In 2015, when neural networks were still little
more than laboratory curiosities, Merzmensch began compiling evidence. Not with
scissors, as Tzara once did, but through Google’s Deep Dream and the early
GANs, where memory itself could be trained, distorted, and replayed. These
first experiments were less inventions than continuations: traces in a lineage
that runs from Dada’s cut-ups to Surrealist Cadavre Exquis, from Barthes’s
intertextual swarm to the post-anthropocentric murmurs of today. His case is
clear, AI is not an interruption of poetry but another witness, another
participant in its ongoing scene of collaboration.
What results are not poems in the conventional
sense but documents of encounter. The bulk of machine output is dismissed,
routine, formulaic, unconvincing. Yet anomalies remain: stutters of Beckett,
absurdities reminiscent of Lynch, recursive loops worthy of Oulipo. These
fragments are preserved, not corrected, and entered into the record as they
stand. They operate as exhibits: objects found in the wild of computation,
artifacts that testify to an emergent form of authorship neither strictly human
nor wholly machinic.
To approach these works is to enter an archive of
residues and misfires, where poetry is less a finished product than a forensic
trace. In Merzmensch’s practice, the machine is both material and witness,
reflecting our culture only to warp it, exposing seams of bias, repetition, and
invention. What remains are poems that fail, poems that astonish, records of a
dialogue in progress. They belong not to us, not to them, but to the evidence
itself.
You’ve worked with AI as a creative partner for
years. If the goal were not visual art but poetry, what would you keep from
your own collaborative methods, and what would you discard?
Indeed, even if my first encounters with Generative AI can be
dated back to 2015, as I experimented with Google Deep Dreams (https://medium.com/merzazine/deep-dream-comes-true-eafb97df6cc5?sk=6d50ebb59584b487183385009ba50f54)
and later Generative Adversarial Networks (https://medium.com/data-science/visual-ai-trained-on-my-memories-a54c55967c28?sk=3e37e2716ccee2101ba07025e5bcfd60),
my primary focus at the beginning was on poetry and textual work.
 |
Self-portrait as Merzmensch, 2015 (Google Deep Dream) |
I’ve recognized in this kind of collaboration with AI a
direct continuation of the Avant-Garde's experiments. Let’s think back about
Tristan Tzara and his Manifesto (https://medium.com/merzazine/lakrobuchi-part-2-four-horned-unicorns-12865a0b4590?sk=99ee4daa9f9dc92a7fd7f59e01b4a373),
in which the poem is generated randomly (by accidental combinations of cut-out
words), but still: “The poem will resemble you” (Tzara).
Or Surrealists with their Cadavre Exquis (https://medium.com/merzazine/today-is-fridai-time-travels-literary-games-and-playing-exquisite-corpse-with-artificial-eaf7c43d9095),
another collaborative creation with controlled actions - yet all participants
are “prompting” their counterparts.
For me, it was a fascinating process to run it with a
machine. How does AI interpret human culture? How does our civilization look
like through the prism of neural networks? These questions motivated me to
incorporate different AI approaches in my creative journey.
Yet my second approach went even further: to stop
collaborating and to let the machine write by itself. A controversial take,
which is surely reflected in the weltangst by many creatives: the
machine becomes creative by itself. For me, this fear remains unfounded. There
are a lot of writers out there. Do they really see each other as competitors?
Are there artisans or rather artists? Human culture is based on intertextuality,
or as Roland Barthes wrote:
“a text consists of multiple writings, issuing from several
cultures and entering into dialogue with each other, into parody, into
contestation;” (“Death of the Author”, Aspen 5+6, https://ubu.punctumbooks.com/aspen/aspen5and6/threeEssays.html#barthes).
And if the authors observe a machine entering their scene, why not embrace the
new narratives? Why not appreciate new ways of finding meanings beyond
words?
The answer is simple: many are afraid of being cannibalized.
But even if a machine generates millions of content pieces, it does not
diminish a single line of human writing. Or put it another way: Arthur Rimbaud
created around 100 poems in his life. His contemporary, Victor Hugo, wrote ca.
3000 poems. Had Hugo cannibalized Rimbaud?
Imagine now a machine creating poems in vast, almost
inflationary quantities and publishing them as on-demand books on Amazon. Would
these writers—because behind every AI there is still a human—replace other
poets if their/machine’s work were just mediocre? Who would buy and read such
poetry, who would adore and celebrate it? And if, on the contrary, the
machine’s verses were genuinely inspiring, moving, even mesmerising—would that
be harmful to other, human poets?
After all, it isn’t about quantity - it’s about quality, and
quality in terms of poetry is a very subjective matter. AI cannot feel
emotions, yet its creations can stir them in us. And I know many poems of
machine origins, which touch and move me deeply.
If a machine carries the biases of its training
data, can we ever expect it to produce poetry that challenges those biases—or
must the human always act as the critical counterweight?
The thing is, machines are as biased as our human
civilization, which means they're very biased. And if we don’t like our
reflection, the last thing to blame is the mirror. We probably will never be
able to liberate machines from biases, like we will never liberate ourselves.
Define “bias”. Who might take that holy duty to decide what’s right and what’s
wrong opinionatively? We see right now in different countries around the world
how diverse the ethical concepts are, and how dogmatically they (also: we) protect
our values, and offend the foreign. No, we, humans, are biased. We should deal
with the plank in our eye first, before claiming about speck of sawdust in the
eye of the machine.
Yes, thanks to AI, we can visualise such biases, expose them,
and play with them. Humans should be a critical counterweight to humans and
machines in the same intensity. So seen, machine-produced poetry challenges our
own biases, and we should embrace it and scrutinize ourselves, as we do with
machines. The second part is surely the easier one, so people become
neo-Luddites in their narcissistic, anthropocentric self-righteousness. Saving
humanity from being endangered by machines is a comforting narrative to dress
up our dogmatic beliefs into a humanist messianism.
We, humans, know it pretty well, of what we are capable (in
good and bad ways). And we are always up to projecting our own maleficent
potential on the machines. Yet a post-anthropocentric age is coming.
In your experience, does the machine contribute
more as a generator of raw material or as a structural thinker—a kind of
co-architect for meaning?
Both, because I have several approaches to this. But I tend
instead to let AI write by itself.
For example, I train (and not just “finetune”) language
models on my own poems, mixing them with public domain books and sources. Then
the language model generates a myriad of texts in various genres, with various
topics, stories, and poems. This is how my series reMERZ (https://medium.com/merzazine/remerz-7920de573f70)
works. For me, this is initially raw material. Ready-made. Objet trouvé. And I
deal with these texts accordingly: by reading them, finding gems, and
presenting them to humans. Even if it’s a “raw material”, I try to keep it as
it is - but not always. We’ve seen that even the most famous ready-made by
Duchamp is modified by “his” signature “R.Mutt”.
The majority of texts (90%) lack convincing poetic power. Yet
around 7% are somehow interesting, so I select them for further use in my
works, stories, screenplays. But the rest, 3% - oh yes! Here we can find unique
irony of the machine, Beckett-esque communication failings, Lynch-esque twists,
Oulipo-alike experiments, and even elements only typical for machines.
These poems I use 1:1 as the original, without intervention.
Here is an ambiguous poem, my favourite, written entirely by
the machine:
 |
“Crazy”, from: EtherPoems 2, 2021 |
Does the psychiatrist call the narrator “crazy” just to
encourage him (“Come on, are you crazy? Keep writing, don’t stop it, throw
your doubts away”). Or is it already a diagnosis (surely not really
professional of a psychiatrist).
In another poem, it created an enigmatic world, which are
slipping away from our understanding, and which is still stable and self-aware
behind the gate to alternative dimension:
https://vimeo.com/572999712?fl=pl&fe=ti
“Emissary”, from: EtherPoems 2, 2021
.Post-athropocentric creatives among us won’t immediately
call this apposition of concepts “BS of a stochastic parrot”, but rather will
join in, co-playing the Demiurg of unknown reality, interpreting and completing
this strangeness with our imagination.
This poem, for example, called “Food Stamps” seems to be an
agglomeration of concepts and meaningless topics, but its absurdity is
especially now very urgent and actual: it’s about decay of society and
government becoming dystopian one:
https://vimeo.com/566273703
Poetry is about feelings (heart) and understandings (mind),
in different dimensions and intensities. Yet the perception counts. There is no
poetry without readers - be it even the authors reading their own texts. Kurt
Schwitters wrote his Ursonate in solitary existence outside civilization - on
the Norwegian Island Hjertøya. He was the only reader of his poetry during this
period. Yet he couldn’t stop writing. That’s real creativity for me - this urge
to express yourself.
In my other project, KI-MERZ-AI, I trained a language model
on Schwitters’ MERZ-Magazines and continued his work into the AI-driven epoch.
Here as well, I hadn’t really intervened. I presented the works as they are -
German literary critic Hannes Bajohr described my project as “Affirmative
Kontrollabgabe” (“Affirmative Ceding of Control”) - and this definition
precisely sums up my project.
(Some Images from KI-MERZ-AI)
AI tends to reproduce learned patterns. Poetry
often thrives on breaking them. How might this tension be navigated in a way
that produces something neither human nor machine could make alone?
It depends. Here you have to stop being just a feeder and
operator of AI, you have to become the poetic engineer. In the case of GPT-2
(the old open-source Model by OpenAI from the year 2019, which I still use),
you have numerous possibilities to change parameters. For example, temperature:
the higher the “fever”, the less “human-like” does the model write. Say, if you
lower the temperature to 0.1, the text model spits out boring primitive text
chunks. If you raise it to 0.7, it becomes a sophisticated and elaborated
writer. If you set it between 0.9 and 1, your model is Dadaist.
In my work The New Flowers for Algernon (https://medium.com/merzazine/the-new-flowers-for-algernon-3c5d526ac9fa),
which is a homage to a short SciFi story by Daniel Keyes, I tried different
temperatures to represent the intellectual rise and decay of the protagonist.
Here you can see how different the same model writes with various temperature
settings. Interestingly, at lower temperatures, you can see weird patterns,
which are typical for such language models. The feedback loops.
Same feedback loops I used as stylistic and semantical twist
for this short videopoem:
https://vimeo.com/572562052
Man’s voice.
So, AI can only break conventions if you let it do it. And
with creative settings “on speed”, it begins to hallucinate in the best desired
ways - expectedly, it creates non-expected twists and phenomena. Still, I see
sometimes parallels to Oulipo experiments, yet here you go beyond mathematical
equations - Transformer in GPT makes this language model something far more
than that “stochastic parrot” (actually, it was probably LSTM models in the
past, it’s not a parrot anymore).
If an AI could be trained exclusively on poetic
texts, what kind of “poetic language” do you think might emerge—would it be
richer or strangely impoverished?
I don’t recommend training AI just on poetry. Our world is
richer than that. Poets don’t live in poems - it would make their reality
limited to the internal metaphors and language. I don’t like semantical
claustrophobia.
After all, it can become very metafictional. Not that I
dislike metafiction, I’m even a big fan of it (Borges, Cortazar, Danielewski
are great masters of metafiction). Yet for poetry we need meanings, feelings,
ideas, concepts, the entire world. You'd better train AI on everything.
I experimented extensively with various datasets. I wanted AI
to write me classical poems about modern topics, so in one of my training
datasets I put scientific papers, Dadaist manifestos, news articles, my own
essays, and a massive volume of English poetry from medieval times till the
19th century. Poetry prevailed in the dataset, and so the results were mostly
poems in the style of the 17th-18th century. It was epigonal, so I reduced the
poetry collection in my dataset. By mixing that with this, I could find the
balance for me by limiting and adding different contexts. This process reminded
me of Alchemy and the pursuit of the Philosopher’s Stone.
At some point, the dataset's energy (not content) was
effectively represented in text generations, allowing me to choose between
different genres, as not all of them were poetry. And language became wilder,
by breaking norms and standards (here I also played with temperature).
At the end, I created the perfect constellation of dataset
and generation settings which allowed me to dive deep into the odd inner world
of machine semantics.
Again, the language and style in generated texts are not
impoverished if the dataset is rich in diversity and the parameters are set for
the most creative condition of the machine.
Sure, with the generation of texts, the entire process is not
finished - it’s only beginning. Here I - as an explorer and discoverer, as a
literary critic and rigorous examiner, start to evaluate texts, how they
resonate in my mind and heart, how they disrupt literary topoi and formulas,
how they distort language.
This is for me the creative collaboration between humans and
machines. And we see great examples for marriage of poetry and machines - in
brilliant works by theVERSEverse group. Our global world literature gets
enriched by the encounter with a machine.
Do you imagine a time when machines will develop
their own internal “poetic traditions,” independent of human culture, and if
so, should we welcome or resist that?
The machines should develop their own poetic language. They
are not here to be epigonal human replacement, not at all. Sure, the uniqueness
of AI-driven poetry changes from model to model, mainly because the language
models are not conceived with the primary goal of writing poems. Probably even,
this approach should be followed by us, poets, to develop a poetic language
model, rich in concepts, language, and metaphors, not for efficiency
optimization or for “explaining scientific papers for me like I’m a 5th
grader”.
Such a model should be trained on a vast amount of texts -
including poetry. For fairness, such a model shall be opt-in based, like Public
Diffusion. Every poet who would like to contribute should be able to provide
her or his poems. Still, training AI models is not stealing, like many suppose.
This wrong narrative reflects a misunderstanding of how the culture emerges -
luckily, post-structuralists wrote a lot about it. Language models don’t steal
concepts or styles. They learn to write, yet after proper training, they don’t
contain texts - they are rather Platonic ideas and forms in vectors of Latent
space. Only by “overfitting” (i.e., an extensive training) can models replicate
originals, but that isn’t what we need.
We need AI to have the capability to experiment with contexts
in its own way. But for this, we need contexts, and we need language. We need
awareness that training on your texts doesn’t steal them, but enriches the
digital future of poetry. Generative AI doesn’t plagiarize, especially advanced
models; they “generate”, not “regenerate”. And if you fear your language and
ideas being stolen, you should stop writing. Because your readers will be
contaminated with your ideas and metaphors - and will steal them in their own
poetry.
Lastly, could you highlight a few poets working
with AI, who we may not have come across yet, but you think are doing important
work.
Yes. Here are some names of poets using AI as poetic engines:
Jörg Piringer, Jaap Blonk, Allison Parrrish, Eran Hadas, Yuri Rydkin, Have,
& Machi Tawara.
What Merzmensch offers is not a final statement
but a growing body of evidence: fragments, anomalies, and experiments that
extend poetry into unfamiliar territory. His Medium archive is itself a kind of
living case file—part manifesto, part laboratory, part exhibition—where readers
can follow the traces of his long engagement with machines. I encourage you to
explore it, not just for the individual works but for the accumulated record: a
decade-long dialogue that shows how poetry can persist, mutate, and reappear in
unexpected forms.
Merzmensch (https://www.merzmensch.com)
is a cultural scholar, publicist, and artist. His work explores the tension
between the avant-garde and collaboration between humans and machines, both in
theory and in practice. Since 2017, he has been writing in his blog Merzazine
about AI and its creative possibilities. His essays, interviews and poems have
appeared in Harper's Magazine, Perspektive, Frankfurter Hefte, Die Zeit, EIKON,
et al. His artworks were exhibited in Berlin, Munich, Frankfurt and New York.
In his book KI-KUNST (AI ART) (second updated edition, 2024,
Wagenbach Verlag), he examines the history and contemporary developments of
generative art and generative AI.
Laura Kerr is an award-winning Canadian visual artist and
poet. In 2012, she was honoured with the Queen Elizabeth II Diamond Jubilee
Medal for her contributions to the arts and her long-standing commitment to art
education.
She recently sold her art school to devote herself fully to
her writing and art practice. Laura currently serves as Vice-President on the
executive board of Plug In ICA, a leading contemporary art centre located on
Treaty 1 territory in Manitoba, Canada.
For over 30 years, she co-owned and taught at Paradise Art
School, specializing in classical and contemporary art education. Throughout
her career, she has explored the intersections of traditional mediums and
digital technology, increasingly blending painting, drawing, and photography
with generative processes.
Her current focus is visual poetry—experimental, image-based
works that merge poetic ambiguity with technological play. By using digital
tools in process-driven ways, she ensures the artist’s hand remains
central—even in collaboration with machines.
She is also developing a body of experimental poetry
criticism, written in collaboration with AI trained on her own work. These
pieces challenge conventional interpretation and embrace uncertainty, forming a
self-reflective loop between maker, machine, and meaning.