LLMs Generate Kitsch

Alexander Koller

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# LLMs Generate Kitsch

Alexander Koller

Saarland University

koller@coli.uni-saarland.de

## Abstract

LLMs are increasingly being used for tasks that have traditionally required human creativity. I argue that when applied to such tasks, they create kitsch. This is a consequence of being trained first to predict average human artifacts and then to maximize human feedback scores, i.e. to generate inoffensive artifacts of mass appeal. I conclude with a discussion of the role of LLMs in supporting programming and research.

I would welcome your comments!

## 1 Introduction

There has been a tremendous amount of recent activity in using large language models (LLMs) and other models of generative AI to produce artifacts that were previously assumed to require creative acts. For instance, GPT 3.5 generates stories and poetry that human annotators rate as highly as human-produced ones (Porter and Machery, 2024); DALL-E generates images that are often indistinguishable from human-generated art and appeals to people; LLMs generate music and even podcasts; and there is an increasing interest in LLMs that can support or even automate the scientific research process (e.g. Jansen et al., 2025).

While the ability to generate computational art is a huge technical advance, these artifacts often leaves behind a feeling of emptiness. The generated music feels formulaic; the generated stories feel “safe” and generic. Generated poetry, such as the example in Figure 1, can recognizably emulate the style of human poets, but often consists of a sequence of shallow tropes. AI-generated art is of increasingly high technical quality (the example poem scans and rhymes well) and often evokes an emotional response in its consumers. But are these responses the kind of novel experience that genuine art provokes, or is the AI-generated work an

> She walks the earth with grace and pride,
> A beauty that cannot be denied,
> With eyes that shine like stars above,
> And lips that speak of endless love.

> But though she wears a smile so sweet,
> A broken heart doth lie beneath,
> For in her chest a pain doth beat,
> A love unrequited, without relief.

> And so she walks with heavy heart,
> A figure haunting in the dark,
> For love, the sweetest of all art,
> Can also leave a painful mark.

<caption>Figure 1: An AI-generated poem in the style of Lord Byron, from Porter and Machery (2024).</caption>

empty surface that efficiently reflects the human reader's or viewer's own emotions back at them?

In this paper, I will argue that content that is generated by today's techniques for generative AI is generally not art, but *kitsch*. Kitsch is widely considered to be bad art, or not even art at all; it describes works that have the superficial characteristics of art, but lack artistic intent, do not enrich the viewer's inner life as real art does, and are designed for mass appeal. While it is difficult to define kitsch precisely, I will review some typical characteristics in Section 2. I will then discuss the ways in which AI-generated art fits these definitions and how our current training regimes promote the generation of kitsch.

My aim in this paper is not to provide a definitive proof of my claim; kitsch is hard to define, and AI-generated art is notoriously hard to evaluate empirically. I instead hope to offer a useful concept for thinking about the differences between art that is produced by humans and by AI, in a time where empirical studies increasingly find it difficult to distinguish between people's responses to them.
Throughout the paper, I will use the word *art* to refer to any activity for which human creators have traditionally required creativity and that are created for the purpose of appreciation by a human *perceiver*. This encompasses text, images, architecture, music, etc. I will broaden my scope to other forms of human creation, such as writing code or doing scientific research, in Section 4.

I am a computational linguist and will therefore mostly talk about LLMs, the type of generative AI with which I am most familiar. However, my arguments are mostly neutral with respect to the modality of the work of art; they apply both to LLMs and to other types of generative AI, and I will include some examples from other modalities.

## 2 What is kitsch?

When I say “kitsch”, any reader of this paper probably has spontaneous examples in mind. Whether we think of garden gnomes or shallow TV shows or overly sentimental music, there is a universal sense among perceivers with even minimal artistic sensibilities that kitsch looks like art on the surface, but something is deeply hollow about it. Kitsch comes across as a deception: it pretends that it is art, but really it evades everything that is deep and meaningful about art and focuses on easy consumption and *mass appeal*.

The word “kitsch” is first attested in the context of the art markets in Munich in the 1870s. This is consistent with the frequent claim that kitsch is a product of the modern era, where the mass appeal of works of kitsch could be monetized through mass production and through distribution in the mass media. Kitsch is generally considered inferior to “real” art, but Ortlieb and Carbon (2019) have argued that the immediate emotional gratification that kitsch offers can give it a positive cognitive value that is simply complementary to that of art.

I will work out some properties of kitsch in more detail, so I can discuss the way in which AI-generated art is kitsch more specifically (Section 3). This section is heavily influenced by Kulka (1996), the most coherent attempt that I could find at actually defining kitsch. All quotes below without an explicit citations are to Kulka’s book.

### 2.1 Appeal to stock emotions

In order to fulfill its purpose of mass appeal, kitsch must address emotions that are shared among a large proportion of the population. It depicts sub-

jects that are capable of arousing such emotions. In the novel *The Unbearable Lightness of Being*, Kundera (1984) describes this connection as follows: “Kitsch causes two tears to flow in quick succession. The first tear says: How nice to see children running on the grass! The second tear says: How nice to be moved, together with all mankind, by children running on the grass! It is the second tear that makes kitsch kitsch.”

As a consequence, the emotions that are evoked by kitsch are simple. Children running on the grass are symbolic of the happiness of childhood; the woman in the poem in Figure 1 is sad and in love, and that is all we can take away from it. We can think of Christian kitsch (plastic Jesus babies), nationalist kitsch (fascist speeches), and local kitsch (Eiffel Tower fridge magnets); these evoke a bundle of shallow emotions that many people connect with certain locations, ideologies, and communities they like. A complex, nuanced emotional landscape will not connect as universally with people and therefore damage the work’s mass appeal; as Kulka says, kitsch addresses “stock emotions”.

The emotional force of kitsch is furthermore derivative of the emotional force of the subject it depicts. People do not buy Eiffel Tower fridge magnets because they find artistic value in them; they buy them to remind themselves of Paris. Similarly, the poem in Figure 1 derives much of its emotional impact from the fact that it is written in the style of Lord Byron, and this evokes the perceiver’s prior emotional responses to other works by Lord Byron or other Romantic poets. Kulka argues that the purpose of a work of kitsch is to symbolize the subject it depicts; in his words, “the appeal of kitsch is totally parasitic on the associations related to its referent”.

### 2.2 Competent but conventional surface form

But if the depiction of an emotionally charged subject is the main purpose of a work of kitsch, this means that effective kitsch makes it as easy as possible for the perceiver to identify this subject. Kulka stipulates that kitsch must depict its subject in a way that is “instantly and effortlessly identifiable”. This has two important consequences for the surface form of kitsch.

First, a work of kitsch must be executed with a certain degree of *technical competence*. If we attempted to produce kitsch by having a toddler draw a replica of the Mona Lisa, the subject of the work would not be effortlessly identifiable, and
therefore unsuitable as kitsch. Kitschy text must
be coherent and grammatically correct; kitschy
images must look realistic; kitschy music must in-
volve reasonable chord progressions. Early models
of generative AI could not ensure these properties
(perhaps image and music generation models still
don't). They were instantly recognizable as tech-
nically deficient, and therefore nobody seriously
took their outputs for either art or kitsch.

Second, “kitsch invariably uses the most *con-*
*ventional*, standard, well-tried, and tested repre-
sentational canons.” Imagine you wanted to pro-
duce kitsch around 1900. You would certainly
not invent cubism as a painting style for your
work, because this would confuse and perhaps
offend your audience, damaging the mass appeal.
Instead, kitsch plays it safe; it executes the current
conventions competently. Kitsch aims to depict its
emotionally charged subject, not to generate artis-
tic innovation.

In other words, just like kitsch is parasitic in
terms of *content* on the emotional force of the
subject it depicts, it is also parasitic in *style* on
other works of art. Other artists must come up with
stylistic innovations; once they become the estab-
lished mainstream, kitsch can then adopt them.

## 2.3 Kitsch as fake art

“Kitsch is fake art, expressing fake emotions,
whose purpose is to deceive the consumer into
thinking he feels something deep and serious,
when in fact he feels nothing at all” (Scruton,
2014). Similarly, Dorfles (1969) defines kitsch as
“something with the external characteristics of art,
but which is in fact a falsification of art.” There is a
sense of deception involved in kitsch: Kitsch pre-
tends to be art, it pretends to enrich our experience
with its subject, but it actually leaves us empty.

It seems counterintuitive at first glance that
kitsch would be “fake” art, when works of kitsch
can in fact be quite beautiful to look at – their
purely *aesthetic value* can be high. One can take
the position that the value of a work is mainly in
its beauty, and all the rest is a matter of taste; this
position then accepts that it is difficult to separate
art from kitsch.

However, this position misses the point that
humans create art for purposes that go beyond
beauty. Artists intend to communicate something
to the perceiver of the work; art seeks to enrich,
enhance, or transform the perceiver's experience
with its subject. In addition to its aesthetic value,

a work of art has an *artistic value* that has to do
with the relevance of this experience and the inno-
vativeness and effectiveness with which it achieves
this. By contrast, there is no particular artistic
intent behind kitsch, except to evoke the emotions
associated with its subject in a way that has mass
appeal. We have also seen that kitsch does not in-
novate with respect to style. Thus, kitsch has very
low artistic value. Our experiences with kitsch feel
hollow because this low artistic value is executed
with a technical competence that makes it look like
art on the surface.

## 3 LLMs generate kitsch

With this clearer understanding of kitsch in mind, I
will now argue that when LLMs and other models
of generative AI are used to generate art, they gen-
erally produce kitsch. This is an almost necessary
consequence of the way in which they are currently
trained. I assume here that the LLM generates
its output without human intervention, and will
discuss the human-in-the-loop case in Section 4.

### 3.1 Conventional surface form

Modern LLMs excel at generating competent sur-
face forms. At least for English, LLMs since
GPT-2 have produced reliably grammatical sen-
tences, and since GPT-3, these have mostly formed
coherent texts. This competence is the result of
pretraining for next-token prediction on very large
quantities of data; this allows LLMs to inherit the
grammaticality and coherence from the human-
written training data. Instruction tuning (Chung et
al., 2024; Ouyang et al., 2022) adds the ability to
follow instructions specified in a prompt, permit-
ting users to (among other things) specify the style
of the surface form.

At the same time, a model that is trained to
predict next tokens is primarily a device for gener-
ating tokens that are likely in the given context,
under the empirical distribution of the training
data. This means that LLMs are specifically
trained to generate *conventional* surface forms:
The training objective rewards the prediction of
next tokens that are frequent continuations in the
training data. While LLMs are theoretically capa-
ble of generating stylistically innovative outputs,
these innovative outputs are of low probability,
or are even impossible when truncation sampling
methods such as top-k sampling are used (Holtz-
man et al., 2020). Thus, like a work of kitsch, LLM
outputs will typically have competent, but conven-
tional surface forms that offer no impediment to
recognizing the subject of the generated work.

## 3.2 Mass appeal

Furthermore, generative AI models are capable of generating outputs with mass appeal. This is illustrated by the current public debate about “AI slop” (Judkis, 2024), cheaply AI-generated art that is being posted en masse to social media and streaming services. While one should distinguish the massive rate of *creation* of AI-generated content from the actual *appeal* of this content to human consumers, there is at least anecdotal evidence for its commercial success. One Youtube channel has 600k subscribers, with individual videos having been viewed 20 million times (Ruwitch, 2025). From a more scientific perspective, studies such as that by Porter and Machinery (2024) have found that AI-generated art (in their case, poetry) is rated higher by human subjects than human-written art; it has “appeal” to at least their random sample of the general population.

Again, the mass appeal of LLM-generated art is not an accident – it is directly caused by the training process. In particular, reinforcement learning from human feedback (RLHF, Ouyang et al., 2022) specifically learns a reward model that aims to capture what responses a human user would “like”, and then uses it to finetune the LLM to generate highly rated answers. RLHF is an engine for promoting outputs with mass appeal.

## 3.3 Fake art

As a final point, a work can only be kitsch if it pretends to be art. LLM-generated cooking recipes and travel guides often feel shallow and derivative, due to the conventionality and mass appeal discussed above. But it is only when LLM outputs are presented as art that they evoke the unique sense of morally offensive deception that kitsch does.

There is no doubt that the quantity of overall content that is generated by LLMs has risen sharply since ChatGPT became available. For instance, Liang et al. (2025) estimated that up to 24% of corporate press releases and 10% of job postings in 2024 were written with LLM support. One could argue that corporate text has never claimed to be art, and therefore cannot be kitsch. Indeed, the use of LLMs for these purposes feels like a tragic commentary on the monotony and redundancy of certain white-collar jobs, but it is not offensive from an artistic perspective.

However, an increasing amount of data also suggests a rapid growth of the AI-based production of content that was traditionally considered art. Approximately half of all photos available on Adobe Stock – 313 million as of April 2025 – are self-reported as AI-generated (Kneschke, 2025). The streaming service Deezer (2025a) reported in April 2025 that 18% of all new music uploaded was AI-generated, according to their AI detection tool. This number had risen to 28% in September (Deezer, 2025b). Broadening the scope to other creative activities that are not specifically art, Daniotti et al. (2025) estimate that 30% of Python code uploaded to Github by contributors in the US in December 2024 was AI-generated.

## 3.4 LLMs generate kitsch

To summarize, generative AI is being used broadly to generate art; its outputs have mass appeal; and the surface form is conventional. This means that AI-generated art satisfies most of the criteria I laid out in Section 2; it is kitsch. This follows from the way in which AI models are being trained.

One criterion which I have not addressed is the reliance of kitsch on the emotional force of the depicted subject and on stock emotions. While this criterion is anecdotally satisfied – the poem in Figure 1 is only one of many examples –, it is difficult to argue for on theoretical grounds. One argument is that LLMs are not humans and therefore cannot have artistic intents that would enrich a human perceiver’s inner life; as a consequence, when they choose a subject for the work they generate, they assign a higher probability to subjects that were frequent in the training data.

I hesitate to make this argument too strongly because it feels a bit cheap, and the way in which LLMs recombine and generalize over the contents in the training data is poorly understood. At the same time, if the notion of “stock emotions” could be operationalized, this criterion would lend itself well to empirical evaluation.

# 4 Kitsch beyond art

Throughout this paper, I have mostly taken a conventional perspective on “art” and therefore “kitsch”: human creative activities that produce artifacts for the purpose of appreciation by a perceiver. However, it seems instructive to me to also apply the criteria of kitsch to other forms of human creativity, such as programming and scientific research. These activities are performed to
achieve an external purpose, which is in contrast
to art; one can apply notions of “elegance” to code
and research, but they are not judged primarily by
aesthetic criteria.

Nonetheless, the thoughts in Section 3 apply to
the use of LLMs in other creative activities too.
We are approaching a point where code genera-
tion with LLMs can generate entire apps (“vibe
coding”) and accelerate human software develop-
ment, and support both the execution of research
and the ideation of new research questions. Fol-
lowing the logic of Section 3, we expect that code
and research that is fully generated by LLMs will
be conventional and optimized for mass appeal.
Kitschy research is commonly called “incremen-
tal”. Initial empirical studies indicate that purely
LLM-based ideation indeed leads to research of
lower quality (Si et al., 2025).

This is not to say that generative AI can't play
any role in supporting the human creative process.
In the hands of a human creator with innovative
artistic, scientific, or technical intents, LLMs can
both accelerate and inform the creative process. To
cite just one prominent recent example, Aaronson
(2025) used GPT-5 to provide a key idea to a proof
in quantum complexity theory. He recognized a
number of GPT-5's initial suggestions as incorrect,
but elicited a useful idea within half an hour of in-
teraction. The literature on using LLMs in research
ideation through human-AI collaboration supports
this anecdotal finding (Li et al., 2025). It will be
interesting to see how future creators will draw the
line between those parts of the creative process
that requires their own ingenuity and those that
they feel comfortable delegating to the machine.

5 Conclusion

In this paper, I have argued that when generative
AI is used to replicate human creative activities, it
generates kitsch. It is optimized for mass appeal,
uses competent but conventional surface forms,
and does not enrich the experiencer's inner world
with respect to its subject. I have argued that
generative AI's propensity to generate kitsch is
an almost necessary consequence of its training
regime, which combines optimizing the likelihood
of the human-produced training data with optimiz-
ing appeal to human experiencers.

In the hands of a human with original artistic
intentions, generative AI may be able to accelerate
and inform their creative process and democratize
the production of art. At the same time, online

media are being flooded with AI-generated kitsch,
which puts human creators at commercial risk.

The outlook that future perceivers of art will be
drowned in appealing and technically competent,
but parasitic and ultimately soulless kitsch is a
dystopia that nobody wants. Are we content to be
what Broch (1969) calls a “kitsch-man”: a “person
who uses this highly considerate mirror so as to
be able to recognize himself in the counterfeit
image it throws back of him”? It is exactly because
generative AI can generate works of such high
technical quality that it forces us to engage with
the difference between aesthetic and artistic value
and the worth of human creation. Perhaps this will
lead us to a clearer common understanding of why
we, as a society, care about art in the first place.

**A note on priority.** I first attempted to upload
this paper to arXiv on 15 October 2025, days after
I discovered that Uhlmann (2025) had also made
the connection between LLMs and kitsch. I have
worked on the present paper, off and on, since
the beginning of September 2024, and I therefore
consider both papers to have been developed in-
dependently. I have deliberately avoided reading
Uhlmann's paper so far in order to prevent acci-
dentally injecting her thoughts into my paper; but
I assume it is an insightful complementary take
from a humanities perspective and worth reading.

**Acknowledgments.** I am grateful to Sarah Breck-
ner, Peter Clark, Yupei Du, Sharon Goldwater,
Xenia Klinge, and Ellie Pavlick for fruitful and
sometimes controversial discussions on the topics
in this paper.

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