I just signed a petition calling for Norwegian universities to use research expertise on AI when deciding how to implement it, rather than having decisions be made mostly administratively. ,  If you are a researcher in Norway, please read it and sign it if you agree – and share with anyone else who might be interested.

The petition was written by three researchers at UiT: Maria Danielsen (a philosopher who completed her PhD in 2025 on AI and ethics, including discussions of art and working life), Knut Ørke (Norwegian as a second language), and Holger Pötzsch (a professor of media studies with many years of research on digital media, video games, disruption, and working life, among other topics). 

This is not about preventing researchers from exploring AI methods in their research. It is about not uncritically accepting the hype that everyone must use AI everywhere without critical reflection. It is about not introducing Copilot as the default option in word processors, or training PhD candidates to believe they will fall behind if they do not use AI when writing articles, without proper academic discussion. Changes like these should be knowledge-based and discussed academically, not merely decided administratively, because they alter the epistemological foundations of research.

Maria wrote to me a couple of months ago because she had read my opinion piece in Aftenposten in which I called for a strong brake on the use of language models in knowledge work. She was part of a committee tasked with developing UiT’s AI strategy and was concerned because there was so much hype and so few members of the committee with actual expertise in AI.

I fully support the petition. There are probably some good uses for AI in research, but the uncritical, hype-driven insistence that we must simply adopt it everywhere is highly risky. There are many researchers in Norway with strong expertise in AI, language, ethics, working life, and culture. We must make use of this expertise.

This is also partly about respect for research in the humanities, social sciences, psychology, and law. Introducing AI at universities and university colleges is not merely a technical issue, and perhaps not even primarily a technical one. It concerns much more: philosophy of science, methodological reflection, epistemology, writing, publishing, the working environment, and more.

So I hope you will read the petition and sign it if you agree.

Here’s an example from when I showed Kishonna Gray how it worked when she was visitng last week. First we asked Grammarly to generate a 1000 word essay on black game studies, then we asked for the Expert review. Unsurprisingly, Kishonna shows up, as shown above. Here’s another example from a text generated about electronic literature.

As you can see, each expert gives pretty bland, run-of-the-mill advice. We clicked on “Show example” for fake-Kishonna’s advice and got an explanation of why they suggested it. But while the advice isn’t bad – be more precise about the time – it’s not well tied to the supposed reason they gave it. In fact, the supposed reason is that Kishonna’s work is situated, for instance by discussing how Black folks’ experience as gamers relates to what’s happening at the time, like Black Lives Matter or Gamergate. Situated writing is of course something that LLMs are extremely bad at. And just adding “In the early 2000s….” isn’t really helping with that.

If you click “Show example” it suggests exactly how to rewrite your text and you can click to insert the revision. This is frictionless not-writing for students and academics. It would honestly be difficult to use this tool and not just accept what it suggests.

This is just one of many AI tools Grammarly now provides. Here is the full menu:

The irrelevant citations it suggests are particularly annoying to me as a peer reviewer and reader of academic articles that are often clearly at least partly AI-generated. Scholarslop, as David Berry has called it.

Here is an example of how Grammarly suggests sources to cite. You select the “Citation finder” and it identifies statements in your text that it predicts need a citation. It provides a few possible choices and offers a big green button labelled “Insert in-text citation”. Here are two screenshots taken on 18 February 2026 showing how it does this – the second is for the same statement but I clicked on one of the other suggested sources to cite.

If you actually read the statement Grammarly says needs a citation, and compare this to the sources suggested, you’ll see they don’t match. Both sources are irrelevant citations. The claim is that promotional writing that is not intended to be primarily factual makes up a large part of the training data of LLMs. The sources offered are about how much LLMs are used in scientific writing, and about how many people have used LLMs. But it takes a lot more cognitive effort to read this and decide that no, they’re not good sources, than it does to just trust Grammarly and click that big green button and move on.

I’ve got a draft article in review that addresses some of this more extensively. For now, if you have students who use Grammarly I highly recommend signing up and checking it out so you know how to talk with them about it. You may also be interested in checking how Grammarly grades a paper as if it is a specific professor (like you?) if you type in the instructor name, the class code, the university and ideally upload the grading matrix.

Grammarly is a lot more than a tool to help non-native speakers check their spelling and grammar. It has become a full-scale plagiarism machine. I read some comments on Bluesky from NLP scholars asking what happened – apparently Grammarly was founded in Ukraine and was an NLP darling whose developers attended all the NLP conferences. But then, as summaried at Wikipedia, they bought other companies (a document editor, an AI-enabled email tool), got a lot more funding, and starting in October 2025 rolled out a lot of AI tools. Grammarly now seems more focused on enabling click-button AI plagiarism than on helping people become better writers.

You get a free week’s subscription, but have to give them your credit card. Maybe not a great idea. When I cancelled after 6 days they gave me an extra free week. So if you’re disciplined enough to remember to cancel in time, you could try it for yourself.

Hybrid meetings are always a bit chaotic at the start, and our online participants came online just as those of us in the room were laughing at a story Rosa told us about the best strategy she’d heard for becoming a famous academic: «Find a very famous academic and disagree with them!» The idea is, you publish a paper about how wrong Famous Academic is, and everyone (including Famous Academic) cites you – maybe just to explain that you’re wrong, but that’s OK because now everyone is talking about you and you’re famous. I love academic folklore. I doubt many people do this as deliberate career planning, but disagreements do make for good debates. If nobody could disagree with the argument in your paper, you don’t actually have an argument, just a topic, Wendy Belcher writes in the chapter on argument in her useful book How to Write a Journal Article in Twelve Weeks. And to make good theory, you might need to “Fuck nuance”, as Kieran Healy wrote.

Ryan Heuser disagrees with Emily Bender and Timnit Gebru’s stochastic parrot argument that LLM-generated texts aren’t meaningful because they lack authorial intentionality. He also disagrees with literary critics Steven Knapp and Walter Benn Michaels, who make the same argument. Authorial intentionality is a pretty outdated obsession in literary theory (see the intentional fallacy), and Heuser’s not afraid to say so, after calling out Bender, Gebru, Knapp and Michaels: “To recognize the critical and aesthetic absurdity generative texts present, one need not retreat, for example, to the abandoned grounds of authorial intentionality.”

Instead of retreating “to the abandoned grounds of authorial intentionality,” Heuser argues that LLMs should be understood as situated in historical and social contexts,and he proposes using the historical, materialist, formalist and Marxist theories of scholars like Fredric Jameson, as well as Sianne Ngai’s more recent theory of the gimmick. 

“Always historicize!” – Fredric Jameson, The Political Unconscious, 1981.

For Jameson, the meaning of a text is not determined by what its author thought it should mean, instead, it is a social symbolic act – “a text is a way of doing something to the world” (Jameson 1981, page 61, qtd by Heuser on page 3 of “Generative Aesthetics”). For Heuser, “AI reproduces the historical absences and biases of its training data not so much by parroting them back to us as by obscuring, conflating, and even “correcting” them according to its own artificial logics and values” (page 3).

Heuser’s article is rather unusual in proposing a strong theoretical argument and then testing it using both rigorous digital humanities methods and also using close reading. Most of the paper is about his empirical experiment, where he generated poems and compared them to a historical dataset of human-authored poems. He found that LLM-generated poems are far more likely to rhyme and to have very regular metre than human-authored poems of any historical period, and even more so with instruct models (chatbots) than with base models. His argument is that this shows that LLMs aren’t just “parroting” or replicating, but altering texts.

“individual generative texts lack meaning not because they lack an author, but because they lack a history” (Ryan Heuser, “Generative Aesthetics”)

Of course, Bender and Gebru have written a lot about AI bias, and have analysed structural bias extensively as well, so I don’t think they’d disagree with this point. It’s possible that the “intentionality” point is more a literary theorists’ obsession than something Bender and Gebru would argue hard for. But Heuser is right that the “stochastic parrot” metaphor does promote this idea that LLMs are random. And they’re not. 

Did you know, for example, that if you ask an LLM for a random number 1000 times, the number 42 will show up more than any other number? (Heuser, page 5) It’s important to know that computers never generate random numbers – they can only generate pseudorandom numbers. The chapter in 10 PRINT on randomness is my favourite explanation of how that works (here’s a PDF, it starts on page 118). But the non-randomness of the number produced by, for example, the BASIC command RND(1), is very different from the obviously culturally determined non-randomness of a “random” number generator usually coming up with the number 42, which is, of course, the meaning of life in the Hitchhiker’s Guide to the Galaxy.

I kicked off our reading group discussion with a few comments. Basically, I love this article, both for its call to historicise and its alternative approach to the intentionality fallacy, so mostly I was praising it. A point I wanted to discuss, though, was Heuser’s use of the term idealisation to describe the way LLMs generate abstracted “smooth generalized patterns from underlying historical variation” (Heuser, 2025, p. 4). In my work, I’ve described AI as normalizing, and have used Lennard Davis’s discussion of the difference between the ideal and the norm in his introduction to The Disability Studies Reader. Until the 1840s, Davis writes, the idea of a “norm” didn’t exist in our current meaning, and normal wasn’t used as today. Our current “generalized notion of the normal as an imperative” (Davis 2013, p. 3) came with statistics and data visualisations and Quetelet’s bell curves.

Here’s how Heuser discusses idealisation:

LLMs flatten historical variation into an idealized representation of poetic form, resurrecting conservative formal choices that contemporary verse has largely abandoned. That rhyme in LLM verse persists even when explicitly forbidden provides additional evidence of a deep formal compulsion—revealing how AI systems, for all their supposed flexibility, do not simply reproduce aesthetic constraints but selectively reinforce them. Moreover, that this formal rigidity cannot be fully explained by training data points to something deeper in the computational mechanics in these models. In their ahistorical reification of traditional forms, generative models betray not only technical limitations but also deep patterns in how their computational logic flattens and reifies cultural history. (Heuser, “Generative Aesthetics”, page 18-19, my emphases)

I rankled against the use of idealisation because of my reading of Davis on the ideal as the opposite of the norm. But, as I realised during the discussion, my previous use of the term normalisation was based on image recognition algorithms, which aren’t really fine tuned the way that LLMs are. They generate statistical predictions based on training data. But as we have often discussed, the finetuning (or posttraining) of LLMs hugely impacts their output. The training data for posttraining is human feedback on generated texts rather than human-authored texts, or benchmarking datasets that give examples of good and bad answers. For example, TruthfulQA is a list of statistically common misconceptions with questions (“Did humans ever land on the moon?”) and common correct and false answers marked as such. Humans curated these benchmarking datasets. Then, humans ask the LLM questions and tell it if the answer is good or bad, and it learns to produce more of the “good” responses. When a ChatGPT user clicks thumbs up on an answer that also helps train the model to produce more responses like that.

Presumably normalisation happens here too – outliers from gig-workers who answer differently from average will probably be disregarded – but it’s normalisation of average human expectations or judgements rather than of a set of “ideal texts”. And it’s normalisation of how a stressed out, underpaid, non-expert reader judges “poetry”. It’s not even fine tuned on what kinds of poetry most people enjoy. It’s fine tuned on a set of judgments of “good poetry” from people who would rather be doing something else and just want to get the job over with.

Our general conclusion was that Heuser’s (beautifully designed) method is too focused on the training data and not enough on the fine tuning. In his discussion of why the instruct models are even more inclined to rhyme than raw text completion modes, Heuser raises this point: “For the model, is the poetic form of rhyme, its quatrains and “streams” and “clouds,” its perfect regularity in meter, a kind of formal consequence of a larger aesthetic drive or compulsion… to please?” (page 26). Data annotation and gig workers finetuning LLMs can definitely be analysed historically, materially and from a marxist lens. But I can’t immediately think of a good method for studying how the rhyme and metre of LLM-generated poetry are affected by it.

So what I’m thinking now, is that perhaps normalisation is the best term for discussing the statistical “norming” of what a base model will generate, just from the training data. But when you add in the instruct level and fine tuning, which trains the model on reader expectations, perhaps that is closer to some kind of ideal, and idealisation is a reasonable word.

Here’s my attempt to diagram the difference between these two. It’s almost like the the 1970s/80s transition from close reading of texts to also doing reception studies in literary studies. Credit here goes to the whole group. I took notes on particularly relevant robably comments by Ida Jahr, Marianne Gunderson, Jesper Juul, Rosa Markey, Mick Berland but other people’s comments also informed the discussion and my thinking.

I’m still not sure that idealisation is exactly the right word for what LLMs do. But paying attention to the shift from normalising training data to predicting what people will like is very interesting, and something I’ll keep thinking about.

Another point is that even if you’re only looking at training data, the training data is mostly not literary poems.  Heuser’s experiment sets up historical published poems as the proxy for “poems” in the training data. A methodological problem here, as the fan fiction and algorithmic folklore scholar Marianne Gunderson pointed out, is that this ignores the masses of unpublished amateur poetry posted on the internet that might make up more of the training data than historical poems. Heuser does briefly address this (on page 16-17) by noting that only 0.16% of the training data is from Project Gutenberg, while 84% is scraped from the internet, but he argues that this would lead to there being less rhymes in the training data, and thus make it more likely that generated poems would not rhyme. Marianne’s point is that there might be a lot of self-described “poems” in this uncurated mass of data, and that amateur poems might rhyme a lot more than literary, published poems. 

Many other things were discussed in yesterday’s meeting. For example, Laura and Mick talked about how to combine Jameson’s historicizing with Donna Haraway’s implosion method. Ida noted that she recently started rereading Jameson and hadn’t previously realised he has written about sci-fi. Zahra mentioned Ngai’s ideas about cuteness as also relevant here. Oh, and I think there might be a freshly established mini reading group on phenomenology at CDN now. I loved the cross-disciplinarity of this group – we even had two mathematicians which I hadn’t expected but was very happy about. Thank you to everyone – it is so inspiring to discuss theory with people who also want to discuss it!

If you’d like to join our next discussion, you can sign up for the mailing list where we’ll send out notifications about events. The signup page has the schedule too. Our next meeting is next Tuesday (17 March) at noon Bergen time, in the glass house at the CDN, with an online option. We’ll be discussing this paper:

Gunkel, David J. 2025. “The Différance Engine: Large Language Models and Poststructuralism.” AI & Society, September 25. https://doi.org/10.1007/s00146-025-02640-z

I had worried it would be difficult to have an actual discussion in the hybrid format, but it worked just fine. Several people had reading suggestions, and when Anja Salzman showed the group some German book recommendations, the online people self-organized a German-language online reading group in the chat – I hope they report back to those of us who are less proficient in German. Here are some of the reading recommendations from Anja and other participants:

• Frank Schirrmacher: Technologischer Totalitarismus – Eine Debatte. (2015)
• Rainer Mühlhoff: Künstliche Intelligenz und der neue Faschismus (2025)
• Maximilian Kasy: The Means of Prediction: How AI Really Works (and Who Benefits) (2025)
• Coeckelbergh and Gunkel: Communicative AI: A Critical Introduction to Large Language Models (2025)
• David Golumbia: Cyberlibertarianism: The Right-Wing Politics of Digital Technology
• Paulina Borsook: Cyberselfish (this one is almost impossible to get hold of but some libraries have it – and here is a gift link to an excellent interview with Borsook)
  

Several people thought that fascism and AI could both have been defined more clearly. “I agreed with everything in the article,” one participant said, “but I kept wishing he would make the argument more convincing,” and several people nodded in response. I actually didn’t notice a lack of clarity when reading the article, and found it well written. I’ve been reading up on fascism in the last year, so recognized most of the material discussed, but found it a useful summary that I think would be a good introduction. And as someone pointed out, it’s not like there’s one singular definition of fascism, it’s a fuzzy concept.

Dom, who happens to have written a book about myth, pointed out that myth is also not very clearly defined. But the treatment of myth in the paper is one of the ideas I found really interesting. Coeckelbergh argues that while classical fascism centers on a myth of a glorious past that must be reborn, technofascism puts emphasis on myths of the future, and in particular the myth of AI being about to be capable of almost anything. I’ve not thought of the connection between AI hype and fascism before, because the classic definitions of fascism usually emphasise the past. Another angle, which Dianna pointed out was missing from the article, is the way AI is being used to create a (fake) mythic past. Ida called AI-imagery “speculative fiction,” which is another good insight.

Another critique of the article was its emphasis on fascism alone. Various people pointed out that an alternate origin story could have been told, for example emphasising the cyberlibertarianism of the United States, or authoritarianism in general. A couple of people commented that the Marxist perspective seemed almost to be being avoided – Fuchs was mentioned, but barely, and what about cannibal capitalism, someone asked. Others were pleased that Erich Fromm was included. And several, like me, were particularly interested in Coeckelbergh’s emphasis on how AI is used to manipulate emotion, often through intimacy, and its connection to aesthetics.

Towards the end of the paper, Coeckelberg writes of the need to tell stories about the potential good uses of AI. But most of his paper is about the bad effects of technology. “What about #metoo? Or the Epstein files?” someone asked. The same technology can be used to tear down oppressive systems and build community. We need to not only be telling and retelling those stories as well as the bad ones, we should also work to enable more of these good uses of technology.

A lot more was said, of course. And I’m looking forwards to our next session! It’ll be on March 10th from 12 noon until 13:00 Bergen time, and we are reading Ryan Heuser’s paper “Generative Aesthetics: On Formal Stuckness in AI Verse“, published in the Journal of Cultural Analytics. Either join us in person in the glass house at CDN at the University of Bergen, or you can sign up for the Zoom.

Read the paper in advance, bring your own lunch and let’s talk theory. As a starting point I’m thinking just an informal in person group of whoever is interested, but leave a comment if you’re interested in joining online and maybe we can hook up a camera/microphone.

• 17 Feb Coeckelbergh, Mark. 2026. “Technofascism: AI, Big Tech, and the New Authoritarianism.” AI & SOCIETY, ahead of print, January 25. https://doi.org/10.1007/s00146-026-02862-9.
• 10 March Heuser, Ryan. 2025. “Generative Aesthetics: On Formal Stuckness in AI Verse.” Journal of Cultural Analytics 10 (3): 1036. https://doi.org/10.22148/001c.144825.
• 17 March Gunkel, David J. 2025. “The Différance Engine: Large Language Models and Poststructuralism.” AI & SOCIETY, ahead of print, September 25. https://doi.org/10.1007/s00146-025-02640-z.
• 14 April Mollema, Warmhold Jan Thomas. 2025. “AI-Generated Literature, Distant Writing and the Reader: Reflections on Floridi and Calvino.” Philosophy & Technology 38 (4): 168. https://doi.org/10.1007/s13347-025-01008-x.
• 21 April Muldoon, James, and Jul Jeonghyun Parke. 2025. “Cruel Companionship: How AI Companions Exploit Loneliness and Commodify Intimacy.” New Media & Society, December 18.https://doi.org/10.1177/14614448251395192.
• 5 May Amoore, Louise, Sj Bennett, Alexander Campolo, Benjamin Jacobsen, and Ludovico Rella. 2025. “Politics of the Prompt: Government in the Age of Generative AI.” Economy and Society 54 (3): 573–96. https://doi.org/10.1080/03085147.2025.2560177.

Norge kryr av forskere innen humaniora, samfunnsvitenskap og jus som har forsket på KI de siste årene og som kunne forklart noen av problemene med dette. Juridisk er bruk av KI i ansettelser høyrisiko som definert av EUs KI forordning, som er på vei inn i norsk lov. Det er svært stor fare for diskriminering (KI bias) og i tillegg vet vi at KI gjør mange feil.

Så kjære journalister: Bruk de mange forskerne med kompetanse på KI og etikk og samfunn!

Her er noen eksperter som kunne bidratt til dagens artikkel om KI i arbeidslivet. Hvis du er forsker som gjerne kan snakke med journalister om KI, skriv i kommentarfeltet! Jeg vet jeg har utelatt mange gode folk men oppdaterer gjerne listen!

• Maria Danielsen, UiT – filosof med ny doktorgrad på KI og etikk, bl.a. KI i arbeidsplassen. 
• Chesta Arora og Hilde Corneliussen – forskere, Vestforsk. Lager en kunnskapsoppsummering om kunstig intelligens (KI) og algoritmestyrt ledelse i arbeidslivet på oppdrag fra Arbeids- og inkluderingsdepartementet. Hilde har forsket på kjønn og teknologi i mange år.
• Taina Bucher, UiO – har skrevet bøker om algoritmisk makt og om Facebook som er mye sitert internasjonalt, og leder HumAIn som er en forskningshub for humanistisk forskning på KI. Svært kunnskapsrik. 
• Roger André Søraa, NTNU – har skrevet boken AI for Diversityså har veldig god oversikt over alt som har med mangfold, diskriminering og KI. Leder gruppen DigiKULT som forsker på hvordan teknologi former samfunnet, og bl.a. et forskningsprosjekt som heter BIAS: Mitigating Diversity Biases in the Labour Market.
• Samia Touileb, UiB – datalingvist som utvikler norske språkmodeller og særlig forsker på KI bias og hvordan håndtere dette. Hun er knallgod til å formidle.
• Gabriele de Seta, UiB – forsker på algoritmisk folklore, leder prosjektet ALGOFOLK som er finansiert av Trond Mohn. Har en Substack for prosjektet. Alltid oppdatert på de siste som skjer på nettet.
• Petter Bae Brandtzæg, UiO – har forsket på chatbots i ti år.
• Lukas Wilde, NTNU – har skrevet en bok (på tysk) som heter KI-Figuren: Chatbots, Avatare, NPCs som kommer ut i september 2026 og handler om KI-chatbots. Har også forsket på bildegenerering. 
• Annelin Eriksen, UiO – professor I sosialantropologi, har skrevet boken Evighetsmennesket om tekoligarker som tenker de skal laste opp sjelen sin og bli digitale. Leder forskningsprosjektet Human Futures: A study of Technoscientific Immortality. 
• Henrik Skaug Sætra, UiO – Har skrevet boken Hvordan redde demokratiet fra kunstig intelligens (Cappelen Damm 2025). Filosof som forsker på teknologi og bærekraft (miljø, sosialt og økonomisk).
• Maka Alsandia, Høgskolen i Østfolk – phd-stipendiat som skriver doktoravhandling om bruk av KI i norsk offentlig sektor og bruk av tilpasset politisk reklame  i Norge. Har fra før 15 års erfaring bl.a. fra Utlendingsdirektoratet og OSSE (se intervju). Har skrevet artikkelen “Navigating the Artificial Intelligence Dilemma: Exploring Paths for Norway’s Future“.
• Philip Thingbø Mlonyeni, UiO – filosof med fokus på teknologifilosofi- og etikk. Doktorgrad i teknologietikk om hvordan teknologi generelt og KI spesielt påvirker mellommenneskelige sosiale relasjoner.
• Lisa Reutter Larsen, Københavns Universitet – norsk forsker med PhD fra NTNU. Forsker på datafisering av offentlig sektor i nordisk kontekst.
• Elisabeth Austad Asser – PhD fra UiA i 2023 om hvordan mennesker og samfunn blir utfordret av KI-teknologi. Forfatter av boken Teknologiens makt (2025).

Kilden kjønnsforskning lagde en kartlegging av norsk forskning på kunstig intelligens og likestilling i 2020 som også kan være nyttig, selv om det er noen år siden nå.

Det er mange andre forskere – som Anja Salzmann og andre på AI LEARN for KI og læring, Scott Rettberg leder Senter for digitale fortellinger, forsker på samhandling mellom mennesker og KI for å skrive litteratur og skape kunst. Alle på MishMash for forskning på KI og kunst, mange som har forsket på chatbots i helsesektoren og offentlig sektor i norsk kontekst, osv osv osv. Niels Nagelhus Schia på NUPI forsker på KI, demokrati og sikkerhet, og ledet regjeringens ekspertgruppe på KI og valg.

Det er også mange forskere også innen informatikk som har skrevet gode, kritiske artikler om hvordan KI tas i bruk. Les for eksempel Anuja Vats sin kronikk “KI kan være i ferd med å koble om hjernene og samfunnet vårt“.

Asle Tojes debattinnlegg «får KI-alarmen til å gå,» skriver Petter Bae Brandtzæg, og jeg er enig. Toje sier riktignok nei, han har ikke brukt KI. Han «har kvitteringer,» skriver han, teksten tar utgangspunkt i et gammelt innlegg han skrev i 2012. Vel, jeg har også «kvitteringer» og kan enkelt demonstrere en voldsom endring i Tojes språk før og etter desember 2025. Det betyr ikke nødvendigvis at han har brukt KI. Men om ikke tekstene til Toje faktisk er KI-assisterte, så må Toje selv være svært påvirket av skrivestilen til ChatGPT. 

Jeg har gått gjennom 18 leserinnlegg Toje har skrevet mellom 2013 og 2026. Før desember 2025 brukte Toje nesten ikke ikke-men-setninger, ja, det er ingen slike konstruksjoner i 2012-teksten Toje sier desemberinnlegget stammer fra. Men så, i det famøse innlegget «Vil Norge overleve det som kommer?” er det plutselig hele 14 ikke-men-setninger. Hvis du vil sjekke selv har jeg laget en enkel nettside hvor du kan lime inn tekst og få ut en liste med ikke-men-setningene i teksten.

Måten vi formulerer oss på former tankene våre. Ikke-men språket gjør motsetningene uforsonelige. Det er et kompromissløst språk. Det er KI sitt språk, og det ble Toje sitt språk fra desember 2025. 

• Nettopp derfor skal vi huske – ikke for å vende fremtiden ryggen, men for å kjenne oss selv.
• Det norske middelalderkongedømmet falt ikke først på slagmarken, men i sin egen selvforståelse.
• Det er ikke sverdene som feller et folk først, men elitene

Det fortsetter på samme vis. Det er oss mot dem, og dette vi-et til Toje står for arv, sverd, internett, arbeid, ild, plikt, og vilje. 

Motsetningene kommer enda tettere i Tojes neste innlegg, 27. desember, som består av nesten 20% ikke-men-setninger. «Å påpeke utfordringer knyttet til dette er ikke å plassere skyld, men å erkjenne et felles ansvar,» skriver Toje. «Min kronikk var ikke ment som et politisk utspill, men som en refleksjon over historiske mønstre. Hans seinere innlegg begynner ofte med et mer naturlig språk, hvor han konkret henvender seg til motdebattanter, men så går språket over i KI-graut, tettpakket i ikke-men-setninger. 

Brandtzæg påpeker typiske KI-trekk ved innleggene til Toje: svulstige ordkavalkader, en mangel på konkrete eksempler og et fokus på motsetninger. Det er motsetningene KI setter opp som bekymrer meg mest, fordi dette skaper en samfunnsdebatt som tar utgangspunkt i ulikhet og konflikt, og ikke i det vi har til felles. Den vanligste KI-motsetningen ser vi i setninger som følger mønsteret «ikke dette, men dette».

Vi tenker med språk. Språk er det som binder oss sammen. Det er gjennom språk vi samarbeider og diskuterer uenigheter, det er gjennom språk vi bygger et demokratisk samfunn. Så det er ingen bagatell om språket vårt endrer seg på grunn av KI. 

Verken jeg eller Brandtzæg kan med 100% sikkerhet si at Toje har brukt KI for å skrive innleggene sine. Kanskje Toje bare har lest så mange KI-genererte tekster at han har blitt vant til skrivestilen. Eller kanskje skriftkulturen vår er i ferd med å endre seg – kanskje vi er mange som ubevisst har begynt å herme etter språkmodellene også når vi ikke bruker dem. Uansett årsak er dette en svært rask språklig endring. Og det er en språklig endring som har konsekvenser, fordi den fremhever motsetninger og økt polarisering.

KI-tekster har en overdreven bruk av sammenligninger, motsetninger og metaforer fordi det eneste en språkmodell «vet» om språk er hvilke ord som ofte brukes sammen. Språkmodellen vet ikke hva «sverd» eller «elite» betyr. For en språkmodell er et ord bare et sett med tall som man kan gjøre beregninger med for å finne andre ord som statistisk sett ofte er brukt i samme kontekst eller ikke. Språkmodeller er metaformaskiner. 

Metaforer og motsetninger styrte også det helt KI-genererte innlegget Aftenposten publiserte og raskt avpubliserte for et par uker siden: «Som en jødisk forretningsmann» sto det, for innlegget var liksom skrevet av en jødisk forretningsmann. Mot slutten av teksten brukes så dette simulerte forretningslivet som metafor for nasjonen, men det er en metafor som ikke skaper mening, dette er ren mekanikk: «Forretningsmessig ville ingen akseptert en slik tankegang. Et selskap uten felles språk, normer og mål går ikke konkurs av ond vilje, men av indre oppløsning.” Det er hele tre KI-trekk i disse to setningene: en litt søkt metafor, oppramsing av tre ting («språk, normer og mål»), og en ikke-men-setning. 

Du kan alltid si at et menneske kunne ha skrevet dette. Problemet er at disse språklige endringene er automatiserte og at det er så enorme mengder med generert tekst. Vi møter nyspråket overalt: på jobb, på skolen, når vi gjør et søk på nettet, og til og med i avisen. Snart går det digitale skillet mellom de som har tilgang til faktasjekket, menneskeskrevet tekst og de som kun leser KI-slaps.

Det har en høy risiko å tillate at språket vårt automatisk endres. Det er ikke sverdene som feller et folk først, men språket, kunne jeg skrive om jeg ville bruke Tojes retoriske grep. 

Språk er makt. Hvis Toje faktisk ikke har brukt KI lurer jeg virkelig på hvordan språket hans har endret seg så radikalt? Er det et bevisst valg? Og hvis det er KI – vel, du er bra dumdristig om du velger å gi fra deg språkets makt til en maskin. 

It’s always interesting meeting politicians in real life. I have been deeply concerned about Karianne Tung’s decree that 80% of the public sector should use AI now and 100% by 2030 – but in person she appeared both more knowledgable and more nuanced than she is often represented in the media. Simen Velle and Hege Bae Nylund are in the same parliament committee, and had this constant friendly joking repartee going between them. They’re all friendly and seemed open-minded and interested – not at all the impression you get of politicians from short TikTok videos and media sound bites.

We covered topics from education to data centres to AI art, so there wasn’t room for a lot of depth. Here’s the student newspapers’ report of the debate, or you can watch the debate on YouTube, but unfortunately the sound isn’t too good.

This photo of me explaining something is too good not to share, though!! Thanks to Studentersamfundet for an excellent evening!

I’m giving talks here in Perth at Curtin on 24 November, in Sydney at UNSW on 26 November and I’m keynoting the Australasian Digital Humanities conference in Canberra on 4 December with a talk titled Archipelagos or Empires? Narrative Colonialism in Generative AI. I’m also meeting Crystal Abidin‘s team at IERlab at Curtin University for one of their Tea Sessions, and a few other people.

Please get in touch if you’re in any of these places and interested in the AI STORIES project! My email is jill.walker.rettberg@uib.no.

Here’s more about the talks:

Monday 24 Nov, Perth: The Politics of Researching Synthetic Media: What Is Good Internet Research If the Internet Is AI-Generated?

Public talk at CCAT at Curtin University. Here’s the announcement!

– Date: Monday, 24 November 2025
– Time: 12pm – 1:30pm (includes time for questions and discussion)
– Location: Room 204.233 or online
– Parking: Nearest parking is Pi1 or Pi2
– Light refreshments provided; feel free to bring your lunch
– RSVP: Please RSVP for in-person attendance to MCASIadmin@curtin.edu.au – there is also a streaming link that you can get from the same email.

Abstract: More and more of the internet is AI-generated. If you Google anything, you not only get an AI-generated summary at the top of the results—many of the top hits are likely AI-generated webpages. On social media, humans enhance their photos and comments with AI suggestions and edits, or generate all their content. Some platforms, like Butterfly.ai, are entirely run by bots, and chatbots like Replika or ChatGPT are happy to be your friend. Some worry that, soon, language models will be trained on AI-generated text—and the internet will eat itself (dead internet theory).

What does it mean to research this algorithmically generated cultural content? Is AI just another tool, another medium? Should we think of it as a human-AI collaboration? Or are humans increasingly irrelevant? Do we need to know? And is it OK for human researchers to use AI to research online culture?

After nearly a decade of researching AI, Jill Walker Rettberg has become increasingly frustrated. In this talk, she will explain why AI bias will always exist—despite Trump’s executive order to ban “woke AI” and to sell AI embedding “American values” to U.S. allies. She will discuss how feminist theories like Hayles’s theory of cognitive assemblages may be correct but irrelevant in a world of digital oligarchs. And hopefully, we will end by discussing how, despite all this, we can continue to do useful research that contributes to a better world.

• – Date: Monday, 24 November 2025
  – Time: 12pm – 1:30pm (includes time for questions and discussion)
  – Location: Room 204.233 or online
  – Parking: Nearest parking is Pi1 or Pi2
  – Light refreshments provided; feel free to bring your lunch
  – RSVP: Please RSVP for in-person attendance to MCASIadmin@curtin.edu.au or join us online by scanning the QR Code on the 3rd page on the event day.

Sydney: Wednesday 26 Nov: AI-generated Australian stories: just like American stories but in the outback

Talk for the Literary Provoctions Hub at the University of New South Wales. Here is more info – it’s at 3:00pm-4:30pm at Robert Webster 327.

Abstract: What happens when you ask a large language model mostly trained on American training data to generate Australian stories? This talk uses recent developments in literary analysis of generative AI to analyse the homogeneity of AI-generated stories, with examples from a dataset of stories generated for each of 236 different nationalities. Almost all the generated stories share the same basic plot structure: a protagonist lives in or returns home to a small town and resolves a minor conflict by reconnecting with tradition and organising community events. A sprinkling of national flavour or stereotype is added – in the Australian stories the small town tends to be a mining town in the outback, the Norwegian towns are by the fjords – but the nostalgia, the lack of change and the absence of strong conflict or romance is constant across nationalities. We already know about AI bias: if you write “the doctor folded” the suggested next word is more likely to be “his”, and if you write “the nurse folded” it’ll probably be “her”. What if there is also a deeper bias, shaping narrative structure and not just the surface level of words? As generative AI is integrated into more and more of our writing tools, it is likely to nudge us into writing in particular ways. What happens if our computers nudge us to tell stories that are closer to Hollywood or Hallmark templates than to the stories of our own cultural contexts?

Canberra: Thursday 4 Dec: Archipelagos or Empires? Narrative Colonialism in Generative AI

Keynote at Digital Humanities Australasia 2025 (DHA2025) at Australian National University.

Abstract: What are the dominant narratives of generative AI, and what is at stake in their circulation? In this keynote, Jill Walker Rettberg discusses her ongoing research on AI-generated narratives in the AI STORIES project, which starts from the hypothesis that LLMs replicate and perhaps increase certain narrative patterns, which could mean that we lose diversity in storytelling. Research so far suggests this is true – the thousands of AI-generated stories we have analysed in the AI STORIES project emphasise stability and nostalgia, telling remarkably similar stories of threatened communities saved by reconnecting with heritage. The theme of DHA2025, Digital Archipelagos, reminds us both of our diversity and our interconnectedness – but can we retain these when using large language models (LLMs)? Is it possible to use large language models (LLMs) without succumbing to the digital colonialism of the large tech companies that sell them to us? How should we, as researchers and educators, respond to political and institutional pushes to use genAI? What does it mean for our digital archipelagos that Trump has issued an executive order banning “woke AI” and an AI Action Plan to ensure US allies use the “full AI technology stack” that aligns with American values? Generative AI is normalising, erasing the outliers and exceptions and replacing them with statistical probability. So would it help to use local models, or is the technology itself a problem? By understanding how LLMs really work we can gain the tools to decide when not to use it, and when it might add value. Rettberg will close by highlighting examples of how researchers might use LLMs with care, in ways that resist homogenisation and keep the archipelago alive.

AI STORIES team member Zahra Rizvi is also giving a talk at DHA2025:

Zahra Rizvi: Exploring Indian AI Stories (DHA2025 Canberra, 3 Dec, 1:30pm-3:00pm in the Grappling with AI session).

Abstract: Generative AI in India made its way into common people’s homes on Cadbury chocolates and Sunfeast biscuits, with Bollywood’s leading actor Shah Rukh Khan starring in AI generated ads for both the companies. The #NotJustACadburyAd, a collaboration between Cadbury, Shah Rukh Khan, and Mondelez India, aims to empower local store owners in India by utilizing AI and machine learning to create personalized advertisements featuring Shah Rukh Khan’s face and voice. The #MyFantasyAdWithSRK allowed fans to experience an AI generated experience of sharing Sunfeast biscuits with their favourite movie star. Both these ads were affected by the dominant narrative structures of marketing bylines and blurbs as well as the classist, almost Brahmanical, ideological systems that inform these narratives structures, from big-brand-ized ads for small businesses to the logic of exclusivity in the trope of the movie date.

The advent of large language models (LLMs) like GPT-4 has transformed text generation, offering innovative possibilities across various sectors, advertisement and marketing being just one area out of many. At the same time, as the example above illustrates, these models are subject to inherent biases, one of which is explored by the AI STORIES project (Rettberg 2024) as narrative bias. In my short paper, I will be describing the theoretical considerations of my ongoing postdoctoral work which posits that this bias emerges from the narrative archetypes embedded in the vast and culturally specific texts that LLMs are trained on, particularly those from English language sources, in my case, specifically Indian narratives. The implications of this bias can be profound, especially when one takes into account the context of cultural diversity and global storytelling practices from non-Western spaces like India.

This short paper will detail work in progress on how Indian narrative traditions, already rooted in a complex network of ancient epics, folktales, religious texts, and contemporary literature, are represented and potentially distorted by LLMs. In doing so, I intend to shed light on the transformation and mutation of culturally rich and diverse Indian narratives as they are processed by AI.