was generated by AI, and models must respect existing copyright and other legal obligations. However, the question of authorship of purely AI-created content remains clear: without significant human creative contribution, the content has no copyright protection.
This means that if AI creates an image from a single text description and a person just presses a button without further input, ownership of the work doesn’t arise. But if a person adds their own concept, edits, changes colors, comes up with an idea – then they are the “author.”
This leads to a new understanding of creative work: Creation is now an interaction between human and machine, not one-sided production.
This approach is supported by research from the University of Oxford and other institutions, which showed that artists using AI achieve 25% to 50% higher productivity and their works are better rated by peers if they effectively use AI as a tool.
Consequences for the Job Market and Culture
Generative content creation has not only technological but also economic impact.
Adobe introduced the Firefly tool (public beta was released in 2023), which enables graphic designers, designers, and content creators to quickly create drawings, colors, templates – and various efficiency gains are attributed to it: from 50% acceleration in image acquisition and content creation to 60% cost savings in some cases. Research shows that across various functions such as content and marketing, the average time savings for marketing content is approximately 11.4 hours per week per employee, freeing up capacity for strategic and creative work.
This doesn’t mean people are losing jobs. On the contrary – many people working with AI say their work has improved, become easier, become more creative.
On the other hand, tensions are emerging. Some exhibitions have begun requiring creators to disclose whether and how AI was used – and some galleries are already refusing works that are not clearly labeled.
In music – some platforms address the surge of AI-generated tracks primarily through anti-spam and anti-fraud measures; elsewhere (e.g., Deezer), visible labeling of detected AI content is also being introduced.
This raises the question: What is “authentic” in art?
What Happens Next? How to Deal With It?
This development is unstoppable. But it doesn’t have to be frightening.
The first step is to distinguish between automation and the end of creative work. AI is not a replacement for creators – it’s a tool that can help, simplify, and accelerate.
But for this to work, we need to be clear:
- When output uses AI, it must be clear who contributed what.
- Don’t abuse AI to forge works or spread false information.
- Support education in digital literacy – whether for children, students, or adults.
Some studies and research from institutions focused on labor productivity and AI, including MIT, suggest that the effective use of AI is linked to better outcomes if workers understand its capabilities and limitations.
And so we return to the beginning: When AI writes articles, music, works of art – what does it mean for us?
It means that creation is not exclusively human. It means that art and information become faster, more accessible, and more widespread.
But at the same time – and this is key – authentic work still needs soul, experience, choice, and emotional weight.
So the question you can ask yourself:
When artificial intelligence creates a work that moves you forward, what does that mean to you – and who actually created that move?
Content Transparency and AI Assistance
How this article was created:
This article was generated with artificial intelligence assistance. Specifically, we used the Qwen3-30B-A3B-2507 language model, running locally in LM‑Studio. Our editorial team established the topic, research direction, and primary sources; the AI then generated the initial structure and draft text.
Want to know more about this model? Read our article about Qwen3-30B-A3B-2507.
Editorial review and fact-checking:
- ✓ The text was editorially reviewed
- ✓ Fact-checking: All key claims and data were verified
- ✓ Fact corrections and enhancement: Our editorial team corrected factual inaccuracies and added subject matter expertise
AI model limitations (important disclaimer):
Language models can generate plausible-sounding but inaccurate or misleading information (known as “hallucinations”). We therefore strongly recommend:
- Verifying critical facts in primary sources (official documentation, peer-reviewed research, subject matter authorities)
- Not relying on AI-generated content as your sole information source for decision-making
- Applying critical thinking when reading
Technical details:
- Model: Qwen3-30B-A3B-2507 (Apache 2.0 licence)
- Execution: Running locally in LM-Studio
- Learn more: Official repository
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