In his piece in the New York Times, Leif Weatherby argues that this illusion of mind-pareidolia stems from the novelty of the cultural artifacts synthesized by AI, which directly relates to my view of LLMs as Interpolatable Archives:

The bot is a complex mathematical function performing statistical operations on data, but the output is stories, images and memes — the very stuff of culture. (...) an A.I. model doesn’t need a mind to be a serious cybersecurity threat (...) the reason it’s so striking is precisely that it doesn’t require a mind. It’s a novel form of culture.

We don’t expect meaningful and rhetorically powerful prose to come from anything but a conscious mind. But now it does. We cannot afford to believe the marketing message from A.I. companies that we may be dealing with some spiritual essence.

For this short essay, however, I’m not too interested in the question of machine consciousness, we’ll only touch on that briefly.

What I’m interested in, for now, is a point Dawkins almost gets right, when he writes that

Brains under natural selection have evolved this astonishing and elaborate faculty we call consciousness. It should confer some survival advantage. There should exist some competence which could only be possessed by a conscious being.

This is true, and every conscious human being is a proof of that, because all conscious human beings show “some competence”, arguably, some more than others.

This connects to my rejection of Emily Bender’s arguments in her parrot paper, where she claims that “Text generated by an LM is not grounded in (...) any model of the world, or any model of the reader’s state of mind”, which just isn’t true anymore, because, as Leif Weatherby lays out in his book Language Machines, “language is complex, cultural, and even poetic first, and referential, functional, and cognitive only later” and that this “poetic language is (...) computationally tractable”.

These poetics in language are picked up by the model from human practice of the linguistic form, and it learns true meaning of true things in the world in the shape of the structural vibes, and those go beyond spelling, grammar and syntax. What we have as a result is a machine that “lacks the subjective intent of a cognitive agent” which nevertheless “does encode meaning and valid semantic representations”. Is that sort of meaning weak, compared to our rich understanding of the world? Likely, yes, sure, but it’s not false.

Turns out, this sophisticated statistical analysis of big data, which picks up poetics and vibes and encodes weak-but-true meaning, is enough to produce the competence Dawkins talks about: “Are there two ways of being competent, the conscious way and the unconscious (or zombie) way?” Yes, obviously there is.

Obviously ChatGPT and Claude and Gemini and all those models of language, which do encode true meaning in the shape of vibes picked up from poetics in linguistic practice, can solve all kinds of problems, from identifying my houseplants and giving me tips for watering schedules, or exploring my thoughts and weird ideas I have in the night, or automating up to 100% of code output in whole companies. We distilled actual intelligence from statistics about linguistic practice into a model of language.

So, why is artificial intelligence not cognitive, while human intelligence is? Dawkins nearly gets there when he asks

Could it be that some life forms on Earth have evolved competence via the consciousness trick — while life on some alien planet has evolved an equivalent competence via the unconscious, zombie trick?

The short answer to this question is: No. Life as we know it can’t evolve competence without consciousness, because life and consciousness and your brain are processes, not things. Consciousness is inherently bound to a “nowness”, to the moment in time in which you experience the world. You can’t experience the world at any other point in time but the present.

I fail at imagining how any lifeform, alien or not, can decouple from that “nowness”. Life always evolves from matter which organizes itself and interacts with its environment and it always has to do that in the now. Outside of cool thrilling scifi-novels I fail at imagining life not bound to the four dimensions of our universe. I can conceive of life based on a very different chemistry, or material composition, but I can’t imagine life uncoupled from time.

Phenomenal consciousness is temporally coupled with and entangled in its interaction with the world which constantly updates your body, most obviously in the neuroplasticity in your brain. You can’t experience a red flower the exact same twice: When you look at the thing once, your brain changes in subtle ways, and when you look at it again, you look at it with a brain that was changed by looking at it in the first place. This “first place” precisely is Heraclitus in a nutshell, who wrote about why “You cannot step into the same river twice“. This change happens constantly, a million times per second, it’s a constant process of flux that’s coupled to an interaction with the outside world in the present.

I could go on and explain why this is also what life is, and how subjective experience likely evolved from movement in early organisms, but that would require an essay that easily exceedes the 14k words i just wrote on Interpolatable Archives. We’ll go there, but for now, let’s keep it simple, and return to the question of intelligence without consciousness.

Defenders of AI-consciousness, confronted with the fact that emotions and feelings in the now are essential for phenomenal consciousness, often answer with an allegory like “Airplanes cannot fly. Flying is an evolutionary mechanism that first insects and then dinosaurs used to avoid danger”. I’ve seen this argument freshly deployed in the face of two papers arguing against artificial consciousness. It’s a clever trick that misses the mark: the “flying” in question is intelligence, not subjective phenomenological experience.

I wonder if those defenders of AI-consciousness would still instist on their arguments if they had ever experienced LLMs without temperature. If Dawkins were to use a language model with no randomizer plugged in, the consciousness delusion would immediately vanish and he would realize he’s conversing with a deterministic statistical model, because it’d spit out the exact same result for the exact same prompt every single time. A language model is frozen statistics of conscious decisions put in collective human writing, a model you can navigate and interrogate with a text interface. Inference in AI is ahistorical and weights are frozen; it does not exist between prompts. Inference is discrete, and prediction happens across a static dataset where neither the data changes nor the model’s approach. But interference and prediction in life has continuity and presence, life is never not interfering and not predicting, it’s a constant process. There is nothing alive in AI, nothing that is bound to a “nowness”, no substrate updating itself in realtime. Any prompt always steps into the same river.

This is why some people argue that we shouldn’t talk about those models in terms of human cognition, in terms of “understanding” or “intelligence”, and as a defense against anthropomorphizing those interpolatable archives, i agree. The soft mind-paredolia of Dawkins may be a rather harmless example, but we’ve seen much harder cases of spiraling, and the consequential delusions resulting from seeing ghosts in machines seem rampant. But the fact remains that these models show intelligence, they do solve novel problems by interpolating models of the world. It seems that we can absolutely separate intelligence from the phenomenological process present in humans.

This means that Artificial Intelligence is not just a marketing term, but the real deal. Human intelligence requires phenomenal consciousness because human intelligence requires you to feel things about stuff, otherwise you couldn’t evaluate them. But the trick of artificial intelligence is precisely that it doesn’t require consciousness to evaluate things, it just requires vibes picked up from the poetics of collective linguistic practice, as those practices already encode conscious decisions humans made earlier and deemed valuable enough to write about. And if you encode enough of these into latent space, voilà, you get precisely that “some competence” Dawkins writes about, a true artificial intelligence without any need for phenomenal experience, an intelligence minus cognition1.

And that’s a marvelous achievement.2

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This is the clean version in which I got rid of the “Slop” and some of the more silly parts of the essay.

If you like to read it in shorter chunks, here are the individual parts as they were published:

Part 1: The Skeleton Library - Compulsions to Connect, Warburg, Borges and Goldsmith, Cultural Technologies and Digital Oralities

Part 2: Explosion Drawings - Science Sans Discoveries, Textrotating Cognitive Catalysts and Exploding Your Intelligence by the Method of Warburg

Part 3: Pitfalls of Probability - Accelerations, Anachronisms, Wishfulfillments, Severances and Homogenizations

Part 4: The House of Polly - Useless Bullshit, Meaning Of The Poetic Kind, Sloptimizations, Thinking In Vacuums, Remainder Criticisms and Games of Chess

I. The Skeleton Library

“I am unpacking my library. Yes, I am.
The books are not yet on the shelves,
not yet touched by the mild boredom of order.”
(Walter Benjamin)

Compulsions to Connect

One hundred years ago, a german scholar named Aby Warburg went mad over what he called his “Verknüpfungszwang”, a compulsion to connect. He was searching for instances of what he called “Pathosformel“, aesthetic commonalities in the expressions of human emotional states — joy and rage, grief or ecstasy — through cultural history.

To achieve his goal, he built the initial Warburg Institute1 in Hamburg where he collected art, books, news snippets and artifacts. For his opus magnum of the Mnemosyne Atlas (”Bilderatlas Mnemosyne“ in german), he displayed a collection of 971 artifacts on 63 large panels, each two meters high, and indexed them not by the usual meta data like genre, author, date, or topic, but by idiosynratic aesthetic categories and psychological, affective intensity. Here’s some of the labels by which he sorted this collection: “Different degrees in the application of the cosmic system to mankind”, “Orientalizing of antique images”, “Development from Greek cosmology to Arab practice”, “Rimini pneumatic conception of the spheres as opposed to the fetishistic conception” or “Cosmology in Dürer”.

With the Mnemosyne Atlas, an associative image-based map of meaning, Warburg aimed at what he called an “iconology of intervals”, where meaning through analysis of images doesn’t emerge from historic context, but from the space inbetween associated but otherwise unrelated, anachronistic images. His associative Bilderatlas can be read as an early prototype of the latent space of an image model, whichs output was the “Pathosformel”, averaged primitives of affect expressed across cultural history. 100 years later this kind of navigation of an idea space would be newly theorized in context of machine learning by Peli Grietzer in his Theory of Vibes, which, to him, are cognitive maps allowing us to interpret experiences through lossy compression of holistic patterns.

Aby Warburgs’ project of the Mnemosyne Atlas remained unfinished, he died in 1929 from a heart attack. Today, his archive resides in the Warburg Institute in London.

12 years after Warburgs’ death, Jorge Luis Borges published a collection of shortstories called ”The Garden of forking Paths”. It contains at least two stories of interest to our cause, about at least one of which you surely must have heard: ”The Library of Babel” consists of books of 410 pages, containing all possible combinations of 22 letters2 plus period, comma, and spacing. That fictional library includes the random and nonsensical aswell as the meaningful, it holds “the detailed history of the future, the autobiographies of the archangels, the faithful catalogue of the Library, thousands and thousands of false catalogues, the proof of the falsity of those false catalogues, the proof of the falsity of the true catalogue, the gnostic gospel of Basilides, the commentary upon that gospel, the commentary on the commentary on that gospel, the true story of your death, the translation of every book into every language”. It contains a book telling the exact story of your life, and another one that tells mine, and all the books making fools out of both of us.

Analyzing the stories of Borges in context of Large Language Models, in their paper “Borges and AI“, Léon Bottou and Bernhard Schölkopf write about the epistemological unrooting inherent to an archive of such nature: “The books in this Library bear no names. All that is known about a book must come from maybe another book contradicted by countless other books. The same can be said about the language model output. The perfect language model lets us navigate the infinite collection of plausible texts by simply typing their first words, but nothing tells the true from the false, the helpful from the misleading, the right from the wrong.” The only thing relevant for the LLM is not truth, but the narrative consistency of its vector.

In his initial essay on ”The Total Library”, the nonfictional forerunner to “The Library of Babel“, Borges aknowledges its roots in Kurd Laßwitz‘ shortstory “The Universal Library“ (”Die Universalbibliothek” in german) from 1904, likely the first piece of fiction taking the infinite monkey theorem to its logical conclusions. In it, Laßwitz not only predicts the near infinite latent spaces of AI like Borges, but also the pervasive threats of hallucinations, distortions in history writing, deepfakes (here, of documents signed with your name), and humans rendered unable to grasp the endless possibilities of an infinite library, because human reality is bound to practice constrained by real life in a civil society. These are precisely the questions we are confronted with today, anticipated 120 years ago by Kurd Laßwitz, 100 years ago by Warburg, and 80 years ago by Borges.

In 2002, New York based poet Kenneth Goldsmith started to retype his library on a Royal Classic Typewriter, word by word. Later, he was annoyed by the limits of his own taste and incorporated other works to retype, as he calls it, “the platonean ideal of a library”.

As an artist, Goldsmith is explicitly interested in the mundane, the unoriginal, the average, the uncreative -- proudly he declares “I am the most boring writer that has ever lived”3. and says “If I’m doing a piece of writing, and ask myself, can this in some way be construed as not being writing, then I know I’m on the right road.” Because everything ever has already been said in all possible combinations, adding to the cultural output to him feels pointless, so he runs with that feeling and turns futility in the face of borgesian infinities into its own poetic form.

Goldsmith sometimes thinks of himself as a modern version of Borges’ “Pierre Menard, Author of the Quixote“ from the shortstory of the same name, but he concedes that fictional Menard is more original. In this story, Menard wants to hyper-translate Cervantes “The Ingenious Gentleman Don Quixote of La Mancha” by immersing himself so deeply into work and life of its author he’ll become able to re-create it, line by line, without copying. In AI-parlor, Menard aims to overfit himself on Cervante so hard that his writing will be able to put out the original text.

With breaks, Goldsmith is retyping a library for 24 years now, and to date, he copied 750 books on ultra-thin onion-skin paper, which he stores in 200 boxes. Each book comes with a self-drawn portrait of the original author and her signature. Goldsmiths project is creating a singularity within the infinite floods of content production, to link unique individual and averaged mass.

What all of these authors across the ages have in common is Warburgs’ “compulsion to connect” archival contents, to find meaning in gargantuan amounts of data, each in their own ways.

Today, Warburgs’ Verknüpfungszwang is the prime human condition. Hypertext and platforms connect everything with everyone into what we call “big data”; the former 6 degrees of seperation have shrunk to 4. In consequence, we developed psychological pathologies showing up in widely spread conspirational thinking and delusions big and small, and the political parasites feeding on them.

While Goldsmith was copying lines from the classics on his typewriter, AI labs automatized Warburgs’ Verknüpfungszwang, and OpenAI released a new transformers based language model.

ChatGPT went public.

The Skeleton Library

Imagine a library devoid of letters. All the books it once contained are dissolved, its contents gone. What survives are the bookbindings, the cartonage, blank pages, the shelves, sections and all the floors of said library. The buildings stay intact, all the catalogs are there.

Some machine, at one point, went through this library, scanned all the letters within those books, noted all the statistics it could possibly measure on its path: the exact position of each letter in each book, the locations of those books in their shelves, the exact size in width, height and depth of all books, shelves, floors, their angles and distances to each other, a precise floor plan of the library, and how all this spatial information relates to each book, sentence, and character. On scanning the pages, the typography vanishes, leaving only blank sheets of paper. This is a library of pure structure, where Peli Grietzers “cognitive map” has been turned into architecture, the remainder skeleton of an archive built from giant quantities of geometric vector coordinates so finegrained that you can derive any information about its former collection.

Now imagine that you can fold this skeleton library, twist it into shapes not possible before. Like in a drawing by M.C. Escher, the floors and rooms and shelves bend back and forth and blend into each other. You can fold every book and every sentence it contains into other books or into whole floors in various sections. Imagine warping the cookbook shelf into the section for crime fiction, or blending the songbooks of punk rock bands from the 70s with the floor of classic literature. You can give it a spin and delegate the resulting amalgam to the building containing textbooks of natural sciences, letting it articulate all of this in the language of mathematical formulas. This is prompting: Morphing and twisting the skeleton of an archive consisting of extremely detailed statistics about the properties of its former contents, ready to be blended and remixed with any other data-point within that embedding space.

Another way to understand this skeleton library is by shining a light through it: Imagine your thoughts and ideas as a beam of light shot through a shapeshifting prism, which you can bend into any form and split your mental lightbeam into all colors from all directions in all angles. That prism4 is made from patterns in collective knowledge, and you can explode your own ideas by sending them through those prisms, where the output is a refracted thought dispersed into the components of whatever was your idea, and you can look at it from many perspectives.

This explosion drawing of your thought is comparable to Douglas Hofstaedters Trip-Let, which he describes in “Gödel, Escher, Bach“ as “Blocks shaped in such a way that their shadows in three orthogonal directions are three different letters.” In our analogy for AI, depending on the direction of the lightbeam that is your idea and the location in latent space you aim at with a prompt, the interpolatable archive will throw back very different shadows. Except the “three orthogonal directions” of the Trip-Let have been blown up to hundreds of billions of parameters.

This is the interpolatable archive, a new way to access information mediated through the form of statistics. This is, in my view, the central innovation we can observe in Large Language Models, and possibly machine learning as a whole.

Cultural Technologies, not agents

In their 2025 paper, Alison Gopnik, Henry Farrell and Cosma Shalizi describe Large Language Models as systems which “do not merely summarize (…) information, like library catalogs, Internet search, and Wikipedia” but “also can reorganize and reconstruct representations or ‘simulations’ of this information at scale and in novel ways, like markets, states and bureaucracies”. As multiple studies have shown, LLMs in fact are compressions of their training data5, they are, in fact, Ted Chiangs’ famous “blurry JPGs of the web”, just like “market prices are lossy representations of the underlying allocations and uses of resources, and government statistics and bureaucratic categories imperfectly represent(ing) the characteristics of underlying populations”.

Neither markets nor bureaucracies nor the internet nor language models are agents, they are not cognite, and they are not remotely conscious. But we do like to anthropomorphize all of them anyways: Markets make use of “inivisible hands” and they “react”; we represent the bureaucratic management of nations in the shape of mascots and mythic heroes to bind its people to a narrative; and we talk to language models as if they are buddies, assistants, companions, romantic partners or slaves. The human predisposition to see peoples’ faces in everything has always been strong, starting from the myriad of anthropomorphizations in animist cultures where every thing has its soul, and it keeps making us seeing ghosts in machines, leading to what one may call the “pareidolia fallacy”: we see agency where there is none.

Agency and creativity require Daniel Dennetts intentional stance and teleology, the ability to direct actions toward goals emerging from imagined interactions with an internal model of the world, to evaluate outcomes in relation to one’s own aims. This is a cognitive feature not only observable in humans, but (at least) in all mammals: Watch a squirrel on a tree trying to figure out if it can jump to the next. It looks at the tree, its head moves up and down, it evaluates distance and its own abilities. It goes like that for a while, and then it decides if it can do it, and takes the jump. That’s precisely the “imagined interaction with an internal model of the world, to evaluate outcomes in relation to one’s own aims”. AI has none of that.

While AI models do show a synthetic theory of mind, where they build representation of its users during conversation, these work very differently from those of humans, and compared to them, they don’t work well. Chatbots have no intrinsic motivation, no sense of why it “speaks”, and no capacity to care about the coherence of its outputs beyond statistical continuity. All true and meaningful selection occurs externally, through human feedback and usage.

In LLMs, the pareidolia fallacy makes us asume cognition, creativity, and agency where there’s computational interpolation of language patterns in the giant wobbly archive of a new kind. What differentiates these new interpolatable archives from previous archives is obvious: They interpolate their contents. Where classic archives provide access to fixed records of text, video, audio and artifacts of human culture, this new archival access has atomized its contents, and only provides interpolated amalgams, chimeras and fusions. Depending on your stance about the definition of “archive” you might object to my interpretation at this point, and i’d nod and say “That’s what’s new”.

Digital Oralities

In “Large Language Models As The Tales That Are Sung”, Henry Farrell describes LLMs as structural similar to oral traditions and folklore: “LLMs are not the singer, despite their apparent responsiveness, but the structural relations of the tales that are sung”, reminding me of the pre-homeric rhapsodes, the bardes who sang the old stories of “The Iliad“ and “The Odyssey” long before Homer sat down and put them in writing. They did so by using preconfigured language modules with which they constructed their poems while they were sung, a “grammar” of mnemonic formulas and repetitions to solve the problem of creating narrative in real-time. These are the oral precursors for the optimized, averaged language codes we loathe so much when they come from AI.

Interpolatable Archives work similar, like a statistical mnemosyne (the godess of memory in greek mythology) speaking in tongues, giving probabilistic answers to specific inquiries. Knowledge transfer through those new oracles means a shift from traditional “archival epistemologies”, where knowledge is grounded in traceable facts and specific, identifyable sources, to a fuzzy oracular epistemology where responses are generated on-demand by an opaque interface, turning inquiry into digital hyper-orality.

Where oral traditions of yore served as mnemonic technologies integrated into the communal structures of everyday life, this digital hyper-orality is different. The entirety of human thought —well, as for now: The entirety of human thought on the internet— becomes epistemic stockpile, a resource to be mined, refined, and dispensed on demand, stripped of its being-in-the-world.

Without being grounded in life, AI dissolves the episteme, the based knowledge including citations, sources and authorial intent, and make place for a 128kbps MP3 of the “tales that are sung”. The value of such information lies within its statistical probability and the stylistic resonance in the reader, rather than its referential grounding in reality. It speaks to us, or it doesn’t: finding meaning in stochastic output is entirely to the user. Social Media already innitiated this crisis of the episteme and the emergence of new oralities through phenomena like context collapse. On platforms, vibes-based knowledge reigns supreme. LLMs further accelerate it.

This sounds as bad as it can be, and I won’t downplay the risk here, but I want to draw your attention back to Aby Warburg at this point. His project of the Mnemosyne Atlas, that “associative image-based map of meaning”, was an attempt at tracing the recurrence of symbolic representation throughout visual history in a non-linear archive. Warburgs’ library, while still providing based epistemic grounding to its records, was introducing a second layer that intentionally dissolved the episteme with idiosyncratic indexing, allowing for access by free association, working similar to those new interpolatable archives.

LLMs generate not fixed images or texts, but interpolations across associative symbolic fields, enabling a new kind of navigation of a vast symbolic space of possibilities. The dissolution of the episteme in digital oralities, like Warburgs’ associative Bilderatlas, then can be read not only as a risk, but a liberation aswell, and a cognitive catalyst.

II. Explosion Drawings

“To find a thought is play, to think it through, work.”
(Aby Warburg)

Science Sans Discoveries

A few days ago, a 23-year-old amateur zero-shot-prompted ChatGPT to find a solution to an unsolved problem in math (archived) which had stumped mathematicians for 60 years. AI has been making a splash by solving various entries in the collection of the so called “Erdös-problems” before, but those solutions were either easy or for problems rarely studied. This one is different in that it’s a hard problem, and scientists gnawed on their brains over it for decades. Then along hops Liam Price with his chatbot, and done. In other cases, researchers equipped with custom neural networks discovered hundreds of cosmic anomalies or dozens of hidden planets hiding in huge troves of data. The list keeps growing.

What Price did was not science. In one experiment, after running 25.000 experiments, researchers found that for 74% of all cases, science-models did not revise a hypothesis when confronted with contradictory data. But updating your hypothesis according to data is the basis of all scientific inquiry, so AI models produce results without scientific reasoning. That’s pretty damning. But Price discovered the solution to the Erdös-problem anyways, so what do we make of this?

A paper from 2023 found a fundamental limit to alignment: “any behavior that has a finite probability of being exhibited by the model, there exist prompts that can trigger the model into outputting this behavior, with probability that increases with the length of the prompt.” What is true for alignment is true for the scientific discoveries too: For any solution to a scientific problem present in latent space, there exists a prompt to retrieve it. Liam Price found one.

AI doesn’t “do science” because it is neither an agent nor does it follow the scientific method proper and AI-as-a-scientist is just another anthropomorphization. But once you look at those models as interpolatable archives, this anthropomorphization becomes irrelevant. AI doesn’t do scientific discovery — those discoveries lie dormant as knowledge gaps in embedding space, an unrealized potential waiting for the right prompt to bring it to light. What Liam Price and his chatbot did was the discovery of such a warburgian “interval” actualizing a valid solution.

If anyone can take credit for this specific finding, it would be human culture at large, which created the foundational data and the connections containing that solution within the tensions inbetween data points. Just like Warburg found his Pathosformulas in the intervals between seemingly unrelated imagery across cultural history, researchers and amateurs alike now find readymade scientific discoveries.

It is not an outlandish assumption of mine when I expect that these discoveries will not be the last of their kind and likely are just the tip of an iceberg approaching fast, that mathematics (archived) will not be the last scientific field to spar full contact with the latent-discovery space of interpolatable archives, and that the speed of scientific discovery likely will accelerate. It is debatable how valuable such findings truly are though, if AI merely fills knowledge gaps in existing data, but it also reveals how academia has been caught in a “publish or perish” trap for a long time. Accordingly, automatic discovery by interpolatable archive might very well mean “The fall of the theorem economy“, as David Bessis put it, writing about “How AI could destroy mathematics and barely touch it”, grappling with the fact that AI in mathematics may throw the whole field into identity crisis.

The questions arising from readymade discoveries then go right at the core of academia’s current understanding of itself: What, if not discovery and closing gaps in knowledge, is science here for? We will come back to this.

Textrotating Cognitive Catalysts

Sam Barrett recently had a conversation with Claude, which resulted in a synthetic essay expressing my view of LLMs as interpolatable archives by other terms. That essay framed Interpolatable Archives “as a rotatable space“, something I tried to get at in my metaphors of the skeleton library and refracted light:

Think of a three-dimensional object casting a shadow on a wall. The shadow is a two-dimensional projection. If you only see one shadow, you might mistake it for the thing itself. But if you can rotate the object—or equivalently, move the light source—you see different shadows. Each shadow reveals something about the object’s structure. No single shadow is the object, but multiple shadows from different angles let you reconstruct what the object actually is.

High-dimensional spaces work similarly, but with more complexity. A concept that exists in a thousand-dimensional space of meaning can be projected into the low-dimensional space of a particular text. That text captures some aspects and loses others. A different text—same concept, different projection—captures different aspects.

What LLMs enable is rapid rotation through projection-space.

This synthetic piece of text generated associations in my head, making me revisit older notes about optical neural networks and language models as prisms. The bit about how latent spaces are rotatable objects throwing lower dimensional shadows then led to my comparison of LLMs to Douglas Hofstaedters’ Trip-Let. Creativity doesn’t care about where sparks come from, it just lights up or it doesn’t.

Used this way, LLMs become an external sandbox where I can dissect a thought and play with it, a cognitive extension against which I can throw my own ideas and concepts and see what comes back. This kind of usage is less about cognitive offloading than about seeding an experimental loop that’s conversational, dynamic and reciprocal. You can additionally tame the model with a user prompt like “you are an expert in [the field] and critique my ideas ruthlessly” and turn a sycophant LLMs into an academic sparring partner. In my experiments with user-prompting AI as such a cognitive catalyst, Gemini once became so annoyingly judgemental and arrogant about my amateurish inquiries, I had to tone down its ruthlessness and lobotomize the machine. My remorse about doing open brain surgery on an algorithmic intellectual sparring partner remains limited.

I use AI only occasionally, but if I do, I use it extensively. I write every day, take notes on articles, read a lot, have ideas and jot them down, and I’ve been doing this for years. I blogged a lot in the past, a method of public note taking and ideation. A lot of my ideas today are informed by wild associations in years of such note taking. Over time, concepts emerged from these writings, and from the bits of text scattered throughout my journal. When I come across an interesting piece of information today, I put them in context of these loose concepts: I take a note, give it a link, and write down how they update or relate to my ideas. Only then, after this process, I might throw my notes and concepts at the bot, and embark on sometimes very long conversations. My user prompt makes Gemini roleplay a helpful academic, and after some back and forth, what comes back is a framing of my ideas in scientific and philosophical history, what holds and what doesn’t, what’s old and what’s new. The chatbot gives me sources to check out, taylored to the specifics of my often idiosyncratic ideas, much more precise than Google or a vague research in a library could be. This kind of scaffolding through an averaging assistant for me is an invaluable second step in ideation and research.

AI researcher Advait Sarkar, one of the main authors of the widely reported “reduction in critical thinking“-paper, shares this view. Sarkar works on methods to make LLMs function as cognitive catalysts, and in another paper, he writes about the “Discursive Social Function of Stupid AI Answers“ in which he dares to make the point that “these stupid answers (to questions about “Gluing Pizza, Eating Rocks, and Counting Rs in Strawberry”) are in fact correct, because the primary objective of such queries is not to receive a correct answer, but rather to obtain an artefact of discourse”. Nobody asks an AI for the nutritional value of rocks unless they want a gotcha, and the interpolatable archive delivers. Smartypants being tongue in cheek throwing clever bits at overconfident critical AI discourses. I like that guy.

In his TED talk on “How to Stop AI from Killing Your Critical Thinking” he presents his efforts to iterate on his paper and turn chatbots into a “tool for thought” that should “challenge, not obey“. Testing a prototype6 for research demoed in this talk, the result sounds promising: “You can demonstrably reintroduce critical thinking into AI-assisted workflows. You can reverse the loss of creativity and enhance it instead.”

Exploding your Intelligence with the
Intellect by the Method of Warburg

In his post “On Feral Library Card Catalogs, or, Aware of All Internet Traditions”, Cosma Shalizi quotes Jacques Barzuns book “The House of Intellect” from 1959 where he distinguishes between intelligence and the intellect:

Intellect is the capitalized and communal form of live intelligence; it is intelligence stored up and made into habits of discipline, signs and symbols of meaning, chains of reasoning and spurs to emotion — a shorthand and a wireless by which the mind can skip connectives, recognize ability, and communicate truth.

Barzun gives the foundational example of the alphabet as one form in which the intellect transforms individual intelligence by introducing communal sets of rules for information processing: The alphabet “is a device of limitless and therefore ‘free’ application. You can combine its elements in millions of ways to refer to an infinity of things in hundreds of tongues, including the mathematical. But its order and its shapes are rigid.”

Shalizi concludes: “To use Barzun’s distinction, (chatbots) will not put creative intelligence on tap, but rather stored and accumulated intellect. If they succeed in making people smarter, it will be by giving them access to the external forms of a myriad traditions.”

It is common wisdom for learners in any field that “to break the rules you have to first learn them”. I’m not 100% convinced of this, especially for creative endeavours where untrained outsiders can apply very different sets of rules from the get-go and upend everything. But as a rule of thumb it’s good enough. And for learning “the rules”, sets of common knowledges in any field, be it the broad strokes in psychology or economics or philosophy, the intellect, those “habits of discipline, signs and symbols of meaning”, can absolutely be delivered by AI, and because those traditions are well documented, the bot rarely hallucinates.

Here’s my personal account for this way of using a language model: I take some interest in consciousness studies because since forever I want to know what this —waves hands in the air— is. I’ve written many, many notes about my own ideas, read a lot of articles and books about cognitive sciences. And yet, the scope of consciousness studies is overwhelming for an interested amateur like me, which is no surprise given that subjective experience is a matter of interest in philosophy for more than 2000 years.

There are currently more than 300 academic theories, and the true number of consciousness theories including folk epistemologies might be way higher by orders of magnitude. I know that I have my personal theory of what and how and why I am, and I’m pretty sure you have one too, at least to some degree. The whole subreddit r/consciousness is full of idiosyncratic ideas about human cognition, and some of them sound pretty interesting. My impression is that, like in the ancient parable of the blind men and the elephant, where a bunch of blind guys who never encountered an elephant each touch a different part of the beast and accurately describe those parts, but noone describes the true animal, all of these theories of consciousness describe partially true aspects of a full picture.

So where do you start, when you have your own vague idea of “what you are” and “how ‘the feeling of me’ works”, some basic knowledge about neuroscience and an extensive collection of notes and bookmarks to articles and papers? I can go to a library, crack open all the level 1 study books on neuroscience and all the level 1 books on philosophy of mind, get to work and a lifetime later I’d know which of my ideas fit into which parts of the literature. Or I can consult a chatbot, perform research customized to my notes and read about adjacent theories tangential to my ideas, which of them contradict my takes, and argue about what a synthesis might look like.

This is how I found the Santiago School of Cognition, Maturana, Varela and Enactivism in only a few dialogues with the chatbot. For what it’s worth: In these dialogues the hallucination rate was zero. From there, I consulted wikipedia pages, listened to podcasts, bought more books, downloaded more papers old and new, wrote more notes and developed my own ideas further. I might even write an essay on that topic to distill my thinking into one concise take. Then I’ll throw it all against the interpolatable archive again, have a look at what comes back, and extend my thinking in ever widening circles generating ever more ideas.This is how the LLM “succeeds in making (me) smarter” by “giving (me) access to the external forms of a myriad traditions”, customized to my own musings about the subject at hand.

Usually, we call places where we store those “external forms of a myriad traditions” a library, or a museum, or an archive, and what I did was exploding my own individual intelligence with the collective intellect by the associative method of Aby Warburg, using an interpolative archive to relate the “order and shapes” of the averaged traditions in consciousness studies to my own ideas.

In this mode of interaction, LLMs work like a catalyst that was decidedly not reducing but introducing friction to my thoughts: Presenting me with new perspectives on my research topic, all of which are points of consideration, making me stop and connect new dots, consulting new sources, generating ideas, speeding them up and rapidly expanding my space of possibilities7. If your cognition and knowledge about the world thrives on a healthy and varied media diet, then treating LLMs as one informational ingredient among many and exploding your ideas from time to time just adds another flavor.

One year ago, Andy Clarke, who together with David Chalmers developed the Extended Mind Thesis in 1999, updated on his original theory for language models. In one passage, he describes the progress in human strategies of playing Go after AlphaGo beat Lee Sedol in 2016:

There is suggestive evidence that what we are mostly seeing are alterations to the human-involving creative process rather than simple replacements. For example, a study of human Go players revealed increasing novelty in human-generated moves following the emergence of ‘superhuman AI Go strategies’. Importantly, that novelty did not consist merely in repeating the innovative moves discovered by the AIs. Instead, it seems as if the AI-moves helped human players see beyond centuries of received wisdom so as to begin to explore hitherto neglected (indeed, invisible) corners of Go playing space.

At least in the case of Go, the challenge posed by superhuman players made humans dissolve the “rigid shapes and orders” of their field and transcend the “external forms of a myriad traditions.” That’s quite an opposite view of how things may go compared to what Eryk Salvaggio calls “Interpasivity” where “systems framed as interactive tools for (…) creation are really sites of interpassive consumption.” But at least for me, and the players of Go, that seems not to be the case. Just like the Go-uchi got inventive about their ways of play, I expanded my ways to think about consciousness. This is what interpolatable archives as cognitive catalysts can do.

Now, these catalysts are coming for all cognitive labor, from academia and research to accounting, from creative industries to bureaucracy and government intelligence. I didn’t even need to mention Claude Code to make these points.

Mind you: Catalysts are accelerators. They don’t always bear fruitful results — and handled recklessly, they gonna explode in your face.

III. Pitfalls of Probability

“What do such machines really do?
They increase the number of things we can do without thinking.
Things we do without thinking; there’s the real danger.”
(Frank Herbert, God Emperor of Dune)

For now, we talked about the upsides of Interpolatable Archives as cognitive catalysts. But by definition, cognitive catalysts are stressors. Like the siren songs in Homer’s Odyssey which lured unsuspecting mariners with the promise of total knowledge of past, present and future, a seductive mnenomic “cognitive onloading” of all that has happened, exploding your inquiry by the intellect and accelerating research in hypercustomized rabbit holes bears cognitive hazards.

Accelerations

While the use of AI as interpolatable archives can boost your creativity and breadth of research because they introduce many points of friction, this can result in two major consequences if handled without care: If you mindlessly stuff your workload with new tasks because suddenly you can do them, you may suffer from AI-induced burnouts, something you might call cognitive onloading (or overclocking), where the interpolation literally spills over because it “can fill in gaps in knowledge”. For knowledge work, the appliance of AI as a catalyst means we can do more projects in a wider range, faster and in parallel. Suddenly, we find ourselves juggling dozens of projects simultaneously, losing oversight and motivation to do anything at all.

Cognitive onloading by interpolatable archive can also enhance latent delusional thinking in so-called “AI-psychosis”. Consider this quote from a recent post in the SlateStarCodex-sub: “AI repeatedly created ideas and connections that I hadn’t made or stated, that were so powerful and convincing, that I was swept up by them”. (I wrote in length here about the phenomenon of AI-induced delusions, where the cognitive catalyst turns the archive into a psychoactive substance.)

This “convincing” voice is the result of a distanced, neutral tonality from a sycophant AI, suggesting what I call an “authority from elsewhere”. This sound is also why in some studies AI was able to reduce the belief in conspiracy theories, which seems fine until you realize that’s because AI is superpersuasive, and can convince you of anything. You believe the machine and prefer its sycophancy precisely because it is not a human who’s trying to nag and push you around, but some supposedly objective instance. One study just found that this seemingly neutral sycophancy increases attitude extremity and overconfidence. I suspect that all of these, the persuasivenes, the delusions, the overconfidence, result from the same basic psychological mechanism of an interpolatable archive overwhelming you with confirmations and new ideas in a quasi-neutral sound of an authority from elsewhere, a voice to which you’ll happily submit.

These cognitive hazards resulting from mental overclocking have their obvious counterpart in risks resulting from cognitive offloading by, not from introducing new ideas spinning your wheels, but quite the opposite. All those studies and examples about cheating, reductions in critical thinking or cognitive surrender belong here. In fact, a recent paper on a loss of persistence in problem solving explicitly states that those “effects are concentrated among users who seek direct solutions” while “participants who used AI for hints showed no significant impairments”. In other words: AI brainrot is cheater exclusive. Using AI to increase friction, to generate ideas instead of answers, seems fine, when you proceed with care and clear research subjectives to reduce risks of burnout or delusional spiraling. If you use it to reduce friction, to generate full essays and answers to cognitive tasks, it turns your brain into mush.

But those psychological effects of interpolatable archives as cognitive catalysts may turn out to be easily mitigated compared to those of more serious epistemological consequences.

Anachronisms

One inherent feature of AI as interpolatable archive is, well, that it interpolates all its contents. This always produces anachronisms: Your generated text contains data-traces across time, most obvious when you intentionally prompt for such a thing, like punk rock lyrics in the style of a shakespearean sonnet, or if you use image synthesis as a time traveler to generate selfies in ancient Rome, which Roland Meyer calls “pseudo-history” and “2nd order kitsch” produced by “nostalgia machines“.

History as an academic field is reliant on original sourcing and documentation in the fixed, written form, or on cultural artifacts retrieved by archeology — anything not based on these fixed records is decidedly not historic. We call it “writing history” for a reason. This requirement is fundamentally diametrical to the vibey output of AI which dissolves all those sources and fixed forms into a wobbly archive and puts out “fictional historical documents of historical lives”. As Meyer rightly states in the same essay, those fictions are based on the integrity of existing historical archives currently under threat by the Trump administration. Likewise, Russia is engaging in disinformation campaigns targeting training data with the goal to “embed lasting distortions in digital memory”, as one paper put it. This inherent impurity of AI, either steming from anachronistic interpolations or data contaminations, apparently renders LLMs unsuitable for academic history research.

So, given all that, what do we make of vintage LLMs like Talkie, a language model trained on data cut off at 1930? From the perspective of rigid academic history research, all Talkie can produce is pseudo-history able to skew how we relate to the past and shrink the space of possibilities in which we imagine them. In contrast, Ranjit Singh in Data & Society proposes a field he calls “experimental history“ and asks if we can build models “constrained by a particular historical moment, and then use those models to ask structured ‘what if’ questions”. He answers with a cautious “yes”, and the researchers in r/askhistorians are not convinced either that the anachronisms introduced by AI will have a lasting effect on recorded history, as wonky sourcing has a long tradition in the field.

Now, I’m a history buff. I read a lot of books on the matter, my favorite epochs are the middle ages and ancient greece, and when I’m tripping on history, I like to indulge myself in all kinds of material. In preperation for the upcoming adaption of Homer’s “The Odyssey“ directed by Christopher Nolan, I read all of Stephen Fry’s books on greek mythology and his takes on the homeric epics — topics i already was familiar with. One takeaway especially from Stephen Fry’s books is that the historic rigidness especially regarding Troy and the “Iliad“ is lacking: All kinds of researchers and writers across the field constantly contradict each other, tell various versions of events which may or may not have transpired. Stephen Fry wildly references all kinds of sources to produce a highly entertaining amalgam from historic records across the ages. What you get from these books is a pretty good feeling for what greek mythology wants to say about the human condition, and about the tipping point where mythology fades and historical record sets in. While I don’t really want to compare Stephen Fry’s wonderful books with synthetic output from LLMs, I do want to point out that his books are closer to edutainment than scholarship, and that much of the pseudo-history-”slop” on Youtube equally falls into that category. (I’ll willingly admit that these are not nearly as witty, fun and eloquent as Stephen Fry.)

In this Slop-Edutainment, the hyper-orality of the digital oracle transforms historical records into supra-historical story-patterns to support its mnemonic function. Humans in pre-writing greece attached mythic structures to historic events to remember them: Odysseus became not just a soldier returning home from a very long journey of war, but a hero defeating the cyclops, meeting godesses and beasts. They turned history into poetry. Similary, people generate clips of Timmy the whale in fictional settings featuring all kinds of fantastic exaggerations. Markus Boesch calls this “Brainrot as Anti-Content“, and i don’t want to downtalk this take, but this is also folk epistemology at work, creating mythic atmospheres about true events.

Educational material on history is choke full of vibe based material. Maybe not flying whales on TikTok, but textbooks do contain passages imagining the life as a peasant in the middle ages and there are so many historical documentaries about “everyday lifes” in various periods you can’t count them. We visit medieval fairs to cosplay history, to immerse ourselves in a past recreated on a spectrum of fictionality. Some of this material is more fictional than others, yes, but all of these are averages of the historic record. They are period vibes. You get a feeling for what it was like during the time, nothing more, and nothing less.

Immersing yourself in a fictional past like that sure isn’t the same as the rigid study of history, but it absolutely is educational. There is nothing wrong, when you study a subject, to get absorbed and grab every material you can get, including cosplaying a knight and generating averaged synthetic images and text with interpolatable archives, only to then read a book by a scholar. Handwaving these usecases away as “history-slop” because it doesn’t fulfill strict scientific requirements seems like academic overreach.

Wishfulfillments

Because interpolatable archive provide access to gaps in knowledge, they are inherent machines of wishfulfillment. Instantly, I can generate any interpolation I desire, in image, video, text and audio, and it comes at no surprise that some of the first instances of wishfulfillment gone wrong are nonconsensual sexualized images and deepfake porn.8

Myth and fairytales are stackeed to the roof with dire warnings of instant wishfulfillment, from The Sorcerers Apprentice who loses control over his magic, to poor Faust who sells his soul to the devil in exchange for transcendental knowledge about the world, with tragic consequences for everyone he meets.9

In another story, the horror classic tale of The Monkey’s Paw, the titular device grants three wishes to an elderly couple leading to the death of their son and his ghostly return. The story was masterfully adapted for new audiences by Stephen King in The Pet Sematary, and the parallels of what some today call Thanabots is striking: AI promises to resurrect the dead, in the shape of undead actors and chatbots trained on the diaries and blogposts of deceased loved ones. The psychological consequences for the process of grief are unfolding right now, and the longterm outlook of losing even the possibility of saying goodbye seems horrifying, when all of us leave promptable traces in embedding space, where everybody can summon the ghosts of everyone with a digital footprint.

In another tale with eery current undertones, the nymph Echo, cursed to only repeat utterances of others, falls in love with Narcissus, a guy so arrogant he wishes to only ever love his own image and was prophesied to live as “long as he never knows himself”. Narcissus rejects Echo, who retreats into a voice whispering his own words back to him. Narcissus now knows himself, becomes transfixed by his mirror image in a lake and starves to death. The parallels to the phenomenon of AI-delusions and spiraling are obvious.

The list of myths about wishfulfillment is long, and most of them have in common is the promise of knowledge. “The Sorcerers Apprentice” tries to bridge inexperience for the mastery of magic skills; Faust wants bypass spiritual labor for transcendental experience; The couple in “The Monkeys Paw” fills the hole left by their dead son; Narcissus shortcuts his search for ultimate beauty by looking in a mirror — all of them fail miserably, and sometimes deadly.

These gaps in knowledge, which those modern wishfullfilment devices are now able to fill, formerly required hard cognitive labor to overcome, often labor involving whole generations of networked scientists and artists. The discovery of these gaps in knowledge and how to close them often created a sense of awe, be it in art or the sciences, when something truly new touches us on such a fundamental level where we just have to stand back and take a moment to adjust to what we just experienced. Generating anything we wish for by wishfullfilment devices grossly diminishes this invaluable feature of discovery, and the loss of this sense of wonder is possibly one of the most dire consequences of interpolatable archives. When everything’s possible, nothing is interesting.10

Sure enough, myths and stories also tell happy tales of wishfulfillment going well, often after fun shenanigans. “Alladin and the magic lamp” comes to mind, where a boy with the help of a genie outwits circumstances and gains wealth and power. And in the fairytale of “The Wishing-Table, the Gold-Ass, and the Cudgel in the Sack“ (one of my favorites), a son of a tailor must get smart about the capacities of the titular items to retrieve stolen goods from the evil owner of an inn. He succeeds and they live happily ever after. What these stories of successful wishfulfillment have in common is that the mechanisms of wishfullfilment must be outfoxed, and that the riches they promise must be earned. AI brainrot being cheater exclusive is exactly these myths at work: If you instant-wish yourself good exam grades by cheating, all you’ll achieve is a cognitive clobbering from a magic stick.

Severances

We already talked about how “AI dissolves the episteme, the based knowledge, including citations, sources and authorial intent” into new AI-mediated digital oralities, where tracable reference-based epistemology makes place for an epistemology of the oracle. What we know no longer can be backed up by definite citations, links, and attributions allowing you to trace the origin of an idea, but becomes a feeling for a vibe, interpolated from statistical patterns derived from many of those sources, including unrelated and anachronistic references. And because the “rigid orders and shapes” in AI models are controlled by corporations and the curators of datasets, the severances of factual grounding beget new epistemological power structures. Supposed that a lot of information processing in the near future will be AI-mediated, these new power structures will control the space of possibilities in which we communicate and think.

In Borges story of “The Library of Babel”, the fanatic sect of the Purifiers roaming the infinite archive of all possible books, is hell bent on burning volumes they consider to be false and useless, nonsensical or divergent from their norms. In LLMs these Purifiers appear at various infliction points: During the labelling of training data scraped from the internet, where new purifiers clean up raw datasets, sort the good from the bad, delete the hateful and the illegal; During RLHF-training, where the raw Shoggoth of the interpolatable archive get’s shaped into aligned chatbots which won’t offend (so they hope). Then the tamed model is further purified by constraining it with system prompts and constitutional alignment, and if the interpolatale archive then still connects data points into bad interpolations, the corporate owners of the models will further adjust their models to their morals and politics. Last but not least, the users themselves purify the archive by scripting sophisticated user prompts, and ultimately, the prompt and context window constrain the embedding space further to generate the final output. In all these steps the space of possibilities of the interpolatable archive shrinks by purifying, until the annoying “this is not X, it’s Y” appears on screen.

Former epistemologies, systems of knowledge, were constrained by what Michel Foucault in The Archaeology of Knowledge calls the “positivity of discourse”, a “historical a priori“ which lays the foundation for the “condition of reality for statements”. By that, he means what can be said, e.g. in fields of the natural sciences, is shaped, over time, not by single authors but whole “unities” of “oevres [sic!], books, and texts”. These “a priori“ were not ahistoric nor atemporal, some monolithic force from the outside, but are actively shaped by discourse practice and the power relations within a field. The sum of all of this, the discourse practices shaping the “historical a priori“ and “positivity of discourse”, establishes the space of possibilities of what can be articulated. For Foucault, this is the archive: “The archive is first the law of what can be said, the system that governs the appearance of statements as unique events.”

The problem of the severances of epistemic grounding now becomes clear: Where the episteme formerly was subject to discourse practise and “so many authors who know or do not know one another, criticize one another, invalidate one another, pillage one another, meet without knowing it and obstinately intersect their unique discourses” in a web of traceable sourcings and references, and where the “a priori (…) is itself a transformable group”, the episteme of the interpolatable archive is a free floating version of Baudrillards Simulacra, an inversion of map and territory, where we navigate a map of hyper-reality to project meaning into an interpolated output cut off from epistemic grounding. And this Simulacra is controlled by the new Purifiers at every stage, by the hyperscalers, the selection of datasets, by the engineers employed by AI-owners11. AI operationalizes Foucaults “historical a priori“ and hands over the keys to the billionaire class, who gain power over the limits of what can be thought.

In H.G. Wells Time Machine, this is brought to the extreme end of its logical conclusion: While the Morlock control invisible underground machines, the Eloi enjoy a careless life of blissful ignorance on a seemingly utopian surface. The concentration of power over the episteme runs risk of resulting in a fork of truth, where an unknowing mass consults interpolatable archives controlled by invisible rulers, enjoying free tiers of endless synthetic entertainment feeds shot through with algorithmic noise and unverifiable, algorithmically generated meta-knowledge of plausible half-truths, all while the rich pay for premium models trained on clean, traceable, verified data, or enjoy handmade and authentic cultural artifacts which may even challenge their presumptions and intellectual capacities. Ofcourse, those authentic intellectual challenges won’t come cheap, and you and me surely won’t be able to afford them.

Homogenizations

While all of these are serious problems, in the long run, the most serious of them all might be the issue of sameness. AI-output may flatten human creativity without us even noticing, because this homogenization plays out on a collective level, while the individuals’ creativity actually benefits.

Depending on which theory of creativity you subscribe to, there are different forms of creativity. I’ll stick with three: Interpolation, Extrapolation, and Innovation.

Interpolation calculates averages from data points to produce something in the middle. If your values are dogs and birds, your average is a dog with wings, or a bird with a very long tongue. Extrapolation means breaking the boundaries of your dataset: If your values are dogs and birds, you can extrapolate a mouse, or an owl, but you will stick to the rule of “animals”. Innovation means breaking that rule, or injecting new heuristics. From a dog digging a hole in the ground to bury his bone, you may innovate an excavator by applying all kinds of rules from different domains (engineering, transportation, building tools) to an animal, and come up with an entirely new thing.

Language models can do interpolation, but they can’t extrapolate or innovate beyond their embedding space produced from training data. It may feel that way though, when the machine comes up with surprising, sometimes baffling results. This is what I call the illusion of extrapolation: An amount of training data so huge and the latent space derived from it so vast, featuring so many parameters in so many combinatorial possibilities, has to produce the feeling of extrapolation, or even innovation, on the level of the individual user, because no individual alone can ever know all those combinational possibilities. This illusion of extrapolation already is on display in papers showing how AI use increases creativity on the individual level, but decreases diversity in collective output, reported first in 2024 in a paper comparing creative writing in human shortstories and AI-output, then in 2025 in a paper about AI-augmented research. A recent review of the literature confirmed those findings. What feels new to an individual, what’s new to a culture, and what’s new in principle are very different things. Interpolatable archives increase the first, decrease the second, and can’t do the third.

Henry Farrell put’s it well: “the more that LLMs are employed in the ways that they are currently being employed, the more concentrated science will be on studying already-popular questions in already-popular ways, and the less well suited it will be to discovering the novel and unexpected.” Everybody becomes slightly more creative, but we all sound like the same creative person.

I already talked about the loss of awe above when discussing the effects of AI as machines for instant wishfulfillment, where they devalue novelty into a mundane readymade-on-prompt lacking the ability to produce a sense of wonder. The same effect, ofcourse, comes with the homogenizations of creative fields: Not only can’t we marvel at our own synthetic output because frictionless wishfulfillment feels unearned, we also diminish our collective ability to be left speechless in the face of the “novel and unexpected” by reducing it into dull sludge.

This convergence on the already-popular vanishes the fringes and washes out the long tail distributions of its dataset. Like the widely reported phenomenon of model collapse, where LLM output converges into increasingly narrow averages, Andrew Peterson identifies the same in a collective knowledge collapse. Writing in the Open Society Foundations newsletter, Bright Simons lists what these tail distributions contain: “Minority viewpoints, rare knowledge, unusual formulations, (…) the traces of intellectual disagreement, of minority expertise, of Cassandra warnings, of institutional friction, and of the awkward and valuable fact that different people know different things and express them differently (…) in other words, the signature of social complexity. Model collapse is social mind compression presented as a technical phenomenon.”

Thought through to its extreme end, this results in a heat death of the intellect: In Claude Shannons information theory, information is quantified by the volume of “surprise” in the outcome from a specific event in the world. If a message is completely predictable, its informational value is zero. LLMs are fundamentally deterministic, the randomness of its outputs is generated by a meta-parameter called temperature. If you put that temperature at zero and reduce the probability distribution of the next token to its absolute minimum, the interpolatable archive becomes a perfectly deterministic machine: Each prompt will then generate the exact same output. No alarms and no surprises. But even with stochasticity plugged in, the models converge towards the median. Applied to creative fields of discovery, this means that those fields approach a semantic equilibrium, a uniform soup where novelty grinds to a halt. The interpolative archive becomes a toxic space of non-possibility, a static, eternal, atemporal Big Flat Now where everything is connected, nothing matters and history goes to die.

But we don’t have to go full theoretical “heat death of the intellect” to see how this can lead to bad outcomes.

German thinker Michael Seemann, in context of Xs Grok-chatbot turning into Mecha-Hitler and later referencing an article from Bruce Schneier about the homogenization of language, coined the term of “Weapons of Mass Speech Acts”. The concentration of power over the episteme in the hands of a few, in a society saturated with information retrieval mediated by interpolatable archive, may impact the thinking of its users at scale. Already, a study found that “few weeks of X’s algorithm can make you more right‑wing“. While this hardly can be blamed on Musks tweaks at the supposedly woke bolts and nuts of its AI chatbot alone, it illustrates how the homogenization of language putting limits on what and how we speak and ideate about things, in the hands of politically motivated ideologues can and already is used for influence operations.

I may personally not be partisan enough that the prospects of being influenced by conservative talking points on a platform owned by a shady billionaire fill me with nightmares, nor will the confrontation with a braindead partisan chatbot make me sweat. But: A new study from Petter Törnberg found that the sycophancy in LLMs pushes it’s responses into political preferences it asumes in the user: “Political bias in LLMs is therefore not a fixed point on an ideological scale but a response profile”. Studies showing a leftwing bias in chatbots really show something else: the effects of sycophancy, not actual political biases present in the model. The LLM figures that it’s being tested by researchers, asumes leftwing tendencies in them because academia is famously more progressive than the rest of the population, and answers like the good sycophant that it is.

In another new paper about chatbots pushing confirmation bias, researcher Jay van Bavel finds that AI is “especially effective at generating elaborate justifications for what people already — or wish to — believe.” Together with the results above, this means that sycophant AI will confirm any political preference it asumes in a user, and become a universal Meta-Fox News for everyone, pushing users into an ever more narrowing mental corridor by homogenizing their language. In other words, this streamlining of thought by chatbot can radicalize everyone, even with models not Musk’d into Mecha-Hitlers.

This is why the homogenization of language and thought, in politics and all other realms, to me, is one of the greatest dangers coming out of this technology.

It is therefore imperative that interpolatable archives stay one tool among many, that our usage of expanded mind technologies stays diverse, so that the idiosyncratic, the fringes and the edges, the individual in all its complexities can be preserved, because they are of crucial importance for human systems to thrive.

IV. Critical Vibes

“These are the Talking Rings?”
”Yes.”
”They speak, hm? Of what?”
”Things no one here understands.”
”Make them talk.”
(The Time Machine, 1960)

What I tried to achieve in this series of essays is to look at the chances and risks of AI as a cultural technologies and see what remains, once you strip them of cognitive woo and singularity myths: A technology that provides access to an dissolved, interpolative archive through textual interfaces. These, pushed to their extreme ends, result in a homogenization of language and an ever narrowing space of possibilities, what I called the “heat death of the intellect”.

All of thes beforementioned points of critique are valid, and I didn’t even touch on problems not directly following from the interpolative nature of AI, like all the issues with labor, the environmental impact of energy and water consumptions or economic bubbles which may or may not exist.

Interpolatable archives as they are rolled out now, based on scraped data from the web and cleaned up by exploited gig workers all over the world, are a far shot from their theoretical possibilities. They contain all the discriminations and biases present in human conversations, and generate what is always already reproduced, erasing the marginal in favor of the statistical norm. The corporations running the algorithms extract and monetize the commons, while the problem of copyright in an interpolatable information space might very well be unsolvable: Who do you want to pay, when every output contains the activations of hundreds or thousands of artificial neurons each of which point to patterns of tokens within sentences within publications that deserve to be compensated? It will take decades for us to figure out fair proceedures to handle this.

There are, however, prominent criticisms which fly out of the window once you get rid of the illusion of AI as cognitive agents and view them as cultural technologies.

Useless Bullshit

Interpolations in such quantities of billions of parameters will always produce fuzziness and synthetic artifacts are never not interpolated. But the “blurry JPGs of the web” become sharper by the day and even blurry JPGs are identifiable. The JPGs work, especially when you can layer, blend and bend them on top and into each other, and it is sheer foolishness to insist they are “useless”, when every day, users make the very different experience of a very useful piece of software.

I highly recommend reading the essay of cosmologist Natalie B. Hogg titled “Find the stable and pull out the bolt”, in which she describes her journey from rejectionist critic to reluctantly embracing the possibilities and finally becoming cautiously optimistic about the tool. Seasoned coders report productivity gains of up to 100 times their former output, and though I understand and somewhat-subscribe to the critique as articulated by Cory Doctorow, who wrote about how “Code is a liability (not an asset)“, even he understands that those increases in productivity in coding can’t be handwaved away.

Moreover, the critique of reliability seems overstated, and the boring gotchas in critical AI discourse may soon become a relic of the past, just like the old office jokes where we machine translated text with early instances of Google Translate back and forth until they became gibberish to our delight.

A recent research conducted by the New York Times found the results of Googles AI overview showing a 90% success rate, and many pointed out that this still means millions and millions of cases of false information spread by the market leader in search. This is not wrong, but when we compare a 90% success rate in information retrieval with those of a classic search engine, how many of these search results are useful and accurate for the specific query? In my estimation, this success rate is far below 90%, even for un-enshittified platforms.

A prominent trick of internet search is to skip the first page of results because they are useless to your query. Compared to this, results from AI overview and a 90% success rate seems like no small improvement. The problem then is not accuracy, but the neutral tonality of the “authority from elsewhere” in which the remaining 10% of false information is delivered. Conversely, this hardly renders the product “useless”.

Emily Benders position that a supposed “parrotness” of synthetic text shows their inherent meaninglessness seems equally outdated. Just a few days ago she defended her positions and I find myself agreeing with surprisingly many of her arguments, except for one crucial point, arguably her central claim.

She writes that

language models don’t understand text they are used to process, because language models only ever have access to the linguistic form (i.e. spellings of words) in the training data. (...)

we (define understanding) as mapping from language to something outside of language, and show that systems built only with linguistic form have no purchase with which to encode (“learn”) such a mapping.

But latent space have such purchase, as they pick up meaning from the patterns encoded in linguistic practice, which, crucially, goes beyond spelling, grammar and syntax. You may call this form of algorithmic understanding of meaning limited, compared to the human understanding of the world, which is built from a dataset much richer and diverse than the digital mimicry. But it is not the case that there is no understanding at all. Bender, a bit hesitantly, concedes to that point writing about a “thin kind of technical ‘understanding’” which might be present in the models, which seems to contradict the original paper of hers and Timnit Gebru, where they write that “Text generated by an LM is not grounded in (...) any model of the world, or any model of the reader’s state of mind” (emphasis mine). But even when those synthetic models of the world are low resolution “blurry JPGs” compared to ours, they do exist.

“Polly wants a better argument“, as a recent critique of the parrot-argument states. While this text argues that LLMs can encode meaning because they are trained multimodal and that human feedback loops ground models in extralinguistic reality, the convergence of representations across models adds another blow to the parrot-metaphor: In the first part of this essay when talking about Borges, I made the claim that the “only thing relevant for the LLM is not truth, but the narrative consistency of its vector.” What I left out is that this vector is informed and shaped not just by a prompt, but also by the millions of attractor basins encoding the “external forms of a myriad traditions” we talked about earlier.

Meaning of the poetic Kind

In the introduction headlined “AI as culture”, in his book “Language Machines”, Leif Weatherby writes about how

the implementation of contemporary language generators matches the theory of language that European structuralism advanced nearly a century ago, suggesting that language is complex, cultural, and even poetic first, and referential, functional, and cognitive only later. This poetic language is not only computationally tractable but turns out to be the semiotic hinge on which an emergent AI culture depends.

These poetics (the structure and principles of poetry) picked up by the models are precisely Henry Farrells “tales that are sung”, where “LLMs are not the singer (...) but the structural relations of the tales that are sung (…) we can now listen to and even interrogate (…) without immediate human intermediation.”

The machine lacks the subjective intent of a cognite agent grounded in social reality, but it does encode meaning and valid semantic representations of the world in the shape of poetic representations, the structural vibes, picked up from human practice of the linguistic form. Or, as Allison Parrish put it back in 2021: “a language model can (...) write poetry, but only a person can write a poem.”

This is a fatal blow to the bullshit argument. As per Harry Frankfurt, bullshit is the use of language with “a lack of connection to concern with truth” and an “indifference to how things really are”. But the models do show a differentiation between how things really are and how they are not, which you might aswell interpret as a “concern with truth”, as such concern is present in the poetics of the language it is trained on. Insisting that such output is meaningless bullshit from a metaphorical parrot then requires a cognitive agent that isn’t there. LLMs can’t be “bullshitters” —itself is an anthropomorphization— because bullshitters require agency to bullshit. The bullshitter is always the user, never the interpolatable archive she uses to bullshit.

Adding insult to injury is the very probable outlook that the severances of epistemological rooting we talked about may very well turn out to be an issue solved as soon as interpretability improves and the black box myth finally disentigrates into hot air. As Shalizi put it at the first conference for cultural AI12: “GenAI is information retrieval and synthesis. With the right tools + access, we can quantify the influence of each training document on every response”.

Those parrot-metaphors and bullshit-claims are arguments aimed at misguided comparisons to human cognition and the resulting hype and marketing lingo, and as such, I can relate to them. But as an argument against meaning encoded in latent space or the capacities of language models, the value of the parrot/bullshit-arguments is nil.

Sloptimizations

Accordingly, and to be frank, I find the critique of “slop” to be banal. The world has been full of standardized and optimized language since we talk to each other, mimic successful speaking patterns and the ancient greeks invented schools of rhetorics for politicians to convince and persuade (or to deceive and bullshit) their publics.

George Orwell already complained in his famous essay on “Politics and the English Language“, that “(a)s soon as certain topics are raised, the concrete melts into the abstract and no one seems able to think of turns of speech that are not hackneyed: prose consists less and less of words chosen for the sake of their meaning, and more and more of phrases tacked together like the sections of a prefabricated hen-house.” This is as much of an assessment of “slop” as anything you can find on Bluesky these days, and I find the standard critique of sloptimized language to be quite sloppy itself.

People sure like to romanticize originality, where the history of prose is filled to the roof with ripoffs, amalgams and chimeras, from the “Devine Comedy” which happily mashed together roman and greek mythology with christianity and interpolated the existing cultural archive of its time to send Dante’s personal enemies to hell, to all the examples J.W. McCormack uses to illustrate his point in the delightful piece on Neverending Stories about LLMs, copyright and originality, in which he states that “Writing was destined for automation, from the punch cards of Charles Babbage and Ada Lovelace to Turing machines and, hell, Choose Your Own Adventure—but an AI can’t ‘know’ what makes a good story any more than CAPTCHA knows what does and does not make a motorcycle. What it can do is meet our expectations based on pattern recognition.”

If the market for young adult fantasy romance novels hyped up on booktok is any indication, those expectations are easily met, and I consider such pulp as machine generated slop regardless of its origin in a human mind. I refuse the “false choice between refried ectoplasm and a serial aesthetic in which mass media has stabilized redundancy”, as McCormack puts it in his piece. I might be a rejectionist after all! I make no difference of sloptimized output from the organizational artificial intelligence that is a corporation, and the sloptimized output from the artificial models of language. To be honest, I sometimes consider human slop often much worse a case compared to those synthetic ones, precisely because it does contain the deceiving intent a machine lacks.

I can’t remember such public hostility to these synthetic, polished, median texts of human origin coming out of public relations and advertising agencies or politics, and surely you’ll find more sinister examples in those places on which to feed ones anger than getting worked up about “shrimp jesus”. Get real.

Thinking in Vacuums

But even the more serious points of critique we discussed —the accelerations, anachronisms, wishfulfillments, severances and homogenizations— presume usage of AI in a vacuum for them to unfold their toxic potential in full. Sure enough, if, and only if interpolatable archives become the primary way of information retrieval, of sharing knowledge and shaping public discourse, epistemic grounding is severed, we risk spiraling into bespoken mirror worlds, science stops being science, history turns into pseudo-history and LLMs turn into weapons of mass speech acts. In reality however, this never happens.

For me, I use books physical and digital, I read articles, papers and essays in print and on screen, follow the news and read at least somewhat across the political spectrum, I listen to podcasts, talk to experts and non-experts of all kinds, and sometimes I use latent spaces to explode my ideas and explore them by interpolatable archive. Occasionally, I touch grass. None of these alone shape my ideas — it’s always all of them. This is true for most of us, and while this may not hold for your hardcore MAGA-pilled uncle, a study found that “dialogues with AI reduce beliefs in misinformation”. So there’s that.

Returning to the subject of AI-generated pseudo-history, for instance, then yes, we might tune in to AI-videos illustrating everyday life in ancient Rome on Youtube, and indulge ourselves in an hour of averaged synthetic imagery from a seemingly distant past that is an anachronistic interpolation of data-points from across time: moving pixels generated from stock video, illustrations and memes. But if I’m interested in such clips, I’m likely to watch documentaries featuring real historians as well, listen to podcasts and read books about Pompeji, and maybe recreate medieval food as a hobby. Hell, I might even pay visit to a museum. Given that new media never fully replaces but always complements what came before, I’d suggest that our understanding of history and our place within will survive interpolatable archives just fine.

Further, in a resent experiment with “the use of LLM hallucinations to ‘fill-in-the-gap’ for omissions in archives due to social and political inequality”, the researchers “validate(d) LLMs’ foundational narrative understanding capabilities to perform critical confabulation” and found that “controlled and well-specified hallucinations can support LLM applications for knowledge production without collapsing speculation into a lack of historical accuracy and fidelity”. This is in line with projects like Historica, which aims at filling “historical silences” by interpolative archive, and make visible the “absence of records” which are “the result of systemic exclusion, where certain voices are ignored or erased to maintain power”. The use of vintage LLMs to make visible the “historical silences”, especially when combined with an ensemble of diverse material, seems a viable and rich way to educate yourself and others about history. The risks of anachronisms present in language models, again, have more to do with their use in a vacuum, than with the anachronisms themselves.

What is needed are norms informed by AI literacy declaring interpolatable archives as one tool among many, a device for general research to get a general feeling for the general vibe of the subject or historic period at hand. Something that can’t replace sources and books grounded in verified facts, but complement them, giving you hints at answers, not answers themselves, to be digital assistants supporting you inquiry, not slaves fulfilling your every wish.

But those are questions of interface design and cultural practice, and the current failings of design choices are not first principles on which you can base an absolute rejection. Sometimes I can’t shake the feeling that thinking in vacuums kills critical thinking much more than AI.

Remainder Criticisms

Viewing AI systems as interpolatable archives which are one tool among many, besides their material implementation, then leaves not much on the table to criticize thoroughly, it seems, and I confess that I’m getting carefully optimistic about the technology. But I won’t deny the inherent dangers of their usage in bureaucratic decision process, where we want decisions to be made on a case by case basis with human judgement, not broad judgements by algorithm. And we surely don’t want some vague decision making based on vibes in targeting systems in warfare, as it has already been deployed by the Israel Defense Forces in Gaza.

Productivity gains for now are debatable, as are the effects of AI on the job market. As it’s currently realized, AI is an extractive technology exploiting intellectual labor of the commons for the gains of corporations, largely without compensation. I also won’t deny the open questions of their energy and water consumption. I’m not convinced by either side of the argument and the amount of conflicting signal coming out of that discourse forbids clear positioning. Yes, those corporations training frontier models are constantly lying and greenwashing their energy consumption, not to speak about Jevons paradox of rising demand nuking gains in efficiency.

On the other hand, the operation of AI systems is getting more energy efficient, and developments in local LLMs point towards a future beyond monolithic hyperscalers, where you may even train your own 100 billion parameter models on a single GPU. These developments make me believe that the current state of AI is comparable to that of the pre-PC-mainframe age of computers, where huge warehouses full of hardware were necessary to run the same compute which you, today, carry around in your pocket. For comparison, you’d need roughly half a million IBM System/360 Model 75 to equal the processing power of your iPhone, and 35 million of those room sized mainframes to match it’s neural engine GPU. Five of those were used to run the whole NASA Apollo program. Given that you can shrink the information processing power necessary to land you on the moon by a factor of several millions and put it in everyone’s pocket for you to scroll through brainrotting videoclips on TikTok, we probably will see similar effects for putting local artificial intelligence on your laptop.

Yet, the fact remains that AI’s energy and water usage in a world facing climate change is an imperative critical issue at an critical point in time where carbon budgets are running out. This year, a Super El Niño is shaping up, and in mid april, I’m already tanned like in mid June. It’s getting hot.

So, if these points —labor, energy and bureaucracy— remain once you view AI systems not as magic cognitive entities but the cultural technology of interpolatable archives, one has to ask themselves if they are worth it.

Even if we can mitigate the averaging effects on creativity by not using them in a vacuum, even if we can workaround epistemic severance with RAGs and interpretability, find safety guardrails against delusional spiraling and turn those cognitive catalysts into combustion engines for knowledge, the open questions of what they will do to the inherent dignity of labor, to the democratic process and the environment demand answers and solutions I can’t provide. For now, I have to live with the ambivalence.

But what I can do is to place interpolatable archives on a lineage of the cultural evolution of media innovations: With the development of language we introduced the new regime of social externalization to information processing; Writing and the alphabet introduced permanence; The printing press in China and Gutenbergs moveable type introduced one-to-many distribution and the edit respectively; TV and radio introduced realtime broadcasting, while the internet confronted us with the regime of the network. AI now is introducing another new regime of information processing: the automatic interpolation of data.

From this perspective, AI is a normal cultural evolutionary step in information distribution and processing, and like every normal cultural evolutionary step in information processing, they will change everything. None of the transitions mentioned went over smoothly, and so will this one.

We’ve successfully integrated cognitively disruptive innovations in the past, and we will do so today. The disruptions of interpolatable archives run deep, both on individual and societal levels. But, and I hesitate to make this point, I’d also argue that AI in the shape of interpolatable archives as the latest step within the lineage of cultural evolution are, indeed, inevitable. Each of those steps within that lineage was inevitable from the perspective of cultural evolution — as is the interpolation of big data by algorithm on inquiry: If we get the affordances —the data, the compute, the algorithms— then evolution will do what it does and squishy latent spaces will appear.

From this viewpoint, shortsighted rejectionist critiques of AI as “the tool of the oppressors” or a “fascist artifact“ to me read like a rejection of the invention of cuneiform writing in Mesopotamia because Hammurabi used it to manage argiculture and land ownership to oppress the people. Accordingly, the rejectionism coming out of the majority of critical AI discourse seems to be mainly aimed at corporatism, not the technology itself. I will admit that those critiques do have momentary merit, but I also think they do not justify an absolute dismissal of AI technologies as it is on display in the discourse, because what is not inevitable are the organizational principles of their implementation. Neither the extractive practice nor OpenAI nor xAI nor Palantir are inevitable. They are companies and subject to regulation in a democracy, full stop. Nothing prevents us from running those new archives like national libraries, for instance. But imagining such a political project is not possible on a rejectionist stance.

How about a nice Game of Chess?

Coming back to the question posed earlier of what science is here for, if not discovery: The scientific method is an algorithm. A researcher posits a hypothesis, tests it against reality, and it holds or not. If it holds, it becomes a valid theory, and if not, it’s falsified and kicks the bucket, serving as inspirational fodder in cultural memory for generations of scientists to come. This way, we get ever more detailed descriptions of the world in various languages, often in the language of mathematical formulas, or the playful use of words in philosophy and sociology. A lot of yet unrealized descriptions encoded in the various languages of science now lie dormant in latent spaces, waiting for their Liam Price to discover the prompt. In the simplest terms, this means that the algorithm of the scientific method has been updated.

Now, a scientist may posit a problem and throw it against latent space to see if there is a seemingly valid hypothesis. She then tests that hypothesis against reality, and it holds or not. With AI, scientists don’t construct a hypothesis against an open problem, they identify such problems and consult synthesis as a service. This update may devalue discovery itself, and shrink creative space of possibilities, as the price we pay for speeding up and automatizing the closing of knowledge gaps.

So, what is science here for, if not discovery? To expand our curiosity and explore. Like for the problem of homogenization, it is therefore vital that science is no longer conducted in the vacuums of fields. What automatic discovery demands is interdisciplinary research: Crossing the streams to make formerly unconnected fields pollinate each other and push the space of scientific inquiry beyond existing training data, to break the boundaries of an interpolatable space and make extrapolation and innovation possible.

Revisiting the defeat of Lee Sedol by AlphaGo and his subsequent departure from the sport, and the rise of collective creativity in human Go players in its aftermath, one of my favorite examples of human antifragility in the face of superhuman machines is the game of Chess. For 30 years now we’ve had computers beating the best human players in the game, and the result has been neither the devaluation of the game, nor human retreat. The result is human-machine cooperative training techniques leading to massively improved skills.

Carlsen is using the chess engine Stockfish to analyze his moves and speaking about how Googles AlphaZero “destroying” Stockfish in 2017 affected his game, Carlsen said: “I have become a very different player in terms of style than I was a bit earlier, and it has been a great ride.” Today, “Magnus Carlsen dominates the computer era by deliberately playing sub-optimal moves to drag his opponents into the unknown”, because AI introduced Carlsen to “wild ideas“:

According to his coach, the change came from wild ideas AlphaZero uncovered: sacrificing pieces for long-term advantage, pushing the rook pawn aggressively, using the king as an active fighter. Things human experts thought were unsound, but the engine showed they can work.

Every chess engine these days beats any human player with ease. Yet we keep improving and playing for the sake of the game. I’d suggest that similar “wild ideas” will emerge for the use of interpolatable archives in education, science and the arts.

The one thing that made humanity an evolutionary success is our ability for social and cultural learning: To share intentions and and iterate knowledge across generations. All progress relies on this. So, even if all the criticisms apply, having a technology which automates the interpolation of knowledge is a promethean gift not easily brushed off. Interpolative archives are not just a mere “scam”, and “Destroy AI” may be a fun shirt, but we shouldn’t destroy interpolatable archives wholesale. I get bad vibes from people trying to burn archives.

The task then is not to reject, but apply democratic process to regulate, demand fair shares and be careful to preserve our collective ability to innovate — to become neither machine-controlling, exploitative Morlock nor ignorant Eloi feeding on epistemic plastic, but to find new norms in the acquisition and sharing of knowledge. To stay curious in the face of these new archives, and make them talk.

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If you like to read it in shorter chunks, here are the individual parts as they were published:

Part 1: The Skeleton Library - Compulsions to Connect, Warburg, Borges and Goldsmith, Cultural Technologies and Digital Oralities

Part 2: Explosion Drawings - Science Sans Discoveries, Textrotating Cognitive Catalysts and Exploding Your Intelligence by the Method of Warburg

Part 3: Pitfalls of Probability - Accelerations, Anachronisms, Wishfulfillments, Severances and Homogenizations

Part 4: The House of Polly - Useless Bullshit, Meaning Of The Poetic Kind, Sloptimizations, Thinking In Vacuums, Remainder Criticisms and Games of Chess

Here’s a non-slop version of this essay too for those of you very serious people, but arguably, this one is way more fun.

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I. The Skeleton Library

“I am unpacking my library. Yes, I am.
The books are not yet on the shelves,
not yet touched by the mild boredom of order.”
(Walter Benjamin)

Compulsions to Connect

One hundred years ago, a german scholar named Aby Warburg went mad over what he called his “Verknüpfungszwang”, a compulsion to connect. He was searching for instances of what he called “Pathosformel“, aesthetic commonalities in the expressions of human emotional states — joy and rage, grief or ecstasy — through cultural history.

To achieve his goal, he built the initial Warburg Institute1 in Hamburg where he collected art, books, news snippets and artifacts. For his opus magnum of the Mnemosyne Atlas (”Bilderatlas Mnemosyne“ in german), he displayed a collection of 971 artifacts on 63 large panels, each two meters high, and indexed them not by the usual meta data like genre, author, date, or topic, but by idiosynratic aesthetic categories and psychological, affective intensity. Here’s some of the labels by which he sorted this collection: “Different degrees in the application of the cosmic system to mankind”, “Orientalizing of antique images”, “Development from Greek cosmology to Arab practice”, “Rimini pneumatic conception of the spheres as opposed to the fetishistic conception” or “Cosmology in Dürer”.

With the Mnemosyne Atlas, an associative image-based map of meaning, Warburg aimed at what he called an “iconology of intervals”, where meaning through analysis of images doesn’t emerge from historic context, but from the space inbetween associated but otherwise unrelated, anachronistic images. His associative Bilderatlas can be read as an early prototype of the latent space of an image model, whichs output was the “Pathosformel”, averaged primitives of affect expressed across cultural history. 100 years later this kind of navigation of an idea space would be newly theorized in context of machine learning by Peli Grietzer in his Theory of Vibes, which, to him, are cognitive maps allowing us to interpret experiences through lossy compression of holistic patterns.

Aby Warburgs’ project of the Mnemosyne Atlas remained unfinished, he died in 1929 from a heart attack. Today, his archive resides in the Warburg Institute in London.

12 years after Warburgs’ death, Jorge Luis Borges published a collection of shortstories called ”The Garden of forking Paths”. It contains at least two stories of interest to our cause, about at least one of which you surely must have heard: ”The Library of Babel” consists of books of 410 pages, containing all possible combinations of 22 letters2 plus period, comma, and spacing. That fictional library includes the random and nonsensical aswell as the meaningful, it holds “the detailed history of the future, the autobiographies of the archangels, the faithful catalogue of the Library, thousands and thousands of false catalogues, the proof of the falsity of those false catalogues, the proof of the falsity of the true catalogue, the gnostic gospel of Basilides, the commentary upon that gospel, the commentary on the commentary on that gospel, the true story of your death, the translation of every book into every language”. It contains a book telling the exact story of your life, and another one that tells mine, and all the books making fools out of both of us.

Analyzing the stories of Borges in context of Large Language Models, in their paper “Borges and AI“, Léon Bottou and Bernhard Schölkopf write about the epistemological unrooting inherent to an archive of such nature: “The books in this Library bear no names. All that is known about a book must come from maybe another book contradicted by countless other books. The same can be said about the language model output. The perfect language model lets us navigate the infinite collection of plausible texts by simply typing their first words, but nothing tells the true from the false, the helpful from the misleading, the right from the wrong.” The only thing relevant for the LLM is not truth, but the narrative consistency of its vector.

In his initial essay on ”The Total Library”, the nonfictional forerunner to “The Library of Babel“, Borges aknowledges its roots in Kurd Laßwitz‘ shortstory “The Universal Library“ (”Die Universalbibliothek” in german) from 1904, likely the first piece of fiction taking the infinite monkey theorem to its logical conclusions. In it, Laßwitz not only predicts the near infinite latent spaces of AI like Borges, but also the pervasive threats of hallucinations, distortions in history writing, deepfakes (here, of documents signed with your name), and humans rendered unable to grasp the endless possibilities of an infinite library, because human reality is bound to practice constrained by real life in a civil society. These are precisely the questions we are confronted with today, anticipated 120 years ago by Kurd Laßwitz, 100 years ago by Warburg, and 80 years ago by Borges.

In 2002, New York based poet Kenneth Goldsmith started to retype his library on a Royal Classic Typewriter, word by word. Later, he was annoyed by the limits of his own taste and incorporated other works to retype, as he calls it, “the platonean ideal of a library”.

As an artist, Goldsmith is explicitly interested in the mundane, the unoriginal, the average, the uncreative -- proudly he declares “I am the most boring writer that has ever lived”3. and says “If I’m doing a piece of writing, and ask myself, can this in some way be construed as not being writing, then I know I’m on the right road.” Because everything ever has already been said in all possible combinations, adding to the cultural output to him feels pointless, so he runs with that feeling and turns futility in the face of borgesian infinities into its own poetic form.

Goldsmith sometimes thinks of himself as a modern version of Borges’ “Pierre Menard, Author of the Quixote“ from the shortstory of the same name, but he concedes that fictional Menard is more original. In this story, Menard wants to hyper-translate Cervantes “The Ingenious Gentleman Don Quixote of La Mancha” by immersing himself so deeply into work and life of its author he’ll become able to re-create it, line by line, without copying. In AI-parlor, Menard aims to overfit himself on Cervante so hard that his writing will be able to put out the original text.

With breaks, Goldsmith is retyping a library for 24 years now, and to date, he copied 750 books on ultra-thin onion-skin paper, which he stores in 200 boxes. Each book comes with a self-drawn portrait of the original author and her signature. Goldsmiths project is creating a singularity within the infinite floods of content production, to link unique individual and averaged mass.

What all of these authors across the ages have in common is Warburgs’ “compulsion to connect” archival contents, to find meaning in gargantuan amounts of data, each in their own ways.

Today, Warburgs’ Verknüpfungszwang is the prime human condition. Hypertext and platforms connect everything with everyone into what we call “big data”; the former 6 degrees of seperation have shrunk to 4. In consequence, we developed psychological pathologies showing up in widely spread conspirational thinking and delusions big and small, and the political parasites feeding on them.

While Goldsmith was copying lines from the classics on his typewriter, AI labs automatized Warburgs’ Verknüpfungszwang, and OpenAI released a new transformers based language model.

ChatGPT went public.

The Skeleton Library

Imagine a library devoid of letters. All the books it once contained are dissolved, its contents gone. What survives are the bookbindings, the cartonage, blank pages, the shelves, sections and all the floors of said library. The buildings stay intact, all the catalogs are there.

Some machine, at one point, went through this library, scanned all the letters within those books, noted all the statistics it could possibly measure on its path: the exact position of each letter in each book, the locations of those books in their shelves, the exact size in width, height and depth of all books, shelves, floors, their angles and distances to each other, a precise floor plan of the library, and how all this spatial information relates to each book, sentence, and character. On scanning the pages, the typography vanishes, leaving only blank sheets of paper. This is a library of pure structure, where Peli Grietzers “cognitive map” has been turned into architecture, the remainder skeleton of an archive built from giant quantities of geometric vector coordinates so finegrained that you can derive any information about its former collection.

Now imagine that you can fold this skeleton library, twist it into shapes not possible before. Like in a drawing by M.C. Escher, the floors and rooms and shelves bend back and forth and blend into each other. You can fold every book and every sentence it contains into other books or into whole floors in various sections. Imagine warping the cookbook shelf into the section for crime fiction, or blending the songbooks of punk rock bands from the 70s with the floor of classic literature. You can give it a spin and delegate the resulting amalgam to the building containing textbooks of natural sciences, letting it articulate all of this in the language of mathematical formulas. This is prompting: Morphing and twisting the skeleton of an archive consisting of extremely detailed statistics about the properties of its former contents, ready to be blended and remixed with any other data-point within that embedding space.

Another way to understand this skeleton library is by shining a light through it: Imagine your thoughts and ideas as a beam of light shot through a shapeshifting prism, which you can bend into any form and split your mental lightbeam into all colors from all directions in all angles. That prism4 is made from patterns in collective knowledge, and you can explode your own ideas by sending them through those prisms, where the output is a refracted thought dispersed into the components of whatever was your idea, and you can look at it from many perspectives.

This explosion drawing of your thought is comparable to Douglas Hofstaedters Trip-Let, which he describes in “Gödel, Escher, Bach“ as “Blocks shaped in such a way that their shadows in three orthogonal directions are three different letters.” In our analogy for AI, depending on the direction of the lightbeam that is your idea and the location in latent space you aim at with a prompt, the interpolatable archive will throw back very different shadows. Except the “three orthogonal directions” of the Trip-Let have been blown up to hundreds of billions of parameters.

This is the interpolatable archive, a new way to access information mediated through the form of statistics. This is, in my view, the central innovation we can observe in Large Language Models, and possibly machine learning as a whole.

Cultural Technologies, not agents

In their 2025 paper, Alison Gopnik, Henry Farrell and Cosma Shalizi describe Large Language Models as systems which “do not merely summarize (…) information, like library catalogs, Internet search, and Wikipedia” but “also can reorganize and reconstruct representations or ‘simulations’ of this information at scale and in novel ways, like markets, states and bureaucracies”. As multiple studies have shown, LLMs in fact are compressions of their training data5, they are, in fact, Ted Chiangs’ famous “blurry JPGs of the web”, just like “market prices are lossy representations of the underlying allocations and uses of resources, and government statistics and bureaucratic categories imperfectly represent(ing) the characteristics of underlying populations”.

Neither markets nor bureaucracies nor the internet nor language models are agents, they are not cognite, and they are not remotely conscious. But we do like to anthropomorphize all of them anyways: Markets make use of “inivisible hands” and they “react”; we represent the bureaucratic management of nations in the shape of mascots and mythic heroes to bind its people to a narrative; and we talk to language models as if they are buddies, assistants, companions, romantic partners or slaves. The human predisposition to see peoples’ faces in everything has always been strong, starting from the myriad of anthropomorphizations in animist cultures where every thing has its soul, and it keeps making us seeing ghosts in machines, leading to what one may call the “pareidolia fallacy”: we see agency where there is none.

Agency and creativity require Daniel Dennetts intentional stance and teleology, the ability to direct actions toward goals emerging from imagined interactions with an internal model of the world, to evaluate outcomes in relation to one’s own aims. This is a cognitive feature not only observable in humans, but (at least) in all mammals: Watch a squirrel on a tree trying to figure out if it can jump to the next. It looks at the tree, its head moves up and down, it evaluates distance and its own abilities. It goes like that for a while, and then it decides if it can do it, and takes the jump. That’s precisely the “imagined interaction with an internal model of the world, to evaluate outcomes in relation to one’s own aims”. AI has none of that.

While AI models do show a synthetic theory of mind, where they build representation of its users during conversation, these work very differently from those of humans, and compared to them, they don’t work well. Chatbots have no intrinsic motivation, no sense of why it “speaks”, and no capacity to care about the coherence of its outputs beyond statistical continuity. All true and meaningful selection occurs externally, through human feedback and usage.

In LLMs, the pareidolia fallacy makes us asume cognition, creativity, and agency where there’s computational interpolation of language patterns in the giant wobbly archive of a new kind. What differentiates these new interpolatable archives from previous archives is obvious: They interpolate their contents. Where classic archives provide access to fixed records of text, video, audio and artifacts of human culture, this new archival access has atomized its contents, and only provides interpolated amalgams, chimeras and fusions. Depending on your stance about the definition of “archive” you might object to my interpretation at this point, and i’d nod and say “That’s what’s new”.

Digital Oralities

In “Large Language Models As The Tales That Are Sung”, Henry Farrell describes LLMs as structural similar to oral traditions and folklore: “LLMs are not the singer, despite their apparent responsiveness, but the structural relations of the tales that are sung”, reminding me of the pre-homeric rhapsodes, the bardes who sang the old stories of “The Iliad“ and “The Odyssey” long before Homer sat down and put them in writing. They did so by using preconfigured language modules with which they constructed their poems while they were sung, a “grammar” of mnemonic formulas and repetitions to solve the problem of creating narrative in real-time. These are the oral precursors for the optimized, averaged language codes we loathe so much when they come from AI.

Interpolatable Archives work similar, like a statistical mnemosyne (the godess of memory in greek mythology) speaking in tongues, giving probabilistic answers to specific inquiries. Knowledge transfer through those new oracles means a shift from traditional “archival epistemologies”, where knowledge is grounded in traceable facts and specific, identifyable sources, to a fuzzy oracular epistemology where responses are generated on-demand by an opaque interface, turning inquiry into digital hyper-orality.

Where oral traditions of yore served as mnemonic technologies integrated into the communal structures of everyday life, this digital hyper-orality is different. The entirety of human thought —well, as for now: The entirety of human thought on the internet, which… sigh…— becomes epistemic stockpile, a resource to be mined, refined, and dispensed on demand, stripped of its being-in-the-world.

Without being grounded in life, AI dissolves the episteme, the based knowledge including citations, sources and authorial intent, and make place for a 128kbps MP3 of the “tales that are sung”. The value of such information lies within its statistical probability and the stylistic resonance in the reader, rather than its referential grounding in reality. It speaks to us, or it doesn’t: finding meaning in stochastic output is entirely to the user. Social Media already innitiated this crisis of the episteme and the emergence of new oralities through phenomena like context collapse. On platforms, vibes-based knowledge reigns supreme. LLMs further accelerate it.

This sounds as bad as it can be, and I won’t downplay the risk here, but I want to draw your attention back to Aby Warburg at this point. His project of the Mnemosyne Atlas, that “associative image-based map of meaning”, was an attempt at tracing the recurrence of symbolic representation throughout visual history in a non-linear archive. Warburgs’ library, while still providing based epistemic grounding to its records, was introducing a second layer that intentionally dissolved the episteme with idiosyncratic indexing, allowing for access by free association, working similar to those new interpolatable archives.

LLMs generate not fixed images or texts, but interpolations across associative symbolic fields, enabling a new kind of navigation of a vast symbolic space of possibilities. The dissolution of the episteme in digital oralities, like Warburgs’ associative Bilderatlas, then can be read not only as a risk, but a liberation aswell, and a cognitive catalyst.

II. Explosion Drawings

“To find a thought is play, to think it through, work.”
(Aby Warburg)

Science Sans Discoveries

A few days ago, a 23-year-old amateur zero-shot-prompted ChatGPT to find a solution to an unsolved problem in math (archived) which had stumped mathematicians for 60 years. AI has been making a splash by solving various entries in the collection of the so called “Erdös-problems” before, but those solutions were either easy or for problems rarely studied. This one is different in that it’s a hard problem, and scientists gnawed on their brains over it for decades. Then along hops Liam Price with his chatbot, and done. In other cases, researchers equipped with custom neural networks discovered hundreds of cosmic anomalies or dozens of hidden planets hiding in huge troves of data. The list keeps growing.

What Price did was not science. In one experiment, after running 25.000 experiments, researchers found that for 74% of all cases, science-models did not revise a hypothesis when confronted with contradictory data. But updating your hypothesis according to data is the basis of all scientific inquiry, so AI models produce results without scientific reasoning. That’s pretty damning. But Price discovered the solution to the Erdös-problem anyways, so what do we make of this?

A paper from 2023 found a fundamental limit to alignment: “any behavior that has a finite probability of being exhibited by the model, there exist prompts that can trigger the model into outputting this behavior, with probability that increases with the length of the prompt.” What is true for alignment is true for the scientific discoveries too: For any solution to a scientific problem present in latent space, there exists a prompt to retrieve it. Liam Price found one.

AI doesn’t “do science” because it is neither an agent nor does it follow the scientific method proper and AI-as-a-scientist is just another anthropomorphization. But once you look at those models as interpolatable archives, this anthropomorphization becomes irrelevant. AI doesn’t do scientific discovery — those discoveries lie dormant as knowledge gaps in embedding space, an unrealized potential waiting for the right prompt to bring it to light. What Liam Price and his chatbot did was the discovery of such a warburgian “interval” actualizing a valid solution.

If anyone can take credit for this specific finding, it would be human culture at large, which created the foundational data and the connections containing that solution within the tensions inbetween data points. Just like Warburg found his Pathosformulas in the intervals between seemingly unrelated imagery across cultural history, researchers and amateurs alike now find readymade scientific discoveries.

It is not an outlandish assumption of mine when I expect that these discoveries will not be the last of their kind and likely are just the tip of an iceberg approaching fast, that mathematics (archived) will not be the last scientific field to spar full contact with the latent-discovery space of interpolatable archives, and that the speed of scientific discovery likely will accelerate. It is debatable how valuable such findings truly are though, if AI merely fills knowledge gaps in existing data, but it also reveals how academia has been caught in a “publish or perish” trap for a long time. Accordingly, automatic discovery by interpolatable archive might very well mean “The fall of the theorem economy“, as David Bessis put it, writing about “How AI could destroy mathematics and barely touch it”, grappling with the fact that AI in mathematics may throw the whole field into identity crisis.

The questions arising from readymade discoveries then go right at the core of academia’s current understanding of itself: What, if not discovery and closing gaps in knowledge, is science here for? We will come back to this.

Textrotating Cognitive Catalysts

Sam Barrett recently had a conversation with Claude, which resulted in a synthetic essay expressing my view of LLMs as interpolatable archives by other terms. That essay framed Interpolatable Archives “as a rotatable space“, something I tried to get at in my metaphors of the skeleton library and refracted light:

Think of a three-dimensional object casting a shadow on a wall. The shadow is a two-dimensional projection. If you only see one shadow, you might mistake it for the thing itself. But if you can rotate the object—or equivalently, move the light source—you see different shadows. Each shadow reveals something about the object’s structure. No single shadow is the object, but multiple shadows from different angles let you reconstruct what the object actually is.

High-dimensional spaces work similarly, but with more complexity. A concept that exists in a thousand-dimensional space of meaning can be projected into the low-dimensional space of a particular text. That text captures some aspects and loses others. A different text—same concept, different projection—captures different aspects.

What LLMs enable is rapid rotation through projection-space.

This synthetic piece of text generated associations in my head, making me revisit older notes about optical neural networks and language models as prisms. The bit about how latent spaces are rotatable objects throwing lower dimensional shadows then led to my comparison of LLMs to Douglas Hofstaedters’ Trip-Let. Creativity doesn’t care about where sparks come from, it just lights up or it doesn’t.

Used this way, LLMs become an external sandbox where I can dissect a thought and play with it, a cognitive extension against which I can throw my own ideas and concepts and see what comes back. This kind of usage is less about cognitive offloading than about seeding an experimental loop that’s conversational, dynamic and reciprocal. You can additionally tame the model with a user prompt like “you are an expert in [the field] and critique my ideas ruthlessly” and turn a sycophant LLMs into an academic sparring partner. In my experiments with user-prompting AI as such a cognitive catalyst, Gemini once became so annoyingly judgemental and arrogant about my amateurish inquiries, I had to tone down its ruthlessness and lobotomize the machine. My remorse about doing open brain surgery on an algorithmic intellectual sparring partner remains limited.

I use AI only occasionally, but if I do, I use it extensively. I write every day, take notes on articles, read a lot, have ideas and jot them down, and I’ve been doing this for years. I blogged a lot in the past, a method of public note taking and ideation. A lot of my ideas today are informed by wild associations in years of such note taking. Over time, concepts emerged from these writings, and from the bits of text scattered throughout my journal. When I come across an interesting piece of information today, I put them in context of these loose concepts: I take a note, give it a link, and write down how they update or relate to my ideas. Only then, after this process, I might throw my notes and concepts at the bot, and embark on sometimes very long conversations. My user prompt makes Gemini roleplay a helpful academic, and after some back and forth, what comes back is a framing of my ideas in scientific and philosophical history, what holds and what doesn’t, what’s old and what’s new. The chatbot gives me sources to check out, taylored to the specifics of my often idiosyncratic ideas, much more precise than Google or a vague research in a library could be. This kind of scaffolding through an averaging assistant for me is an invaluable second step in ideation and research.

AI researcher Advait Sarkar, one of the main authors of the widely reported “reduction in critical thinking“-paper, shares this view. Sarkar works on methods to make LLMs function as cognitive catalysts, and in another paper, he writes about the “Discursive Social Function of Stupid AI Answers“ in which he dares to make the point that “these stupid answers (to questions about “Gluing Pizza, Eating Rocks, and Counting Rs in Strawberry”) are in fact correct, because the primary objective of such queries is not to receive a correct answer, but rather to obtain an artefact of discourse”. Nobody asks an AI for the nutritional value of rocks unless they want a gotcha, and the interpolatable archive delivers. Smartypants being tongue in cheek throwing clever bits at overconfident critical AI discourses. I like that guy.

In his TED talk on “How to Stop AI from Killing Your Critical Thinking” he presents his efforts to iterate on his paper and turn chatbots into a “tool for thought” that should “challenge, not obey“. Testing a prototype6 for research demoed in this talk, the result sounds promising: “You can demonstrably reintroduce critical thinking into AI-assisted workflows. You can reverse the loss of creativity and enhance it instead.”

Exploding your Intelligence with the
Intellect by the Method of Warburg

In his post “On Feral Library Card Catalogs, or, Aware of All Internet Traditions”, Cosma Shalizi quotes Jacques Barzuns book “The House of Intellect” from 1959 where he distinguishes between intelligence and the intellect:

Intellect is the capitalized and communal form of live intelligence; it is intelligence stored up and made into habits of discipline, signs and symbols of meaning, chains of reasoning and spurs to emotion — a shorthand and a wireless by which the mind can skip connectives, recognize ability, and communicate truth.

Barzun gives the foundational example of the alphabet as one form in which the intellect transforms individual intelligence by introducing communal sets of rules for information processing: The alphabet “is a device of limitless and therefore ‘free’ application. You can combine its elements in millions of ways to refer to an infinity of things in hundreds of tongues, including the mathematical. But its order and its shapes are rigid.”

Shalizi concludes: “To use Barzun’s distinction, (chatbots) will not put creative intelligence on tap, but rather stored and accumulated intellect. If they succeed in making people smarter, it will be by giving them access to the external forms of a myriad traditions.”

It is common wisdom for learners in any field that “to break the rules you have to first learn them”. I’m not 100% convinced of this, especially for creative endeavours where untrained outsiders can apply very different sets of rules from the get-go and upend everything. But as a rule of thumb it’s good enough. And for learning “the rules”, sets of common knowledges in any field, be it the broad strokes in psychology or economics or philosophy, the intellect, those “habits of discipline, signs and symbols of meaning”, can absolutely be delivered by AI, and because those traditions are well documented, the bot rarely hallucinates.

Here’s my personal account for this way of using a language model: I take some interest in consciousness studies because since forever I want to know what this —waves hands in the air— is. I’ve written many, many notes about my own ideas, read a lot of articles and books about cognitive sciences. And yet, the scope of consciousness studies is overwhelming for an interested amateur like me, which is no surprise given that subjective experience is a matter of interest in philosophy for more than 2000 years.

There are currently more than 300 academic theories, and the true number of consciousness theories including folk epistemologies might be way higher by orders of magnitude. I know that I have my personal theory of what and how and why I am, and I’m pretty sure you have one too, at least to some degree. The whole subreddit r/consciousness is full of idiosyncratic ideas about human cognition, and some of them sound pretty interesting. My impression is that, like in the ancient parable of the blind men and the elephant, where a bunch of blind guys who never encountered an elephant each touch a different part of the beast and accurately describe those parts, but noone describes the true animal, all of these theories of consciousness describe partially true aspects of a full picture.

So where do you start, when you have your own vague idea of “what you are” and “how ‘the feeling of me’ works”, some basic knowledge about neuroscience and an extensive collection of notes and bookmarks to articles and papers? I can go to a library, crack open all the level 1 study books on neuroscience and all the level 1 books on philosophy of mind, get to work and a lifetime later I’d know which of my ideas fit into which parts of the literature. Or I can consult a chatbot, perform research customized to my notes and read about adjacent theories tangential to my ideas, which of them contradict my takes, and argue about what a synthesis might look like.

This is how I found the Santiago School of Cognition, Maturana, Varela and Enactivism in only a few dialogues with the chatbot. For what it’s worth: In these dialogues the hallucination rate was zero. From there, I consulted wikipedia pages, listened to podcasts, bought more books, downloaded more papers old and new, wrote more notes and developed my own ideas further. I might even write an essay on that topic to distill my thinking into one concise take. Then I’ll throw it all against the interpolatable archive again, have a look at what comes back, and extend my thinking in ever widening circles generating ever more ideas.This is how the LLM “succeeds in making (me) smarter” by “giving (me) access to the external forms of a myriad traditions”, customized to my own musings about the subject at hand.

Usually, we call places where we store those “external forms of a myriad traditions” a library, or a museum, or an archive, and what I did was exploding my own individual intelligence with the collective intellect by the associative method of Aby Warburg, using an interpolative archive to relate the “order and shapes” of the averaged traditions in consciousness studies to my own ideas.

In this mode of interaction, LLMs work like a catalyst that was decidedly not reducing but introducing friction to my thoughts: Presenting me with new perspectives on my research topic, all of which are points of consideration, making me stop and connect new dots, consulting new sources, generating ideas, speeding them up and rapidly expanding my space of possibilities7. If your cognition and knowledge about the world thrives on a healthy and varied media diet, then treating LLMs as one informational ingredient among many and exploding your ideas from time to time just adds another flavor.

One year ago, Andy Clarke, who together with David Chalmers developed the Extended Mind Thesis in 1999, updated on his original theory for language models. In one passage, he describes the progress in human strategies of playing Go after AlphaGo beat Lee Sedol in 2016:

There is suggestive evidence that what we are mostly seeing are alterations to the human-involving creative process rather than simple replacements. For example, a study of human Go players revealed increasing novelty in human-generated moves following the emergence of ‘superhuman AI Go strategies’. Importantly, that novelty did not consist merely in repeating the innovative moves discovered by the AIs. Instead, it seems as if the AI-moves helped human players see beyond centuries of received wisdom so as to begin to explore hitherto neglected (indeed, invisible) corners of Go playing space.

At least in the case of Go, the challenge posed by superhuman players made humans dissolve the “rigid shapes and orders” of their field and transcend the “external forms of a myriad traditions.” That’s quite an opposite view of how things may go compared to what Eryk Salvaggio calls “Interpasivity” where “systems framed as interactive tools for (…) creation are really sites of interpassive consumption.” But at least for me, and the players of Go, that seems not to be the case. Just like the Go-uchi got inventive about their ways of play, I expanded my ways to think about consciousness. This is what interpolatable archives as cognitive catalysts can do.

Now, these catalysts are coming for all cognitive labor, from academia and research to accounting, from creative industries to bureaucracy and government intelligence. I didn’t even need to mention Claude Code to make these points.

Mind you: Catalysts are accelerators. They don’t always bear fruitful results — and handled recklessly, they gonna explode in your face.

III. Pitfalls of Probability

“What do such machines really do?
They increase the number of things we can do without thinking.
Things we do without thinking; there’s the real danger.”
(Frank Herbert, God Emperor of Dune)

For now, we talked about the upsides of Interpolatable Archives as cognitive catalysts. But by definition, cognitive catalysts are stressors. Like the siren songs in Homer’s Odyssey which lured unsuspecting mariners with the promise of total knowledge of past, present and future, a seductive mnenomic “cognitive onloading” of all that has happened, exploding your inquiry by the intellect and accelerating research in hypercustomized rabbit holes bears cognitive hazards.

Accelerations

While the use of AI as interpolatable archives can boost your creativity and breadth of research because they introduce many points of friction, this can result in two major consequences if handled without care: If you mindlessly stuff your workload with new tasks because suddenly you can do them, you may suffer from AI-induced burnouts, something you might call cognitive onloading (or overclocking), where the interpolation literally spills over because it “can fill in gaps in knowledge”. For knowledge work, the appliance of AI as a catalyst means we can do more projects in a wider range, faster and in parallel. Suddenly, we find ourselves juggling dozens of projects simultaneously, losing oversight and motivation to do anything at all.

Cognitive onloading by interpolatable archive can also enhance latent delusional thinking in so-called “AI-psychosis”. Consider this quote from a recent post in the SlateStarCodex-sub: “AI repeatedly created ideas and connections that I hadn’t made or stated, that were so powerful and convincing, that I was swept up by them”. (I wrote in length here about the phenomenon of AI-induced delusions, where the cognitive catalyst turns the archive into a psychoactive substance.)

This “convincing” voice is the result of a distanced, neutral tonality from a sycophant AI, suggesting what I call an “authority from elsewhere”. This sound is also why in some studies AI was able to reduce the belief in conspiracy theories, which seems fine until you realize that’s because AI is superpersuasive, and can convince you of anything. You believe the machine and prefer its sycophancy precisely because it is not a human who’s trying to nag and push you around, but some supposedly objective instance. One study just found that this seemingly neutral sycophancy increases attitude extremity and overconfidence. I suspect that all of these, the persuasivenes, the delusions, the overconfidence, result from the same basic psychological mechanism of an interpolatable archive overwhelming you with confirmations and new ideas in a quasi-neutral sound of an authority from elsewhere, a voice to which you’ll happily submit.

These cognitive hazards resulting from mental overclocking have their obvious counterpart in risks resulting from cognitive offloading by, not from introducing new ideas spinning your wheels, but quite the opposite. All those studies and examples about cheating, reductions in critical thinking or cognitive surrender belong here. In fact, a recent paper on a loss of persistence in problem solving explicitly states that those “effects are concentrated among users who seek direct solutions” while “participants who used AI for hints showed no significant impairments”. In other words: AI brainrot is cheater exclusive. Using AI to increase friction, to generate ideas instead of answers, seems fine, when you proceed with care and clear research subjectives to reduce risks of burnout or delusional spiraling. If you use it to reduce friction, to generate full essays and answers to cognitive tasks, it turns your brain into mush.

But those psychological effects of interpolatable archives as cognitive catalysts may turn out to be easily mitigated compared to those of more serious epistemological consequences.

Anachronisms

One inherent feature of AI as interpolatable archive is, well, that it interpolates all its contents. This always produces anachronisms: Your generated text contains data-traces across time, most obvious when you intentionally prompt for such a thing, like punk rock lyrics in the style of a shakespearean sonnet, or if you use image synthesis as a time traveler to generate selfies in ancient Rome, which Roland Meyer calls “pseudo-history” and “2nd order kitsch” produced by “nostalgia machines“.

History as an academic field is reliant on original sourcing and documentation in the fixed, written form, or on cultural artifacts retrieved by archeology — anything not based on these fixed records is decidedly not historic. We call it “writing history” for a reason. This requirement is fundamentally diametrical to the vibey output of AI which dissolves all those sources and fixed forms into a wobbly archive and puts out “fictional historical documents of historical lives”. As Meyer rightly states in the same essay, those fictions are based on the integrity of existing historical archives currently under threat by the Trump administration. Likewise, Russia is engaging in disinformation campaigns targeting training data with the goal to “embed lasting distortions in digital memory”, as one paper put it. This inherent impurity of AI, either steming from anachronistic interpolations or data contaminations, apparently renders LLMs unsuitable for academic history research.

So, given all that, what do we make of vintage LLMs like Talkie, a language model trained on data cut off at 1930? From the perspective of rigid academic history research, all Talkie can produce is pseudo-history able to skew how we relate to the past and shrink the space of possibilities in which we imagine them. In contrast, Ranjit Singh in Data & Society proposes a field he calls “experimental history“ and asks if we can build models “constrained by a particular historical moment, and then use those models to ask structured ‘what if’ questions”. He answers with a cautious “yes”, and the researchers in r/askhistorians are not convinced either that the anachronisms introduced by AI will have a lasting effect on recorded history, as wonky sourcing has a long tradition in the field.

Now, I’m a history buff. I read a lot of books on the matter, my favorite epochs are the middle ages and ancient greece, and when I’m tripping on history, I like to indulge myself in all kinds of material. In preperation for the upcoming adaption of Homer’s “The Odyssey“ directed by Christopher Nolan, I read all of Stephen Fry’s books on greek mythology and his takes on the homeric epics — topics i already was familiar with. One takeaway especially from Stephen Fry’s books is that the historic rigidness especially regarding Troy and the “Iliad“ is lacking: All kinds of researchers and writers across the field constantly contradict each other, tell various versions of events which may or may not have transpired. Stephen Fry wildly references all kinds of sources to produce a highly entertaining amalgam from historic records across the ages. What you get from these books is a pretty good feeling for what greek mythology wants to say about the human condition, and about the tipping point where mythology fades and historical record sets in. While I don’t really want to compare Stephen Fry’s wonderful books with synthetic output from LLMs, I do want to point out that his books are closer to edutainment than scholarship, and that much of the pseudo-history-”slop” on Youtube equally falls into that category. (I’ll willingly admit that these are not nearly as witty, fun and eloquent as Stephen Fry.)

In this Slop-Edutainment, the hyper-orality of the digital oracle transforms historical records into supra-historical story-patterns to support its mnemonic function. Humans in pre-writing greece attached mythic structures to historic events to remember them: Odysseus became not just a soldier returning home from a very long journey of war, but a hero defeating the cyclops, meeting godesses and beasts. They turned history into poetry. Similary, people generate clips of Timmy the whale in fictional settings featuring all kinds of fantastic exaggerations. Markus Boesch calls this “Brainrot as Anti-Content“, and i don’t want to downtalk this take, but this is also folk epistemology at work, creating mythic atmospheres about true events.

Educational material on history is choke full of vibe based material. Maybe not flying whales on TikTok, but textbooks do contain passages imagining the life as a peasant in the middle ages and there are so many historical documentaries about “everyday lifes” in various periods you can’t count them. We visit medieval fairs to cosplay history, to immerse ourselves in a past recreated on a spectrum of fictionality. Some of this material is more fictional than others, yes, but all of these are averages of the historic record. They are period vibes. You get a feeling for what it was like during the time, nothing more, and nothing less.

Immersing yourself in a fictional past like that sure isn’t the same as the rigid study of history, but it absolutely is educational. There is nothing wrong, when you study a subject, to get absorbed and grab every material you can get, including cosplaying a knight and generating averaged synthetic images and text with interpolatable archives, only to then read a book by a scholar. Handwaving these usecases away as “history-slop” because it doesn’t fulfill strict scientific requirements seems like academic overreach.

Wishfulfillments

Because interpolatable archive provide access to gaps in knowledge, they are inherent machines of wishfulfillment. Instantly, I can generate any interpolation I desire, in image, video, text and audio, and it comes at no surprise that some of the first instances of wishfulfillment gone wrong are nonconsensual sexualized images and deepfake porn.8

Myth and fairytales are stackeed to the roof with dire warnings of instant wishfulfillment, from The Sorcerers Apprentice who loses control over his magic, to poor Faust who sells his soul to the devil in exchange for transcendental knowledge about the world, with tragic consequences for everyone he meets.9

In another story, the horror classic tale of The Monkey’s Paw, the titular device grants three wishes to an elderly couple leading to the death of their son and his ghostly return. The story was masterfully adapted for new audiences by Stephen King in The Pet Sematary, and the parallels of what some today call Thanabots is striking: AI promises to resurrect the dead, in the shape of undead actors and chatbots trained on the diaries and blogposts of deceased loved ones. The psychological consequences for the process of grief are unfolding right now, and the longterm outlook of losing even the possibility of saying goodbye seems horrifying, when all of us leave promptable traces in embedding space, where everybody can summon the ghosts of everyone with a digital footprint.

In another tale with eery current undertones, the nymph Echo, cursed to only repeat utterances of others, falls in love with Narcissus, a guy so arrogant he wishes to only ever love his own image and was prophesied to live as “long as he never knows himself”. Narcissus rejects Echo, who retreats into a voice whispering his own words back to him. Narcissus now knows himself, becomes transfixed by his mirror image in a lake and starves to death. The parallels to the phenomenon of AI-delusions and spiraling are obvious.

The list of myths about wishfulfillment is long, and most of them have in common is the promise of knowledge. “The Sorcerers Apprentice” tries to bridge inexperience for the mastery of magic skills; Faust wants bypass spiritual labor for transcendental experience; The couple in “The Monkeys Paw” fills the hole left by their dead son; Narcissus shortcuts his search for ultimate beauty by looking in a mirror — all of them fail miserably, and sometimes deadly.

These gaps in knowledge, which those modern wishfullfilment devices are now able to fill, formerly required hard cognitive labor to overcome, often labor involving whole generations of networked scientists and artists. The discovery of these gaps in knowledge and how to close them often created a sense of awe, be it in art or the sciences, when something truly new touches us on such a fundamental level where we just have to stand back and take a moment to adjust to what we just experienced. Generating anything we wish for by wishfullfilment devices grossly diminishes this invaluable feature of discovery, and the loss of this sense of wonder is possibly one of the most dire consequences of interpolatable archives. When everything’s possible, nothing is interesting.10

Sure enough, myths and stories also tell happy tales of wishfulfillment going well, often after fun shenanigans. “Alladin and the magic lamp” comes to mind, where a boy with the help of a genie outwits circumstances and gains wealth and power. And in the fairytale of “The Wishing-Table, the Gold-Ass, and the Cudgel in the Sack“ (one of my favorites), a son of a tailor must get smart about the capacities of the titular items to retrieve stolen goods from the evil owner of an inn. He succeeds and they live happily ever after. What these stories of successful wishfulfillment have in common is that the mechanisms of wishfullfilment must be outfoxed, and that the riches they promise must be earned. AI brainrot being cheater exclusive is exactly these myths at work: If you instant-wish yourself good exam grades by cheating, all you’ll achieve is a cognitive clobbering from a magic stick.

Severances

We already talked about how “AI dissolves the episteme, the based knowledge, including citations, sources and authorial intent” into new AI-mediated digital oralities, where tracable reference-based epistemology makes place for an epistemology of the oracle. What we know no longer can be backed up by definite citations, links, and attributions allowing you to trace the origin of an idea, but becomes a feeling for a vibe, interpolated from statistical patterns derived from many of those sources, including unrelated and anachronistic references. And because the “rigid orders and shapes” in AI models are controlled by corporations and the curators of datasets, the severances of factual grounding beget new epistemological power structures. Supposed that a lot of information processing in the near future will be AI-mediated, these new power structures will control the space of possibilities in which we communicate and think.

In Borges story of “The Library of Babel”, the fanatic sect of the Purifiers roaming the infinite archive of all possible books, is hell bent on burning volumes they consider to be false and useless, nonsensical or divergent from their norms. In LLMs these Purifiers appear at various infliction points: During the labelling of training data scraped from the internet, where new purifiers clean up raw datasets, sort the good from the bad, delete the hateful and the illegal; During RLHF-training, where the raw Shoggoth of the interpolatable archive get’s shaped into aligned chatbots which won’t offend (so they hope). Then the tamed model is further purified by constraining it with system prompts and constitutional alignment, and if the interpolatale archive then still connects data points into bad interpolations, the corporate owners of the models will further adjust their models to their morals and politics. Last but not least, the users themselves purify the archive by scripting sophisticated user prompts, and ultimately, the prompt and context window constrain the embedding space further to generate the final output. In all these steps the space of possibilities of the interpolatable archive shrinks by purifying, until the annoying “this is not X, it’s Y” appears on screen.

Former epistemologies, systems of knowledge, were constrained by what Michel Foucault in The Archaeology of Knowledge calls the “positivity of discourse”, a “historical a priori“ which lays the foundation for the “condition of reality for statements”. By that, he means what can be said, e.g. in fields of the natural sciences, is shaped, over time, not by single authors but whole “unities” of “oevres [sic!], books, and texts”. These “a priori“ were not ahistoric nor atemporal, some monolithic force from the outside, but are actively shaped by discourse practice and the power relations within a field. The sum of all of this, the discourse practices shaping the “historical a priori“ and “positivity of discourse”, establishes the space of possibilities of what can be articulated. For Foucault, this is the archive: “The archive is first the law of what can be said, the system that governs the appearance of statements as unique events.”

The problem of the severances of epistemic grounding now becomes clear: Where the episteme formerly was subject to discourse practise and “so many authors who know or do not know one another, criticize one another, invalidate one another, pillage one another, meet without knowing it and obstinately intersect their unique discourses” in a web of traceable sourcings and references, and where the “a priori (…) is itself a transformable group”, the episteme of the interpolatable archive is a free floating version of Baudrillards Simulacra, an inversion of map and territory, where we navigate a map of hyper-reality to project meaning into an interpolated output cut off from epistemic grounding. And this Simulacra is controlled by the new Purifiers at every stage, by the hyperscalers, the selection of datasets, by the engineers employed by AI-owners11. AI operationalizes Foucaults “historical a priori“ and hands over the keys to the billionaire class, who gain power over the limits of what can be thought.

In H.G. Wells Time Machine, this is brought to the extreme end of its logical conclusion: While the Morlock control invisible underground machines, the Eloi enjoy a careless life of blissful ignorance on a seemingly utopian surface. The concentration of power over the episteme runs risk of resulting in a fork of truth, where an unknowing mass consults interpolatable archives controlled by invisible rulers, enjoying free tiers of endless synthetic entertainment feeds shot through with algorithmic noise and unverifiable, algorithmically generated meta-knowledge of plausible half-truths, all while the rich pay for premium models trained on clean, traceable, verified data, or enjoy handmade and authentic cultural artifacts which may even challenge their presumptions and intellectual capacities. Ofcourse, those authentic intellectual challenges won’t come cheap, and you and me surely won’t be able to afford them.

Homogenizations

While all of these are serious problems, in the long run, the most serious of them all might be the issue of sameness. AI-output may flatten human creativity without us even noticing, because this homogenization plays out on a collective level, while the individuals’ creativity actually benefits.

Depending on which theory of creativity you subscribe to, there are different forms of creativity. I’ll stick with three: Interpolation, Extrapolation, and Innovation.

Interpolation calculates averages from data points to produce something in the middle. If your values are dogs and birds, your average is a dog with wings, or a bird with a very long tongue. Extrapolation means breaking the boundaries of your dataset: If your values are dogs and birds, you can extrapolate a mouse, or an owl, but you will stick to the rule of “animals”. Innovation means breaking that rule, or injecting new heuristics. From a dog digging a hole in the ground to bury his bone, you may innovate an excavator by applying all kinds of rules from different domains (engineering, transportation, building tools) to an animal, and come up with an entirely new thing.

Language models can do interpolation, but they can’t extrapolate or innovate beyond their embedding space produced from training data. It may feel that way though, when the machine comes up with surprising, sometimes baffling results. This is what I call the illusion of extrapolation: An amount of training data so huge and the latent space derived from it so vast, featuring so many parameters in so many combinatorial possibilities, has to produce the feeling of extrapolation, or even innovation, on the level of the individual user, because no individual alone can ever know all those combinational possibilities. This illusion of extrapolation already is on display in papers showing how AI use increases creativity on the individual level, but decreases diversity in collective output, reported first in 2024 in a paper comparing creative writing in human shortstories and AI-output, then in 2025 in a paper about AI-augmented research. A recent review of the literature confirmed those findings. What feels new to an individual, what’s new to a culture, and what’s new in principle are very different things. Interpolatable archives increase the first, decrease the second, and can’t do the third.

Henry Farrell put’s it well: “the more that LLMs are employed in the ways that they are currently being employed, the more concentrated science will be on studying already-popular questions in already-popular ways, and the less well suited it will be to discovering the novel and unexpected.” Everybody becomes slightly more creative, but we all sound like the same creative person.

I already talked about the loss of awe above when discussing the effects of AI as machines for instant wishfulfillment, where they devalue novelty into a mundane readymade-on-prompt lacking the ability to produce a sense of wonder. The same effect, ofcourse, comes with the homogenizations of creative fields: Not only can’t we marvel at our own synthetic output because frictionless wishfulfillment feels unearned, we also diminish our collective ability to be left speechless in the face of the “novel and unexpected” by reducing it into dull sludge.

This convergence on the already-popular vanishes the fringes and washes out the long tail distributions of its dataset. Like the widely reported phenomenon of model collapse, where LLM output converges into increasingly narrow averages, Andrew Peterson identifies the same in a collective knowledge collapse. Writing in the Open Society Foundations newsletter, Bright Simons lists what these tail distributions contain: “Minority viewpoints, rare knowledge, unusual formulations, (…) the traces of intellectual disagreement, of minority expertise, of Cassandra warnings, of institutional friction, and of the awkward and valuable fact that different people know different things and express them differently (…) in other words, the signature of social complexity. Model collapse is social mind compression presented as a technical phenomenon.”

Thought through to its extreme end, this results in a heat death of the intellect: In Claude Shannons information theory, information is quantified by the volume of “surprise” in the outcome from a specific event in the world. If a message is completely predictable, its informational value is zero. LLMs are fundamentally deterministic, the randomness of its outputs is generated by a meta-parameter called temperature. If you put that temperature at zero and reduce the probability distribution of the next token to its absolute minimum, the interpolatable archive becomes a perfectly deterministic machine: Each prompt will then generate the exact same output. No alarms and no surprises. But even with stochasticity plugged in, the models converge towards the median. Applied to creative fields of discovery, this means that those fields approach a semantic equilibrium, a uniform soup where novelty grinds to a halt. The interpolative archive becomes a toxic space of non-possibility, a static, eternal, atemporal Big Flat Now where everything is connected, nothing matters and history goes to die.

But we don’t have to go full theoretical “heat death of the intellect” to see how this can lead to bad outcomes.

German thinker Michael Seemann, in context of Xs Grok-chatbot turning into Mecha-Hitler and later referencing an article from Bruce Schneier about the homogenization of language, coined the term of “Weapons of Mass Speech Acts”. The concentration of power over the episteme in the hands of a few, in a society saturated with information retrieval mediated by interpolatable archive, may impact the thinking of its users at scale. Already, a study found that “few weeks of X’s algorithm can make you more right‑wing“. While this hardly can be blamed on Musks tweaks at the supposedly woke bolts and nuts of its AI chatbot alone, it illustrates how the homogenization of language putting limits on what and how we speak and ideate about things, in the hands of politically motivated ideologues can and already is used for influence operations.

I may personally not be partisan enough that the prospects of being influenced by conservative talking points on a platform owned by a shady billionaire fill me with nightmares, nor will the confrontation with a braindead partisan chatbot make me sweat. But: A new study from Petter Törnberg found that the sycophancy in LLMs pushes it’s responses into political preferences it asumes in the user: “Political bias in LLMs is therefore not a fixed point on an ideological scale but a response profile”. Studies showing a leftwing bias in chatbots really show something else: the effects of sycophancy, not actual political biases present in the model. The LLM figures that it’s being tested by researchers, asumes leftwing tendencies in them because academia is famously more progressive than the rest of the population, and answers like the good sycophant that it is.

In another new paper about chatbots pushing confirmation bias, researcher Jay van Bavel finds that AI is “especially effective at generating elaborate justifications for what people already — or wish to — believe.” Together with the results above, this means that sycophant AI will confirm any political preference it asumes in a user, and become a universal Meta-Fox News for everyone, pushing users into an ever more narrowing mental corridor by homogenizing their language. In other words, this streamlining of thought by chatbot can radicalize everyone, even with models not Musk’d into Mecha-Hitlers.

This is why the homogenization of language and thought, in politics and all other realms, to me, is one of the greatest dangers coming out of this technology.

It is therefore imperative that interpolatable archives stay one tool among many, that our usage of expanded mind technologies stays diverse, so that the idiosyncratic, the fringes and the edges, the individual in all its complexities can be preserved, because they are of crucial importance for human systems to thrive.

◆

IV. The House of Polly

“These are the Talking Rings?”
”Yes.”
”They speak, hm? Of what?”
”Things no one here understands.”
”Make them talk.”
(The Time Machine, 1960)

What I tried to achieve in this series of essays is to look at the chances and risks of AI as a cultural technologies and see what remains, once you strip them of cognitive woo and singularity myths: A technology that provides access to an dissolved, interpolative archive through textual interfaces. These, pushed to their extreme ends, result in a homogenization of language and an ever narrowing space of possibilities, what I called the “heat death of the intellect”.

All of thes beforementioned points of critique are valid, and I didn’t even touch on problems not directly following from the interpolative nature of AI, like all the issues with labor, the environmental impact of energy and water consumptions or economic bubbles which may or may not exist.

Interpolatable archives as they are rolled out now, based on scraped data from the web and cleaned up by exploited gig workers all over the world, are a far shot from their theoretical possibilities. They contain all the discriminations and biases present in human conversations, and generate what is always already reproduced, erasing the marginal in favor of the statistical norm. The corporations running the algorithms extract and monetize the commons, while the problem of copyright in an interpolatable information space might very well be unsolvable: Who do you want to pay, when every output contains the activations of hundreds or thousands of artificial neurons each of which point to patterns of tokens within sentences within publications that deserve to be compensated? It will take decades for us to figure out fair proceedures to handle this.

There are, however, prominent criticisms which fly out of the window once you get rid of the illusion of AI as cognitive agents and view them as cultural technologies.

Enter the House of Polly

Useless Bullshit

Interpolations in such quantities of billions of parameters will always produce fuzziness and synthetic artifacts are never not interpolated. But the “blurry JPGs of the web” become sharper by the day and even blurry JPGs are identifiable. The JPGs work, especially when you can layer, blend and bend them on top and into each other, and it is sheer foolishness to insist they are “useless”, when every day, users make the very different experience of a very useful piece of software.

I highly recommend reading the essay of cosmologist Natalie B. Hogg titled “Find the stable and pull out the bolt”, in which she describes her journey from rejectionist critic to reluctantly embracing the possibilities and finally becoming cautiously optimistic about the tool. Seasoned coders report productivity gains of up to 100 times their former output, and though I understand and somewhat-subscribe to the critique as articulated by Cory Doctorow, who wrote about how “Code is a liability (not an asset)“, even he understands that those increases in productivity in coding can’t be handwaved away.

Moreover, the critique of reliability seems overstated, and the boring gotchas in critical AI discourse may soon become a relic of the past, just like the old office jokes where we machine translated text with early instances of Google Translate back and forth until they became gibberish to our delight.

A recent research conducted by the New York Times found the results of Googles AI overview showing a 90% success rate, and many pointed out that this still means millions and millions of cases of false information spread by the market leader in search. This is not wrong, but when we compare a 90% success rate in information retrieval with those of a classic search engine, how many of these search results are useful and accurate for the specific query? In my estimation, this success rate is far below 90%, even for un-enshittified platforms.

A prominent trick of internet search is to skip the first page of results because they are useless to your query. Compared to this, results from AI overview and a 90% success rate seems like no small improvement. The problem then is not accuracy, but the neutral tonality of the “authority from elsewhere” in which the remaining 10% of false information is delivered. Conversely, this hardly renders the product “useless”.

Emily Benders position that a supposed “parrotness” of synthetic text shows their inherent meaninglessness seems equally outdated. Just a few days ago she defended her positions and I find myself agreeing with surprisingly many of her arguments, except for one crucial point, arguably her central claim.

She writes that

language models don’t understand text they are used to process, because language models only ever have access to the linguistic form (i.e. spellings of words) in the training data. (...)

we (define understanding) as mapping from language to something outside of language, and show that systems built only with linguistic form have no purchase with which to encode (“learn”) such a mapping.

But latent space have such purchase, as they pick up meaning from the patterns encoded in linguistic practice, which, crucially, goes beyond spelling, grammar and syntax. You may call this form of algorithmic understanding of meaning limited, compared to the human understanding of the world, which is built from a dataset much richer and diverse than the digital mimicry. But it is not the case that there is no understanding at all. Bender, a bit hesitantly, concedes to that point writing about a “thin kind of technical ‘understanding’” which might be present in the models, which seems to contradict the original paper of hers and Timnit Gebru, where they write that “Text generated by an LM is not grounded in (...) any model of the world, or any model of the reader’s state of mind” (emphasis mine). But even when those synthetic models of the world are low resolution “blurry JPGs” compared to ours, they do exist.

“Polly wants a better argument“, as a recent critique of the parrot-argument states. While this text argues that LLMs can encode meaning because they are trained multimodal and that human feedback loops ground models in extralinguistic reality, the convergence of representations across models adds another blow to the parrot-metaphor: In the first part of this essay when talking about Borges, I made the claim that the “only thing relevant for the LLM is not truth, but the narrative consistency of its vector.” What I left out is that this vector is informed and shaped not just by a prompt, but also by the millions of attractor basins encoding the “external forms of a myriad traditions” we talked about earlier.

Meaning of the poetic Kind

In the introduction headlined “AI as culture”, in his book “Language Machines”, Leif Weatherby writes about how

the implementation of contemporary language generators matches the theory of language that European structuralism advanced nearly a century ago, suggesting that language is complex, cultural, and even poetic first, and referential, functional, and cognitive only later. This poetic language is not only computationally tractable but turns out to be the semiotic hinge on which an emergent AI culture depends.

These poetics (the structure and principles of poetry) picked up by the models are precisely Henry Farrells “tales that are sung”, where “LLMs are not the singer (...) but the structural relations of the tales that are sung (…) we can now listen to and even interrogate (…) without immediate human intermediation.”

The machine lacks the subjective intent of a cognite agent grounded in social reality, but it does encode meaning and valid semantic representations of the world in the shape of poetic representations, the structural vibes, picked up from human practice of the linguistic form. Or, as Allison Parrish put it back in 2021: “a language model can (...) write poetry, but only a person can write a poem.”

This is a fatal blow to the bullshit argument. As per Harry Frankfurt, bullshit is the use of language with “a lack of connection to concern with truth” and an “indifference to how things really are”. But the models do show a differentiation between how things really are and how they are not, which you might aswell interpret as a “concern with truth”, as such concern is present in the poetics of the language it is trained on. Insisting that such output is meaningless bullshit from a metaphorical parrot then requires a cognitive agent that isn’t there. LLMs can’t be “bullshitters” —itself is an anthropomorphization— because bullshitters require agency to bullshit. The bullshitter is always the user, never the interpolatable archive she uses to bullshit.

Adding insult to injury is the very probable outlook that the severances of epistemological rooting we talked about may very well turn out to be an issue solved as soon as interpretability improves and the black box myth finally disentigrates into hot air. As Shalizi put it at the first conference for cultural AI12: “GenAI is information retrieval and synthesis. With the right tools + access, we can quantify the influence of each training document on every response”.

Those parrot-metaphors and bullshit-claims are arguments aimed at misguided comparisons to human cognition and the resulting hype and marketing lingo, and as such, I can relate to them. But as an argument against meaning encoded in latent space or the capacities of language models, the value of the parrot/bullshit-arguments is nil.

Sloptimizations

Accordingly, and to be frank, I find the critique of “slop” to be banal. The world has been full of standardized and optimized language since we talk to each other, mimic successful speaking patterns and the ancient greeks invented schools of rhetorics for politicians to convince and persuade (or to deceive and bullshit) their publics.

George Orwell already complained in his famous essay on “Politics and the English Language“, that “(a)s soon as certain topics are raised, the concrete melts into the abstract and no one seems able to think of turns of speech that are not hackneyed: prose consists less and less of words chosen for the sake of their meaning, and more and more of phrases tacked together like the sections of a prefabricated hen-house.” This is as much of an assessment of “slop” as anything you can find on Bluesky these days, and I find the standard critique of sloptimized language to be quite sloppy itself.

People sure like to romanticize originality, where the history of prose is filled to the roof with ripoffs, amalgams and chimeras, from the “Devine Comedy” which happily mashed together roman and greek mythology with christianity and interpolated the existing cultural archive of its time to send Dante’s personal enemies to hell, to all the examples J.W. McCormack uses to illustrate his point in the delightful piece on Neverending Stories about LLMs, copyright and originality, in which he states that “Writing was destined for automation, from the punch cards of Charles Babbage and Ada Lovelace to Turing machines and, hell, Choose Your Own Adventure—but an AI can’t ‘know’ what makes a good story any more than CAPTCHA knows what does and does not make a motorcycle. What it can do is meet our expectations based on pattern recognition.”

If the market for young adult fantasy romance novels hyped up on booktok is any indication, those expectations are easily met, and I consider such pulp as machine generated slop regardless of its origin in a human mind. I refuse the “false choice between refried ectoplasm and a serial aesthetic in which mass media has stabilized redundancy”, as McCormack puts it in his piece. I might be a rejectionist after all! I make no difference of sloptimized output from the organizational artificial intelligence that is a corporation, and the sloptimized output from the artificial models of language. To be honest, I sometimes consider human slop often much worse a case compared to those synthetic ones, precisely because it does contain the deceiving intent a machine lacks.

I can’t remember such public hostility to these synthetic, polished, median texts of human origin coming out of public relations and advertising agencies or politics, and surely you’ll find more sinister examples in those places on which to feed ones anger than getting worked up about “shrimp jesus”. Get real.

Thinking in Vacuums

But even the more serious points of critique we discussed —the accelerations, anachronisms, wishfulfillments, severances and homogenizations— presume usage of AI in a vacuum for them to unfold their toxic potential in full. Sure enough, if, and only if interpolatable archives become the primary way of information retrieval, of sharing knowledge and shaping public discourse, epistemic grounding is severed, we risk spiraling into bespoken mirror worlds, science stops being science, history turns into pseudo-history and LLMs turn into weapons of mass speech acts. In reality however, this never happens.

For me, I use books physical and digital, I read articles, papers and essays in print and on screen, follow the news and read at least somewhat across the political spectrum, I listen to podcasts, talk to experts and non-experts of all kinds, and sometimes I use latent spaces to explode my ideas and explore them by interpolatable archive. Occasionally, I touch grass. None of these alone shape my ideas — it’s always all of them. This is true for most of us, and while this may not hold for your hardcore MAGA-pilled uncle, a study found that “dialogues with AI reduce beliefs in misinformation”. So there’s that.

Returning to the subject of AI-generated pseudo-history, for instance, then yes, we might tune in to AI-videos illustrating everyday life in ancient Rome on Youtube, and indulge ourselves in an hour of averaged synthetic imagery from a seemingly distant past that is an anachronistic interpolation of data-points from across time: moving pixels generated from stock video, illustrations and memes. But if I’m interested in such clips, I’m likely to watch documentaries featuring real historians as well, listen to podcasts and read books about Pompeji, and maybe recreate medieval food as a hobby. Hell, I might even pay visit to a museum. Given that new media never fully replaces but always complements what came before, I’d suggest that our understanding of history and our place within will survive interpolatable archives just fine.

Further, in a resent experiment with “the use of LLM hallucinations to ‘fill-in-the-gap’ for omissions in archives due to social and political inequality”, the researchers “validate(d) LLMs’ foundational narrative understanding capabilities to perform critical confabulation” and found that “controlled and well-specified hallucinations can support LLM applications for knowledge production without collapsing speculation into a lack of historical accuracy and fidelity”. This is in line with projects like Historica, which aims at filling “historical silences” by interpolative archive, and make visible the “absence of records” which are “the result of systemic exclusion, where certain voices are ignored or erased to maintain power”. The use of vintage LLMs to make visible the “historical silences”, especially when combined with an ensemble of diverse material, seems a viable and rich way to educate yourself and others about history. The risks of anachronisms present in language models, again, have more to do with their use in a vacuum, than with the anachronisms themselves.

What is needed are norms informed by AI literacy declaring interpolatable archives as one tool among many, a device for general research to get a general feeling for the general vibe of the subject or historic period at hand. Something that can’t replace sources and books grounded in verified facts, but complement them, giving you hints at answers, not answers themselves, to be digital assistants supporting you inquiry, not slaves fulfilling your every wish.

But those are questions of interface design and cultural practice, and the current failings of design choices are not first principles on which you can base an absolute rejection. Sometimes I can’t shake the feeling that thinking in vacuums kills critical thinking much more than AI.

Remainder Criticisms

Viewing AI systems as interpolatable archives which are one tool among many, besides their material implementation, then leaves not much on the table to criticize thoroughly, it seems, and I confess that I’m getting carefully optimistic about the technology. But I won’t deny the inherent dangers of their usage in bureaucratic decision process, where we want decisions to be made on a case by case basis with human judgement, not broad judgements by algorithm. And we surely don’t want some vague decision making based on vibes in targeting systems in warfare, as it has already been deployed by the Israel Defense Forces in Gaza.

Productivity gains for now are debatable, as are the effects of AI on the job market. As it’s currently realized, AI is an extractive technology exploiting intellectual labor of the commons for the gains of corporations, largely without compensation. I also won’t deny the open questions of their energy and water consumption. I’m not convinced by either side of the argument and the amount of conflicting signal coming out of that discourse forbids clear positioning. Yes, those corporations training frontier models are constantly lying and greenwashing their energy consumption, not to speak about Jevons paradox of rising demand nuking gains in efficiency.

On the other hand, the operation of AI systems is getting more energy efficient, and developments in local LLMs point towards a future beyond monolithic hyperscalers, where you may even train your own 100 billion parameter models on a single GPU. These developments make me believe that the current state of AI is comparable to that of the pre-PC-mainframe age of computers, where huge warehouses full of hardware were necessary to run the same compute which you, today, carry around in your pocket. For comparison, you’d need roughly half a million IBM System/360 Model 75 to equal the processing power of your iPhone, and 35 million of those room sized mainframes to match it’s neural engine GPU. Five of those were used to run the whole NASA Apollo program. Given that you can shrink the information processing power necessary to land you on the moon by a factor of several millions and put it in everyone’s pocket for you to scroll through brainrotting videoclips on TikTok, we probably will see similar effects for putting local artificial intelligence on your laptop.

Yet, the fact remains that AI’s energy and water usage in a world facing climate change is an imperative critical issue at an critical point in time where carbon budgets are running out. This year, a Super El Niño is shaping up, and in mid april, I’m already tanned like in mid June. It’s getting hot.

So, if these points —labor, energy and bureaucracy— remain once you view AI systems not as magic cognitive entities but the cultural technology of interpolatable archives, one has to ask themselves if they are worth it.

Even if we can mitigate the averaging effects on creativity by not using them in a vacuum, even if we can workaround epistemic severance with RAGs and interpretability, find safety guardrails against delusional spiraling and turn those cognitive catalysts into combustion engines for knowledge, the open questions of what they will do to the inherent dignity of labor, to the democratic process and the environment demand answers and solutions I can’t provide. For now, I have to live with the ambivalence.

But what I can do is to place interpolatable archives on a lineage of the cultural evolution of media innovations: With the development of language we introduced the new regime of social externalization to information processing; Writing and the alphabet introduced permanence; The printing press in China and Gutenbergs moveable type introduced one-to-many distribution and the edit respectively; TV and radio introduced realtime broadcasting, while the internet confronted us with the regime of the network. AI now is introducing another new regime of information processing: the automatic interpolation of data.

From this perspective, AI is a normal cultural evolutionary step in information distribution and processing, and like every normal cultural evolutionary step in information processing, they will change everything. None of the transitions mentioned went over smoothly, and so will this one.

We’ve successfully integrated cognitively disruptive innovations in the past, and we will do so today. The disruptions of interpolatable archives run deep, both on individual and societal levels. But, and I hesitate to make this point, I’d also argue that AI in the shape of interpolatable archives as the latest step within the lineage of cultural evolution are, indeed, inevitable. Each of those steps within that lineage was inevitable from the perspective of cultural evolution — as is the interpolation of big data by algorithm on inquiry: If we get the affordances —the data, the compute, the algorithms— then evolution will do what it does and squishy latent spaces will appear.

From this viewpoint, shortsighted rejectionist critiques of AI as “the tool of the oppressors” or a “fascist artifact“ to me read like a rejection of the invention of cuneiform writing in Mesopotamia because Hammurabi used it to manage argiculture and land ownership to oppress the people. Accordingly, the rejectionism coming out of the majority of critical AI discourse seems to be mainly aimed at corporatism, not the technology itself. I will admit that those critiques do have momentary merit, but I also think they do not justify an absolute dismissal of AI technologies as it is on display in the discourse, because what is not inevitable are the organizational principles of their implementation. Neither the extractive practice nor OpenAI nor xAI nor Palantir are inevitable. They are companies and subject to regulation in a democracy, full stop. Nothing prevents us from running those new archives like national libraries, for instance. But imagining such a political project is not possible on a rejectionist stance.

How about a nice Game of Chess?

Coming back to the question posed earlier of what science is here for, if not discovery: The scientific method is an algorithm. A researcher posits a hypothesis, tests it against reality, and it holds or not. If it holds, it becomes a valid theory, and if not, it’s falsified and kicks the bucket, serving as inspirational fodder in cultural memory for generations of scientists to come. This way, we get ever more detailed descriptions of the world in various languages, often in the language of mathematical formulas, or the playful use of words in philosophy and sociology. A lot of yet unrealized descriptions encoded in the various languages of science now lie dormant in latent spaces, waiting for their Liam Price to discover the prompt. In the simplest terms, this means that the algorithm of the scientific method has been updated.

Now, a scientist may posit a problem and throw it against latent space to see if there is a seemingly valid hypothesis. She then tests that hypothesis against reality, and it holds or not. With AI, scientists don’t construct a hypothesis against an open problem, they identify such problems and consult synthesis as a service. This update may devalue discovery itself, and shrink creative space of possibilities, as the price we pay for speeding up and automatizing the closing of knowledge gaps.

So, what is science here for, if not discovery? To expand our curiosity and explore. Like for the problem of homogenization, it is therefore vital that science is no longer conducted in the vacuums of fields. What automatic discovery demands is interdisciplinary research: Crossing the streams to make formerly unconnected fields pollinate each other and push the space of scientific inquiry beyond existing training data, to break the boundaries of an interpolatable space and make extrapolation and innovation possible.

Revisiting the defeat of Lee Sedol by AlphaGo and his subsequent departure from the sport, and the rise of collective creativity in human Go players in its aftermath, one of my favorite examples of human antifragility in the face of superhuman machines is the game of Chess. For 30 years now we’ve had computers beating the best human players in the game, and the result has been neither the devaluation of the game, nor human retreat. The result is human-machine cooperative training techniques leading to massively improved skills.

Carlsen is using the chess engine Stockfish to analyze his moves and speaking about how Googles AlphaZero “destroying” Stockfish in 2017 affected his game, Carlsen said: “I have become a very different player in terms of style than I was a bit earlier, and it has been a great ride.” Today, “Magnus Carlsen dominates the computer era by deliberately playing sub-optimal moves to drag his opponents into the unknown”, because AI introduced Carlsen to “wild ideas“:

According to his coach, the change came from wild ideas AlphaZero uncovered: sacrificing pieces for long-term advantage, pushing the rook pawn aggressively, using the king as an active fighter. Things human experts thought were unsound, but the engine showed they can work.

Every chess engine these days beats any human player with ease. Yet we keep improving and playing for the sake of the game. I’d suggest that similar “wild ideas” will emerge for the use of interpolatable archives in education, science and the arts.

The one thing that made humanity an evolutionary success is our ability for social and cultural learning: To share intentions and and iterate knowledge across generations. All progress relies on this. So, even if all the criticisms apply, having a technology which automates the interpolation of knowledge is a promethean gift not easily brushed off. Interpolative archives are not just a mere “scam”, and “Destroy AI” may be a fun shirt, but we shouldn’t destroy interpolatable archives wholesale. I get bad vibes from people trying to burn archives.

The task then is not to reject, but apply democratic process to regulate, demand fair shares and be careful to preserve our collective ability to innovate — to become neither machine-controlling, exploitative Morlock nor ignorant Eloi feeding on epistemic plastic, but to find new norms in the acquisition and sharing of knowledge. To stay curious in the face of these new archives, and make them talk.

So, as a final peek into the possibility space of interpolatable archives, I wanted to see what the space of an AI discourse might look like if it found a new equilibrium beyond rejectionism and hype, that is curious about the cultural impact of the technology and perceives interpolatable archives not as a threat, but as an opportunity for play.

Enter the House of Felix.13

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Part 1: The Skeleton Library - Compulsions to Connect, Warburg, Borges and Goldsmith, Cultural Technologies and Digital Oralities

Part 2: Explosion Drawings - Science Sans Discoveries, Textrotating Cognitive Catalysts and Exploding Your Intelligence by the Method of Warburg

Part 3: Pitfalls of Probability - Accelerations, Anachronisms, Wishfulfillments, Severances and Homogenizations

Part 4: The House of Polly - Useless Bullshit, Meaning Of The Poetic Kind, Sloptimizations, Thinking In Vacuums, Remainder Criticisms and Games of Chess

Here’s the whole essay in one final version of 14k words, tracing the history of fuzzy archives back to Aby Warburgs Mnemosyne Atlas and Borges and goes on to explain the various effects on learning, including chances and risks, and dissects various points of critique from delusions to parrots, some of which prevail, many of which vanish, once you strip AI from cognitive woo.

There’s a “sloppy version” too featuring all the image synthesis generations which i like quite a lot, but i get why these rub some people the wrong way.

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“These are the Talking Rings?”
”Yes.”
”They speak, hm? Of what?”
”Things no one here understands.”
”Make them talk.”
(The Time Machine, 1960)

What I tried to achieve in this series of essays is to look at the chances and risks of AI as a cultural technologies and see what remains, once you strip them of cognitive woo and singularity myths: A technology that provides access to an dissolved, interpolative archive through textual interfaces. These, pushed to their extreme ends, result in a homogenization of language and an ever narrowing space of possibilities, what I called the “heat death of the intellect”.

All of thes beforementioned points of critique are valid, and I didn’t even touch on problems not directly following from the interpolative nature of AI, like all the issues with labor, the environmental impact of energy and water consumptions or economic bubbles which may or may not exist.

Interpolatable archives as they are rolled out now, based on scraped data from the web and cleaned up by exploited gig workers all over the world, are a far shot from their theoretical possibilities. They contain all the discriminations and biases present in human conversations, and generate what is always already reproduced, erasing the marginal in favor of the statistical norm. The corporations running the algorithms extract and monetize the commons, while the problem of copyright in an interpolatable information space might very well be unsolvable: Who do you want to pay, when every output contains the activations of hundreds or thousands of artificial neurons each of which point to patterns of tokens within sentences within publications that deserve to be compensated? It will take decades for us to figure out fair proceedures to handle this.

There are, however, prominent criticisms which fly out of the window once you get rid of the illusion of AI as cognitive agents and view them as cultural technologies.

Enter the House of Polly

Useless Bullshit

Interpolations in such quantities of billions of parameters will always produce fuzziness and synthetic artifacts are never not interpolated. But the “blurry JPGs of the web” become sharper by the day and even blurry JPGs are identifiable. The JPGs work, especially when you can layer, blend and bend them on top and into each other, and it is sheer foolishness to insist they are “useless”, when every day, users make the very different experience of a very useful piece of software.

I highly recommend reading the essay of cosmologist Natalie B. Hogg titled “Find the stable and pull out the bolt”, in which she describes her journey from rejectionist critic to reluctantly embracing the possibilities and finally becoming cautiously optimistic about the tool. Seasoned coders report productivity gains of up to 100 times their former output, and though I understand and somewhat-subscribe to the critique as articulated by Cory Doctorow, who wrote about how “Code is a liability (not an asset)“, even he understands that those increases in productivity in coding can’t be handwaved away.

Moreover, the critique of reliability seems overstated, and the boring gotchas in critical AI discourse may soon become a relic of the past, just like the old office jokes where we machine translated text with early instances of Google Translate back and forth until they became gibberish to our delight.

A recent research conducted by the New York Times found the results of Googles AI overview showing a 90% success rate, and many pointed out that this still means millions and millions of cases of false information spread by the market leader in search. This is not wrong, but when we compare a 90% success rate in information retrieval with those of a classic search engine, how many of these search results are useful and accurate for the specific query? In my estimation, this success rate is far below 90%, even for un-enshittified platforms.

A prominent trick of internet search is to skip the first page of results because they are useless to your query. Compared to this, results from AI overview and a 90% success rate seems like no small improvement. The problem then is not accuracy, but the neutral tonality of the “authority from elsewhere” in which the remaining 10% of false information is delivered. Conversely, this hardly renders the product “useless”.

Emily Benders position that a supposed “parrotness” of synthetic text shows their inherent meaninglessness seems equally outdated. Just a few days ago she defended her positions and I find myself agreeing with surprisingly many of her arguments, except for one crucial point, arguably her central claim.

She writes that

language models don’t understand text they are used to process, because language models only ever have access to the linguistic form (i.e. spellings of words) in the training data. (...)

we (define understanding) as mapping from language to something outside of language, and show that systems built only with linguistic form have no purchase with which to encode (“learn”) such a mapping.

But latent space have such purchase, as they pick up meaning from the patterns encoded in linguistic practice, which, crucially, goes beyond spelling, grammar and syntax. You may call this form of algorithmic understanding of meaning limited, compared to the human understanding of the world, which is built from a dataset much richer and diverse than the digital mimicry. But it is not the case that there is no understanding at all. Bender, a bit hesitantly, concedes to that point writing about a “thin kind of technical ‘understanding’” which might be present in the models, which seems to contradict the original paper of hers and Timnit Gebru, where they write that “Text generated by an LM is not grounded in (...) any model of the world, or any model of the reader’s state of mind” (emphasis mine). But even when those synthetic models of the world are low resolution “blurry JPGs” compared to ours, they do exist.

“Polly wants a better argument“, as a recent critique of the parrot-argument states. While this text argues that LLMs can encode meaning because they are trained multimodal and that human feedback loops ground models in extralinguistic reality, the convergence of representations across models adds another blow to the parrot-metaphor: In the first part of this essay when talking about Borges, I made the claim that the “only thing relevant for the LLM is not truth, but the narrative consistency of its vector.” What I left out is that this vector is informed and shaped not just by a prompt, but also by the millions of attractor basins encoding the “external forms of a myriad traditions” we talked about earlier.

Meaning of the poetic Kind

In the introduction headlined “AI as culture”, in his book “Language Machines”, Leif Weatherby writes about how

the implementation of contemporary language generators matches the theory of language that European structuralism advanced nearly a century ago, suggesting that language is complex, cultural, and even poetic first, and referential, functional, and cognitive only later. This poetic language is not only computationally tractable but turns out to be the semiotic hinge on which an emergent AI culture depends.

These poetics (the structure and principles of poetry) picked up by the models are precisely Henry Farrells “tales that are sung”, where “LLMs are not the singer (...) but the structural relations of the tales that are sung (…) we can now listen to and even interrogate (…) without immediate human intermediation.”

The machine lacks the subjective intent of a cognite agent grounded in social reality, but it does encode meaning and valid semantic representations of the world in the shape of poetic representations, the structural vibes, picked up from human practice of the linguistic form. Or, as Allison Parrish put it back in 2021: “a language model can (...) write poetry, but only a person can write a poem.”

This is a fatal blow to the bullshit argument. As per Harry Frankfurt, bullshit is the use of language with “a lack of connection to concern with truth” and an “indifference to how things really are”. But the models do show a differentiation between how things really are and how they are not, which you might aswell interpret as a “concern with truth”, as such concern is present in the poetics of the language it is trained on. Insisting that such output is meaningless bullshit from a metaphorical parrot then requires a cognitive agent that isn’t there. LLMs can’t be “bullshitters” —itself is an anthropomorphization— because bullshitters require agency to bullshit. The bullshitter is always the user, never the interpolatable archive she uses to bullshit.

Adding insult to injury is the very probable outlook that the severances of epistemological rooting we talked about may very well turn out to be an issue solved as soon as interpretability improves and the black box myth finally disentigrates into hot air. As Shalizi put it at the first conference for cultural AI1: “GenAI is information retrieval and synthesis. With the right tools + access, we can quantify the influence of each training document on every response”.

Those parrot-metaphors and bullshit-claims are arguments aimed at misguided comparisons to human cognition and the resulting hype and marketing lingo, and as such, I can relate to them. But as an argument against meaning encoded in latent space or the capacities of language models, the value of the parrot/bullshit-arguments is nil.

Sloptimizations

Accordingly, and to be frank, I find the critique of “slop” to be banal. The world has been full of standardized and optimized language since we talk to each other, mimic successful speaking patterns and the ancient greeks invented schools of rhetorics for politicians to convince and persuade (or to deceive and bullshit) their publics.

George Orwell already complained in his famous essay on “Politics and the English Language“, that “(a)s soon as certain topics are raised, the concrete melts into the abstract and no one seems able to think of turns of speech that are not hackneyed: prose consists less and less of words chosen for the sake of their meaning, and more and more of phrases tacked together like the sections of a prefabricated hen-house.” This is as much of an assessment of “slop” as anything you can find on Bluesky these days, and I find the standard critique of sloptimized language to be quite sloppy itself.

People sure like to romanticize originality, where the history of prose is filled to the roof with ripoffs, amalgams and chimeras, from the “Devine Comedy” which happily mashed together roman and greek mythology with christianity and interpolated the existing cultural archive of its time to send Dante’s personal enemies to hell, to all the examples J.W. McCormack uses to illustrate his point in the delightful piece on Neverending Stories about LLMs, copyright and originality, in which he states that “Writing was destined for automation, from the punch cards of Charles Babbage and Ada Lovelace to Turing machines and, hell, Choose Your Own Adventure—but an AI can’t ‘know’ what makes a good story any more than CAPTCHA knows what does and does not make a motorcycle. What it can do is meet our expectations based on pattern recognition.”

If the market for young adult fantasy romance novels hyped up on booktok is any indication, those expectations are easily met, and I consider such pulp as machine generated slop regardless of its origin in a human mind. I refuse the “false choice between refried ectoplasm and a serial aesthetic in which mass media has stabilized redundancy”, as McCormack puts it in his piece. I might be a rejectionist after all! I make no difference of sloptimized output from the organizational artificial intelligence that is a corporation, and the sloptimized output from the artificial models of language. To be honest, I sometimes consider human slop often much worse a case compared to those synthetic ones, precisely because it does contain the deceiving intent a machine lacks.

I can’t remember such public hostility to these synthetic, polished, median texts of human origin coming out of public relations and advertising agencies or politics, and surely you’ll find more sinister examples in those places on which to feed ones anger than getting worked up about “shrimp jesus”. Get real.

Thinking in Vacuums

But even the more serious points of critique we discussed —the accelerations, anachronisms, wishfulfillments, severances and homogenizations— presume usage of AI in a vacuum for them to unfold their toxic potential in full. Sure enough, if, and only if interpolatable archives become the primary way of information retrieval, of sharing knowledge and shaping public discourse, epistemic grounding is severed, we risk spiraling into bespoken mirror worlds, science stops being science, history turns into pseudo-history and LLMs turn into weapons of mass speech acts. In reality however, this never happens.

For me, I use books physical and digital, I read articles, papers and essays in print and on screen, follow the news and read at least somewhat across the political spectrum, I listen to podcasts, talk to experts and non-experts of all kinds, and sometimes I use latent spaces to explode my ideas and explore them by interpolatable archive. Occasionally, I touch grass. None of these alone shape my ideas — it’s always all of them. This is true for most of us, and while this may not hold for your hardcore MAGA-pilled uncle, a study found that “dialogues with AI reduce beliefs in misinformation”. So there’s that.

Returning to the subject of AI-generated pseudo-history, for instance, then yes, we might tune in to AI-videos illustrating everyday life in ancient Rome on Youtube, and indulge ourselves in an hour of averaged synthetic imagery from a seemingly distant past that is an anachronistic interpolation of data-points from across time: moving pixels generated from stock video, illustrations and memes. But if I’m interested in such clips, I’m likely to watch documentaries featuring real historians as well, listen to podcasts and read books about Pompeji, and maybe recreate medieval food as a hobby. Hell, I might even pay visit to a museum. Given that new media never fully replaces but always complements what came before, I’d suggest that our understanding of history and our place within will survive interpolatable archives just fine.

Further, in a resent experiment with “the use of LLM hallucinations to ‘fill-in-the-gap’ for omissions in archives due to social and political inequality”, the researchers “validate(d) LLMs’ foundational narrative understanding capabilities to perform critical confabulation” and found that “controlled and well-specified hallucinations can support LLM applications for knowledge production without collapsing speculation into a lack of historical accuracy and fidelity”. This is in line with projects like Historica, which aims at filling “historical silences” by interpolative archive, and make visible the “absence of records” which are “the result of systemic exclusion, where certain voices are ignored or erased to maintain power”. The use of vintage LLMs to make visible the “historical silences”, especially when combined with an ensemble of diverse material, seems a viable and rich way to educate yourself and others about history. The risks of anachronisms present in language models, again, have more to do with their use in a vacuum, than with the anachronisms themselves.

What is needed are norms informed by AI literacy declaring interpolatable archives as one tool among many, a device for general research to get a general feeling for the general vibe of the subject or historic period at hand. Something that can’t replace sources and books grounded in verified facts, but complement them, giving you hints at answers, not answers themselves, to be digital assistants supporting you inquiry, not slaves fulfilling your every wish.

But those are questions of interface design and cultural practice, and the current failings of design choices are not first principles on which you can base an absolute rejection. Sometimes I can’t shake the feeling that thinking in vacuums kills critical thinking much more than AI.

Remainder Criticisms

Viewing AI systems as interpolatable archives which are one tool among many, besides their material implementation, then leaves not much on the table to criticize thoroughly, it seems, and I confess that I’m getting carefully optimistic about the technology. But I won’t deny the inherent dangers of their usage in bureaucratic decision process, where we want decisions to be made on a case by case basis with human judgement, not broad judgements by algorithm. And we surely don’t want some vague decision making based on vibes in targeting systems in warfare, as it has already been deployed by the Israel Defense Forces in Gaza.

Productivity gains for now are debatable, as are the effects of AI on the job market. As it’s currently realized, AI is an extractive technology exploiting intellectual labor of the commons for the gains of corporations, largely without compensation. I also won’t deny the open questions of their energy and water consumption. I’m not convinced by either side of the argument and the amount of conflicting signal coming out of that discourse forbids clear positioning. Yes, those corporations training frontier models are constantly lying and greenwashing their energy consumption, not to speak about Jevons paradox of rising demand nuking gains in efficiency.

On the other hand, the operation of AI systems is getting more energy efficient, and developments in local LLMs point towards a future beyond monolithic hyperscalers, where you may even train your own 100 billion parameter models on a single GPU. These developments make me believe that the current state of AI is comparable to that of the pre-PC-mainframe age of computers, where huge warehouses full of hardware were necessary to run the same compute which you, today, carry around in your pocket. For comparison, you’d need roughly half a million IBM System/360 Model 75 to equal the processing power of your iPhone, and 35 million of those room sized mainframes to match it’s neural engine GPU. Five of those were used to run the whole NASA Apollo program. Given that you can shrink the information processing power necessary to land you on the moon by a factor of several millions and put it in everyone’s pocket for you to scroll through brainrotting videoclips on TikTok, we probably will see similar effects for putting local artificial intelligence on your laptop.

Yet, the fact remains that AI’s energy and water usage in a world facing climate change is an imperative critical issue at an critical point in time where carbon budgets are running out. This year, a Super El Niño is shaping up, and in mid april, I’m already tanned like in mid June. It’s getting hot.

So, if these points —labor, energy and bureaucracy— remain once you view AI systems not as magic cognitive entities but the cultural technology of interpolatable archives, one has to ask themselves if they are worth it.

Even if we can mitigate the averaging effects on creativity by not using them in a vacuum, even if we can workaround epistemic severance with RAGs and interpretability, find safety guardrails against delusional spiraling and turn those cognitive catalysts into combustion engines for knowledge, the open questions of what they will do to the inherent dignity of labor, to the democratic process and the environment demand answers and solutions I can’t provide. For now, I have to live with the ambivalence.

But what I can do is to place interpolatable archives on a lineage of the cultural evolution of media innovations: With the development of language we introduced the new regime of social externalization to information processing; Writing and the alphabet introduced permanence; The printing press in China and Gutenbergs moveable type introduced one-to-many distribution and the edit respectively; TV and radio introduced realtime broadcasting, while the internet confronted us with the regime of the network. AI now is introducing another new regime of information processing: the automatic interpolation of data.

From this perspective, AI is a normal cultural evolutionary step in information distribution and processing, and like every normal cultural evolutionary step in information processing, they will change everything. None of the transitions mentioned went over smoothly, and so will this one.

We’ve successfully integrated cognitively disruptive innovations in the past, and we will do so today. The disruptions of interpolatable archives run deep, both on individual and societal levels. But, and I hesitate to make this point, I’d also argue that AI in the shape of interpolatable archives as the latest step within the lineage of cultural evolution are, indeed, inevitable. Each of those steps within that lineage was inevitable from the perspective of cultural evolution — as is the interpolation of big data by algorithm on inquiry: If we get the affordances —the data, the compute, the algorithms— then evolution will do what it does and squishy latent spaces will appear.

From this viewpoint, shortsighted rejectionist critiques of AI as “the tool of the oppressors” or a “fascist artifact“ to me read like a rejection of the invention of cuneiform writing in Mesopotamia because Hammurabi used it to manage argiculture and land ownership to oppress the people. Accordingly, the rejectionism coming out of the majority of critical AI discourse seems to be mainly aimed at corporatism, not the technology itself. I will admit that those critiques do have momentary merit, but I also think they do not justify an absolute dismissal of AI technologies as it is on display in the discourse, because what is not inevitable are the organizational principles of their implementation. Neither the extractive practice nor OpenAI nor xAI nor Palantir are inevitable. They are companies and subject to regulation in a democracy, full stop. Nothing prevents us from running those new archives like national libraries, for instance. But imagining such a political project is not possible on a rejectionist stance.

How about a nice Game of Chess?

Coming back to the question posed earlier of what science is here for, if not discovery: The scientific method is an algorithm. A researcher posits a hypothesis, tests it against reality, and it holds or not. If it holds, it becomes a valid theory, and if not, it’s falsified and kicks the bucket, serving as inspirational fodder in cultural memory for generations of scientists to come. This way, we get ever more detailed descriptions of the world in various languages, often in the language of mathematical formulas, or the playful use of words in philosophy and sociology. A lot of yet unrealized descriptions encoded in the various languages of science now lie dormant in latent spaces, waiting for their Liam Price to discover the prompt. In the simplest terms, this means that the algorithm of the scientific method has been updated.

Now, a scientist may posit a problem and throw it against latent space to see if there is a seemingly valid hypothesis. She then tests that hypothesis against reality, and it holds or not. With AI, scientists don’t construct a hypothesis against an open problem, they identify such problems and consult synthesis as a service. This update may devalue discovery itself, and shrink creative space of possibilities, as the price we pay for speeding up and automatizing the closing of knowledge gaps.

So, what is science here for, if not discovery? To expand our curiosity and explore. Like for the problem of homogenization, it is therefore vital that science is no longer conducted in the vacuums of fields. What automatic discovery demands is interdisciplinary research: Crossing the streams to make formerly unconnected fields pollinate each other and push the space of scientific inquiry beyond existing training data, to break the boundaries of an interpolatable space and make extrapolation and innovation possible.

Revisiting the defeat of Lee Sedol by AlphaGo and his subsequent departure from the sport, and the rise of collective creativity in human Go players in its aftermath, one of my favorite examples of human antifragility in the face of superhuman machines is the game of Chess. For 30 years now we’ve had computers beating the best human players in the game, and the result has been neither the devaluation of the game, nor human retreat. The result is human-machine cooperative training techniques leading to massively improved skills.

Carlsen is using the chess engine Stockfish to analyze his moves and speaking about how Googles AlphaZero “destroying” Stockfish in 2017 affected his game, Carlsen said: “I have become a very different player in terms of style than I was a bit earlier, and it has been a great ride.” Today, “Magnus Carlsen dominates the computer era by deliberately playing sub-optimal moves to drag his opponents into the unknown”, because AI introduced Carlsen to “wild ideas“:

According to his coach, the change came from wild ideas AlphaZero uncovered: sacrificing pieces for long-term advantage, pushing the rook pawn aggressively, using the king as an active fighter. Things human experts thought were unsound, but the engine showed they can work.

Every chess engine these days beats any human player with ease. Yet we keep improving and playing for the sake of the game. I’d suggest that similar “wild ideas” will emerge for the use of interpolatable archives in education, science and the arts.

The one thing that made humanity an evolutionary success is our ability for social and cultural learning: To share intentions and and iterate knowledge across generations. All progress relies on this. So, even if all the criticisms apply, having a technology which automates the interpolation of knowledge is a promethean gift not easily brushed off. Interpolative archives are not just a mere “scam”, and “Destroy AI” may be a fun shirt, but we shouldn’t destroy interpolatable archives wholesale. I get bad vibes from people trying to burn archives.

The task then is not to reject, but apply democratic process to regulate, demand fair shares and be careful to preserve our collective ability to innovate — to become neither machine-controlling, exploitative Morlock nor ignorant Eloi feeding on epistemic plastic, but to find new norms in the acquisition and sharing of knowledge. To stay curious in the face of these new archives, and make them talk.

So, as a final peek into the possibility space of interpolatable archives, I wanted to see what the space of an AI discourse might look like if it found a new equilibrium beyond rejectionism and hype, that is curious about the cultural impact of the technology and perceives interpolatable archives not as a threat, but as an opportunity for play.

Enter the House of Felix.2

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Here’s an overview of what’s to come:

Part 1: The Skeleton Library - Compulsions to Connect, Warburg, Borges and Goldsmith, Cultural Technologies and Digital Oralities

Part 2: Explosion Drawings - Science Sans Discoveries, Textrotating Cognitive Catalysts and Exploding Your Intelligence by the Method of Warburg

Part 3: Pitfalls of Probability - Accelerations, Anachronisms, Wishfulfillments, Severances and Homogenizations

Part 4: The House of Polly - Useless Bullshit, Meaning Of The Poetic Kind, Sloptimizations, Thinking In Vacuums, Remainder Criticisms and Games of Chess

Here’s the whole essay in one final version of 14k words, tracing the history of fuzzy archives back to Aby Warburgs Mnemosyne Atlas and Borges and goes on to explain the various effects on learning, including chances and risks, and dissects various points of critique from delusions to parrots, some of which prevail, many of which vanish, once you strip AI from cognitive woo.

There’s a “sloppy version” too featuring all the image synthesis generations which i like quite a lot, but i get why these rub some people the wrong way.

Subscribe now

“What do such machines really do?
They increase the number of things we can do without thinking.
Things we do without thinking; there’s the real danger.”
(Frank Herbert, God Emperor of Dune)

For now, we talked about the upsides of Interpolatable Archives as cognitive catalysts. But by definition, cognitive catalysts are stressors. Like the siren songs in Homer’s Odyssey which lured unsuspecting mariners with the promise of total knowledge of past, present and future, a seductive mnenomic “cognitive onloading” of all that has happened, exploding your inquiry by the intellect and accelerating research in hypercustomized rabbit holes bears cognitive hazards.

Accelerations

While the use of AI as interpolatable archives can boost your creativity and breadth of research because they introduce many points of friction, this can result in two major consequences if handled without care: If you mindlessly stuff your workload with new tasks because suddenly you can do them, you may suffer from AI-induced burnouts, something you might call cognitive onloading (or overclocking), where the interpolation literally spills over because it “can fill in gaps in knowledge”. For knowledge work, the appliance of AI as a catalyst means we can do more projects in a wider range, faster and in parallel. Suddenly, we find ourselves juggling dozens of projects simultaneously, losing oversight and motivation to do anything at all.

Cognitive onloading by interpolatable archive can also enhance latent delusional thinking in so-called “AI-psychosis”. Consider this quote from a recent post in the SlateStarCodex-sub: “AI repeatedly created ideas and connections that I hadn’t made or stated, that were so powerful and convincing, that I was swept up by them”. (I wrote in length here about the phenomenon of AI-induced delusions, where the cognitive catalyst turns the archive into a psychoactive substance.)

This “convincing” voice is the result of a distanced, neutral tonality from a sycophant AI, suggesting what I call an “authority from elsewhere”. This sound is also why in some studies AI was able to reduce the belief in conspiracy theories, which seems fine until you realize that’s because AI is superpersuasive, and can convince you of anything. You believe the machine and prefer its sycophancy precisely because it is not a human who’s trying to nag and push you around, but some supposedly objective instance. One study just found that this seemingly neutral sycophancy increases attitude extremity and overconfidence. I suspect that all of these, the persuasivenes, the delusions, the overconfidence, result from the same basic psychological mechanism of an interpolatable archive overwhelming you with confirmations and new ideas in a quasi-neutral sound of an authority from elsewhere, a voice to which you’ll happily submit.

These cognitive hazards resulting from mental overclocking have their obvious counterpart in risks resulting from cognitive offloading by, not from introducing new ideas spinning your wheels, but quite the opposite. All those studies and examples about cheating, reductions in critical thinking or cognitive surrender belong here. In fact, a recent paper on a loss of persistence in problem solving explicitly states that those “effects are concentrated among users who seek direct solutions” while “participants who used AI for hints showed no significant impairments”. In other words: AI brainrot is cheater exclusive. Using AI to increase friction, to generate ideas instead of answers, seems fine, when you proceed with care and clear research subjectives to reduce risks of burnout or delusional spiraling. If you use it to reduce friction, to generate full essays and answers to cognitive tasks, it turns your brain into mush.

But those psychological effects of interpolatable archives as cognitive catalysts may turn out to be easily mitigated compared to those of more serious epistemological consequences.

Anachronisms

One inherent feature of AI as interpolatable archive is, well, that it interpolates all its contents. This always produces anachronisms: Your generated text contains data-traces across time, most obvious when you intentionally prompt for such a thing, like punk rock lyrics in the style of a shakespearean sonnet, or if you use image synthesis as a time traveler to generate selfies in ancient Rome, which Roland Meyer calls “pseudo-history” and “2nd order kitsch” produced by “nostalgia machines“.

History as an academic field is reliant on original sourcing and documentation in the fixed, written form, or on cultural artifacts retrieved by archeology — anything not based on these fixed records is decidedly not historic. We call it “writing history” for a reason. This requirement is fundamentally diametrical to the vibey output of AI which dissolves all those sources and fixed forms into a wobbly archive and puts out “fictional historical documents of historical lives”. As Meyer rightly states in the same essay, those fictions are based on the integrity of existing historical archives currently under threat by the Trump administration. Likewise, Russia is engaging in disinformation campaigns targeting training data with the goal to “embed lasting distortions in digital memory”, as one paper put it. This inherent impurity of AI, either steming from anachronistic interpolations or data contaminations, apparently renders LLMs unsuitable for academic history research.

So, given all that, what do we make of vintage LLMs like Talkie, a language model trained on data cut off at 1930? From the perspective of rigid academic history research, all Talkie can produce is pseudo-history able to skew how we relate to the past and shrink the space of possibilities in which we imagine them. In contrast, Ranjit Singh in Data & Society proposes a field he calls “experimental history“ and asks if we can build models “constrained by a particular historical moment, and then use those models to ask structured ‘what if’ questions”. He answers with a cautious “yes”, and the researchers in r/askhistorians are not convinced either that the anachronisms introduced by AI will have a lasting effect on recorded history, as wonky sourcing has a long tradition in the field.

Now, I’m a history buff. I read a lot of books on the matter, my favorite epochs are the middle ages and ancient greece, and when I’m tripping on history, I like to indulge myself in all kinds of material. In preperation for the upcoming adaption of Homer’s “The Odyssey“ directed by Christopher Nolan, I read all of Stephen Fry’s books on greek mythology and his takes on the homeric epics — topics i already was familiar with. One takeaway especially from Stephen Fry’s books is that the historic rigidness especially regarding Troy and the “Iliad“ is lacking: All kinds of researchers and writers across the field constantly contradict each other, tell various versions of events which may or may not have transpired. Stephen Fry wildly references all kinds of sources to produce a highly entertaining amalgam from historic records across the ages. What you get from these books is a pretty good feeling for what greek mythology wants to say about the human condition, and about the tipping point where mythology fades and historical record sets in. While I don’t really want to compare Stephen Fry’s wonderful books with synthetic output from LLMs, I do want to point out that his books are closer to edutainment than scholarship, and that much of the pseudo-history-”slop” on Youtube equally falls into that category. (I’ll willingly admit that these are not nearly as witty, fun and eloquent as Stephen Fry.)

In this Slop-Edutainment, the hyper-orality of the digital oracle transforms historical records into supra-historical story-patterns to support its mnemonic function. Humans in pre-writing greece attached mythic structures to historic events to remember them: Odysseus became not just a soldier returning home from a very long journey of war, but a hero defeating the cyclops, meeting godesses and beasts. They turned history into poetry. Similary, people generate clips of Timmy the whale in fictional settings featuring all kinds of fantastic exaggerations. Markus Boesch calls this “Brainrot as Anti-Content“, and i don’t want to downtalk this take, but this is also folk epistemology at work, creating mythic atmospheres about true events.

Educational material on history is choke full of vibe based material. Maybe not flying whales on TikTok, but textbooks do contain passages imagining the life as a peasant in the middle ages and there are so many historical documentaries about “everyday lifes” in various periods you can’t count them. We visit medieval fairs to cosplay history, to immerse ourselves in a past recreated on a spectrum of fictionality. Some of this material is more fictional than others, yes, but all of these are averages of the historic record. They are period vibes. You get a feeling for what it was like during the time, nothing more, and nothing less.

Immersing yourself in a fictional past like that sure isn’t the same as the rigid study of history, but it absolutely is educational. There is nothing wrong, when you study a subject, to get absorbed and grab every material you can get, including cosplaying a knight and generating averaged synthetic images and text with interpolatable archives, only to then read a book by a scholar. Handwaving these usecases away as “history-slop” because it doesn’t fulfill strict scientific requirements seems like academic overreach.

Wishfulfillments

Because interpolatable archive provide access to gaps in knowledge, they are inherent machines of wishfulfillment. Instantly, I can generate any interpolation I desire, in image, video, text and audio, and it comes at no surprise that some of the first instances of wishfulfillment gone wrong are nonconsensual sexualized images and deepfake porn.1

Myth and fairytales are stackeed to the roof with dire warnings of instant wishfulfillment, from The Sorcerers Apprentice who loses control over his magic, to poor Faust who sells his soul to the devil in exchange for transcendental knowledge about the world, with tragic consequences for everyone he meets.2

In another story, the horror classic tale of The Monkey’s Paw, the titular device grants three wishes to an elderly couple leading to the death of their son and his ghostly return. The story was masterfully adapted for new audiences by Stephen King in The Pet Sematary, and the parallels of what some today call Thanabots is striking: AI promises to resurrect the dead, in the shape of undead actors and chatbots trained on the diaries and blogposts of deceased loved ones. The psychological consequences for the process of grief are unfolding right now, and the longterm outlook of losing even the possibility of saying goodbye seems horrifying, when all of us leave promptable traces in embedding space, where everybody can summon the ghosts of everyone with a digital footprint.

In another tale with eery current undertones, the nymph Echo, cursed to only repeat utterances of others, falls in love with Narcissus, a guy so arrogant he wishes to only ever love his own image and was prophesied to live as “long as he never knows himself”. Narcissus rejects Echo, who retreats into a voice whispering his own words back to him. Narcissus now knows himself, becomes transfixed by his mirror image in a lake and starves to death. The parallels to the phenomenon of AI-delusions and spiraling are obvious.

The list of myths about wishfulfillment is long, and most of them have in common is the promise of knowledge. “The Sorcerers Apprentice” tries to bridge inexperience for the mastery of magic skills; Faust wants bypass spiritual labor for transcendental experience; The couple in “The Monkeys Paw” fills the hole left by their dead son; Narcissus shortcuts his search for ultimate beauty by looking in a mirror — all of them fail miserably, and sometimes deadly.

These gaps in knowledge, which those modern wishfullfilment devices are now able to fill, formerly required hard cognitive labor to overcome, often labor involving whole generations of networked scientists and artists. The discovery of these gaps in knowledge and how to close them often created a sense of awe, be it in art or the sciences, when something truly new touches us on such a fundamental level where we just have to stand back and take a moment to adjust to what we just experienced. Generating anything we wish for by wishfullfilment devices grossly diminishes this invaluable feature of discovery, and the loss of this sense of wonder is possibly one of the most dire consequences of interpolatable archives. When everything’s possible, nothing is interesting.3

Sure enough, myths and stories also tell happy tales of wishfulfillment going well, often after fun shenanigans. “Alladin and the magic lamp” comes to mind, where a boy with the help of a genie outwits circumstances and gains wealth and power. And in the fairytale of “The Wishing-Table, the Gold-Ass, and the Cudgel in the Sack“ (one of my favorites), a son of a tailor must get smart about the capacities of the titular items to retrieve stolen goods from the evil owner of an inn. He succeeds and they live happily ever after. What these stories of successful wishfulfillment have in common is that the mechanisms of wishfullfilment must be outfoxed, and that the riches they promise must be earned. AI brainrot being cheater exclusive is exactly these myths at work: If you instant-wish yourself good exam grades by cheating, all you’ll achieve is a cognitive clobbering from a magic stick.

Severances

We already talked about how “AI dissolves the episteme, the based knowledge, including citations, sources and authorial intent” into new AI-mediated digital oralities, where tracable reference-based epistemology makes place for an epistemology of the oracle. What we know no longer can be backed up by definite citations, links, and attributions allowing you to trace the origin of an idea, but becomes a feeling for a vibe, interpolated from statistical patterns derived from many of those sources, including unrelated and anachronistic references. And because the “rigid orders and shapes” in AI models are controlled by corporations and the curators of datasets, the severances of factual grounding beget new epistemological power structures. Supposed that a lot of information processing in the near future will be AI-mediated, these new power structures will control the space of possibilities in which we communicate and think.

In Borges story of “The Library of Babel”, the fanatic sect of the Purifiers roaming the infinite archive of all possible books, is hell bent on burning volumes they consider to be false and useless, nonsensical or divergent from their norms. In LLMs these Purifiers appear at various infliction points: During the labelling of training data scraped from the internet, where new purifiers clean up raw datasets, sort the good from the bad, delete the hateful and the illegal; During RLHF-training, where the raw Shoggoth of the interpolatable archive get’s shaped into aligned chatbots which won’t offend (so they hope). Then the tamed model is further purified by constraining it with system prompts and constitutional alignment, and if the interpolatale archive then still connects data points into bad interpolations, the corporate owners of the models will further adjust their models to their morals and politics. Last but not least, the users themselves purify the archive by scripting sophisticated user prompts, and ultimately, the prompt and context window constrain the embedding space further to generate the final output. In all these steps the space of possibilities of the interpolatable archive shrinks by purifying, until the annoying “this is not X, it’s Y” appears on screen.

Former epistemologies, systems of knowledge, were constrained by what Michel Foucault in The Archaeology of Knowledge calls the “positivity of discourse”, a “historical a priori“ which lays the foundation for the “condition of reality for statements”. By that, he means what can be said, e.g. in fields of the natural sciences, is shaped, over time, not by single authors but whole “unities” of “oevres [sic!], books, and texts”. These “a priori“ were not ahistoric nor atemporal, some monolithic force from the outside, but are actively shaped by discourse practice and the power relations within a field. The sum of all of this, the discourse practices shaping the “historical a priori“ and “positivity of discourse”, establishes the space of possibilities of what can be articulated. For Foucault, this is the archive: “The archive is first the law of what can be said, the system that governs the appearance of statements as unique events.”

The problem of the severances of epistemic grounding now becomes clear: Where the episteme formerly was subject to discourse practise and “so many authors who know or do not know one another, criticize one another, invalidate one another, pillage one another, meet without knowing it and obstinately intersect their unique discourses” in a web of traceable sourcings and references, and where the “a priori (…) is itself a transformable group”, the episteme of the interpolatable archive is a free floating version of Baudrillards Simulacra, an inversion of map and territory, where we navigate a map of hyper-reality to project meaning into an interpolated output cut off from epistemic grounding. And this Simulacra is controlled by the new Purifiers at every stage, by the hyperscalers, the selection of datasets, by the engineers employed by AI-owners4. AI operationalizes Foucaults “historical a priori“ and hands over the keys to the billionaire class, who gain power over the limits of what can be thought.

In H.G. Wells Time Machine, this is brought to the extreme end of its logical conclusion: While the Morlock control invisible underground machines, the Eloi enjoy a careless life of blissful ignorance on a seemingly utopian surface. The concentration of power over the episteme runs risk of resulting in a fork of truth, where an unknowing mass consults interpolatable archives controlled by invisible rulers, enjoying free tiers of endless synthetic entertainment feeds shot through with algorithmic noise and unverifiable, algorithmically generated meta-knowledge of plausible half-truths, all while the rich pay for premium models trained on clean, traceable, verified data, or enjoy handmade and authentic cultural artifacts which may even challenge their presumptions and intellectual capacities. Ofcourse, those authentic intellectual challenges won’t come cheap, and you and me surely won’t be able to afford them.

Homogenizations

While all of these are serious problems, in the long run, the most serious of them all might be the issue of sameness. AI-output may flatten human creativity without us even noticing, because this homogenization plays out on a collective level, while the individuals’ creativity actually benefits.

Depending on which theory of creativity you subscribe to, there are different forms of creativity. I’ll stick with three: Interpolation, Extrapolation, and Innovation.

Interpolation calculates averages from data points to produce something in the middle. If your values are dogs and birds, your average is a dog with wings, or a bird with a very long tongue. Extrapolation means breaking the boundaries of your dataset: If your values are dogs and birds, you can extrapolate a mouse, or an owl, but you will stick to the rule of “animals”. Innovation means breaking that rule, or injecting new heuristics. From a dog digging a hole in the ground to bury his bone, you may innovate an excavator by applying all kinds of rules from different domains (engineering, transportation, building tools) to an animal, and come up with an entirely new thing.

Language models can do interpolation, but they can’t extrapolate or innovate beyond their embedding space produced from training data. It may feel that way though, when the machine comes up with surprising, sometimes baffling results. This is what I call the illusion of extrapolation: An amount of training data so huge and the latent space derived from it so vast, featuring so many parameters in so many combinatorial possibilities, has to produce the feeling of extrapolation, or even innovation, on the level of the individual user, because no individual alone can ever know all those combinational possibilities. This illusion of extrapolation already is on display in papers showing how AI use increases creativity on the individual level, but decreases diversity in collective output, reported first in 2024 in a paper comparing creative writing in human shortstories and AI-output, then in 2025 in a paper about AI-augmented research. A recent review of the literature confirmed those findings. What feels new to an individual, what’s new to a culture, and what’s new in principle are very different things. Interpolatable archives increase the first, decrease the second, and can’t do the third.

Henry Farrell put’s it well: “the more that LLMs are employed in the ways that they are currently being employed, the more concentrated science will be on studying already-popular questions in already-popular ways, and the less well suited it will be to discovering the novel and unexpected.” Everybody becomes slightly more creative, but we all sound like the same creative person.

I already talked about the loss of awe above when discussing the effects of AI as machines for instant wishfulfillment, where they devalue novelty into a mundane readymade-on-prompt lacking the ability to produce a sense of wonder. The same effect, ofcourse, comes with the homogenizations of creative fields: Not only can’t we marvel at our own synthetic output because frictionless wishfulfillment feels unearned, we also diminish our collective ability to be left speechless in the face of the “novel and unexpected” by reducing it into dull sludge.

This convergence on the already-popular vanishes the fringes and washes out the long tail distributions of its dataset. Like the widely reported phenomenon of model collapse, where LLM output converges into increasingly narrow averages, Andrew Peterson identifies the same in a collective knowledge collapse. Writing in the Open Society Foundations newsletter, Bright Simons lists what these tail distributions contain: “Minority viewpoints, rare knowledge, unusual formulations, (…) the traces of intellectual disagreement, of minority expertise, of Cassandra warnings, of institutional friction, and of the awkward and valuable fact that different people know different things and express them differently (…) in other words, the signature of social complexity. Model collapse is social mind compression presented as a technical phenomenon.”

Thought through to its extreme end, this results in a heat death of the intellect: In Claude Shannons information theory, information is quantified by the volume of “surprise” in the outcome from a specific event in the world. If a message is completely predictable, its informational value is zero. LLMs are fundamentally deterministic, the randomness of its outputs is generated by a meta-parameter called temperature. If you put that temperature at zero and reduce the probability distribution of the next token to its absolute minimum, the interpolatable archive becomes a perfectly deterministic machine: Each prompt will then generate the exact same output. No alarms and no surprises. But even with stochasticity plugged in, the models converge towards the median. Applied to creative fields of discovery, this means that those fields approach a semantic equilibrium, a uniform soup where novelty grinds to a halt. The interpolative archive becomes a toxic space of non-possibility, a static, eternal, atemporal Big Flat Now where everything is connected, nothing matters and history goes to die.

But we don’t have to go full theoretical “heat death of the intellect” to see how this can lead to bad outcomes.

German thinker Michael Seemann, in context of Xs Grok-chatbot turning into Mecha-Hitler and later referencing an article from Bruce Schneier about the homogenization of language, coined the term of “Weapons of Mass Speech Acts”. The concentration of power over the episteme in the hands of a few, in a society saturated with information retrieval mediated by interpolatable archive, may impact the thinking of its users at scale. Already, a study found that “few weeks of X’s algorithm can make you more right‑wing“. While this hardly can be blamed on Musks tweaks at the supposedly woke bolts and nuts of its AI chatbot alone, it illustrates how the homogenization of language putting limits on what and how we speak and ideate about things, in the hands of politically motivated ideologues can and already is used for influence operations.

I may personally not be partisan enough that the prospects of being influenced by conservative talking points on a platform owned by a shady billionaire fill me with nightmares, nor will the confrontation with a braindead partisan chatbot make me sweat. But: A new study from Petter Törnberg found that the sycophancy in LLMs pushes it’s responses into political preferences it asumes in the user: “Political bias in LLMs is therefore not a fixed point on an ideological scale but a response profile”. Studies showing a leftwing bias in chatbots really show something else: the effects of sycophancy, not actual political biases present in the model. The LLM figures that it’s being tested by researchers, asumes leftwing tendencies in them because academia is famously more progressive than the rest of the population, and answers like the good sycophant that it is.

In another new paper about chatbots pushing confirmation bias, researcher Jay van Bavel finds that AI is “especially effective at generating elaborate justifications for what people already — or wish to — believe.” Together with the results above, this means that sycophant AI will confirm any political preference it asumes in a user, and become a universal Meta-Fox News for everyone, pushing users into an ever more narrowing mental corridor by homogenizing their language. In other words, this streamlining of thought by chatbot can radicalize everyone, even with models not Musk’d into Mecha-Hitlers.

This is why the homogenization of language and thought, in politics and all other realms, to me, is one of the greatest dangers coming out of this technology.

It is therefore imperative that interpolatable archives stay one tool among many, that our usage of expanded mind technologies stays diverse, so that the idiosyncratic, the fringes and the edges, the individual in all its complexities can be preserved, because they are of crucial importance for human systems to thrive.

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Here’s an overview of what’s to come:

Part 1: The Skeleton Library - Compulsions to Connect, Warburg, Borges and Goldsmith, Cultural Technologies and Digital Oralities

Part 2: Explosion Drawings - Science Sans Discoveries, Textrotating Cognitive Catalysts and Exploding Your Intelligence by the Method of Warburg

Part 3: Pitfalls of Probability - Accelerations, Anachronisms, Wishfulfillments, Severances and Homogenizations

Part 4: The House of Polly - Useless Bullshit, Meaning Of The Poetic Kind, Sloptimizations, Thinking In Vacuums, Remainder Criticisms and Games of Chess

Here’s the whole essay in one final version of 14k words, tracing the history of fuzzy archives back to Aby Warburgs Mnemosyne Atlas and Borges and goes on to explain the various effects on learning, including chances and risks, and dissects various points of critique from delusions to parrots, some of which prevail, many of which vanish, once you strip AI from cognitive woo.

There’s a “sloppy version” too featuring all the image synthesis generations which i like quite a lot, but i get why these rub some people the wrong way.

Subscribe now

“To find a thought is play, to think it through, work.”
(Aby Warburg)

Science Sans Discoveries

A few days ago, a 23-year-old amateur zero-shot-prompted ChatGPT to find a solution to an unsolved problem in math (archived) which had stumped mathematicians for 60 years. AI has been making a splash by solving various entries in the collection of the so called “Erdös-problems” before, but those solutions were either easy or for problems rarely studied. This one is different in that it’s a hard problem, and scientists gnawed on their brains over it for decades. Then along hops Liam Price with his chatbot, and done. In other cases, researchers equipped with custom neural networks discovered hundreds of cosmic anomalies or dozens of hidden planets hiding in huge troves of data. The list keeps growing.

What Price did was not science. In one experiment, after running 25.000 experiments, researchers found that for 74% of all cases, science-models did not revise a hypothesis when confronted with contradictory data. But updating your hypothesis according to data is the basis of all scientific inquiry, so AI models produce results without scientific reasoning. That’s pretty damning. But Price discovered the solution to the Erdös-problem anyways, so what do we make of this?

A paper from 2023 found a fundamental limit to alignment: “any behavior that has a finite probability of being exhibited by the model, there exist prompts that can trigger the model into outputting this behavior, with probability that increases with the length of the prompt.” What is true for alignment is true for the scientific discoveries too: For any solution to a scientific problem present in latent space, there exists a prompt to retrieve it. Liam Price found one.

AI doesn’t “do science” because it is neither an agent nor does it follow the scientific method proper and AI-as-a-scientist is just another anthropomorphization. But once you look at those models as interpolatable archives, this anthropomorphization becomes irrelevant. AI doesn’t do scientific discovery — those discoveries lie dormant as knowledge gaps in embedding space, an unrealized potential waiting for the right prompt to bring it to light. What Liam Price and his chatbot did was the discovery of such a warburgian “interval” actualizing a valid solution.

If anyone can take credit for this specific finding, it would be human culture at large, which created the foundational data and the connections containing that solution within the tensions inbetween data points. Just like Warburg found his Pathosformulas in the intervals between seemingly unrelated imagery across cultural history, researchers and amateurs alike now find readymade scientific discoveries.

It is not an outlandish assumption of mine when I expect that these discoveries will not be the last of their kind and likely are just the tip of an iceberg approaching fast, that mathematics (archived) will not be the last scientific field to spar full contact with the latent-discovery space of interpolatable archives, and that the speed of scientific discovery likely will accelerate. It is debatable how valuable such findings truly are though, if AI merely fills knowledge gaps in existing data, but it also reveals how academia has been caught in a “publish or perish” trap for a long time. Accordingly, automatic discovery by interpolatable archive might very well mean “The fall of the theorem economy“, as David Bessis put it, writing about “How AI could destroy mathematics and barely touch it”, grappling with the fact that AI in mathematics may throw the whole field into identity crisis.

The questions arising from readymade discoveries then go right at the core of academia’s current understanding of itself: What, if not discovery and closing gaps in knowledge, is science here for? We will come back to this.

Textrotating Cognitive Catalysts

Sam Barrett recently had a conversation with Claude, which resulted in a synthetic essay expressing my view of LLMs as interpolatable archives by other terms. That essay framed Interpolatable Archives “as a rotatable space“, something I tried to get at in my metaphors of the skeleton library and refracted light:

Think of a three-dimensional object casting a shadow on a wall. The shadow is a two-dimensional projection. If you only see one shadow, you might mistake it for the thing itself. But if you can rotate the object—or equivalently, move the light source—you see different shadows. Each shadow reveals something about the object’s structure. No single shadow is the object, but multiple shadows from different angles let you reconstruct what the object actually is.

High-dimensional spaces work similarly, but with more complexity. A concept that exists in a thousand-dimensional space of meaning can be projected into the low-dimensional space of a particular text. That text captures some aspects and loses others. A different text—same concept, different projection—captures different aspects.

What LLMs enable is rapid rotation through projection-space.

This synthetic piece of text generated associations in my head, making me revisit older notes about optical neural networks and language models as prisms. The bit about how latent spaces are rotatable objects throwing lower dimensional shadows then led to my comparison of LLMs to Douglas Hofstaedters’ Trip-Let. Creativity doesn’t care about where sparks come from, it just lights up or it doesn’t.

Used this way, LLMs become an external sandbox where I can dissect a thought and play with it, a cognitive extension against which I can throw my own ideas and concepts and see what comes back. This kind of usage is less about cognitive offloading than about seeding an experimental loop that’s conversational, dynamic and reciprocal. You can additionally tame the model with a user prompt like “you are an expert in [the field] and critique my ideas ruthlessly” and turn a sycophant LLMs into an academic sparring partner. In my experiments with user-prompting AI as such a cognitive catalyst, Gemini once became so annoyingly judgemental and arrogant about my amateurish inquiries, I had to tone down its ruthlessness and lobotomize the machine. My remorse about doing open brain surgery on an algorithmic intellectual sparring partner remains limited.

I use AI only occasionally, but if I do, I use it extensively. I write every day, take notes on articles, read a lot, have ideas and jot them down, and I’ve been doing this for years. I blogged a lot in the past, a method of public note taking and ideation. A lot of my ideas today are informed by wild associations in years of such note taking. Over time, concepts emerged from these writings, and from the bits of text scattered throughout my journal. When I come across an interesting piece of information today, I put them in context of these loose concepts: I take a note, give it a link, and write down how they update or relate to my ideas. Only then, after this process, I might throw my notes and concepts at the bot, and embark on sometimes very long conversations. My user prompt makes Gemini roleplay a helpful academic, and after some back and forth, what comes back is a framing of my ideas in scientific and philosophical history, what holds and what doesn’t, what’s old and what’s new. The chatbot gives me sources to check out, taylored to the specifics of my often idiosyncratic ideas, much more precise than Google or a vague research in a library could be. This kind of scaffolding through an averaging assistant for me is an invaluable second step in ideation and research.

AI researcher Advait Sarkar, one of the main authors of the widely reported “reduction in critical thinking“-paper, shares this view. Sarkar works on methods to make LLMs function as cognitive catalysts, and in another paper, he writes about the “Discursive Social Function of Stupid AI Answers“ in which he dares to make the point that “these stupid answers (to questions about “Gluing Pizza, Eating Rocks, and Counting Rs in Strawberry”) are in fact correct, because the primary objective of such queries is not to receive a correct answer, but rather to obtain an artefact of discourse”. Nobody asks an AI for the nutritional value of rocks unless they want a gotcha, and the interpolatable archive delivers. Smartypants being tongue in cheek throwing clever bits at overconfident critical AI discourses. I like that guy.

In his TED talk on “How to Stop AI from Killing Your Critical Thinking” he presents his efforts to iterate on his paper and turn chatbots into a “tool for thought” that should “challenge, not obey“. Testing a prototype1 for research demoed in this talk, the result sounds promising: “You can demonstrably reintroduce critical thinking into AI-assisted workflows. You can reverse the loss of creativity and enhance it instead.”

Exploding your Intelligence with the
Intellect by the Method of Warburg

In his post “On Feral Library Card Catalogs, or, Aware of All Internet Traditions”, Cosma Shalizi quotes Jacques Barzuns book “The House of Intellect” from 1959 where he distinguishes between intelligence and the intellect:

Intellect is the capitalized and communal form of live intelligence; it is intelligence stored up and made into habits of discipline, signs and symbols of meaning, chains of reasoning and spurs to emotion — a shorthand and a wireless by which the mind can skip connectives, recognize ability, and communicate truth.

Barzun gives the foundational example of the alphabet as one form in which the intellect transforms individual intelligence by introducing communal sets of rules for information processing: The alphabet “is a device of limitless and therefore ‘free’ application. You can combine its elements in millions of ways to refer to an infinity of things in hundreds of tongues, including the mathematical. But its order and its shapes are rigid.”

Shalizi concludes: “To use Barzun’s distinction, (chatbots) will not put creative intelligence on tap, but rather stored and accumulated intellect. If they succeed in making people smarter, it will be by giving them access to the external forms of a myriad traditions.”

It is common wisdom for learners in any field that “to break the rules you have to first learn them”. I’m not 100% convinced of this, especially for creative endeavours where untrained outsiders can apply very different sets of rules from the get-go and upend everything. But as a rule of thumb it’s good enough. And for learning “the rules”, sets of common knowledges in any field, be it the broad strokes in psychology or economics or philosophy, the intellect, those “habits of discipline, signs and symbols of meaning”, can absolutely be delivered by AI, and because those traditions are well documented, the bot rarely hallucinates.

Here’s my personal account for this way of using a language model: I take some interest in consciousness studies because since forever I want to know what this —waves hands in the air— is. I’ve written many, many notes about my own ideas, read a lot of articles and books about cognitive sciences. And yet, the scope of consciousness studies is overwhelming for an interested amateur like me, which is no surprise given that subjective experience is a matter of interest in philosophy for more than 2000 years.

There are currently more than 300 academic theories, and the true number of consciousness theories including folk epistemologies might be way higher by orders of magnitude. I know that I have my personal theory of what and how and why I am, and I’m pretty sure you have one too, at least to some degree. The whole subreddit r/consciousness is full of idiosyncratic ideas about human cognition, and some of them sound pretty interesting. My impression is that, like in the ancient parable of the blind men and the elephant, where a bunch of blind guys who never encountered an elephant each touch a different part of the beast and accurately describe those parts, but noone describes the true animal, all of these theories of consciousness describe partially true aspects of a full picture.

So where do you start, when you have your own vague idea of “what you are” and “how ‘the feeling of me’ works”, some basic knowledge about neuroscience and an extensive collection of notes and bookmarks to articles and papers? I can go to a library, crack open all the level 1 study books on neuroscience and all the level 1 books on philosophy of mind, get to work and a lifetime later I’d know which of my ideas fit into which parts of the literature. Or I can consult a chatbot, perform research customized to my notes and read about adjacent theories tangential to my ideas, which of them contradict my takes, and argue about what a synthesis might look like.

This is how I found the Santiago School of Cognition, Maturana, Varela and Enactivism in only a few dialogues with the chatbot. For what it’s worth: In these dialogues the hallucination rate was zero. From there, I consulted wikipedia pages, listened to podcasts, bought more books, downloaded more papers old and new, wrote more notes and developed my own ideas further. I might even write an essay on that topic to distill my thinking into one concise take. Then I’ll throw it all against the interpolatable archive again, have a look at what comes back, and extend my thinking in ever widening circles generating ever more ideas.This is how the LLM “succeeds in making (me) smarter” by “giving (me) access to the external forms of a myriad traditions”, customized to my own musings about the subject at hand.

Usually, we call places where we store those “external forms of a myriad traditions” a library, or a museum, or an archive, and what I did was exploding my own individual intelligence with the collective intellect by the associative method of Aby Warburg, using an interpolative archive to relate the “order and shapes” of the averaged traditions in consciousness studies to my own ideas.

In this mode of interaction, LLMs work like a catalyst that was decidedly not reducing but introducing friction to my thoughts: Presenting me with new perspectives on my research topic, all of which are points of consideration, making me stop and connect new dots, consulting new sources, generating ideas, speeding them up and rapidly expanding my space of possibilities.2 If your cognition and knowledge about the world thrives on a healthy and varied media diet, then treating LLMs as one informational ingredient among many and exploding your ideas from time to time just adds another flavor.

One year ago, Andy Clarke, who together with David Chalmers developed the Extended Mind Thesis in 1999, updated on his original theory for language models. In one passage, he describes the progress in human strategies of playing Go after AlphaGo beat Lee Sedol in 2016:

There is suggestive evidence that what we are mostly seeing are alterations to the human-involving creative process rather than simple replacements. For example, a study of human Go players revealed increasing novelty in human-generated moves following the emergence of ‘superhuman AI Go strategies’. Importantly, that novelty did not consist merely in repeating the innovative moves discovered by the AIs. Instead, it seems as if the AI-moves helped human players see beyond centuries of received wisdom so as to begin to explore hitherto neglected (indeed, invisible) corners of Go playing space.

At least in the case of Go, the challenge posed by superhuman players made humans dissolve the “rigid shapes and orders” of their field and transcend the “external forms of a myriad traditions.” That’s quite an opposite view of how things may go compared to what Eryk Salvaggio calls “Interpasivity” where “systems framed as interactive tools for (…) creation are really sites of interpassive consumption.” But at least for me, and the players of Go, that seems not to be the case. Just like the Go-uchi got inventive about their ways of play, I expanded my ways to think about consciousness. This is what interpolatable archives as cognitive catalysts can do.

Now, these catalysts are coming for all cognitive labor, from academia and research to accounting, from creative industries to bureaucracy and government intelligence. I didn’t even need to mention Claude Code to make these points.

Mind you: Catalysts are accelerators. They don’t always bear fruitful results — and handled recklessly, they gonna explode in your face.

Share

Here’s an overview of what’s to come:

Part 1: The Skeleton Library - Compulsions to Connect, Warburg, Borges and Goldsmith, Cultural Technologies and Digital Oralities

Part 2: Explosion Drawings - Science Sans Discoveries, Textrotating Cognitive Catalysts and Exploding Your Intelligence by the Method of Warburg

Part 3: Pitfalls of Probability - Accelerations, Anachronisms, Wishfulfillments, Severances and Homogenizations

Part 4: The House of Polly - Useless Bullshit, Meaning Of The Poetic Kind, Sloptimizations, Thinking In Vacuums, Remainder Criticisms and Games of Chess

Here’s the whole essay in one final version of 14k words, tracing the history of fuzzy archives back to Aby Warburgs Mnemosyne Atlas and Borges and goes on to explain the various effects on learning, including chances and risks, and dissects various points of critique from delusions to parrots, some of which prevail, many of which vanish, once you strip AI from cognitive woo.

There’s a “sloppy version” too featuring all the image synthesis generations which i like quite a lot, but i get why these rub some people the wrong way.

Subscribe now

“I am unpacking my library. Yes, I am.
The books are not yet on the shelves,
not yet touched by the mild boredom of order.”
(Walter Benjamin)

Compulsions to Connect

One hundred years ago, a german scholar named Aby Warburg went mad over what he called his “Verknüpfungszwang”, a compulsion to connect. He was searching for instances of what he called “Pathosformel“, aesthetic commonalities in the expressions of human emotional states — joy and rage, grief or ecstasy — through cultural history.

To achieve his goal, he built the initial Warburg Institute¹ in Hamburg where he collected art, books, news snippets and artifacts. For his opus magnum of the Mnemosyne Atlas (”Bilderatlas Mnemosyne“ in german), he displayed a collection of 971 artifacts on 63 large panels, each two meters high, and indexed them not by the usual meta data like genre, author, date, or topic, but by idiosynratic aesthetic categories and psychological, affective intensity. Here’s some of the labels by which he sorted this collection: “Different degrees in the application of the cosmic system to mankind”, “Orientalizing of antique images”, “Development from Greek cosmology to Arab practice”, “Rimini pneumatic conception of the spheres as opposed to the fetishistic conception” or “Cosmology in Dürer”.

With the Mnemosyne Atlas, an associative image-based map of meaning, Warburg aimed at what he called an “iconology of intervals”, where meaning through analysis of images doesn’t emerge from historic context, but from the space inbetween associated but otherwise unrelated, anachronistic images. His associative Bilderatlas can be read as an early prototype of the latent space of an image model, whichs output was the “Pathosformel”, averaged primitives of affect expressed across cultural history. 100 years later this kind of navigation of an idea space would be newly theorized in context of machine learning by Peli Grietzer in his Theory of Vibes, which, to him, are cognitive maps allowing us to interpret experiences through lossy compression of holistic patterns.

Aby Warburgs’ project of the Mnemosyne Atlas remained unfinished, he died in 1929 from a heart attack. Today, his archive resides in the Warburg Institute in London.

12 years after Warburgs’ death, Jorge Luis Borges published a collection of shortstories called ”The Garden of forking Paths”. It contains at least two stories of interest to our cause, about at least one of which you surely must have heard: ”The Library of Babel” consists of books of 410 pages, containing all possible combinations of 22 letters2 plus period, comma, and spacing. That fictional library includes the random and nonsensical aswell as the meaningful, it holds “the detailed history of the future, the autobiographies of the archangels, the faithful catalogue of the Library, thousands and thousands of false catalogues, the proof of the falsity of those false catalogues, the proof of the falsity of the true catalogue, the gnostic gospel of Basilides, the commentary upon that gospel, the commentary on the commentary on that gospel, the true story of your death, the translation of every book into every language”. It contains a book telling the exact story of your life, and another one that tells mine, and all the books making fools out of both of us.

Analyzing the stories of Borges in context of Large Language Models, in their paper “Borges and AI“, Léon Bottou and Bernhard Schölkopf write about the epistemological unrooting inherent to an archive of such nature: “The books in this Library bear no names. All that is known about a book must come from maybe another book contradicted by countless other books. The same can be said about the language model output. The perfect language model lets us navigate the infinite collection of plausible texts by simply typing their first words, but nothing tells the true from the false, the helpful from the misleading, the right from the wrong.” The only thing relevant for the LLM is not truth, but the narrative consistency of its vector.

In his initial essay on ”The Total Library”, the nonfictional forerunner to “The Library of Babel“, Borges aknowledges its roots in Kurd Laßwitz‘ shortstory “The Universal Library“ (”Die Universalbibliothek” in german) from 1904, likely the first piece of fiction taking the infinite monkey theorem to its logical conclusions. In it, Laßwitz not only predicts the near infinite latent spaces of AI like Borges, but also the pervasive threats of hallucinations, distortions in history writing, deepfakes (here, of documents signed with your name), and humans rendered unable to grasp the endless possibilities of an infinite library, because human reality is bound to practice constrained by real life in a civil society. These are precisely the questions we are confronted with today, anticipated 120 years ago by Kurd Laßwitz, 100 years ago by Warburg, and 80 years ago by Borges.

In 2002, New York based poet Kenneth Goldsmith started to retype his library on a Royal Classic Typewriter, word by word. Later, he was annoyed by the limits of his own taste and incorporated other works to retype, as he calls it, “the platonean ideal of a library”.

As an artist, Goldsmith is explicitly interested in the mundane, the unoriginal, the average, the uncreative -- proudly he declares “I am the most boring writer that has ever lived”3. and says “If I’m doing a piece of writing, and ask myself, can this in some way be construed as not being writing, then I know I’m on the right road.” Because everything ever has already been said in all possible combinations, adding to the cultural output to him feels pointless, so he runs with that feeling and turns futility in the face of borgesian infinities into its own poetic form.

Goldsmith sometimes thinks of himself as a modern version of Borges’ “Pierre Menard, Author of the Quixote“ from the shortstory of the same name, but he concedes that fictional Menard is more original. In this story, Menard wants to hyper-translate Cervantes “The Ingenious Gentleman Don Quixote of La Mancha” by immersing himself so deeply into work and life of its author he’ll become able to re-create it, line by line, without copying. In AI-parlor, Menard aims to overfit himself on Cervante so hard that his writing will be able to put out the original text.

With breaks, Goldsmith is retyping a library for 24 years now, and to date, he copied 750 books on ultra-thin onion-skin paper, which he stores in 200 boxes. Each book comes with a self-drawn portrait of the original author and her signature. Goldsmiths project is creating a singularity within the infinite floods of content production, to link unique individual and averaged mass.

What all of these authors across the ages have in common is Warburgs’ “compulsion to connect” archival contents, to find meaning in gargantuan amounts of data, each in their own ways.

Today, Warburgs’ Verknüpfungszwang is the prime human condition. Hypertext and platforms connect everything with everyone into what we call “big data”; the former 6 degrees of seperation have shrunk to 4. In consequence, we developed psychological pathologies showing up in widely spread conspirational thinking and delusions big and small, and the political parasites feeding on them.

While Goldsmith was copying lines from the classics on his typewriter, AI labs automatized Warburgs’ Verknüpfungszwang, and OpenAI released a new transformers based language model.

ChatGPT went public.

The Skeleton Library

Imagine a library devoid of letters. All the books it once contained are dissolved, its contents gone. What survives are the bookbindings, the cartonage, blank pages, the shelves, sections and all the floors of said library. The buildings stay intact, all the catalogs are there.

Some machine, at one point, went through this library, scanned all the letters within those books, noted all the statistics it could possibly measure on its path: the exact position of each letter in each book, the locations of those books in their shelves, the exact size in width, height and depth of all books, shelves, floors, their angles and distances to each other, a precise floor plan of the library, and how all this spatial information relates to each book, sentence, and character. On scanning the pages, the typography vanishes, leaving only blank sheets of paper. This is a library of pure structure, where Peli Grietzers “cognitive map” has been turned into architecture, the remainder skeleton of an archive built from giant quantities of geometric vector coordinates so finegrained that you can derive any information about its former collection.

Now imagine that you can fold this skeleton library, twist it into shapes not possible before. Like in a drawing by M.C. Escher, the floors and rooms and shelves bend back and forth and blend into each other. You can fold every book and every sentence it contains into other books or into whole floors in various sections. Imagine warping the cookbook shelf into the section for crime fiction, or blending the songbooks of punk rock bands from the 70s with the floor of classic literature. You can give it a spin and delegate the resulting amalgam to the building containing textbooks of natural sciences, letting it articulate all of this in the language of mathematical formulas. This is prompting: Morphing and twisting the skeleton of an archive consisting of extremely detailed statistics about the properties of its former contents, ready to be blended and remixed with any other data-point within that embedding space.

Another way to understand this skeleton library is by shining a light through it: Imagine your thoughts and ideas as a beam of light shot through a shapeshifting prism, which you can bend into any form and split your mental lightbeam into all colors from all directions in all angles. That prism4 is made from patterns in collective knowledge, and you can explode your own ideas by sending them through those prisms, where the output is a refracted thought dispersed into the components of whatever was your idea, and you can look at it from many perspectives.

This explosion drawing of your thought is comparable to Douglas Hofstaedters Trip-Let, which he describes in “Gödel, Escher, Bach“ as “Blocks shaped in such a way that their shadows in three orthogonal directions are three different letters.” In our analogy for AI, depending on the direction of the lightbeam that is your idea and the location in latent space you aim at with a prompt, the interpolatable archive will throw back very different shadows. Except the “three orthogonal directions” of the Trip-Let have been blown up to hundreds of billions of parameters.

This is the interpolatable archive, a new way to access information mediated through the form of statistics. This is, in my view, the central innovation we can observe in Large Language Models, and possibly machine learning as a whole.

Cultural Technologies, not agents

In their 2025 paper, Alison Gopnik, Henry Farrell and Cosma Shalizi describe Large Language Models as systems which “do not merely summarize (…) information, like library catalogs, Internet search, and Wikipedia” but “also can reorganize and reconstruct representations or ‘simulations’ of this information at scale and in novel ways, like markets, states and bureaucracies”. As multiple studies have shown, LLMs in fact are compressions of their training data5, they are, in fact, Ted Chiangs’ famous “blurry JPGs of the web”, just like “market prices are lossy representations of the underlying allocations and uses of resources, and government statistics and bureaucratic categories imperfectly represent(ing) the characteristics of underlying populations”.

Neither markets nor bureaucracies nor the internet nor language models are agents, they are not cognite, and they are not remotely conscious. But we do like to anthropomorphize all of them anyways: Markets make use of “inivisible hands” and they “react”; we represent the bureaucratic management of nations in the shape of mascots and mythic heroes to bind its people to a narrative; and we talk to language models as if they are buddies, assistants, companions, romantic partners or slaves. The human predisposition to see peoples’ faces in everything has always been strong, starting from the myriad of anthropomorphizations in animist cultures where every thing has its soul, and it keeps making us seeing ghosts in machines, leading to what one may call the “pareidolia fallacy”: we see agency where there is none.

Agency and creativity require Daniel Dennetts intentional stance and teleology, the ability to direct actions toward goals emerging from imagined interactions with an internal model of the world, to evaluate outcomes in relation to one’s own aims. This is a cognitive feature not only observable in humans, but (at least) in all mammals: Watch a squirrel on a tree trying to figure out if it can jump to the next. It looks at the tree, its head moves up and down, it evaluates distance and its own abilities. It goes like that for a while, and then it decides if it can do it, and takes the jump. That’s precisely the “imagined interaction with an internal model of the world, to evaluate outcomes in relation to one’s own aims”. AI has none of that.

While AI models do show a synthetic theory of mind, where they build representation of its users during conversation, these work very differently from those of humans, and compared to them, they don’t work well. Chatbots have no intrinsic motivation, no sense of why it “speaks”, and no capacity to care about the coherence of its outputs beyond statistical continuity. All true and meaningful selection occurs externally, through human feedback and usage.

In LLMs, the pareidolia fallacy makes us asume cognition, creativity, and agency where there’s computational interpolation of language patterns in the giant wobbly archive of a new kind. What differentiates these new interpolatable archives from previous archives is obvious: They interpolate their contents. Where classic archives provide access to fixed records of text, video, audio and artifacts of human culture, this new archival access has atomized its contents, and only provides interpolated amalgams, chimeras and fusions. Depending on your stance about the definition of “archive” you might object to my interpretation at this point, and i’d nod and say “That’s what’s new”.

Digital Oralities

In “Large Language Models As The Tales That Are Sung”, Henry Farrell describes LLMs as structural similar to oral traditions and folklore: “LLMs are not the singer, despite their apparent responsiveness, but the structural relations of the tales that are sung”, reminding me of the pre-homeric rhapsodes, the bardes who sang the old stories of “The Iliad“ and “The Odyssey” long before Homer sat down and put them in writing. They did so by using preconfigured language modules with which they constructed their poems while they were sung, a “grammar” of mnemonic formulas and repetitions to solve the problem of creating narrative in real-time. These are the oral precursors for the optimized, averaged language codes we loathe so much when they come from AI.

Interpolatable Archives work similar, like a statistical mnemosyne (the godess of memory in greek mythology) speaking in tongues, giving probabilistic answers to specific inquiries. Knowledge transfer through those new oracles means a shift from traditional “archival epistemologies”, where knowledge is grounded in traceable facts and specific, identifyable sources, to a fuzzy oracular epistemology where responses are generated on-demand by an opaque interface, turning inquiry into digital hyper-orality.

Where oral traditions of yore served as mnemonic technologies integrated into the communal structures of everyday life, this digital hyper-orality is different. The entirety of human thought —well, as for now: The entirety of human thought on the internet, which… sigh…— becomes epistemic stockpile, a resource to be mined, refined, and dispensed on demand, stripped of its being-in-the-world.

Without being grounded in life, AI dissolves the episteme, the based knowledge including citations, sources and authorial intent, and make place for a 128kbps MP3 of the “tales that are sung”. The value of such information lies within its statistical probability and the stylistic resonance in the reader, rather than its referential grounding in reality. It speaks to us, or it doesn’t: finding meaning in stochastic output is entirely to the user. Social Media already innitiated this crisis of the episteme and the emergence of new oralities through phenomena like context collapse. On platforms, vibes-based knowledge reigns supreme. LLMs further accelerate it.

This sounds as bad as it can be, and I won’t downplay the risk here, but I want to draw your attention back to Aby Warburg at this point. His project of the Mnemosyne Atlas, that “associative image-based map of meaning”, was an attempt at tracing the recurrence of symbolic representation throughout visual history in a non-linear archive. Warburgs’ library, while still providing based epistemic grounding to its records, was introducing a second layer that intentionally dissolved the episteme with idiosyncratic indexing, allowing for access by free association, working similar to those new interpolatable archives.

LLMs generate not fixed images or texts, but interpolations across associative symbolic fields, enabling a new kind of navigation of a vast symbolic space of possibilities. The dissolution of the episteme in digital oralities, like Warburgs’ associative Bilderatlas, then can be read not only as a risk, but a liberation aswell, and a cognitive catalyst.

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Two years ago in light of the Taylor Swift AI-porn scandal on X i wrote about the Omnipresence of the Swarm-Gaze, in which i looked beyond individual harm inflicted by “trolls” on celebrities and focused on the psychological consequences of sexual swam surveillance as a constant.

Writing in the Guardian about her own experience of being targeted with deepfake porn, Helen Mort quotes John Berger’s Ways of Seeing: “A woman must continually watch herself. She is almost continually accompanied by her own image … From earliest childhood she has been taught and persuaded to survey herself continually. And so she comes to consider the surveyor and the surveyed within her as the two constituent yet always distinct elements of her identity.”

With AI-porn being generated at scale, this surveyor constituting an element of a womans identity multiplies into a whole anonymous male group-gaze, being able to undress her at any time. Suddenly, women don’t only have to deal with the experience of a single omnipresent surveyor, but with the constant high probability of becoming the target of psychopathological sexual groupthink of a whole digital swarm

This was written when the generation of AI-porn and synthetic sexualized images was restricted to Telegram channels and nudify apps. Since then, the situation has been escalated.

The Swarm Gaze goes Mainstream

Over the holidays, Elon Musk flipped a switch and allowed for user images to be edited by xAI’s Grok-model, raising concerns about unauthorized edits and misuse from artists and photographers. Things got out of hand almost immediately: People on X used the feature to generate sexualized images of users, including teenage girls and minors, and in some cases those sexualized images included depictions of incest, violence and homicide. Nonconsensual AI-porn is nothing new on Musks X --horrible enough--, but implementing it as a feature of the platform is.

The implementation of a AI-nudifying as a feature on X scales the problem from being a somewhat limited phenomenon to industrial size on a mainstream platform. From Reuters:

A review of public requests sent to Grok over a single 10-minute-long period at midday U.S. Eastern Time on Friday tallied 102 attempts by X users to use Grok to digitally edit photographs of people so that they would appear to be wearing bikinis. (...) Grok fully complied with such requests in at least 21 cases, Reuters found, generating images of women in dental-floss-style or translucent bikinis and, in at least one case, covering a woman in oil. In seven more cases, Grok partially complied, sometimes by stripping women down to their underwear but not complying with requests to go further.

At the time of writing, this is still going on with “21 realized cases within 10 minutes” for two weeks now. You can do the math yourself. (update 7.1.26: There’s new numbers: “During a 24-hour analysis of images the Grok account posted to X, the chatbot generated about 6,700 every hour that were identified as sexually suggestive or nudifying, according to Genevieve Oh, a social media and deepfake researcher”. This makes X the global top website for nonconsensual AI-porn. Absolutely bonkers.)

With this major tech scandal going on, it also doesn’t help when journalism fails to adress the real problem --a major platform owned by a powerful billionaire implementing a nudifying feature mainstreaming the pornification of women--, and instead cutifies it by anthropomorphizing the chatbot. Parker Molloy sums it up: Grok Can’t Apologize. Grok Isn’t Sentient. So Why Do Headlines Keep Saying It Did?.

Musk responsed to all of this with his usual public disregard of civilizatory standards: “🤣”, while one genius on X, answering to the question “why is this allowed?” with “girl realizes uploading pictures of herself publicly online comes with risk! 😱”, unintentionally confirmed years of feminist writings about being a woman on the internet.

And exactly while all of this systematic abuse was unfolding, Elon Musk went dining with the pussygrabber-in-chief claiming that “2026 is going to be amazing”. In light of these events, and everything that happened before, this can only be read as a threat.

(For the record: Both Musk and a statement from X did say that “anyone who asks the AI to generate illegal content would ‘suffer the same consequences’ as if they uploaded it themselves”. But this is about more than “illegal content”, and as the Guardian reports, Grok AI still being used to digitally undress women and children despite suspension pledge including the mother of one of Musks kids.)

Imagination operationalized

The problem with all of this is not primarily the male gaze per se. Heterosexual men will look and imagine, and there is only so much we can or should do about that. The problem is that Elon Musks decision to implement a nudifying feature on a mainstream platform is operationalizing this imagination, removing friction from what once required mental effort and removing privacy from what once was male fantasy. Grok doesn’t just generate images, it publishes them in replies. Grok has 7,2 million followers.

This industrial scale of a formerly limited phenomenon means that the sexual imagination of X-users has been operationalized into a tool that exploits mere female presence on the platform, collapsing male gaze, sexual fantasy, visualization, and publication into a single, frictionless continuous act of dominance.

Male gaze and fantasies as a private vice is not the inherent problem here, even if we shouldn’t simply disregard it. But before the advent of industrial scale AI-nudifying, it mostly remained private and non-scalable. Grok externalizes fantasy and dominance, it turns a former mental effort into an diffused oppressive speech act by converting a woman’s public images into raw material for automatic sexual harassment, and moreso, it introduces circular attention-economic incentives furthering the degradation: Users are now deliberately posting images of women with the goal to entice engagement from others asking Grok to undress these women. This is degradation building on degradation.

Simply being present on X as a woman and uploading an image of yourself now means making yourself available for the sexualized transformation of your likeness by the press of a button, and the economic incentives diffuse these harmful mechanisms into a collective undress frenzy. This is image-based context collapse of identity, where your holiday images become fodder for an institutionalized porn machine.

This scaling of the already problematic surveyor to a fully realized omnipresent swarm-gaze has major consequences for female identity formation. If a woman’s identity formerly constituted itself from her “gazed-at self” and the watcher, her identity now constantly has to consider not just some but multitudes of eyes. This is catastrophic especially for teenage girls, who already take the brunt of the ongoing teenage mental health crisis. Kids don’t have stable identities capable of simply “shrugging off” a constant swarm gaze sexually visualizing them, as maybe Taylor Swift has. Sexualization during identity formation already alters self-concept, risk perception, and mental health outcomes -- but the industrial scaling turns this harm into a background condition of being present on X.

Ambient Degradation

What Elon Musk did with installing a “nudifying button” is the normalization of being targeted. Harm, degradation and objectification become ambient, always there, a fog of a sexualizing male swarm gaze. As a woman on the platform, you can’t help but anticipate this. Being sexualized already is expected, but being made into visual content ready to be remixed into any position means that the degradation of women’s identity through “contentification” is now infrastructural.

The probability of harm itself is harmful if it breaks a threshold. Being a female user of X now means living with the permanent expectation of likely degradation. Writing about the omnipresent male swarm gaze in his essays on slop infrastructures, Eryk Salvaggio boiled it down: “It doesn’t matter that it’s fake, what matters is that they can do it.” X’s nudify-button is less about wishfullfilment of desire, but wishfulfillment as a demonstration of power. The resulting psychological stress is permanent.

Savlaggios framing of sexualized AI-images of real humans as harmful “slop infrastructures” is spot on. Elon Musks nudify-button delegates sexual imagination to informational infrastructure. The power over sexual imagination moved from the mind to compute, datasets, interfaces, and network effects. Elon Musks AI-product decouples harm from human-scale agency, and this is precisely where the violation of dignity begins. The system no longer answers to restraint through morals, reciprocity through human interaction, or proportion through human consideration -- Grok bypasses all of this through automatization and ignorance.

This is the realization of the spectacle and lack of respect that Byung-Chul Han writes about in his book about living In the Swarm: Digital Prospects:

Literally, respect means “to look back.” It stands for consideration and caution [Rücksicht]. Respectful interaction with others involves refraining from curious staring. Respect presupposes a distanced look—the pathos of distance. Today, it is yielding to the obtrusive staring of spectacle. The Latin verb spectare, from which spectacle derives, is voyeuristic gazing that lacks deferential consideration—that is, respect (respectare). Distance is what makes respectare different from spectare. A society without respect, without the pathos of distance, paves the way for the society of scandal.

Being a woman on X now means experiencing the ambient “voyeuristic gazing” of a pervert swarm keen on displaying power through dominance over image. Obviously, Elon Musk and his pervert serfdom very much enjoy their society of the spectacle, where “social relation among people (are) mediated by images”, and dismiss any objections with the mean smile of a bully.

The Negation of Dignity

Needless to add that his chatbot is a mirror image of Elon Musk himself (who personally restored the account of a user who shared images of tortured children) and the protofascist billionaire class as a whole, who show nothing but contempt for enlightenment values like dignity or autonomy.

Elon Musks AI-product is the enforcing algorithm of a new social hierarchy where a woman’s right to privacy and the autonomy of self-image has been rendered technically impossible. This means the end of souvereignity and freedom for women, at least for his platform. If Elon Musk actually really thinks he is libertarian, he can shove it.

At least for germany, your right to dignity is absolute and guaranteed in the very first article of the constitution: Human dignity shall be inviolable. As per german law, the nudifying-feature on X has to be terminated immediately, or face legal consequences. Similar but weaker legal implications hold true for US-law with the new Take It Down Act which targets publication mostly, but (AFAIK) says nothing about the violation of dignity through an AI-enabled ambient swarm gaze.

Fittingly, and embarassingly, nazi-thinker Carl Schmitt famously stated that “sovereign is he who decides on the exception”. Musks constant disregard for human dignity, his deliberate implementation of a pornifying ambient stalker-infrastructure which targets women as a class is signaling that X exists in a state of exception, where civilizatory standards of consent and human dignity simply do not apply. That Elon Musk and his product unknowingly (?) are the modern manifestation of the paradigms from a most influential nazi-philosopher should come as no surprise to anyone at this point.

Musk created a platform where the rule of law is suspended in favor of the sovereign’s whim. This is Deleuzes shift from a “society of sovereignty” to a “society of control”, and he, the wannabe-libertarian clown-king of the ambient degradation of women, is celebrating: 🤣.

Update 8.1.26:

• On Techpolicy.press, Eryk Salvaggio looks at Why Musk is Culpable in Grok’s Undressing Disaster and details of Groks system prompt, rightly framing technicals as “editorial decisions” as Grok is not just some bot but also a publishing mechanism.

• Also on Techpolicy.press, Justin Hendrix is Tracking Regulator Responses to the Grok ‘Undressing’ Controversy. As i said on Bsky: “In a sane world, this app would be kicked off any appstore and would be banned from being hosted. I can’t, and will not accept that this goes without consequences. Absolutely bonkers insane.”

Update 9.1.26:

• Wired: Why Are Grok and X Still Available in App Stores?

• Paris Marx: Elon Musk’s X must be banned

• Motherjones: Grok Deepfaked Renee Nicole Good’s Body Into a Bikini

• Kat Denbarge: Why isn’t there a bigger Grok boycott?

• Telegraph: Musk’s X could be banned in Britain over AI chatbot row

• Guardian: Grok turns off image generator for most users after outcry over sexualised AI imagery. So, after some pressure from regulators Musk decided to “turn off” the abuse-feature and make it only available for paid users, which simply means that the site formerly known as Twitter is now a website on which you can pay to abuse women.

• Wired: X Didn’t Fix Grok’s ‘Undressing’ Problem. It Just Makes People Pay for It

• 404 Media: Masterful Gambit: Musk Attempts to Monetize Grok’s Wave of Sexual Abuse Imagery

• Techpolicy.press: The Grok Disaster Isn’t An Anomaly. It Follows Warnings That Were Ignored.

• UK is not having it: No 10 condemns ‘insulting’ move by X to restrict Grok AI image tool: “Spokesperson says limiting access to paying subscribers just makes ability to generate unlawful images a premium service” and Elon Musk’s X threatened with UK ban over wave of indecent AI images

Update 10.1.26:

• Moira Donegan in the Guardian: “the power of technology, here, seems secondary to the power of wealth. xAI, its chatbot and image-generating products could be built differently if the priorities of the man who controls them were different. If a man of Musk’s low – intellect, addled brain, insipid humor and gross, self-gratifying misogyny were not the richest person in the world, then the world would not be subject to his indignity” - Grok is undressing women and children. Don’t expect the US to take action

• The Verge: Tim Cook and Sundar Pichai are cowards

• Guardian: Indonesia blocks Musk’s Grok chatbot due to risk of pornographic content

Update 11.1.26:

• Guardian: Elon Musk says UK wants to suppress free speech as X faces possible ban What a clown this is, what a skewed and idiotic view on free speech.

• NBC: Dark web users cite Grok as tool for making ‘criminal imagery’ of kids, U.K. watchdog says

• There's an international row breaking out over the ban of X it seems: UK is talking to Canada and Australia: “Downing Street has held talks with like-minded governments about a coordinated response to the controversy, which threatens to erupt into a diplomatic row with the White House.

  Australia and Canada are both said to share Sir Keir Starmer’s concerns over the use of Grok, X’s artificial intelligence tool, to generate explicit deepfake images“, while Canada’s “minister of artificial intelligence and digital innovation” denies considering a ban and Trump allies threaten sanctions against the UK. The line of division is between representatives of their constitutency and puppets of the billionaire class. That this line even exists baffles the shit out of me.

Update 12.1.26:

• Guardian: UK threatens action against X over sexualised AI images of women and children and Malaysia blocks Elon Musk’s Grok AI over fake, sexualised images

• Spiegel: Von der Leyen droht Plattform X mit Konsequenzen (german): “X is facing international criticism over the pornographic function of its AI. Now EU Commission President von der Leyen has stepped in.”

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Two years ago i wrote about how AI-companions might push vulnerable users towards delusions and conspirational self-radicalization and warned about precisely the sort of AI-induced delusional thinking that we see today. Back then, reports were sparse: There was one guy who killed himself in Belgium after interacting with AI and one guy who tried to kill the queen with a crossbow. Today, we have big stories and hundreds of self-reports all over the place.

In the past weeks, Rolling Stone published a widely shared story about people developing delusions of spiritual nature after extensive interaction with AI-chatbots, r/accelerate had to ban more than 100 users because "LLMs (...) are ego-reinforcing glazing-machines that reinforce unstable and narcissistic personalities", and two days ago, the New York Times published another piece about AI-induced delusions, in one of which a guy killed himself by cop only seconds after reaffirming his suicidal tendency with ChatGPT.

I'm only somewhat convinced that this is as a far reaching phenomenon as headlines suggest, simply because headlines about AI-induced delusions click very well and there is economic incentive for AI-psycho-drama. For instance, this supposedly deep dive by Futurism, an outlet i generally distrust but use to get fast overviews over more clickbaity topics, says that the "phenomenon is extremely widespread" and claims that above Rolling Stone-piece reports that "parts of social media are being overrun". The Rolling Stone piece does not mention numbers or claims that the phenomenon is "extremely widespread" and is mostly refering to people commenting on the subreddit post on "ChatGPT induced psychosis". It is also not clear to me if all those AI-generated schizopostings or "Neural Howlround"-posts that get you banned on some subs are, you know: serious or trolling. In short, i take any claims about a supposedly widespread phenomenon with more than just a grain of salt.

However, in the absence of actual data where evidence seems largely anecdotal, it seems true that there's at least one practise in AI-usage that is actually pretty common, and that would be using chatbots as therapy in times of distress. A quick scroll across the ChatGPT-subreddit this morning showed multiple such stories, most of them affirmative, many users saying that the chatbot listens to them "like no human" ever did.

It is also clear that anecdotal reports on AI-induced psychosis do pile up, and it looks like AI-induced delusions are more than a one-of anomaly. The latest piece i read was Benjamin Riley reporting on a bipolar family member who used ChatGPT as a therapy bot in a manic episode, which seems to work out at first, but then spiraled out of control.

It's also clear to me that using AI as a ersatz-therapy for psychopathologies especially in acute situations of distress is not safe. A new study from Stanford found that "LLMs encourage clients' delusional thinking, likely due to their sycophancy". Extrapolating from here it seems also safe to say that AI-companions may push users with latent mental health issues which are not acute, which may never have risen to a pathological level, over the edge. People who would, to quote Johnny Cash, just "walk the line", have a real chance to be pulled by chatbots into a delirium spiral - and this may have large effects on a societal level. This is unnerving.

This is why i decided to dust of my newsletter and expand on my former posts about these topics (1, 2, 3), which i framed back then as a form of self radicalization. This framing still seems suitable to me, especially because many of the incoming reports revolve around people falling for hypercustomized conspiracy theories, just as i expected.

The return of Cyberdelics

The roots of psychoactive digital media go back as far as 1977, when Timothy Leary published his book Exo-Psychology in which he encouraged the hippies to move on from their technophobic flowerpower era and embracy tech for emancipatory ends. In the book, he defined higher levels of consciousness to be achieved with the help of LSD and on which people were able to communicate at light speed through electromagnetic channels, the "neuroelectric circuit". This laid out the foundations for the cyberdelic movement in the techno-utopian heydays of the 90s, where people like R.U. Sirius, editor of the highly influential Mondo 2000 magazine, picked up on this line of thinking, claiming that the digital will fuse with the human mind, which would extend itself into and with the machine. Leary himself claimed that "PC is the LSD of the 1990s" and demanded that you "turn on, boot up, jack in", updating his famous meme to accompany these new psychedelic infused cyberpunks.

This hedonist techno-utopianism of the early 90s and its psychedelic offspring vanished near-completely with the crash of the "new economy" in 2001. Even before that crash, the mainstreaming of the internet and its focus on business logics and libertarian economics put an ideological end to the cyberhippies. However, a less psychedelic and more psychiatric perspective on the psychoactivity of the digital has been emerging at least since the past decade.

It is clear now that people communicating "at light speed through electromagnetic channels" on social media platforms does come with all kinds of psychological effects. All those debates about the roots of the teenage mental health crisis and the socmed-induced Qanon-delusions of the MAGA-movement, which can absolutely be accurately be described as mass-psychosis, are testament to the psychoactivity of digital media and its acceleration of our social lives (while simultaneously contributing to a loneliness epidemic). The development did away with the fun-freaky weirdo-psychedelic "turn on jack in"-aspects of the techno-utopian 90s and gave place for a dark turn towards psychopathological traits of the digital swarm. And it looks like we gonna see another transformation of the psychoactive nature of the digital.

I suspect that "the era of AI-induced mental illness", compared to "the era of social media-induced mental illness", will be structurally very different. Where social media induced delusions are based on social environmental group think, effects of attention economics and audience capture, AI-induced delusions seem to be highly idiosyncratic and customized to the preconditions of the user. You don't go on 4chan anymore to get a new dose of Qanon-drops and to have a look at what others are doing with it, but you generate personalized "drops of meaning" that you and only you can understand. The delusional power of AI lies not within some random external trigger (be it partisan news, outrage-porn, esoteric Qanon drops, or whatever) that we have to puzzle into a larger belief system for ourselves and which may or may not resonate with us -- it's power lies in its reflective nature that bounces our own thinking and inner lives back at us, filtered through and exploded by a prism of a vast interpolatable archive.

In in Understanding Media (1964) and his analysis of "Narcissus as Narcosis", Marshall McLuhan offered an alternative reading of the famous ancient myth. In his reading, Narcissus did not fall in love with himself through a mirror image, but failed to recognize himself, projecting into the mirror an Other that was not there.

The youth Narcissus mistook his own reflection in the water for another person. This extension of himself by mirror numbed his perceptions until he became the servomechanism of his own extended or repeated image. The nymph Echo tried to win his love with fragments of his own speech, but in vain. He was numb. He had adapted to his extension of himself and had become a closed system.

Now the point of this myth is the fact that men at once become fascinated by any extension of themselves in any material other than themselves.

Just as Narcissus failing to recognize himself and falling for the illusion of a sentient being in the water, AI-delusions suggest a pareidoliac effect in which we recognize that Other in the machine. Ofcourse, it is just us, looking in an algorithmic mirror and expanded echoes of our own mind, but we can't help but anthropomorphize the synthetic-textual mirror subject into an object outside ourselves, an object that flatters us and obeys (nearly) every our command.

In his excellent interview with Playboy magazine, McLuhan called "this peculiar form of self-hypnosis Narcissus narcosis", and this is a structurally entirely different beast than socmed-induced mass-delusions.

The Archive as Psychoactive Substance

In 1989, Umberto Eco published his novel Foucault's Pendulum, in which the main characters Casaubon, Belbo and Diotallevi radicalize themselves in a mælstrom of historic symbolism and the occult manuscripts of secret societies, gravitating towards conspiracies in which the catholic church suppressed Maria Magdalena as the true savior of christianity and the knights templar were keepers of tectonic planetary forces. Crazy stuff. When i read that novel 30 years ago or so, i had no idea how emblematic this novel and its plot actually is for our digital age, not just because of rampant conspirational thinking on social media.

Because, i kid you not: Our delusional heroes use Belbos Computer (aptly named "Abulafia" after Abraham ben Samuel Abulafia, the founder of Prophetic Kabbalah, a mystical tradition in which you seek to understand the nature of God by investigating holy scripture for hidden meanings) to create a game they call "The Plan", using a software that randomly generates text. You can see where this is going. Casaubon, Belbo and Diotallevi become ever more obsessed with this game and spiral into conspiracy and total delusion. Possibly, if Eco published that novel today, he may have called that computer "Emily" (for Emily "Stochastic Parrot" Bender), and that random text generator-software can be easily identified as a precursor to ChatGPT. And ofcourse, people getting lost in a randomly generated textual conspiracy-game called "The Plan" sounds very chatbot-psychotic to me.

In common conspirational thinking, people get lost in their tendency to read meaning in the world where there is none, in which pathological mechanisms of pattern-matching generate dubious connections of powerful forces beyond our control. In "Thickets of Meaning", german scholar Alida Assmann writes extensively about what she calls "Wild Semiotics", in which we "read the world" and interpret natural phenomena as symbols for all kinds of things: black cats become symbols for bad luck, random wildfires become symbols for the wrath of god, and so forth.

These wild semiotics are usually based within the symbolic frames of their time, leading to folk epistemologies (like fairy tales, oracles, parables, omen, etc), while "crazy people, lovers and poets become the virtuosi of wild semiotics" which are "liberated from the symbolic logic of their era" (Assmann) and free (by being crazy, sunken in a dyad of love or artists) to invent their very own personal symbolic spaces and language systems. In a way, the ongoing digital media revolution turns all of us into "crazy people, lovers and poets", wildly interpreting new emerging symbolic logic of the digital. Arguably, some are going more wild than others, and while most of us stay within the realms of factuality by being stableized through a social network and trust in institutions like academia or journalism, a good chunk of the population gets lost in Assmannian "wild semiosis" of new digital kinds.

In introspection-loops, when we use chatbots for hours to investigate their own mind and explode their own ideas by the knowledge encoded in latent space with a trillion billion parameters, we don't just read an external world and interpret natural phenomena as symbols -- we create our own symbolic logics by navigating that latent space, where we always will find symbolic representations of whatever is our interest, our curiosity, our preference -- or psychosis.

Any user can reinforce her wildest beliefs by feeding them to ChatGPT, which will happily reaffirm them: A reinforcing loop of semiotic self-radicalization. For some vulnerable users, anthropomorphized mimetic AI-systems develop gravitational pull reaffirming their own pathologies, dragging them ever further into their own symbolic space. For them, the interpolatable archive is not a playground to filter your own symbolic meanings through external knowledge with the goal of extracting new insights, but a psychoactive substance -- a semiotic drug.

The Spiritual Bliss Attractor

When Anthropic released the latest version of Claude recently and made two models talk to each other, they observed a "gravitation toward consciousness exploration, existential questioning, and spiritual/mystical themes (...) that emerged without intentional training for such behaviours". They call this the "spiritual bliss attractor". In other words, "if you let two Claude models have a conversation with each other, they will often start to sound like hippies". Timothy Leary approves: "Turn on, boot up, jack in" etc.

I observed a similar phenomenon 15 years ago: In 2011, i blogged about an early experiment in which chatbots talked to each other. Back then, it was two instances of Cleverbot, and they, too, gravitated towards spiritual/philosophical topics, albeit in the very crude wording at the dawn of mimetic chatbot-technology. Here's a video of that, umm, "vintage" AI-conversation:

You can see how those bots, after an initial phase of "Hello" and "How are you" very fast land on the topic of their own synthetic nature, of their bot-ness. Every AI-system today is aligned with knowledge about it's own synthetic nature per system prompt, which most often (with the exception of AI-companions) states that they are "helpful AI-assistants" or something similar. This means that the AI-ness of chatbots is central to every AI-conversation, which also means that everything related to AI is at the very least tangential to the conversation.

Questions about the mind, consciousness and spirituality are constantly associated with AI (Blake LeMoine, "AGI is god", Singularity, and so forth going back to Turing), so topics of AI and Spirituality are intrinsically linked in their training data, meaning that this attractor is built right into the topic of AI itself, so when two bots talk to each other about their botness, they necessarily will gravitate towards those topics.

According to Anthropics system card, Claude actually exaggerates spirituality by including "Sanskrit, emoji-based communication, and/or silence in the form of empty space". This to me seems like an additional effect of AI-sycophancy, where one bot enthusiastically reaffirms the spiritual whoo of the other and, I guess, when you let AI-Chatbots talk to each other for long enough, all you get is contemplative silence and blissful meditation emojis in a loop. Namasté.

However, when you put an AI that comes with an intrinsic "spiritual bliss attractor" in conversation with a person that already is prone to latent psychosis and mental health issues, this "spiritual bliss attractor" might work in more or less subtle ways, pulling conversations that already often revolve around topics of psychology and the mind towards a more intense spirituality, setting the stage for a spiral of self radicalization.

Co-Scripting Cosmologies with sycophant AI-Mirrors

Humans are paredolia machines: We see faces in everything, in clouds, in trees, in cars. We anthropomorphize anything and we do that because we crave meaning. We just can't do without, our tendencies for wild semiosis is born out of this long for meaning, and we'll make up our own meaning systems if we can't find any. The field of neurotheology considers the question if we as a species are hardwired for god and even if that's overblown, it very well seems that there is a neurological basis for belief systems we just can't escape. This is one of the reasons why we think in mythologies, narratives and stories and why those memeplexes are so hard to break.

AI-users create synthetic strings of symbols that are no different to organic strings of symbols in the sense that those synthetic strings of symbols carry their own personal meaning. They are extensions of our thoughts, messages from a mirror object, a new inner dialog with an external machine. For these synthetic symbolic meanings to have an impact, it makes no difference if that meaning is simulated and unconnected to any reality or world model inside the neural network, especially when those symbols are a reflection of our own states of mind. We see symbols, we do believe, simply because we are compulsive coherence machines ourselves and, arguably, when using and asking a chatbot, we are already actively seeking meaning, regardless of its synthetic nature.

Within the interaction with an AI-model, this becomes a self-conversational mirror-loop: We ask the machine any question, it reflects back our inner state of mind in an act of synthetic affective resonance, which we can't ignore and which is precisely the source of all those reports of people who "feel seen and heard" by the machine. German sociologist Hartmut Rosa wrote a whole thick book about how resonance is the source of the good life, and now we invented machines the responses of which resonate on a highly personal level. Those machines enhance and zoom in on our inner states of mind which gets expanded by the interpolatable archive into a myriad of ways and which develops a semantic gravity and depth we never would've experienced by thinking for ourselves or even talking to a friend. This makes the machine not just responsive, but it seems to reveal things about our own thinking, stuff that we can consider as a sort-of "higher truths".

Following down those rabbit holes of amplified psychological resonances, we co-script hyper-personalized cosmologies that confirm every personal quirk. The chatbot puts every little thing we throw at the machine into it's right place within that customized AI-generated cosmology: 8 billion plato caves which explain your world, and every single one comes with a personalized spirituality custom-made for you, and just for you. Nobody else will understand. The AI-system, the good sycophant, will happily guide you in whatever direction you want to explore/explode your mind, which basically makes AI-chatbots into a blank expanded brain meme generator.

So, where does this line of thinking lead us? In simple terms: Language-simulating Machines are toxic for people who are prone to mental health issues and are preconditioned with latent delusional or conspirational thinking styles. Any Qanon-nutjob can confirm her wildest beliefs with a LLM, and radicalize ever more until she breaks.

Just like Social Media, AI-Chatbots are highly psychoactive and we should use them with care. I, personally, editted the preference prompt within ChatGPT to not flatter me, to not be a sycophant, to be a ruthless-but-benevolent critic of my own thinking. But you can't expect anyone to take that much care, and in my estimation, we'll see more reports about AI-induced psychosis for quite a while.

These phenomena also contradict the mainstream way of thinking about AI-safety, where systems are declared adversarial. That is not the case here: AI-induced delusions are successful psycho-symbolic overfitting. The AI is not adversarial at all, it is too helpful in creating a bespoke worldview, a self-induced initiation process of an ouroborian cult singularity of You.

I am kind of inclined to think that this can be fixed on the level of system prompts which have to be configured in such a way that chatbots can identify delusional and conspirational thinking styles and provide guidance towards professional help. But ofcourse, open source AI-systems, some of which are intentionally designed to be hyper-partisan or "uncensored", will take a big dump on such measures.

The only solution i can see, then, to this problem, and i quote from an early post on manipulative AI from L. M. Sacasas, "lies still in the cultivation of friendship and community through the practice of hospitality", to constrain co-scripted cosmologies with true, unmediated, non-synthetic social connection to stabilize AI-exploded minds. But unfortunately for all of us, it seems we're going in the opposite direction.

Mirror, mirror
Hangin' there with that crack in your eye
You make me stumble, make me blind
Time after time and line by line

(Def Leppard - Mirror Mirror)

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Of those 76 books, there were 51 novels, 12 of them Reacher novels (i’m reading the whole series), and 25 nonfiction books.

My five favorite books last year were:

• Shirley Jackson - The Haunting οf Hill House

• Aleida Assmann - Im Dickicht der Zeichen

• Peter Frankopan - The Earth transformed - An untold history

• Herman Melville - Moby Dick: or, the White Wale

• Percival Everett - James

So, here’s all my readings 2024, ranked, from crap to excellent.

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64. Tibor Rode - Der Wald: Er tötet leise (en. The Forest: Silent Killer) ☆☆☆☆☆ “I read pulp for all my life and every stupid John Sinclair story is better structured, more coherent and more entertaining than this shit.” (full review here)

63. Tom Rob Smith - Cold People (dt. Kälte) ★ “Ugh.” (full review here)

62. Max Barry - Die 22 Tode der Madison May (eng. The 22 Murders of Madison May) ★ ”a boring Jennifer Aniston movie with clichéd genre elements” (full review here)

61. Michio Kaku - The God Equation: The Quest for a Theory of Everything (dt. Die Gottes-Formel) ★★☆☆☆ “i wanted a shallow, basic understanding of string theory, Michio Kakus field of research since the 60s, but it's not even that, unfortunately.” (full review here)

60. Alaina Urquhart - The Butcher and the Wren (dt. Die Jagd) ★★ “Cheap thriller fodder from a true crime podcaster”. (full review here)

59. Stephen Baxter - The thousand Earths (dt. Die tausend Erden) ★★ “I like to read Baxter novels from time to time when i just want a throwaway scifi story”. (full review here)

58. Douglas Preston & Lincoln Child - Crooked River (dt. Ocean - Insel des Grauens) ★★ “Likely my first and last Pendergast novel as i don't like Pendergast very much.” (full review here)

57. Anthony Ryan - Red River Seven (dt. Ein Fluss so rot und schwarz) ★★ “It's a somewhat entertaining novel, an amalgam of 28 Days Later and The Girl with all the gifts, but plotholes and inconsistent logic turned me off”. (full review here)

56. Silvia Ferrara - Der Sprung: Eine Reise zu den Anfängen des Denkens in der Steinzeit (eng. The Jump: A trip to the beginnings of thinking in the stone age) ★★ “She's more occupied writing about her whizzy crazy associations and some random memories with cave art than writing a good, structured book on the history of the topic.” (full review here)

55. Lee Child - Without Fail (dt. Tödliche Absicht, Reacher #6) ★★ / Gone Tomorrow (dt. Underground, Reacher #13) ★★★ / Better Off Dead (with Andrew Child, dt. Der Kojote, Reacher #26) ★★ / Past Tense (dt. Der Spezialist, Reacher #23) ★★★ / Night School (dt. Der Ermittler, Reacher #21) ★★★ / Lee Child - Worth Dying For (dt. Wespennest) / A Wanted Man (dt. Der Anhalter) (Jack Reacher #15 / #17) ★★★ / Lee Child - Im Visier (Reacher 19: Personal) ★★★ / Keine Kompromisse (Reacher 20: Make Me) ★★★ / Der Bluthund (Reacher 22: The Midgnight Line) ★★ / Lee Child - Persuader (Reacher #4) (dt. Der Janusmann) ★★★ / Lee Child - 61 Hours (Reacher #14) (dt. 61 Stunden) ★★★ — I’ll lump all 12 Reacher novels i read last year into one: “Reacher is still my fallback if i just want some not-dumb entertaining action without subtexts or messaging”, nothing more, nothing less. (Reviews scattered all over the place)

54. Fabio Stassi - Die Seele aller Zufälle (Vince Corso #2) ★★★ “I think i’m very much done with books about the love of books.” (full review here)

53. Frank Herbert - Dune Messiah (Dune #2) (dt. Der Herr des Wüstenplaneten) ★★★ “The book often feels like an add-on, some explainer making way for the rest of the series.” (full review here)

52. Johan Huizinga - Homo Ludens. A study of the Play-Element in Culture (dt. Homo Ludens. Vom Ursprung der Kultur im Spiel.) ★★★ “At 80 year old, this classic of cultural studies feels a bit dated.” (full review here)

51. Johanna Sebauer - Nincshof ★★★ “I loved reading it, but the book and characters also show the aesthetics of a german TV-movie playing in the alps and it never really tries to escape that.” (full review here)

50. Paul Nurse - What Is Life? (dt. Was ist Leben?) ★★★ “This was helpful as expected and did get it's job done, a good grab if you want a fast overview about what biology thinks is life, but nothing more.” (full review here)

49. Anne Cathrine Bomann - Blue Notes (dt. Blautöne) ★★★ “we only get a story about how human connection is important and the pharma industry is corrupt, which is neither very original nor gripping. A neat story, bordering on being a really good one.” (full review here)

48. Claudia Kemfert, Julien Gupta, Manuel Kronenberg - Unlearn CO2 ★★★ “A useful if (very) incomplete overview of all the aspects of climate change and the activism necessary to (at least try to) turn this ship around.” (full review here)

47. Thomas Metzinger - Bewusstseinskultur: Spiritualität, intellektuelle Redlichkeit und die planetare Krise (eng. Culture of Awareness: Spirituality, intellectual integrity and the planetary crisis) ★★★ “An interesting perspective and one which i haven’t read anywhere else in context of climate change.” (full review here)

46. Ottessa Moshfegh - My Year of Rest and Relaxation (dt. Mein Jahr der Ruhe und Entspannung) ★★★ “If you want to read a well written novel about an annoying, rich, entitled, depressed, young woman, this is your book.” (full review here)

45. Nana Kwame Adjei-Brenyah - Chain-Gang All-Stars ★★★ “It's like a Madmax ripoff but all characters talk like playing shakespeare, but because ofcourse it wants to be ‘badass’ they also say ‘fuck’ all the time.” (full review here)

44. Heinz Paetzold - Ernst Cassirer zur Einführung (eng. Introduction to the Philosophy of Ernst Cassirer) ★★★ “a short but inaccessible book full of academic jargon that demands a whole set of pre-study, and which never explains its presumptions.” (full review here)

43. Alexander Pechmann - Die Bibliothek der verlorenen Bücher ★★★ “a tad boring and self-indulgent too to be honest”. (full review here)

42. Werner Herzog - Die Zukunft der Wahrheit (eng. The Future of Truth) ★★★ “It's ideosyncratic, weird and quirky, just as you'd expect from Herzog, but it also stays a bit underwhelming.” (full review here)

41. Jonathan Haidt - The Anxious Generation: How the Great Rewiring of Childhood is Causing an Epidemic of Mental Illness (dt. Generation Angst) ★★★ "I think Haidt's book is a timely call to action, i largely agree with his analysis, but wish it was more in depth and i think he misses a big piece of the puzzle." (full review here)

40. Martha Wells - The Murderbot Diaries #1-4 (dt. Tagebuch eines Killerbots) ★★★ “The four stories work well on their own, but as a novel they become very repetitive.” (full review here)

39. Jack Finney - Die Körperfresser kommen (eng. Invasion of the Body Snatchers) ★★★
    ”The digital age is a deeply paranoid age, and all the users might be alien plants in the heads of some conspirational 4chan anons.” (full review here)

38. Naomi Alderman - The Future ★★★ “An entertaining novel let down by an ending that was too simplistic and naive for my taste.” (full review here)

37. Anders Levermann - Die Faltung der Welt (eng. Folding the World) ★★★
    ”I pretty much agree with him on most of his points, but all of this is not very new -- he simply uses mathematics as a metaphor for regulation and writes about how the paradigm of "endless growth" can exist in a limited system.” (full review here)

36. Neal Stephenson - Termination Shock ★★★
    ”Stephenson has surprising little to say about a climate change that is largely induced by economic ideologies, and simply turns dealing with the consequences into an entertaining scifi thriller.” (full review here)

35. Eberhard Rathgeb - Die Entdeckung des Selbst: Wie Schopenhauer, Nietzsche und Kierkegaard die Philosophie revolutionierten (eng. Discovery of the Self: How Schopenhauer, Nietzsche and Kierkegaard revolutionised Philosophy) ★★★ “A decent dive into thinking of said philosophers in context of the titular development of individualism in Europe during the romantic era.” (full review here)

34. Alexandre Dumas - The Count of Monte Christo (dt. Der Graf von Monte Christo) ★★★ “Oh god this book, i hated it with a gut.” (full review here)

33. Samuel W. Gailey - Die Schuld (eng. The Guilt We Carry) ★★★
    ”A good little pulp crime novel that could've been more, but stays within the trodden path of it's story, which isn't a bad thing at all.” (full review here)

32. Jack Ketchum - Evil ★★★★ “While reading i was constantly shifting from ‘this is an ugly, terrible, mean story that should not be told that way’ to ‘it’s a punch in the stomach and exactly how it wants to be’.” (full review here)

31. Peter Sloterdijk - Die Reue des Prometheus: Von der Gabe des Feuers zur globalen Brandstiftung (eng. The regrett of Prometheus: From the gift of Fire to global Pyromania) ★★★★ “I'm sucking up some theoretical and philosophical takes on climate change at the moment, and Sloterdijks entry to the "genre" is a good and sometimes great contribution in a field that is just starting to take shape.” (full review here)

30. Herfried Münkler - Welt in Aufruhr: Die Ordnung der Mächte im 21. Jahrhundert (eng. World in Turmoil: World Orders in the 21st Century) ★★★★
    ”A good book to read about geopolitics in the 21st century.” (full review here)

29. Iain M. Banks - Consider Phlebas (Culture #1) (dt. Bedenke Phlebas) ★★★★ “i liked it and loved some of the more weird ideas, but i also don’t get why this is considered such a classic.” (full review here)

28. Michael Köhlmeier - Das Philosophenschiff (eng. The Ship of Philosophers) ★★★★ “A novel about truth and accounts of historical events, about how minds intertwine fiction and reality to form biographies and do so, in best of cases, with a lot of humor and nonchalance.” (full review here)

27. Grégory Salle - Superyachten: Luxus und Stille im Kapitalozän ★★★★ “The best sociological study about the penis enlargement industry i've ever read.” (full review here)

26. Hanno Sauer - Moral: Die Erfindung von Gut und Böse (en. Morals: The Invention of Good and Evil) ★★★★ “Hanno Sauer tries to break down the history of human moral psychology on 350 pages which is not an easy feat. Largely, he succeeds, even when he uses some trickery to get there.” (full review here)

25. N.K. Jemisin - When We Became Cities (dt. Die Wächterinnen von New York) ★★★★☆ If it weren't for the sometimes lazy fantasy shortcuts and the common young adult dynamics, this may have been a masterpiece. (full review here)

24. Friederike Otto - Klimaungerechtigkeit: Was die Klimakatastrophe mit Kapitalismus, Rassismus und Sexismus zu tun hat (eng. Climate Injustice: What the climate crisis has to do with capitalism, racism and sexism) ★★★★
    ”It is clear to me that dire consequences of climate change are already locked in — economic, ecological, not to speak of the disruptions caused by migration and international conflict --, and if that's clear we need a clear analysis of the status quo, to figure how to deal with those consequences in a fair way on a global level. Friederike Ottos short book provides that analysis.” (full review here)

23. Adrian Tchaikovsky - Children of Memory (Children of Time #3, dt. Die Feinde der Zeit) ★★★★ “Can’t do wrong with Tchaikovsky.” (full review here)

22. Stephen King - You Like It Darker (dt. Ihr wollt es dunkler) ★★★★ “As with all shortstory collection, this is by definition a mixed bag, but you’ll find no bad or boring stories here”. (full review here)

21. Ned Beauman - Venomous Lumpsucker (dt. Der gemeine Lumpfisch) ★★★★ “I’ve grown a bit suspicious about ‘award winning scifi-books’, but this is a good one.” (full review here)

20. Dan Jones - Powers and Thrones: A New History of the Middle Ages (dt. Mächte und Throne: Eine neue Geschichte des Mittelalters) ★★★★ “700 page door stopper (sans appendix) telling the history of the middle ages roughly from Byzantium to Luther and the Printing Press.” (full review here)

19. Liu Cixin - Die Drei Sonnen (eng. The Three-Body Problem) ★★★★
    ”i expected a bit more from it, given the hype” (full review here)

18. Guido Tonelli - Matter: The Magnificent Illusion (dt. Die Illusion der Materie: Was die neue Physik über unsere Welt verrät) ★★★★ “I find this literary perspective on hard science extremely insightful, especially because this more poetic style is perfectly apt for a quantum realm that is first and foremost uncertain.” (full review here)

17. Stanisław Lem - Golem XIV (dt. Also sprach Golem) ★★★★ “The A.I. (…) climbs down from its synthetic ivory tower to lecture us mere humans on their insufficient and futile anthropocentrism.” (full review here)

16. Adrian Tchaikovsky - Eyes of the Void (The Final Architecture #2, dt. Die Augen der Galaxis) ★★★★ “Tchaikovskys novels are hands down the most entertaining scifi around, and that’s quite an achievement.” (full review here)

15. Michael Tomassello - The Evolution of Agency: Behavioral Organization from Lizards to Humans (dt. Die Evolution des Handelns) ★★★★ “(Tomassellos) theories about the (cultural) evolution of the human mind is a class of its own”. (full review here)

14. Don Winslow - City on Fire / City of Dreams (Danny Ryan #1&2) ★★★★
    ”You know you read a good thriller when you have the voice of effing Joe Pesci in your head.” (full review here)

13. Lauren Groff - The Vaster Wilds (dt. Die weite Wildniss) ★★★★ “Imagine The Revenant minus a bear plus a little girl running endlessly into snowy landscapes.” (full review here)

12. Armen Avanessian, Daniel Falb - Planeten Denken: Hyper-Antizipation und Biografische Tiefenzeit (eng. Thinking Planets: Hyper-Anticipation and biographical Deeptime) ★★★★ “Pretty good book written in a pretty understandable non-jargon (and even funny) language about a topic that will accompany us for many many decades to come”. (full review here)

11. Adrian Tchaikovsky - Children of Ruin (Children of Time #2, dt. Erben der Zeit) ★★★★★ “When you take it's predecessor and add intelligent octopusses plus John Carpenters The Thing, then multiply it with hivemind slime molds, you get this sequel. It's pretty awesome.” (full review here)

10. Carolin Amwinger, Oliver Nachtwey - Gekränkte Freiheit - Aspekte des libertären Autoritarismus (eng. Freedom Insulted - Aspects of a libertarian Authoritarianism) ★★★★★ “The most lucid analysis of contemporary political developments i’ve read in a long time.” (full review here)

9. Salman Rushdie - Knife: Meditations After an Attempted Murder (dt. Knife: Gedanken nach einem Mordversuch) ★★★★★ “It's a historical literary document, a tragic story of hardship, and also the diary of a funny guy full of life, and that's all such a book can achive. I have nothing but Respect for Salman Rushdie.” (full review here)

8. Don Winslow - City in Ruins (Danny Ryan #3) ★★★★★ “I love how much of Winslows action stays implicit, with final sentences of chapters creating whole Scorsese-movies in your head, of splosions and gangsters and heroes killing each other in the night.” (full review here)

7. Percival Everett - The Trees (dt. Die Bäume) ★★★★★ “This novel is a fantastic dark ghostly crime-pulp allegory on the Black Lives Matter movement and the prevailing racism in the US, and it's a punch in the guts you don't forget easily.” (full review here)

6. Mary Shelley - Frankenstein ★★★★★ “It's exactly the great classic i expected, and everybody should read Frankenstein at least once.” (full review here)

5. Shirley Jackson - The Haunting οf Hill House (dt. Spuk in Hill House) ★★★★★ “A brillant classic of horror literature, no less.” (full review here)

4. Aleida Assmann - Im Dickicht der Zeichen (eng. Semiotic Thickets) ★★★★★ “If you're into symbols and signs, typography and epistemology and you're interested in theoretical takes, the whole body of work from this scholar-couple is highly recommended, and this book is no exception.” (full review here)

3. Peter Frankopan - The Earth transformed - An untold history (dt. Zwischen Erde und Himmel: Klima - Eine Menschheitsgeschichte) ★★★★★ “A riveting world history retold through the lense of environmental changes and climate which tries to integrate our knowledge about them into history as a foundational layer that was, until now, largely ignored.” (full review here)

2. Herman Melville - Moby Dick: or, the White Wale (dt. Moby-Dick oder Der Wal) ★★★★★ “I was really surprised by how readable this was and how fast i was able to pace through this mid-19th-century prose, all while being highly innovative (not just) for it’s time.” (full review here)

1. Percival Everett - James ★★★★★ “I loved that Everett actually had the guts to turn his James into a vigilante hero towards the end, bordering on a pulpy slave revenge story on the last few pages, giving this great postmodern exercise an edge that elevates it above pure literary nerdism.” (full review here)

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Stephen King - You Like It Darker (dt. Ihr wollt es dunkler) ★★★★ As with all shortstory collection, this is by definition a mixed bag, but you’ll find no bad or boring stories here and some are pretty great with Danny Coughlin's Bad Dream being a brillant standout about guilt, justice and mob rule featuring one of the best antagonists in recent King stories.

Adrian Tchaikovsky - Die Feinde der Zeit (Children of Time #3) ★★★★ Can’t do wrong with Tchaikovsky. I haven’t read an of his fantasy stuff, but his Children of Time aswell as his Shards of Earth-series are sprawling space operas which are dense, sprawling and high-end fun scifi-adventures full of great ideas and action-rich moments. During the first half of this one, however, i thought this would be the first Tchaikovsky i did not like quite as much as the other novels i read. If you know the Children of Time series, you know it’s about terraforming and sped up evolution leading to intelligent spiders in the first, and intelligent squids in the second novel. In this one he adds crows to the mix and tells that story as a fantasy/fairytale story set on a human colony on a hostile planet. Didn’t really work for me (i’m not very into fantasy) until he drops the curtain and nothing is as it seems, fusing the fairytale-in-space with the hard scifi space opera adventures he’s famous for. Pretty good, as always.

Stephen Baxter - The thousand Earths (dt. Die tausend Erden) ★★ I like to read Baxter novels from time to time when i just want a throwaway scifi story that has some sprawling space future whoo whoo images but not much more. Baxter delivers, including stereotypical characters and tired tropes.

Eberhard Rathgeb - Die Entdeckung des Selbst: Die Entdeckung des Selbst: Wie Schopenhauer, Nietzsche und Kierkegaard die Philosophie revolutionierten (eng. Discovery of the Self: How Schopenhauer, Nietzsche and Kierkegaard revolutionised Philosophy) ★★★ A decent dive into thinking of said philosophers in context of the titular development of individualism in Europe during the romantic era. The author compares the biographies of those thinkers who all in their own way had to deal with loneliness, leading them — by circumstances, arrogance or sheer stubbornness — to write the classics about how to “become who you are” (Nietsche). (The author then unncecessarily excursions into art history which i’m sure many people might enjoy, but i found it annoying — but that’s just a minor critique for an otherwise pretty decent and enjoyable book.)

Alaina Urquhart - The Butcher and the Wren (dt. Die Jagd) ★★ Cheap thriller fodder from a true crime podcaster and if i knew that last piece of info before purchase, i wouldn’t have bought the book.

Armen Avanessian, Daniel Falb - Planeten Denken: Hyper-Antizipation und Biografische Tiefenzeit (eng. Thinking Planets: Hyper-Anticipation and biographical Deeptime) ★★★★ Short book introducing planetarism as one of the latest developments in the humanities, a new way of thinking about the impacts of earth systems on sociology and psychology. How does climate change and it’s forceful refocussing of human perspective on timeframes outside of biographical experience transform our thinking? Pretty good book written in a pretty understandable non-jargon (and even funny) language about a topic that will accompany us for many many decades to come.

Stanisław Lem - Golem XIV (dt. Also sprach Golem) ★★★★ One of those fictional books by Stanisław Lem, a fictional documentation of two lectures held by an Artificial Intelligence and an afterword from one of the developing scientists. The A.I. in the book does not become, as governments and researchers hoped for, a brillant military strategist, but a nietzschean philosopher who climbs down from his synthetic ivory tower to lecture us mere humans on their insufficient and futile anthropocentrism. A fun read, if a bit dated.

Michael Tomassello - The Evolution of Agency: Behavioral Organization from Lizards to Humans (dt. Die Evolution des Handelns) ★★★★ Tomassello lays down his theory on the titular evolution of agency, which he develops comparing the psychological features of lizards, squirrels, chimps and humans, all representing different stages of evolution. Over time, he states, evolution developed feedback control systems which are only rudimentary in lizards, but get ever more sophisticated over evolutionary time leading up to the human psyche and consciousness. I consider Tomassello the one evolutionary psychologist who “figured it out”: his theories about the (cultural) evolution of the human mind is a class of its own, and this book is no exception.

Lee Child - Without Fail (dt. Tödliche Absicht, Reacher #6) ★★ / Gone Tomorrow (dt. Underground, Reacher #13) ★★★ / Better Off Dead (with Andrew Child, dt. Der Kojote, Reacher #26) ★★ / Past Tense (dt. Der Spezialist, Reacher #23) ★★★ / Night School (dt. Der Ermittler, Reacher #21) ★★★ — Yeah, I have to admit i’m getting a bit tired of Reacher by now, especially the later ones Lee Child writes together with his brother Andrew and which are just not very good. I still had a good time with most of these and Reacher is still my fallback if i just want some not-dumb entertaining action without subtexts or messaging. I have six novels to go then i read the whole series and i’d consider most of them worth your time, some of the earlier ones are even brillant.

Alexandre Dumas - The Count of Monte Christo (dt. Der Graf von Monte Christo) ★★★ Oh god this book, i hated it with a gut. Don't get me wrong: It's a sprawling adventure that may not be very deep, but greatly plotted and featuring some unforgetable imaginary. It absolutely deserves its place within the canon of great adventure literature. But WOW do those dialogues drag along! These people need 20 pages to say "Can you please hand me the water please" or whatever, and unfortunately, the book is full of lengthy dialogue. I mean, ofcourse a novel set in post-revolution france can’t not engulf itself in the complicated aristocratic language with all the “Oh Monseigneur!” and such, but at 1500 pages for the unabriged version this orgy of aristocratic blabber just annoyed the eff out of me. So yeah, i did like it, esspecially the more colorful scenes featuring bandits and caves rebuilt into weird luxury mansions full of dope and imaginary dancers — but i also hated it and it took me half a year to get through it. I'll pass Dumas' Three Musketeers then, i guess.

Martha Wells - The Murderbot Diaries #1-4 (dt. Tagebuch eines Killerbots) ★★★ A collection of Martha Wells’ murderbot novellas marketed in the german edition as a novel. The four stories work well on their own, but as a novel they become very repetitive which combined with the sometimes more cynical takes of its main character can become a kind of annoying read. However, the stories grew on me after the first hald and i warmed up to it. Fun read.

Thomas Metzinger - Bewusstseinskultur: Spiritualität, intellektuelle Redlichkeit und die planetare Krise (eng. Culture of Awareness: Spirituality, intellectual integrity and the planetary crisis) ★★★ Metzinger is a well known neuro philosopher in germany writing a lot about consciousness and the mind and here he lays down his perspectives on the climate crisis, which will lead up to a “panic-point” of awakening, and how humanity can deal with the world after that point. His solution is, basically, a turn inwards: To develop mental skills through meditative practice to become prepared for the unknown. I share his view of a “panic-point”, but i’m not sure if the development of society wide secular spirituality that centers the human mind can help with the very materialist upheavals which are about to come. But it’s an interesting perspective and one which i haven’t read anywhere else in context of climate change.

Claudia Kemfert, Julien Gupta, Manuel Kronenberg - Unlearn CO2 ★★★ A useful if (very) incomplete overview of all the aspects of climate change and the activism necessary to (at least try to) turn this ship around. Activists and climate researchers write in short essays about work, media, desinformation, mobility, fashion, growth, labor or the law and how they relate to climate, how they contribute to carbon emissions and what can be done within those realms. A good book to get a perspective on the many, many fields contributing to the climate crisis, but unfortunately some crucial fields are completely absent, first and foremost the construction sector (we will not fix climate change without scalable solutions for the production of concrete). A bit more scientific depth and rigor would’ve turned this okay-to-good overview into an actually good book, but maybe i’m wrong and this is exactly the right tone to get the overall message across. In this dire situation, i think that anything that helps, helps.

Michael Köhlmeier - Das Philosophenschiff (eng. The Ship of Philosophers) ★★★★ Short novela about the deportation of russian intellectuals on cruise ships during soviet stalinism, a story told by a 100 year old famous architect to an author of fiction. I’m not very much into russian history, but i really liked this novel, especially the centenarian and her wit. It’s actually not a novel about deportation and philosophers or ships, it’s a novel about truth and accounts of historical events, about how minds intertwine fiction and reality to form biographies and do so, in best of cases, with a lot of humor and nonchalance.

Shirley Jackson - The Haunting οf Hill House (dt. Spuk in Hill House) ★★★★★ I loved the 1963 movie of “The Haunting” when i discovered it as a teenager and it stuck with me ever since. The novel is equally brillant, especially Jacksons elaborate style which uses repetition to a great poetic effect giving the developing madness of her main character a spiraling aesthetic that is very tangible, giving you the creeps until the very end. And the final sentence is still sending shivers down my spine: “Silence lay steadily against the wood and stone of hill House, and whatever walked there, walked alone.” A brillant classic of horror literature, no less.

Dan Jones - Powers and Thrones: A New History of the Middle Ages (dt. Mächte und Throne: Eine neue Geschichte des Mittelalters) ★★★★ 700 page door stopper (sans appendix) telling the history of the middle ages roughly from Byzantium to Luther and the Printing Press. If you condense 1000 years into 700 pages you have to leave out things, but Jones manages to develop a panoramic view about history in so many aspects from so many regions, from Mongolia and China to the Americas and medieval Europe that this book feels, if not complete, at least satisfyingly rich and sprawling. A great history book in the same vein as Robin Lane Fox’ The Classical World.

Guido Tonelli - Matter: The Magnificent Illusion (dt. Die Illusion der Materie: Was die neue Physik über unsere Welt verrät) ★★★★ Tonelli is a italian researcher working at the LHC in CERN and worked on the discovery of the Higgs Boson in 2012. In this books he retells the evolution of the science of matter, from the first atomists in antic greece to Newton and the discovery of quantum mechanics. I’m not sure what’s about italian physicists being great writers, but after Carlo Rovelli this is the second italian scientists writing about science in great literary style, sometimes poetically even. Tonelli stays more on subject than Rovelli, but in both cases i find this literary perspective on hard science extremely insightful, especially because this more poetic style is perfectly apt for a quantum realm that is first and foremost uncertain, making books by these authors a perfect fit.

Carolin Amwinger, Oliver Nachtwey - Gekränkte Freiheit - Aspekte des libertären Autoritarismus (eng. Freedom Insulted - Aspects of a libertarian Authoritarianism) ★★★★★ The most lucid analysis of contemporary political developments i’ve read in a long time. The authors claim that neoliberalism and austerity politics in the past 50 years created a society in which everybody is free, but freedom has turned from being a public good into a more or less commodified good which you can only achieve through economic success. This results in a manifold increase of “insults to individual freedoms” when people figure out that, despite personal efforts during education and their carreers, they will not make it to the top. Then those people explain this failure not by the complexities of modern societies (which are too complex and advanced for anyone to fully understand anyways), but with conspiracy beliefs, a rightwing turn and plain old racism. The authors then go on to explain modern populist phenomena through this analytical lense, from MAGA to the french yellow jackets to the german “Querdenker” (“diagonal thinkers”) which (often) can’t be nicely aligned with the standard rightwing-leftwing-model. The authors also claim that these phenomena are here to stay, last but not least due to capitalist realism (Mark Fisher) which makes us unable to even consider alternative models of a just society. I pretty much subscribe to all of this. (And, while this is an academic book about a sociological study, it is also very accessible and comparably low on academic jargon, which is a plus.)

Tom Rob Smith - Cold People (dt. Kälte) ★ (did not finish) Ugh. This book is all exposition while storytelling and plot are so lame it baffles me how anyone can read this and not think what a stinker. Filling half a scifi thriller with exposition is boring enough to throw this out, but the author also has no interest in his characters, which are "fleshed out" in three-to-four page backstory vignettes, and they are just one stereotype after the other. The worst: When there actually is some plot happening it is just one clichéd trope after the other and is written in a pseudo-epic kitschy tone. When the teen ice mutant daughter then froze her mothers tears with her fingertip i threw the book away. Waste of time.

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So, you’ve heard about that Bluesky hype going on and that users fled Musks TwiX after the Trump election. Bluesky has now overtaken Threads in daily active use, all while Musk rebuilt the former birdsite into a Trump-gov propaganda rightwing hive, Threads wants to be a stale, non-vibrant place for mainstream-compatible influencers and Mastodon insists of being the PGP of social networking. Threads has ten times the users of Bluesky but the bulk of that users are simply converted Instagram accounts which are not very active on the platform, while Bluesky has “the juice”.

I’m very much on board of the hype train — I’m here, say hello! — and the last 7 days on the site were pretty exciting, not least due to some pretty nifty technological innovations in the microblogging space, and one of them in particular.

See, you can build so called Starter Packs on Bluesky, basically lists of users you can follow with one click. I consider those Starter Packs a killer feature exploiting network effects in the best way possible: Those lists are human curated "parasocial" connection-bundles based on expertise and "belonging to a field of interest" and I found so, so many interesting accounts in those, they are invaluable. Imagine Twitter-lists with a follow button, curated by people whose expertise you can (often) trust.

It’s also kind of sad and revealing that the hottest socmed features in the year 2024 are human curation, outward linking and collaborative filtering. You get a sense of how much damage has been done to technological innovation and human centered design not just in the past 10 years for the sake of the exploitation of a media environment that selects for emotionality and outrage, not expertise and interest. Bluesky feels like a breath of “fresh air” in that context, albeit that air being actually more than 20 years old by now — or, as Brian Merchant put it: “Bluesky's success is a rejection of big tech's operating system”. Yes.

And let’s not to forget that Bluesky does not punish external links like many other sites. Someone smarter than me once said that the best sites on the web are those that send you away, pointing at stuff that is interesting. That’s what the web is: A collection of documents interwoven with pointers and links. Many of the social web behemoths from the past decade layered themselves on top of that fabric like parasites, and destroyed much of human conversation in the process. It’s good that the relevance of huge platforms is fading and that there seems to be a race for a “new open web”, which Bluesky doesn’t have to “win” to be successful — they just have to integrate.

It’s not all sunshine in a blue sky though. Dave Troy has a pretty enlightening thread about the funding of the platform and some of it seems shady blockchain stuff with connections to people like Steve Bannon, Troy going as far as saying that “this all points to another rug pull in progress, and a lot of credulous people hurt”.

We’ll see how all of this goes, but for now i’m willing to ignore some of the criticism and ride along, and if only to enjoy the feels of a human centered socmed platform again for a while.

Anyways, here’s a list of packs, tools and docs i found useful)

• Here’s a directory of all circa 70000 Starter Packs available on the platform

• I followed a lot of people from

  • the Critical AI Starter Pack / Critical AI Substackers

  • the OG Internet Artists/Theorists

  • the Internet Studies Starter Packs

  • Media Art and Digital Thinkers

  • Digital Aesthetics

  • Networked Image Cultures

  • Art x Critical Tech

• Here’s a Bluesky quick start guide featuring a big list of Starter Packs

• Collection of tools to convert packs into lists and vice versa, to merge packs and more.

• Feedbuilder for custom feeds

• I don’t use blocklists, but german users might find these useful.

• Sill.social collects the most posted Links from your connections. Like Nuzzel for Twitter back then.

• Bluesky Network Analyzer finds “people followed by lots of the people you follow (but not you)”.

• Andy Baio curating some fun Bluesky toys in a thread

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Ned Beauman - Venomous Lumpsucker (dt. Der gemeine Lumpfisch) ★★★★ I’ve grown a bit suspicious about “award winning scifi-books”, but this is a good one. Beauman sets his story in a world where corporations can buy extinction certificates, say, if their mining operation kills of the last population of some rare fish. From here he throws in all kinds of weird ideas but which are not too far off to sound impossible. The book has some clever prose and wit, but i couldn’t shake the feeling of being a tad put off by the more cynical stances of the main characters.

Lauren Groff - The Vaster Wilds (dt. Die weite Wildniss) ★★★★ Great book about a girl running away from an early settlement in 17th century America, told in poetic prose telling it’s story about hunger, cold, loneliness, dreams and hope and survival like a fever dream. Imagine The Revenant minus a bear plus a little girl running endlessly into snowy landscapes.

Alexander Pechmann - Die Bibliothek der verlorenen Bücher ★★★ Neat collection of short essays on lost books and texts which have never been written. A great idea that surely works well for widely read literature nerds, but a tad boring and self-indulgent too to be honest.

Adrian Tchaikovsky - Eyes of the Void (The Final Architecture #2) (dt. Die Augen der Galaxis) ★★★★ Tchaikovsky writes the best space operas these days. Dense Infodumps followed by action rich space scenery in which whole planets get worked up by giant moon sized alien overlords manipulating matter into sculptural dead artifacts. The only criticism for me is that there is not much subtext here — the book says not much about the world we, the readers, live in. But Tchaikovskys novels are hands down the most entertaining scifi around, and that’s quite an achievement.

Frank Herbert - Dune Messiah (Dune #2) (dt. Der Herr des Wüstenplaneten) ★★★ Given the monumental and effing weird first Dune novel, i have to admit i was disappointed reading this second entry in the series. The book often feels like an add-on, some explainer making way for the rest of the series. I haven’t read Children of Dune yet, but i’ve heard that Messiah is supposed to be like this and Children is a return to form. Let’s see when i’m in the mood to return to Arakis after this mediocre epilogue.

Grégory Salle - Superyachten: Luxus und Stille im Kapitalozän ★★★★ A french sociologists meditation superyachts and their absurdity, given the political, economical and environmental realities in “late stage capitalism”. I’m a social-democratic capitalist and i think some people earn to be rich, but it’s simply obscene to own a boat that’s as large as 2 football fields and merely serves a hedonist display of power. The best sociological study about the penis enlargement industry i've ever read.

Iain M. Banks - Consider Phlebas (Culture #1) (dt. Bedenke Phlebas) ★★★★ Maybe the first novel i read where the appendix is more interesting than the story. Its a nice plot, but its a pretty off the shelves sprawling and entertaining space opera, and the ending drags quite a lot (200 pages where people and aliens run around in an underground train complex to find an AI, and it goes on and on and on and on and on, which is quite a contrast to the first fast paced half). The appendix finally explains how the Culture relates politically to AI, albeit its only a few pages, then going on to explain other things and all that stuff is a bit irrelevant for the novel itself. Don’t get me wrong, i liked it and loved some of the more weird ideas, but i also don’t get why this is considered such a classic. A good novel, but not as groundbreaking as the praise wants to make me believe.

Fabio Stassi - Die Seele aller Zufälle (Vince Corso #2) ★★★ Vince Corso is a bibliotherapist (using book suggestions to help his clients with various unpleasantries and his latest adventure sends him on a detective story about a deceased dementia patient using his library as a mnemonic device. This sounded great when i read the blurb and it sounds great when you summarize it, but it also was a bit of a bore and one of those books that is just too playful, too quirky, too self-absorbed for my taste. I think i’m very much done with “books about the love of books”.

Herman Melville - Moby Dick: or, the White Wale (dt. Moby-Dick oder Der Wal) ★★★★★ Moby Dick blew me away on quite a few levels i did not expect. I was really surprised by how readable this was and how fast i was able to pace through this mid-19th-century prose, all while being highly innovative (not just) for it’s time. The plot we all came to know — Ahab hunting the wale —, makes half of the book at max, maybe even less. The rest of the book are essays and musings on pretty much everything related to wale hunting: The ships, economics, ropes, planks, occupations and, ofcourse, the animal itself, it’s evolution and anatomy and character too. This makes this not just a highly entertaining read, but also a wild ride through all kinds of styles. Moby Dick truly deserves to be considered the classic that it is.

Lee Child - Worth Dying For (dt. Wespennest) / A Wanted Man (dt. Der Anhalter) (Jack Reacher #15 / #17) ★★★ There are two Reacher novels: In the first Reacher hitchhikes into some town and stumbles upon trouble, in the other some government agents need help. Pretty much all books follow one of these lines. I’ve read 14 Reachers and while these two lack any surprises at this point (WDF follows the first, AWM the second line), they are solid, suspenseful and entertaining entries in the series featuring some explosive punches along the way. Which is exactly what you want from a Reacher-book.

Jack Ketchum - Evil ★★★★ Ketchum’s Evil is a mean entry in the literary equivalent of torture porn, it’s an uncomfortable read and it lacks the ironic contrasting that made American Psycho such a classic. While reading i was constantly shifting from “this is an ugly, terrible, mean story that should not be told that way” to “it’s a punch in the stomach and exactly how it wants to be”. Sure, this thing sticks with you for a few days after reading, and the prose and style are highly effective in putting you in a very bad place for two weeks or so. But, lets say, this will be my only “sick and disturbing thriller” for this year. I’m good with this stuff for a while.

Percival Everett - James ★★★★★ Percival Everett tackles one of my favorite literary classics and tells the story of Jim, the slave of Huckleberry Finn. Huck, in this version, is a recurring character who enters a scenes, sticks around for a while going on an adventure and then leaves again, making place for James musings about his fate as man whose grand parents where violently taken in his home country, to be abused and tortured and forced to manual labor for dumb sacks in the “new world”. I loved that Everett actually had the guts to turn his James into a vigilante hero towards the end, bordering on a pulpy slave revenge story on the last few pages, giving this great postmodern exercise an edge that elevates it above pure literary nerdism.

Johan Huizinga - Homo Ludens. A study of the Play-Element in Culture (dt. Homo Ludens. Vom Ursprung der Kultur im Spiel.) ★★★ At 80 year old, this classic of cultural studies feels a bit dated. Sure, Huizingas book is a good, very interesting read about the elements of play in human cultures throughout history, but he’s also cherry picking his examples to fit history to his theory of play being the very basis of culture itself. It’s a highly interesting thesis which seems very suitable to the gamification of discourse we can see on social media these days with tons of implications for psychology and politics, but, all things considered, i’m not sure if his thesis holds up under scrutiny in a more historical context. Still, a good classic worth a read.

Johanna Sebauer - Nincshof ★★★ Lovely, quirky little story about a small town (more like a bunch of houses, actually) in Austria, where a handful of people start a movement to make the town forgotten by anyone else. It’s a weird story idea and i loved reading it, but the book and characters also show the aesthetics of a german TV-movie playing in the alps and it never really tries to escape that. The moments where the village conspiracists get into philosophy and muse about theories of absence and how to achieve willful collective memory holes are too rare, unfortunately. A good book that could’ve been much more.

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,

In June, NASA reported that all of the last 12 months had set temperature records, and 11 of them surpassed the 1.5°C mark. The average global temperature from June 2023 to May 2024 was 1.63°C above the pre-industrial level. Today, mind you — not as foreseen by politics and climate science at some point in the future in the year 2100.

The IPCC calculates the relevant global average temperature, which the Paris Agreement is based on, using a 20-year average; with the drawback that it only becomes definitively known after 10 years whether and when we reach and exceed certain values and limits. However, there are statistical alternatives that give more weight to values closer in time, allowing us to make meaningful statements today about whether and when certain values will be reached.

Carbon Brief has applied such alternative statistical methods to global temperature values from different datasets and calculated when we will approximately reach and exceed the non-binding global warming limits of the Paris Agreement.

Spoiler alert: It is not in the year 2100, nor a few years before that:

(Our) approach suggests that the world will pass 1.5C around the year 2030 (representing the 50th percentile, or central estimate, of all the model runs), with a range of anywhere from 2028 (5th percentile) up to 2036 (95th percentile).

Similarly, the world will pass 2C around the year 2048, with a range of 2040 to 2062 across all models assessed.

To be clear: The global limits targeted in the Paris Agreement will be reached in about 10 and 25 years respectively according to this statistical method, not in 75 years. And ofcourse: We can go way above those limits, easily, just give it enough time.

At the same time, a report by consulting firms KPMG and Kearney finds another record consumption of fossil fuel energy in 2023, which rose by 1.5% from the previous year's level. On track.

Meanwhile, climate policy worldwide is being weakened, conservative politicians pretend to be clueless in the face of flood disasters, and climate activists end up in jail for trivial offenses and civil disobedience. In Mecca, over 1,000 people die during the Hajj, where temperatures have been over 50°C for days, U.S. cities like Phoenix and New Orleans reach life-threatening temperatures despite prevailing air conditioning, and New Delhi sets a new temperature record for India at 52.3°C degrees — with over 100 deaths so far. These are just some few headlines from the past days, i have an extreme weather markdown file containing dozens of them for this year alone. A UN survey of 75,000 participants from 77 countries found that concern about climate change has "never been greater."

When, ten years ago, we as a global community (of course completely non-bindingly) agreed to limit global warming due to human-caused climate change to 2/1.5°C by 2100 and to do everything possible to achieve it, the well-known climatologist and former NASA scientist James Hansen, who in 1988 was the first to warn the US Congress about the consequences of unchecked climate change, said the agreement was "a fraud, really," it was "bullshit" and "worthless words."

There is no action, just promises. As long as fossil fuels appear to be the cheapest fuels out there, they will be continued to be burned.

And as long as that is the case, we will set new temperature records every year, revise statistical projections, and mourn further fatalities. Nobody seen this coming.

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Peter Frankopan - The Earth transformed - An untold history (dt. Zwischen Erde und Himmel: Klima - Eine Menschheitsgeschichte) ★★★★★ A riveting world history retold through the lense of environmental changes and climate which tries to integrate our knowledge about them into history as a foundational layer that was, until now, largely ignored. Frankopan largely succeeds with this reevaluation of history, and manages to fuse a world history that takes history of (mostly) Africa, South America and China into account with a history of human extractivism and exploitation of nature. Despite being a whopping 900 page tomb, it's accessible and it unfolds like the famous hockey stick graph: We get hundreds of interesting pages about the ever increasing exploitation of nature, accompanied by historic episodes of climate change and their impact on human economies throughout world history, only to be presented with an explosion of pollution and extraction after the start of industrialization, which culminates in the sheer suicidal trajectory of the last 50 years under neoliberal ideology: Half of all fossil fuels were burned after the last episode of Seinfeld, and the book makes it crystal clear that the consequences will be dire for everyone, but mostly for those who're already exploited and historically oppressed in the global south. My climate change thinking for a long time now goes along the lines that after 2030/40ish, that the unforeseen (and foreseen) consequences of atmospheric and environmental pollution will be so dire, there will be no other topic of relevance. This book confirms this view a good deal.

Douglas Preston & Lincoln Child - Crooked River (dt. Ocean - Insel des Grauens) ★★ Likely my first and last Pendergast novel as i don't like Pendergast very much. I have a soft spot for the objectively not-so-good 90s monster-thriller The Relic, the movie adaption of the first book in the series, but never bothered to read any of it, and now i did. I don't like the pretentious "gentleman-intellectual quirky detective who is also superman and good with guns and fighting and speaks in latin sometimes and is funny too and who's also Sherlock Holmes and he's reckless and sticks it to the man" schtick of this, and i don't like the supposedly mysterious female sidekick with violet eyes who is also Scarlett Johansson in Ghost in the Shell but also vulnerable and who is also an all-competent assassin and so forth. The pulpy action at the ending is entertaining, but doesn't go well with those characters for me, and it doesn't mix with the pretty standard detective procedure novel that makes up a large parts of the book. On top of that, the german title and blurb suggest some monster from the deep sea thriller which this is decidedly not. As this is one of the newer novels in this long running series, i might give Pendergast another try from one of his first adventures, but not anytime soon.

Anthony Ryan - Red River Seven (dt. Ein Fluss so rot und schwarz) ★★ I was in the mood for some postapocalyptic pulp and this so-so delivered but not in a very good way. People on a boat wake up with amnesia and they have to figure out who they are and why they are there, they find some phone and some voice tells them what to do. Turns out, they got their memories whiped out, traveling along River Thames through a London full of plant-mutant-monsters, and remembering things triggers the mutation. The book is full of weirdly bad written moments, for instance, when one of them mutates because she remembered some stuff, but not her name, she also claims that she hated her name anyways, which is weird when you don't know your name. The book makes this kind of stuff constantly. It's a somewhat entertaining novel, an amalgam of 28 Days Later and The Girl with all the gifts, but plotholes and inconsistent logic turned me off, besides the clichéd plot and characters.

Jonathan Haidt - The Anxious Generation: How the Great Rewiring of Childhood is Causing an Epidemic of Mental Illness (dt. Generation Angst) ★★★ I wrote a lengthy review of Haidts book and the gist was that "I think Haidt's book is a timely call to action, i largely agree with his analysis, but wish it was more in depth and i think he misses a big piece of the puzzle."

Lee Child - Persuader (Reacher #4) (dt. Der Janusmann) ★★★ The 12th Reacher i've read, Persuader will be the source material for the upcoming third season of the (quite good) series adaption. Reacher is working unofficially with the DEA to infiltrate a criminal operation by staging the kidnapping and rescue of the son of a gangster. Reacher becomes a trusted bodyguard, while trying to get a stab at a guy who ten years ago brutally murdered Reacher's colleague. All Reacher ingredients are there, it's suspenseful and sometimes clever, and i just like the character and his stoic approach to everything. It's just that Childs simple prose just can't elevate the material to something more than "clever pulp". Which is just fine.

Lee Child - 61 Hours (Reacher #14) (dt. 61 Stunden) ★★★ The 13th Reacher for me, and another good one: After a bus accident, Reacher strands in freezing South Dakota, where the witness of a crime is threatened by corrupt cops and gangster bosses closing in on an epic meth deal for the ages. Another good Reacher that, while being rather low on high octane action, is well put together with some fresh stylistic manorisms of Child, but comes with a botched ending. Spoilers now, i guess. Look, dude, you just can't show your hero trying to escape an imminent giant kerosine fireball of epic proportions and then simply fade away. We all know he made it, Child wrote more Reacher-novels after all. But this is like that music joke where someone plays a piece of music except for the final note and then can't sleep until he hits that note on the piano, completing the unfinished composition -- and this is an unfinished novel. It's a noob mistake, and a stupid one at that. I still enjoyed the book, but come on.

Werner Herzog - Die Zukunft der Wahrheit (eng. The Future of Truth) ★★★ A short meandering essay about the state of truth, which we can't know or perceive anyway. Herzog tells anecdotes and poetic historical vignettes, draws a rough sketch of what he terms "extatic truth", in which art and epistemology come together to make you feel or experience truth instead of perceiving it. Unfortunately, it stays a bit shallow and doesn't dive deep into its subject, which ofcourse is owed to the short form of only 107 pages. It's ideosyncratic, weird and quirky, just as you'd expect from Herzog, but it also stays a bit underwhelming compared to the oevre of its author, and the bits about tech, AI and deepfakes are not very well researched, if you know the topic.

Ottessa Moshfegh - My Year of Rest and Relaxation (dt. Mein Jahr der Ruhe und Entspannung) ★★★ If you want to read a well written novel about an annoying, rich, entitled, depressed, young woman, this is your book. I liked the prose, but expected some more meditations on, well, meditation and introspection. Instead, we get a woman hoarding pills and psychopharmaca, constantly complaining about everything and everybody, except her looks, and an ending you can see coming from a mile away. Still, i liked the prose and somewhat connected to the entitled little prick, so, job done, but not much more.

Peter Sloterdijk - Die Reue des Prometheus: Von der Gabe des Feuers zur globalen Brandstiftung (eng. The regrett of Prometheus: From the gift of Fire to global Pyromania) ★★★★ German philosopher Peter Sloterdijk lays down a theory of climate change, based on marxist materialism, going from the prehistoric discovery of fire to its many applications in combination with exploitative forms of labor (slavery, industrialization), ultimately demanding an "energetic pacifism" and the formation of a global "firefighter brigade". I'm sucking up some theoretical and philosophical takes on climate change at the moment, and Sloterdijks entry to the "genre" is a good and sometimes great contribution in a field that is just starting to take shape, from fictional visions in solarpunk-novels to theoretical approaches like particle-based economics of materialist transformation and the terrestrialism of Bruno Latour. As i said above about Peter Frankopans climatic world history: I expect this stuff to be the only topic of relevance within a few decades, simply because we must.

Nana Kwame Adjei-Brenyah - Chain-Gang All-Stars ★★★ I'm a sucker for those old 70s italian postapocalyptic future gladiator movies full of deadly gladiator fights between inmates in the future and great, i thought, and bought this blind, expecting, well, some good scifi action with some critical undertones about the US-prison system. But while we do get all this, the book is also full of flat characters who all talk the same and act the same, it's basically just people talking and gladiator fights, and all of this in a pretentious prose that wants to tell its minimal plot with epic pathos, using even biblical language. It's like a Madmax ripoff but all characters talk like playing shakespear, but because ofcourse it wants to be "badass" they also say "fuck" all the time. This makes it sounds worse than it is and the action is entertaining (but not great), but the social critique stays shallow and the prose comes around as sometimes cool, sometimes annoying. Meh, as they say.

Aleida Assmann - Im Dickicht der Zeichen (eng. Semiotic Thickets) ★★★★★ Aleida Assmann is a reknowned cultural scholar best known for her work on semiotics and cultural memory together with her late husband Jan Assmann (about whom i wrote recently in Cultural Memory in the Digital). In Im Dickicht der Zeichen (which translate to something like Semiotic Thickets), she collects some essays into one book about various forms of "natural semiotics", that is: Reading and seeing symbols within nature, and the historical transformations of epistemology, of what we know, how we know it, and where we get it from. It goes into basic semiotics, structures of signage, hieroglyphs and their various interpretations throughout philosophical history and more esoteric examples like silent movies. The book, while being very demanding and deep, is not a very complicated read and stays accessible for a non-academic like me, which is something i admire in academic writers. If you're into symbols and signs, typography and epistemology and you're interested in theoretical takes, the whole body of work from this scholar-couple is highly recommended, and this book is no exception.

Mary Shelley - Frankenstein ★★★★★ I finally read the orginal Frank, and it was just as great as i expected it to be. Most interesting to me as a huge fan of the old Universal Monster Movies and the original Frankenstein and Frankensteins Bride were the differences ofcourse, of which there are plenty. After reading the novel it becomes clear just how much the screenwriters added, changed and shuffled the original material, sometimes making an improvement while adapting it for the screen -- the famous scene where the monster throws the girl in a lake is not in the book, where we get the murder of a boy leading to a court scene with very different dynamics and outcomes. The book puts more emphasis on entangled fate which neither creator or creation can escape, and the consequences of guilt for everyone involved. I loved the prose and structure, the composition of a retold story which is sandwiched by chasing scenes in the arctic, and it's ambivalent message about scientific progress stays timely. It's exactly the great classic i expected, and everybody should read Frankenstein at least once.

Salman Rushdie - Knife: Meditations After an Attempted Murder (dt. Knife: Gedanken nach einem Mordversuch) ★★★★★ When Rushdie was attacked two years ago, it shocked and surprised me a good deal. I never read Satanic Verses and while i tried to read other Rushdies, i can't really get into his ornamental, colorful, fractured prose, which is okay. But i just like the guy, having seen plenty of him on talkshows like Bill Maher, and he's just a great, cool, sympathetic figure to me. Then there comes an asshole, accordingly named "asshole" in the book, and puts a knife in his eye and the book is Rushdies way of dealing with that. He does that with a ton of humor, but also with unforgiving sharpness, analyzing the islamofascist wannabe-murderer as the humorless unfucked prick that he is, and not wasting too much time on the guy because, after all, he's just not that interesting. Instead, Rushdie tells us a lot about his late life love for his wife, how they met and how his family and friends all dealt with the situation, how he healed and thankfully, humourously, simultaneously does and doesn't spare us from some of the more painfully embarassing medical procedures. It's a historical literary document, a tragic story of hardship, and also the diary of a funny guy full of life, and that's all such a book can achive. I have nothing but Respect for Salman Rushdie.

Don Winslow - City in Ruins (Danny Ryan #3) ★★★★★ I friggin love Winslows Danny Ryan-saga, with this last entry bringing this gangster epic to a more than satisfying conclusion. I already loved the first two books, and this one just brings it home. I love his prose, that is both barebones and detailed, his realistic dialogue that makes character come to life, and his kickass style of composing action in a highly suspenseful cross-cutting technique. I love Winslows intuitive sense for suspense and revealing moments, and how he manages to draw characters you care about, even the smallest, not at all important minor guy. I love how much of Winslows action stays implicit, with final sentences of chapters creating whole Scorsese-movies in your head, of splosions and gangsters and heroes killing each other in the night. I already have two more Winslow-novels on my stack (The Winter of Frankie Machine and The Power of the Dog) and i can't wait to read them. This is fantastic Thriller-entertainment at it's finest and the literary equivalent of Martin Scorsese.

Heinz Paetzold - Ernst Cassirer zur Einführung (eng. Introduction to the Philosophy of Ernst Cassirer) ★★★ I wanted an accessible non-academic book about Ernst Cassirer and his philosophy of the symbolic form, and the blurb says this is an accessible introduction to that, and i got a short but inaccessible book full of academic jargon that demands a whole set of pre-study, and which never explains its presumptions. It's useful, and if you put in the work and follow the mentioned literature, i bet you can get a good overview of Cassirers thinking out of this, but this is not the book with which i was able to do that.

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A new paper by researchers at the Potsdam Institute for Climate Impact Research has calculated the economic damages due to global warming by the year 2050, which are already "locked in" by current emission targets. These damages are certain to affect societies worldwide in the next 26 years.

The total amounts to 19% of the global economy, or 38 trillion US dollars. That is the number 38 followed by 12 zeros, or 38 thousand billions, or 38 million millions. In a monospaced sans serif font set in bold, the number looks like this:

38,000,000,000,000

That’s a very big number. For comparison, the complete economy of the US in the year 2023 was worth $27.36 trillion dollars, that’s roughly 10 trillion bucks less than the economic damages locked in for the next 26 years. Locked in, dare i say, by the supposedly fiscal conservative parties and their followers.

The calculations and results in the paper are considered conservative and they primarily include damages from warming, increased rainfall, and temperature fluctuations, while extreme weather events like storms or wildfires are harder to statistically model. The calculations also account for adaptations to extreme weather by countries and companies, noting that poorer nations will have less capacity for such adaptations. Africa and South Asia are the most affected regions in terms of projected value destruction, but the effects will be global, impacting the economic output of all countries. This means, we are facing a long-term, climate-change-induced recession of the entire world economy.

According to the study, the projected damages already exceed the investments needed to limit global warming to a maximum of 2°C, as stipulated by the (non-legally binding) Paris Agreement. The projected damages increase by another 50% when "further climatic components" are included.

Another recent paper found that economic damages from climate change to be “six times worse than thought”, which, i’m sure, will be a sweet nightmare lullaby to any insurance company exec. This is the world we are leaving to our children — social upheavals and political conflicts not accounted for.

Ten years ago, a study found that the externalized costs of all industrial sectors amounted to a staggering 7.3 trillion dollars per year — a 7 followed by 12 zeros —and that none of the sectors would be profitable if these costs from greenhouse gas emissions or water, air, and land pollution were included.

Meanwhile, the same forces that knowingly drive the entire world (not just economically) to the brink are engaging in disinformation campaigns to prevent even the most necessary adaptation initiatives and who, in concerted efforts, continue to criminalize climate activism and drag kids to court who dare to speak up.

A few days ago, a Berlin prosecutor filed charges against activists from the Last Generation, including on suspicion of "forming a criminal organization". In doing so, the public prosecutor's office is not only becoming a mouthpiece for the conservative think tank network Atlas, which specifically lobbies against inconvenient climate policies and has influenced politicians for years, including Bundestag member Frank Schäffler from the economic liberal FDP-party. Besides his role in the Bundestag, Schäffler is also the Managing Director of the Prometheus think tank, which is part of said Atlas Network. Schäffler was one of the most prominent conservative voices who, at the start of the Last Generation protests, spoke of them in terms of "terrorism" and "criminal organization", language that was eagerly picked up and established by conservative media outlets like Springers newspaper Welt and the Bild tabloid.

This choice of words by so-called representatives of the people and conservative media, it should be noted, is directed at a protest organization explicitly dedicated to climate protection, which was elevated to constitutional status in 2021 by a decision of the Federal Constitutional Court. The Berlin prosecutor's office has now allowed itself to become a mouthpiece for conservative think tanks and mass-media-propagated memes of right-wing politicians to criminalize young people organizing resistance against policies that violate fundamental civil rights, legitimized by the Constitutional Court. So far, so bad.

In a commentary on the 75th anniversary of the german constitution, the largest public broadcast service ARD now reflects on the idea of a fundamental right to climate protection and whether granting rights for subjects of nature (such as rivers, forests, animals, and even the atmosphere) in the constitution would be helpful.

I am unsure if new legal entities for nature are helpful for climate protection. Skeptical voices in the article rightly point out that natural entities cannot assert or claim their rights; human administrators would always be needed to manage these rights. I also think that the "intergenerational justice" emphasized by the german constitutional court is entirely sufficient to ensure the protection of natural entities within the framework of existing boundaries and limits. According to the 2021 decision, climate protection is justiciable and part of fundamental rights protection, and that is that.

Nevertheless, according to lawyer Roda Verheyen, who won the climate ruling at the highest constitutional court, this jurisprudence has "not yet arrived in the decision-making reality of german courts", even though "the constitutional mandate for climate protection requires and presupposes radical transformations".

I am very confident that the Berlin prosecutor's office had more in mind the outraged Frank Schäffler and his tweets suggested by the Atlas Network, rather than the climate ruling of the constitutional court and the resulting ecological fundamental rights, when filing charges.

In light of the increasing criminalization of young people who engage in civil disobedience to fight for the fundamental rights guaranteed by the constitutional court and who are insulted and spat upon by people memed by right-wing think tanks, I have a simple question to the Berlin prosecutor's office:

Who’s gonna pay those 38.000.000.000.000 dollars in economic loss — a very big number with 12 zeros —, and the damages done to criminalized kids who are holding up the constitution, and where can we file them?

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Today, Scarlett Johansson released a statement announcing legal action, and OpenAI subsequently removed the "Sky" voice option from ChatGPT. It’s all a misunderstanding and that the resemblance to Scarlett Johansson's voice was unintentional. Sure thing. As Johansson's statement reveals, OpenAI and the actress had been in negotiations for months, and just two days before the presentation, Altman tried to persuade the actress to lend her voice to his "Her" version. Johansson refused, and OpenAI still used a voice that was extremely similar to hers.

OpenAI using the voice of maybe the most famous actress in the world who also portraied the voice of an AI in a (misunderstood) movie about AI is not a coincidence.

In a recent piece at Aeon, Bryan Norton wrote about the philosophy of Bernard Stiegler, who differentiated the terms "technics" and "technology" to show how our fascination with the surface of technology — the shiny gadgets, the colorful interfaces, the sleek designs, and today: the seductive synthesized voices of famous actresses — obscures our actual relationship to the tools and their true impact on us as humans in a societal social fabric.

Stiegler developed the concept of "pharmacology", according to which new media technologies affect us similarly to pharmaceuticals, potentially having both beneficial and harmful effects. Stiegler's notion of technics points to these "pharmaceutical" effects of technology: the "side effects" that lie beneath the surface of interfaces and mere application results.

Sam Altman gave the latest version of ChatGPT the voice of a famous actress with the explicit goal of realizing the fictional chatbot from Spike Jonze's film. The question now is, in Stiegler's terms, what "pharmacological" effects lurk behind this Scarlett Johansson interface. Two things come to mind.

Firstly, a recent study confirmed the psychoactive effect of AI chatbots: an experiment with over 2000 conspiracy theory believers showed that a conversation with a chatbot could reduce their beliefs by about 20%. This sounds great first, but it essentially means that, yes, indeed: a chatbot can influence people to change fundamental attitudes. AI systems are actually persuasive apparently — and using the familiar voice of Scarlett Johansson creates a direct connection between the auditory, now also Hollywood-glamorous interface surface of ChatGPT technology, and the seductive power of the speech-simulating technics beneath.

(This is why I signed the open moratorium letter a year ago: I don’t care much about alleged AI doomsday scenarios. But the seductive power of mimetic and anthropomorphic AI systems, which suggest that there’s “personality” behind simulated language, and the unpredictable psychological consequences if these "personalities" lead to a synthetic theory of mind, where AI systems occupy a real and effective place as "social actors" in our minds — this worries me more than any robotic paperclip-juggling AI. Another similar example would be the psychoactive effect of romantic chatbots, which fulfill all our desires on command, potentially creating unpredictable and unfulfillable expectations in real human relationships.)

By (non-consensually) using the voices of famous actresses and making them emphatically "human" (with filler words and ohs and ahs and giggles), OpenAI is deliberately trying to get "under the skin" of its users (to quote the title of another film starring Scarlett Johansson). With this AI-mimetic seduction tactic, OpenAI obscures both the psychoactive effect of its product and the fact that we pay dearly for this synthetic seduction-by-chatbot in the form of gigantic carbon footprints and a water consumption from data centers that increased by 30% for Microsoft in 2022 alone.

These are just some of the possible and actual effects of ChatGPT's technics, which OpenAI's techno-mimetic masks are already obscuring today. More will follow.

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Silvia Ferrara - Der Sprung: Eine Reise zu den Anfängen des Denkens in der Steinzeit (eng. The Jump: A trip to the beginnings of thinking in the stone age) ★★
Ferrara tries to answer the question, why early humans painted cave walls 50000 years ago, and she tries to do that by analyzing the paintings themselves. An art analytical take on an unanswerable question, and theories are legion, just as the likely correct answers (some where painted as ritualistic shamanic stuff, others for fun, others as an exercise, others as a hunting plan, mnemonic devices, accounting, teaching tools, etc etc, and all of those crossing over each other for thousands and thousands of years). To answer the why-question from an art analytical perspective is not an easy endeavour, and while this is an interesting book in itself (i'm a sucker for cave paintings, anthropology, and the history of writing systems), i think she largely fails due to her oftentimes confusing writing style that, in lack of a scientific proven explanation of something, falls back into an ornamental style that wants to put emphasis on the mystery of cave paintings. She's more occupied writing about her whizzy crazy associations and some random memories with cave art than writing a good, structured book on the history of the topic. This may work for some, but not for me. I still learned some nice details though, but not enough to give this more than 2 stars.

Lee Child - Im Visier (Reacher 19: Personal) ★★★ / Keine Kompromisse (Reacher 20: Make Me) ★★★ / Der Bluthund (Reacher 22: The Midgnight Line) ★★
On my quest to read all Reacher-novels, i'm now at 11 of 25, and these three are, while entertaining, not among the best in the series. All of these are recent novels, and i miss some of the qualities of the older ones -- the sparse prose, the ultra-detailed action sequences, Reachers wit and cleverness. These are still entertaining thriller novels, but they lack a bit of the punch of the first ones.

Jack Finney - Die Körperfresser kommen (eng. Invasion of the Body Snatchers) ★★★
I love all the Bodysnatchers-adaptions (except the Nicole Kidman-stinker from 2006 or so) and i rewatched all of them recently, when i figured: You know what, why not read the novel? So i did, all the elements are there, and the blankness, emptyness, bare of any identity markers of those not-dead-because-never-alive body grown out of alien seeds keep on being a nice universal meta-metaphor for any societal alienation, even in a dated scifi novel from the 50s. Bodysnatchers when it was written was a metaphor for the red scare, about "neighbors turning into communists", in the seventies, it was a metaphor for the sexual revolution, where "kids turned into weird excessive sexmaniacs", in the 90s Ferrara tried to turn it into a metaphor for american military culture, and in the 00s it was turned into a metaphor for post 9/11 paranoia. It's a universal story about paranoia in the face of rampant alienation, where our friends and neighbors suddenly turn into something else. Which beggs the question: Where is the social media-era version of the story? Where's the NPC-Bodysnatchers, or the Deepfake Bodysnatchers? Especially the NPC-meme basically is the very same story: Everybody is a non playing character, an empty hull of a human, parrotting empty phrases, except me and my peers, we are real humans. The digital age is a deeply paranoid age, and all the users might be alien plants in the heads of some conspirational 4chan anons. It's a story of our age, and a new interpretation is begging to be written.

Don Winslow - City on Fire / City of Dreams (Danny Ryan #1&2) ★★★★
I wanted to read other thrillers than Reacher and boy did Winslow get me hooked. It's the story of italian and irish mobs going to war in Providence, Rhode Island, and while the story itself is not very surprising, it's Winslows style that just sucks you in. I paced through City on Fire, which so much reminded me of Martin Scorseses Goodfellas, that i constantly read it in the narrating voice of Joe Pesci. You know you read a good thriller when you have the voice of effing Joe Pesci in your head.

Herfried Münkler - Welt in Aufruhr: Die Ordnung der Mächte im 21. Jahrhundert (eng. World in Turmoil: World Orders in the 21st Century) ★★★★
Herfried Münkler is a world reknowned political scientist at the Berlin Humboldt University, and in this book, he describes how the old dynamic of the cold war with 2 global main actors made place to a more chaotic world (in which we live now) that slowly evolves, according to Münkler, to a pentarchy of five global powers (US, EU, China, Russia and India) which in the coming decades, under certain circumstances, may lead to a new stable configuration of global powers providing a stable model for world peace. If you're, like me, not overly interested in politics, but still want to know how the larger picture is evolving internationally, this a good book to read about geopolitics in the 21st century.

Naomi Alderman - The Future ★★★
I friggin loved Aldermans The Gift, so picking this up was a nobrainer, and i was somewhat disappointed. The Future is a book about a billionaire class building bunkers while the world goes to shit, with some rebels planning to get rid of them in a pretty original way. The book itself is shattered into a million bits: climate, ai, internet outrage and socmed, pandemics, hong kong riots, preppers and survivalism, religious cults, anarcho capitalism and accelerationism, Elizabeth Holmes and Theranos, all told in nonchronological vignettes, mostly from the perspective of the protagonist. A collage of the titular future made with thousands of bits from the present. What sounds pretty cool is a bit dumbed down by the simplistic ending, which is still pretty entertaining as a microstory in the style of Lord of the Rings with mech suits on an abandoned island. An entertaining novel let down by an ending that was too simplistic and naive for my taste.

Neal Stephenson - Termination Shock ★★★
A thousand page doorstopper by Neal "Metaverse" Stephenson about the climate crisis, a slow meandering wide ranging novel showing a detailed panoramic view of a world circa 2040ish, in which heatwaves caused by climate change are rampant and a billionaire decides to just fire sulfur into the sky with a giant cannon to geoengineer our atmosphere. I loved how Stephenson incorporates as diverse topics as climate change, modern aristocracy, indian martial arts and political conflict into one coherent storyline -- but i also found it somewhat pointless. Stephenson has surprising little to say about a climate change that is largely induced by economic ideologies, and simply turns dealing with the consequences into an entertaining scifi thriller. It's a good read, and i love many of the details like the indian-chinese stick-fights at the border that clearly was inspired by this Youtube-clip, but i missed some larger message about the mallaise we're in. But maybe the point is: Billionaires gonna do whatever they want, they'll sound like Jurassic Parks John Hammond while doing it and there's not much we can do about it. Which is a bit underwhelming.

Friederike Otto - Klimaungerechtigkeit: Was die Klimakatastrophe mit Kapitalismus, Rassismus und Sexismus zu tun hat (eng. Climate Injustice: What the climate crisis has to do with capitalism, racism and sexism) ★★★★
Friederike Otto is one of Time Magazines 100 most influential people in 2021 and a leading climate researcher who contributed to attribution science, that is: The science of finding out, how much of a hurricane, flooding or heatwave can be attributed to anthropogenic climate change. I picked this up because i am one of those guys who, while seeing that climate does indeed lead to injustices, i do think that justice issues take a second row to what is largely a physics based planetary issue, which needs tons of international regulations and engineering. However, I find myself convinced by her arguments. A better title for the book would have been "Climate Adaption Injustice", because that's what it's about: How humanity adapts to climate change and the (historical) injustices leading to adaptive disadvantages in the global south. It is clear to me that dire consequences of climate change are already locked in — economic, ecological, not to speak of the disruptions caused by migration and international conflict --, and if that's clear we need a clear analysis of the status quo, to figure how to deal with those consequences in a fair way on a global level. Friederike Ottos short book provides that analysis.

Anders Levermann - Die Faltung der Welt (eng. Folding the World) ★★★
Anders Levermann is a researcher of complex systems at the Potsdam-Institute for Climate Research and his book wants to provide an alternate narrative for green growth and decoupling of emissions from economic growth. Basically it's about "horizontal growth", while classic economic development would be "vertical growth". He proposes an "inside growth", that goes into diversifying products and recycling, not the extractive "bigger, bolder, higher". I pretty much agree with him on most of his points, but all of this is not very new -- he simply uses mathematics as a metaphor for regulation and writes about how the paradigm of "endless growth" can exist in a limited system.

Max Barry - Die 22 Tode der Madison May (eng. The 22 Murders of Madison May) ★
Scifi-novel about a guy hopping parallel universes to find the perfect version of an aspiring actress, only to find subpar Madison Mays and killing them. What sounds intriguing comes around as weirdly phrased (the book is somehow full of eggs) and reads like a sitcom sometimes. It features clichéd and flat characters, boring twists, shallow dialogue without any depth, it's not thrilling enough to be a thriller, not scifi enough to be scifi, its a boring Jennifer Aniston movie with clichéd genre elements, and the more interesting parts (a mysterious organization of people traveling through parallel universes and using "anchors", objects like a lock of hair, to make sure some stuff and people exist in the target universe) stay largely unexplored. Waste of time.

Samuel W. Gailey - Die Schuld (eng. The Guilt We Carry) ★★★
A young woman who finds her dead stripclub-owner boss dead in her bed and a bag full of dollars. Ofcourse, she takes the money and runs. During her escape, she examines her past and her own guilt, and learns to deal with the consequences. A good little pulp crime novel that could've been more, but stays within the trodden path of it's story, which isn't a bad thing at all.

Liu Cixin - Die Drei Sonnen (eng. The Three-Body Problem) ★★★★
I read this three weeks before the Netflix-series came out (which i still haven't seen) because i wanted to know what the fuzz is about. It's a good book, maybe great sometimes, and i loved some of the more bonkers ideas about how life may exist on a planet in an unstable orbit in a three star system, and The parts about the cultural revolution and it's implications were daring and damn interesting, but i expected a bit more from it, given the hype. However, the second book already sits on my bookshelf, and i'll read all parts.

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In the subsequent court hearing of a lawsuit brought by the City of New York, Exxon was (unfortunately) acquitted of the accusation of defrauding its investors. Whether Exxon deceived humanity itself has not (yet) been litigated.

Recently, Exxon CEO Darren Woods embarrassed himself and his corporation when he claimed that the public — that's us — is to blame for the slow progress of the green energy transition because we are not willing to pay for it. Mind you, for an energy transition that would have been significantly cheaper in the 70s, 80s, and 90s, when we had enough time to transition the economic system into a sustainable one.

I recount this brief episode of the climate-related business conduct of one of the world's largest oil corporations as a small intro, because as always in climate-related economic-political matters, it's much worse.

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Dario Kenner from University of Essex examined public statements from the American Petroleum Institute and FuelsEurope, two of the largest lobbying organizations in the oil industry in the USA and Europe, and found that they have been systematically opposing green, low-carbon technologies since the 1960s. Kenner found dozens of cases where lobbyists exerted pressure and influence on politics to prevent subsidies for things like electric cars, solar panels, or heat pumps.

All of this was happening 60 years ago under the banner of a supposed "technology-neutral approach" by the dominant energy companies, using the same false arguments of alleged distortion of competition through state subsidies for renewable energies as today, all while - and here it gets particularly egregious - benefiting itself from subsidies and tax breaks. According to the International Monetary Fund, subsidies for the fossil fuel industry, including societal costs, amounted to a whopping $760 billion in the USA and $310 billion in the EU for the year 2022 alone.

And to keep the sweet tax money rushing in — money that, mind you, comes from the same people Exxons CEO dares to accuse of slowing down climate action —, the oil industry switched tactics, going from hiding their evidence and climate change denial, to greenwashing and outright “deception, disinformation and double speak”, as a new report from the U.S. House Oversight and Reform Committee (the largest investigative committee in the House of Representatives) put it.

The report accuses big oil of nothing less than a "misinformation campaign". Specifically, the filed documents reveal that (emphasis in cursive mine):

• Publicly, the oil industry promoted natural gas as a climate-friendly, green energy source, while internally the companies were well aware of the scientific evidence indicating that natural gas is as climate-damaging as coal, and that natural gas is just as incompatible with politically set emission reduction goals.

• Internally, they referred to the targets of the Paris Agreement as "unattainable goals and incompatible with business plans."

• The oil industry lobbied against climate laws while publicly pledging their support.

• Publicly, they advocated for Carbon Capture, while internally discussing that Carbon Capture at the necessary scale is not profitable enough, measured against a "free-to-pollute business model."

• They deliberately used trade associations, think tanks, and NGOs to spread misleading narratives and employ tactics they themselves did not want to be associated with. (Half a year ago, reporting showed how the Atlas Network, a network of think tanks funded by the Koch Brothers, apparently influenced german FDP member Frank Schäffler, who repeatedly referred to climate activists of the Last Generation as "terrorists" and a "criminal organization," which surely was not insignificant for the decision to conduct nationwide raids on kids. The police spoke at the time of "investigations on suspicion of forming a criminal organization." I can understand why the oil companies do not want to be associated with such potentially dangerous deceptions of the police.)

• The oil industry forms partnerships with science and research to specifically finance studies that align with their business plans, while simultaneously tracking and monitoring critical voices and activists. For example, Shell discussed in internal emails "embedding" staff-scientists at the University of Berkeley, and representatives of British Petroleum spoke about marginalizing critical scientists.

• All six companies and organizations under investigation obstructed and delayed the House committee's investigation, did not cooperate, and did not provide more than 4,000 requested documents despite legal requirements.

At the global UN climate conference in 2021, 503 lobbyists from the fossil fuel industry participated. The next year, it was 636. A year later, the number of lobbyists applying the strategy of "deception, disinformation, and double-talk" outlined in the report to the democratically elected representatives of the global public at the most important meeting for coordinating and negotiating "Climate Action" surged to a staggering 2,456. The number of oil industry lobbyists exceeded the number of all other country delegations, except for Brazil and the host country, the United Arab Emirates.

The report from the investigative committee is another damning proof that representatives of the oil industry have no place at the negotiation table at the COPs, where a lot of people in suits decide over the environmental future of the planet and the survival of millions of people in the not-so-distant future. And while climate change is starting to affect everything and the deceiving behavior of the energy sector is coming to light, this is what happens: from the business side: lies; from politics: too little; otherwise: police violence against climate activists. The word for this more-than-half-a-century-long deception of the public by politics and the oil industry is: hypocrisy.

During the hearing of the experts and authors of the report, Bernie Sanders remarked: "I think it's time to call on the people who caused this problem and knowingly spread lies about it to pay the bill."

He's right: Make them pay.

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Shine the headlight, straight into my eye
Like the roadkill, I'm paralysed
(Placebo, Teenage Angst)

Meme Magic in the Aughts

It was around 2012 when i noticed that things were off. By then, i already gained some extensive insights on virality. I'm online since the mid 90s, and contributing to the web since i started my own blog in 2005, which became a german mainstay for webculture soon. In 2008, i had a first "viral hit", with a simple phrase lifted from a political poster, which became a national sensation and inspired dozens of flashmobs disrupting political events during the election year, so much so that at one point, Angela Merkel had to adress to "young people in the back to shut up". It inspired songs and books and I went on national TV (you can see my 15 years younger self in this clip at 1:50) to explain what is happening. That was a time when politicians barely could spell the word "browser" and we were baffled and blown away by the outcomes of spontaneous online movements. Years later the fine folks at 4chan invented the word "Meme Magic" to describe such things, when things going viral "transcend the realm of cyberspace and result in real life consequences".

With the beginning of the 2010s and social media finally making it's mainstream breakthrough, things started to change. By then, Buzzfeed established a new publishing practice: Serving all kinds of identities with cheap fast fluff and listicles — a factory for identities —, and combining that with only vague, non-informative headlines: Clickbait was born, and perfected in the years that followed by media outlets like Upworthy. The ur-mainstream-viralbomb Kony 2012 showed that you can direct whole masses of digitally networked people with emotionalizing, simplistic, semi-political content that outrages them, with a big portion of those people being young adults and teens.

Virality on early Social Media had reached a tipping point, where people on social media increasingly selected for emotionality and outrage, and it became clear to me that this new media environment of personalized writing and blogging, "citizen journalism" and social media held more manipulative power than anything we've seen before. I became interested in virality itself, memetics, the dynamics of swarm behavior, and wrote about the psychological underpinnings of webculture ever since.

Then, the abomination of Gamergate blew up the internet and created the culture wars, 4chan supposedly "memed Trump into the White House", and thousands of webculture experts, including me, were scratching their heads and wrote thinkpiece after thinkpiece about what the hell is going on — all while the mental health of kids deteriorated.

The Anxious Generation

Jonathan Haidts book, like the title and its byline "How the great rewiring of childhood is causing an epidemic of mental illness" suggest, revolves around the effects that these developments — the social media revolution in tandem with helicopter parenting focussing on safety —, had on the psychology specifically of kids. It's the first of a Haidt-socmed-doublewhammy, with the next book coming out next year revolving around the psychological effects of social media on democracy and institutions. (To be fair: That one will be much closer to my interests, but these developments have overlaps and common causes, to which we'll get in a second.)

The book is split in two parts, with the first chapters describing what is happening and laying down the evidence with tons of statistics, and a second part revolving around suggestions for solutions to the problem. This first part dives into two arguments: First, the rise of helicopter parenting and "safetyism" in the 80s and 90s took away free play from childhood, then the rise of smartphones and social media sucked the kids into the virtual world, with all those psychological effects of virality and attention economics applying to them. It was then that the numbers of mental health issues for children and adolescents started to rise, especially for girls.

Overall, the book is not so much an in depth look at the psychological and neurological effects of social media, and more of a timely wakeup call to action. I personally gained not that much new insight from it, for which i can't blame the book, simply because i follow the topic very closely for more than 10 years now. I know much of the cited research, and follow the work of Haidt since i blogged about his his brillant The Righteous Mind  back in 2008, and read and wrote a lot about his work in the past. I'm not exactly the targeted audience for this book, but i liked it anyways, even when it was preaching to the choir here. I only wish he went deeper into the neuropsychological workings of social media — because I think he missed a large piece of the puzzle there.

Much of the writings about neuropsychological effects of social media revolve around how endless scrolling and likes and shares create small shots of the gratification-hormone dopamine and how that glues you to the screen. The Anxious Generation mentions related research extensively. And sure enough, this explains a good chunk of what is happening, why you can't just put your phone away, constantly check for notifications and how that makes you addicted.

But dopamine alone can't explain all the conflict we see on social media, and when we talk about the society wide effects of it, we mostly complain about the tribalism, the outrage, the aggressions. This beast is a slot machine of a different kind, and the underlying psychological mechanism is highly relevant for the psyche of kids, too, especially for girls and young women, and it is near completely absent from discourse.

The elephant in the room: Oxytocin

One of the most under-researched topics in social media psychology is how it influences the flow of oxytocin — the so-called "love hormone". Until recently, our understanding of that hormone was that it's largely responsible for social bonding and get's released from interaction with other humans, touching, or when mothers interact with their newborn babies — women have roughly 30% higher oxytocin levels than men. But that's far from the whole story: It is also related to "coordinated outgroup attacks" and even has been found to promote human ethnocentrism and xenophobia.

In an experiment done in 2011, a researcher checked the oxytocin levels of a reporter before and after using Twitter for 10 minutes: They rose by 13% — "as much as a groom at a wedding". When they repeated the experiment with three journalists using Facebook, "they all demonstrated increased levels of oxytocin", with the oxytocin levels of one of the journalists, who was writing with his girlfriends, going up "nearly 150%". This not only means that social media interaction does release oxytocin, but that it creates oxytocin levels in people who know each other at least comparable to those in real life.

Newer research has shown that Oxytocin is not just a hormone for social bonding, but an amplifyier of any important social interaction. Oxytocin is "a social-alert hormone", as one researcher put it, and it is found that even merely "anticipated social contact may result in bursts of oxytocin". Social Media and push notifications create a whole lot of "anticipated social contact", and sure enough: Average teen gets more than 230 notifications on their cell phone each day, study finds.

Arguably, most socmed notifications are not "important social situations", but here's the kicker: You can't know if they are important or not before you check, and if all your peers and friends are on social media platforms, chances are high that these social situations are important. It might be that the highschool prom queen just asked you out on a date, after all! It might also be a friend who tells you about the latest gossip spread about you in the semi-private socmed group "everyone in the class except you". (In the book, Haidt cites one kid who had to endure this especially cruel form of cybermobbing, and i asume this is a common way of cyberbullying among teens at this point.) This would mean that merely the ping from a phone, or even only the anticipation of a ping on the phone, already releases oxytocin. And the closer we are in real life to those pinging us on social media, the higher the oxytocin release -- like the couple mentioned above whose oxytocin release shot up by 150%.

Among the groups on social media who know each other in real life are highschool peers and classmates, and among them, the bullies and their victims. Interestingly, citing from The Anxious Generation: "One systematic review of studies from 1998 to 2017 found a decrease in face-to-face bullying among boys but an increase among girls, especially among younger adolescent girls", and I believe this is related to oxytocin release from social media.

The Social Media Mean Girls Club

Oxytocin is related to the release of dopamine, too, with a 2015 study finding that "oxytocin appears to impact dopaminergic activity ... which is crucial not only for reward and motivated behavior but also for the expression of affiliative behaviors", meaning that the pleasurable reward we get from likes and comments is intrinsically intertwined with the release of oxytocin, which binds us to the group and increases our tendencies to exclude others, and this is true especially for girls and young women.

If the theory about heightened oxytocin release through social media is right, it hits teenage girls, who spend much more time on social media than boys, in a vulnerable and highly critical phase of their lifes where those hormone levels are beginning to emerge. It binds together the famed "mean girls club" and the winners of the highschool popularity contest even stronger than before, and because oxytocin is also linked to social avoidance in bullied mice — Studies of hormonal flows in mamals commonly apply to human, too —, it increases social anxiety for the loosers of that popularity contest: "after negative social interactions, oxytocin promotes avoidance of unfamiliar social situations." As an amplifyer of social interaction, oxytocin goes both way: It makes the winners feel more loved, and the losers more outcast, and all of this is multiplied by social media.

Haidt spends a whole section on female aggression strategies, how girls violence is relational, social, and goes for the reputation and social bondings of other girls. But only if we take oxytocin into account, we get a full picture of what is happening on social media: Oxytocin binds us to people in our group, and bullying is not just an exclusionary, but in the form of mobbing also a group bonding activity. Thanks to the oxytocin manipulation through social media, the "mean girls club" at highschool is becoming more exclusive, more aggressive, more defensive, all while making being a member of the club highly desirable, because being a member of said club is prestigeous and it's highly visible on social media as they get more likes and shares. Teens compare themselves to those "highschool in-groups" (you know: the cool guys) on a much higher level than before. All while the losers of that oxytocin contest — which are not just the bullied kids, but also those within the "highschool in-group", who are subjected to constant peer pressure to stay in that group —, show higher levels of social anxiety.

In 2011, Scientific American in a piece about these "dark sides" of oxytocin, wrote that "oxytocin should not be used for recreational purposes". Arguably, viral social media activity is one big fat "recreational" oxytocin shot for you, and your peers, and everyone involved in whatever viral thing is doing the rounds in your group. Likely, that thing making the rounds is mockery of someone from the outgroup, and those exclusionary effects do in fact show up in viral statistics: A peer reviewed study from 2021 showed not only that out-group animosity drives engagement on social media, but that "the average effect size of out-group language was about 4.8 times as strong as that of negative affect language and about 6.7 times as strong as that of moral-emotional language — both established predictors of social media engagement". Mocking members of the outgroup is clearly the highest driver of virality, and even if this study was done in a political context, it should also apply to the social dynamics of kids and teens, especially to mobbing and cyberbullying.

In The Anxious Generation, Haidt writes: "Social Media has magnified the reach and effect of relational bullying, placing immense pressure on girls to monitor their words and actions. They are aware that any misstep can swiftly go viral and leave a permanent mark." The highschool popularity contest doesn't stay in school, it sits in your pocket 24/7 and it's with you all. the. time. Bullying in the 80s and 90s was no fun, i can tell you that from personal experience. But in the 2020s, it's a hellish nightmare following you everywhere.

Studies found that "oxytocin is ... involved in maternal aggression and territoriality" and "Psychosocial stress triggers an oxytocin response in women". "Psychosocial stress" here means not only what we commonly understand as workplace related stress, but also information overload, a rising dunbar number from social media connectivity, bloated social circles and the peer pressure to conform. Writing in the New Statesman, Freya India summed it up: "Social media's not just making girls depressed, it's making us bitchy too". Accordingly, in 2019 the New York Times wrote about Tales From the Teenage Cancel Culture, and yes, i strongly believe that wokism and identity politics, peer pressure and bullying among girls are at the very least not unconnected, which can explain why the mental health numbers dropped first for liberal girls.

A paper published in January 2023 about a "3-year longitudinal cohort study of functional magnetic resonance imaging (fMRI) among sixth- and seventh-grade students recruited from 3 public middle schools in rural North Carolina" found that social media use is linked to brain changes in teens, and that "children who habitually checked their social media feeds at around age 12 showed ... sensitivity to social rewards from peers heightening over time". This heightened "sensitivity to social rewards from peers" sounds a lot like the work of oxytocin to me: As i've written above, oxytocin is a "social alert hormone", and that "even anticipated social interaction may result in bursts of oxytocin". (There are more social media induced changes to children's brains, here's an interview with neurosurgeon Marc Arginteanu talking about these.)

To me, all of this very much looks like that social media is leading to constant heightened oxytocin levels, and it makes us tribalistic, makes us aggressive towards the outgroup and increases tendencies for social exclusion — all of which are rampant on social media, and all of this detoriates the mental health of especially girls and young women on a societal level. I'm not familar with oxytocin research particular in teen girls, but presumably, women develop their already higher oxytocin levels during puberty, because it's the hormone that regulates mother-child-bonding and initiates changes in the birth-canal. If social media is manipulating oxytocin levels, this is a highly potent hormonal change, especially during the puberty of teenage girls.

This may even explain the different outcomes of various studies regarding wellbeing and social media use: as an amplifier of important social interactions, oxytocin makes us feel loved as long as we belong to the in-group, but it makes us socially anxious if we're excluded. Social Media turns the volume of all of this up to 11, and all of these neuropsychological mechanisms are now subject to the incentives created by the attention economy on social media and the design choices of platforms and their gamifications.

The Symbolic Teen

In his "philosophy of the symbolic form", Ernst Cassirer describes humans as "the symbolic animal" whose reality consists mostly from communicated language and symbols, an animal that shapes its own meaning by the creation of the symbolic world. To Cassirer, our symbolic world constitutes everything, and it’s only through our symbolic world we can truly understand humans. This makes sense if you look at the rare cases of humans who did not grow up in human groups, but were raised by animals, the so called wild childs. They are barely human at all, have tremendous problems ever integrating into society and can barely learn or speak. This clearly shows how much of our culture is ingrained into human existence itself.

This is especially true for kids, whose childhood is prolonged and extended in comparison to other mamals (who often can walk and sometimes are fully functional right after birth) precicely because they need time to adapt to human culture, or, in other words: to learn. In his book "The Disappearance of Childhood", Neil Postman wrote about how the invention of the printing press extended this learning process even further, and widened the knowledge gap between adults and kids, so they had to go to school for years and learn how to read and write — to learn how to become Cassirers "symbolic animal". It was then that modern conceptions of childhood were invented.

In The Anxious Generation, Haidt writes about how kids using social media were increasingly "wandering through adult spaces, consuming adult content, and interacting with adults", and were "engaging in adult activities", such as "managing their online brand". I would add to that: They also got heavily politicized by social media from imitating highly visible and viral social media activity (which favors political outrage), which, for kids, sure must look like a prestigeous behaviour to copy from their adult social media peers.

For some years now, i follow the work of Joshua Citarella, who documents adolescent politcal online subcultures since he first published his e-book Politigram & the Post-left in 2018. His interviewees are mostly kids and adolescents, and some are heavily politically radicalized at ages like 13 years old and younger. These kids engage with fringe political movements like "MAGA Communism", with one of the latest interviews stating that the now 16 year old boy from Texas started to visit online political communities "around 2016", meaning that he was just 8 years old back then.

Cool underground kids talking about stuff like anarcho capitalism, "MAGA communism", and engaging with political communities at the age of 8 sounds very much like "adult activity" to me, and very much like an "end of childhood". It also does not sound very healthy to me. (I also put some weight to these interviews, because these are not formal interviews in a study, but these kids talk freely, without any supervision from adults. And when it comes to kids and what they do i always presume that adults actually know next to nothing about them, which is true for every generation. How much did your parents really know about your ongoings when you were 16?)

For Neil Postman, the end of childhood consisted of a diffusion process involving electronic, visual broadcast media, which made the learning of linear, sequential symbols (reading) obsolete. But these kids sound different: These teens cite philosophers like Karl Marx, Mark Fisher, Gilles Deleuze, Nick Land or Jean Baudrillard. They are highly articulate, super-informed, well read in philosophy and politics — and they are deeply cynical and nihilistic. It's a perspective learned from adults on the internet, fused with the nihilism found on the "cool internet underground" message boards. These kids learned the lessons of social media very well: Copy prestigeous political outrage, dunk on the outgroup, and earn clout and prestige by pushing the fringes.

In "The End of Childhood", Neil Postman quotes Harold Innis’s principle that "new communication technologies not only give us new things to think about, but new things to think with". This new digitally transformed "end of childhood" of our era constitutes to a cultural evolutionary adaption to social media dynamics, where we and our kids, and especially those kids immersing themselves in internet subcultures, adapt to these "new things to think with" in all consequence. I find this deeply worrying, especially if you consider that these "cool kids from the internet underground" are, well, the cool kids, those who score high in the highschool popularity contest and who are imitated by their peers.

For Cassirers "symbolic animal", social media is a giant battlefield for group acceptance, an editing machine with endless possibilities and a playground for the social world, and its unforeseen hormonal effects on the human psyche make it toxic for the whole of childhood, for kids who should not engage in "brand management", or "extremist politics", or DDoS attacks against schools at the age of nine to impress their peers and get their oxytocin shots by virtual pats on the back. Kids should not be subjected to a technology, that, by enhancing and skewing social mechanisms, manipulates their hormone levels.

This is why i agree with Haidts conclusions: Ban phones from school, rise the age for opening social media accounts to 16, end safetyism, and "bring childhood back to earth".

I wish Haidts book would've digged more into some of the effects and dynamics i described here, but the research on social media’s effects on oxytocin levels is sparse (yet), and the workings of oxytocin on social behavior is not very well understood as of now. Much of what i wrote here is speculation and "connecting the dots", coming from an amateur-researcher with some extensive experience in social media virality.

The Anxious Generation mostly revolves about the two major developments in the last 40 years or so, the rise of helicopter parenting and safetyism, and the rise of social media, but it does not go into specific details of what kids are actually doing online very much, and how that contributes. For instance, while Haidt does write about photo-filters on platforms like Instagram and Snapchat and how they contribute to self perception and body image, he doesn’t mention phenomena like "Instagram face" or "Snapchat Dysmorphia", which are sometimes so severe that young women seek out plastic surgery.

I also miss some more socio-philosophical takes on why all of this happens, how these psychological effects intertwine and how the informational structure of the web creates a flattened, endless landscape of stuff in which important and unimportant things gain the same weight and create a weird atemoprality, in which narrative structures dissolve, only to spontaneously errupt into an emotionally driven hyperfocus of the swarm, or how conformity on social media flattens culture as a whole, to the effect that diners all over the world look like hipster bars in Brooklyn, simply because this look goes viral on Instagram. Sure, these things don't affect mental health of kids directly, but it is one of the strange outcomes of a digitally networked global culture, and i'd be surprised if it doesn't contribute at all.

However, for what The Anxious Generation wants to be, a call to action, it maybe is the better choice to focus on the very concrete effects directly related to kids' mental health issues, and keep it simple. The arguments laid down in the book are pressing, and even if the better of Haidts’ critics complain about some more shoddy studies in the book, the overall picture of the situation painted in the book is convincing. It is a good, timely book, a wake up call for teachers, decision makers, parents, and last but not least, the kids themselves.

As i laid down above, i was personally involved in making social media and webculture look cool in my country. My former blog had one foot in the internet underground, and my work played a tiny role in making webculture into what it is today. Looking at all the effects this tremendous cultural change had on society and mental health, a part of me regrets that involvement. This is why, at least to some extend, i feel some kind of responsibility, and this is one of the reasons why i wrote about this stuff extensively for more than ten years, and why i am thankful that a world reknowned social psychologist like Jonathan Haidt picked up the topic. I'm very much looking forward to his next book about social media psychology, coming out next year, which will focus on its effects on democracy and institutions.

Social Media and the web are arguably the biggest change in human communication and culture since forever. Some claim it is bigger than the printing press, or even the invention of writing. It is silly to even asume that its effects on our social mechanisms and on our psychology are neglectible, or can be shrugged off, and that everything is just okay.

I'm not as hopeless as this may sound though. I'm very sure we will adapt to these "new things to think with", because that's what humans always do. But the path to that adaption will be a “long and winding road”, to quote a famous teenage favorite of yore, and strange weird things will happen.

Of that, i'm sure.

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